Sensors | February-1 2023 - Browse Articles

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Feedback </a> </div> <div class="small-6 columns end"> <h3>Information</h3> <p> Visit our dedicated information section to learn more about MDPI. </p> <a target="_blank" href="/authors" class="button button--color button--full-width"> Get Information </a> </div> </div> <a class="close-reveal-modal" aria-label="Close"> <i class="material-icons">clear</i> </a> </div> </div> <div class="middle-column__main ul-spaced"> <div class="content__container content__container--overflow-initial"> <h1> <i>Sensors</i>, Volume 23, Issue 3 (February-1 2023) – 698 articles </h1> <div style="position: absolute; width: 140px; padding-bottom: 15px"> <a href="https://www.mdpi.com/1424-8220/23/3/1188" target="_blank" rel="noopener noreferrer"> <img style="width: 140px; height: auto;" src="/files/uploaded/covers/sensors/cover-sensors-v23-i3.png"/> </a> </div> <div style="margin-left: 160px; min-height: 210px;"> <div style="margin-bottom: 1em;"> <strong>Cover Story</strong> (<a href="/files/uploaded/covers/sensors/big_cover-sensors-v23-i3.png" target="_blank" rel="noopener noreferrer">view full-size image</a>): A new plasmonic configuration is demonstrated for the detection of variations in the bulk refractive index of solutions. The configuration consists of monitoring two diffracted orders resulting from the interaction of a TM-polarized optical beam incident on a grating, operating based on an effect termed the “optical switch”. These two diffracted orders enable differential measurements which cancel the perturbations common to both, leading to an improved detection limit. Bulk sensing is demonstrated under intensity interrogation via the injection of solutions comprising glycerol in water into a fluidic cell. A limit of detection of about 10–6 RIU was achieved. The optical switch configuration is easy to implement and is cost-effective, yielding a highly promising approach for the sensing and the real-time detection of biological species. <a href="https://www.mdpi.com/1424-8220/23/3/1188">View this paper</a> </div> </div> <ul> <li>Issues are regarded as officially published after their release is announced to the <a href="/journal/sensors/toc-alert">table of contents alert mailing list</a>.</li> <li>You may <a href="/journal/sensors/toc-alert">sign up for e-mail alerts</a> to receive table of contents of newly released issues.</li> <li>PDF is the official format for papers published in both, html and pdf forms. 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return false;">Ok</a> </div> </div> <a class="close-reveal-modal" aria-label="Close"> <i class="material-icons">clear</i> </a> </div> </div> <div> <div style="clear: both"></div> </div> </div> </div> <div class="jscroll"> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1057584" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 16 pages, 2795 KiB   </span> <a href="/1424-8220/23/3/1746/pdf?version=1675740383" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Research on Power Allocation in Multiple-Beam Space Division Access Based on NOMA for Underwater Optical Communication" data-journal="sensors"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1424-8220/23/3/1746">Research on Power Allocation in Multiple-Beam Space Division Access Based on NOMA for Underwater Optical Communication</a> <div class="authors"> by <span class="inlineblock "><strong>Yanlong Li</strong>, </span><span class="inlineblock "><strong>Syed Agha Hassnain Mohsan</strong>, </span><span class="inlineblock "><strong>Xiao Chen</strong>, </span><span class="inlineblock "><strong>Riffat Tehseen</strong>, </span><span class="inlineblock "><strong>Shuaixing Li</strong> and </span><span class="inlineblock "><strong>Jianzhao Wang</strong></span> </div> <div class="color-grey-dark"> <em>Sensors</em> <b>2023</b>, <em>23</em>(3), 1746; <a href="https://doi.org/10.3390/s23031746">https://doi.org/10.3390/s23031746</a> - 3 Feb 2023 </div> <a href="/1424-8220/23/3/1746#metrics">Cited by 5</a> | Viewed by 2661 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> To meet the transmission requirements of different users in a multiple-beam access system for underwater optical communication (UWOC), this paper proposes a novel multiple-beam space division multiple access (MB-SDMA) system by utilizing a directional radiation communication beam of the hemispherical LED arrays. The <a href="#" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1746/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> To meet the transmission requirements of different users in a multiple-beam access system for underwater optical communication (UWOC), this paper proposes a novel multiple-beam space division multiple access (MB-SDMA) system by utilizing a directional radiation communication beam of the hemispherical LED arrays. The system’s access users in the different beams are divided into two categories: the users with a single beam and the users with multiple beams. We also propose a power allocation algorithm that guarantees the quality of service (QoS) for single beam and multiple beam access, especially the QoS for edge users, and fairness for all users. An optimization model of power distribution under the constraints of specific light-emitting diode (LED) emission power is established for two scenarios, which ensure the user QoS for edge users and the max–min fairness for fair users. Using the Karush–Kuhn–Tucker (KKT) condition and the bisection method, we obtain the optimal power allocation expression for the two types of users in the optimization model. Through simulation, we verify that the proposed user classification and power allocation method can ensure the fairness of fair users on the premise of ensuring the QoS of edge users. At the same time, we know that the number of users will affect the improvement of the minimum rate, and the throughput of the non-orthogonal multiple access (NOMA) system is greatly improved compared with the traditional orthogonal multiple access (OMA) systems. <a href="/1424-8220/23/3/1746">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/sensors/special_issues/Nonorthogonal_Multiple_Access_sensors ">Non-orthogonal Multiple Access Techniques in Emerging Optical Wireless Sensor Networks</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1746/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1057584"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1057584"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1057584" data-cycle-prev="#prev1057584" data-cycle-progressive="#images1057584" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1057584-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/sensors/sensors-23-01746/article_deploy/html/images/sensors-23-01746-g001-550.jpg?1675740464" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1057584" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1057584-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01746/article_deploy/html/images/sensors-23-01746-g002-550.jpg?1675740463'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1057584-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01746/article_deploy/html/images/sensors-23-01746-g003-550.jpg?1675740460'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1057584-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01746/article_deploy/html/images/sensors-23-01746-g004-550.jpg?1675740461'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1057584-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01746/article_deploy/html/images/sensors-23-01746-g005-550.jpg?1675740465'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1057584-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01746/article_deploy/html/images/sensors-23-01746-g006-550.jpg?1675740466'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1057584-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01746/article_deploy/html/images/sensors-23-01746-g007-550.jpg?1675740459'><p>Figure 7</p></div></script></div></div><div id="article-1057584-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/sensors/sensors-23-01746/article_deploy/html/images/sensors-23-01746-g001-550.jpg?1675740464" title=" <strong>Figure 1</strong><br/> <p>Underwater optical communication network based on the hemispherical LED array.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1746'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01746/article_deploy/html/images/sensors-23-01746-g002-550.jpg?1675740463" title=" <strong>Figure 2</strong><br/> <p>Schematic diagram of the hemispherical multiple-beam space division multiple access transmission.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1746'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01746/article_deploy/html/images/sensors-23-01746-g003-550.jpg?1675740460" title=" <strong>Figure 3</strong><br/> <p>Transmitted beams by the hemispherical LED array.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1746'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01746/article_deploy/html/images/sensors-23-01746-g004-550.jpg?1675740461" title=" <strong>Figure 4</strong><br/> <p>(<b>a</b>) Sum rate of NOMA and OMA under the same QoS. (<b>b</b>) Sum rate of NOMA and OMA under different QoS.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1746'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01746/article_deploy/html/images/sensors-23-01746-g005-550.jpg?1675740465" title=" <strong>Figure 5</strong><br/> <p>(<b>a</b>) Rates of different users under the same QoS. (<b>b</b>) Rates of different users under different QoS.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1746'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01746/article_deploy/html/images/sensors-23-01746-g006-550.jpg?1675740466" title=" <strong>Figure 6</strong><br/> <p>Maximized minimal rates of all users without QoS constraints.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1746'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01746/article_deploy/html/images/sensors-23-01746-g007-550.jpg?1675740459" title=" <strong>Figure 7</strong><br/> <p>(<b>a</b>) The minimum rate varies with SNR in the case of multiple beams. (<b>b</b>) The minimum rate varies with SNR in a multi-user case.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1746'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1057575" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 29 pages, 7174 KiB   </span> <a href="/1424-8220/23/3/1745/pdf?version=1675679533" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Core versus Surface Sensors for Reinforced Concrete Structures: A Comparison of Fiber-Optic Strain Sensing to Conventional Instrumentation" data-journal="sensors"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1424-8220/23/3/1745">Core versus Surface Sensors for Reinforced Concrete Structures: A Comparison of Fiber-Optic Strain Sensing to Conventional Instrumentation</a> <div class="authors"> by <span class="inlineblock "><strong>Ryan Hoult</strong>, </span><span class="inlineblock "><strong>Alex Bertholet</strong> and </span><span class="inlineblock "><strong>João Pacheco de Almeida</strong></span> </div> <div class="color-grey-dark"> <em>Sensors</em> <b>2023</b>, <em>23</em>(3), 1745; <a href="https://doi.org/10.3390/s23031745">https://doi.org/10.3390/s23031745</a> - 3 Feb 2023 </div> <a href="/1424-8220/23/3/1745#metrics">Cited by 9</a> | Viewed by 2707 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> High-resolution distributed reinforcement strain measurements can provide invaluable information for developing and evaluating numerical and analytical models of reinforced concrete structures. A recent testing campaign conducted at UCLouvain in Belgium used fiber-optic sensors embedded along several longitudinal steel rebars of three reinforced concrete <a href="#" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1745/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> High-resolution distributed reinforcement strain measurements can provide invaluable information for developing and evaluating numerical and analytical models of reinforced concrete structures. A recent testing campaign conducted at UCLouvain in Belgium used fiber-optic sensors embedded along several longitudinal steel rebars of three reinforced concrete U-shaped walls. The resulting experimental dataset provides an opportunity to evaluate and compare, for different types of loading, the strain measurements obtained with the fiber-optic sensors in the confined core of the structural member against more conventional and state-of-the-practice sensors that monitor surface displacements and deformations. This work highlights the need to average strain measurements from digital image correlation techniques in order to obtain coherent results with the strains measured from fiber optics, and investigates proposals to achieve this relevant goal for research and engineering practices. The longitudinal strains measured by the fiber optics also provide additional detailed information on the behavior of these wall units compared to the more conventional instrumentation, such as strain penetration into the foundation and head of the wall units, which are studied in detail. <a href="/1424-8220/23/3/1745">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/sensors/special_issues/5S85Q1U4W4 ">Distributed Fibre Optic Sensing Technologies and Applications</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1745/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1057575"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1057575"><i class="fa fa-caret-right"></i></div><div class="absgraph 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src='https://pub.mdpi-res.com/sensors/sensors-23-01745/article_deploy/html/images/sensors-23-01745-g006-550.jpg?1675679636'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1057575-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01745/article_deploy/html/images/sensors-23-01745-g007-550.jpg?1675679638'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1057575-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01745/article_deploy/html/images/sensors-23-01745-g008-550.jpg?1675679629'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1057575-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01745/article_deploy/html/images/sensors-23-01745-g009-550.jpg?1675679623'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1057575-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01745/article_deploy/html/images/sensors-23-01745-g010-550.jpg?1675679639'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1057575-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01745/article_deploy/html/images/sensors-23-01745-g011-550.jpg?1675679634'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1057575-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01745/article_deploy/html/images/sensors-23-01745-g012-550.jpg?1675679619'><p>Figure 12</p></div> --- <div class='openpopupgallery' data-imgindex='12' data-target='article-1057575-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01745/article_deploy/html/images/sensors-23-01745-g013-550.jpg?1675679614'><p>Figure 13</p></div> --- <div class='openpopupgallery' data-imgindex='13' data-target='article-1057575-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01745/article_deploy/html/images/sensors-23-01745-g014-550.jpg?1675679610'><p>Figure 14</p></div> --- <div class='openpopupgallery' data-imgindex='14' data-target='article-1057575-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01745/article_deploy/html/images/sensors-23-01745-g015-550.jpg?1675679624'><p>Figure 15</p></div> --- <div class='openpopupgallery' data-imgindex='15' data-target='article-1057575-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01745/article_deploy/html/images/sensors-23-01745-g016-550.jpg?1675679616'><p>Figure 16</p></div></script></div></div><div id="article-1057575-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/sensors/sensors-23-01745/article_deploy/html/images/sensors-23-01745-g001-550.jpg?1675679631" title=" <strong>Figure 1</strong><br/> <p>(<b>a</b>) Cross-section and reinforcement layout with indication of DFOS rebars and micrometer instruments (M1–M16, clockwise from boundary end of west flange); and (<b>b</b>) Elevation view of wall units with indication of the LVDT chain on the west flange boundary end.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1745'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01745/article_deploy/html/images/sensors-23-01745-g002-550.jpg?1675679626" title=" <strong>Figure 2</strong><br/> <p>(<b>a</b>) Wall cross-section with the different loading positions; and (<b>b</b>) The different measurement instrumentation devices (not to scale; dimensions in mm).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1745'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01745/article_deploy/html/images/sensors-23-01745-g003-550.jpg?1675679641" title=" <strong>Figure 3</strong><br/> <p>Location of the longitudinal rebars instrumented with fiber-optic sensors (purple, full opacity rebars): (<b>a</b>) Elevation view of the wall from the west with fiber-optic channels (“ch”) 1, 2, 3, and 4; and (<b>b</b>) Elevation view of the wall from the south. The LVDT chain to the south, along the boundary end of the west flange, and to the north, along the corner of the west flange-web intersection, is also depicted.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1745'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01745/article_deploy/html/images/sensors-23-01745-g004-550.jpg?1675679609" title=" <strong>Figure 4</strong><br/> <p>(<b>a</b>) Graphical representation of the bonding technique to the rebars and welded segment used in the foundation; (<b>b</b>) Groove made along the longitudinal rebar, showing the rounded and welded segment used in the head to join the two parallel rebar layers; and (<b>c</b>) the connector and termination of the fiber-optic cable was protected in the concrete footing using a plastic orange cone.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1745'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01745/article_deploy/html/images/sensors-23-01745-g005-550.jpg?1675679620" title=" <strong>Figure 5</strong><br/> <p>(<b>a</b>) 3-dimensional representation of the RC U-shaped wall units with foundation blocks and DIC strain results on the surface of the web; and (<b>b</b>) Corner-region “column” of the west flange-web intersection (100 × 100 mm<sup>2</sup> cross-section) with DIC surface, embedded DFOS sensors (Channel 1), and LVDT chain, used in the current research.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1745'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01745/article_deploy/html/images/sensors-23-01745-g006-550.jpg?1675679636" title=" <strong>Figure 6</strong><br/> <p>Photos of the inside edge of the west flange with micrometers (M1, M2, and M3) and base LVDTs attached close to the exterior and interior faces of the flange boundary end (<b>a</b>) Before testing of UW1; and (<b>b</b>) At LS34 of UW3 (position C+) showing flexural-shear crack running through attachment point of M1.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1745'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01745/article_deploy/html/images/sensors-23-01745-g007-550.jpg?1675679638" title=" <strong>Figure 7</strong><br/> <p>Longitudinal strain profiles for unit UW1 subjected to pure flexure at different imposed drifts: (<b>a</b>) Channel 4 of the DFOS; (<b>b</b>) chain of LVDTs located on the west flange boundary end; (<b>c</b>) Channel 1 of the DFOS; and (<b>d</b>) chain of LVDTs located on the west flange-web intersection.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1745'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01745/article_deploy/html/images/sensors-23-01745-g008-550.jpg?1675679629" title=" <strong>Figure 8</strong><br/> <p>Longitudinal strain profiles for unit UW2 subjected to pure torsion at different imposed twists: (<b>a</b>) Channel 4 of the DFOS; (<b>b</b>) Chain of LVDTs located on the west flange boundary end; (<b>c</b>) Channel 1 of the DFOS; and (<b>d</b>) Chain of LVDTs located on the west flange-web intersection.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1745'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01745/article_deploy/html/images/sensors-23-01745-g009-550.jpg?1675679623" title=" <strong>Figure 9</strong><br/> <p>Longitudinal strain profiles for unit UW3 subjected to a combination of flexure and torsion: (<b>a</b>) Channel 4 of the DFOS; (<b>b</b>) Chain of LVDTs located on the west flange boundary end; (<b>c</b>) Channel 1 of the DFOS; and (<b>d</b>) Chain of LVDTs located on the west flange-web intersection.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1745'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01745/article_deploy/html/images/sensors-23-01745-g010-550.jpg?1675679639" title=" <strong>Figure 10</strong><br/> <p>DIC strains profiles for the web-West flange intersection of wall unit UW1 at: (<b>a</b>) Position D (compression); and (<b>b</b>) Position C (tension) at <span class="html-italic">δ</span> = 0.6%. A moving average was used to smooth the DIC profiles using different span base lengths (<span class="html-italic">B<sub>l</sub></span> = 50 mm, 100 mm, and 200 mm). The strain profiles are compared to the profiles measured from the average of the DFOS strain profiles (black solid lines) and LVDT strains (black dashed lines).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1745'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01745/article_deploy/html/images/sensors-23-01745-g011-550.jpg?1675679634" title=" <strong>Figure 11</strong><br/> <p>DIC and DFOS longitudinal strain profiles for the west web-flange intersection of wall unit UW1 subjected to flexure, in tension (Position C): (<b>a</b>) <span class="html-italic">δ</span> = −0.2%; (<b>b</b>) <span class="html-italic">δ</span> = −0.4%; (<b>c</b>) <span class="html-italic">δ</span> = −0.6%; (<b>d</b>) <span class="html-italic">δ</span> = −0.8%, and in compression (Position D); (<b>e</b>) <span class="html-italic">δ</span> = 0.2%; (<b>f</b>) <span class="html-italic">δ</span> = 0.4%; (<b>g</b>) <span class="html-italic">δ</span> = 0.6%; and (<b>h</b>) <span class="html-italic">δ</span> = 0.8%. The thin blue lines are the uncorrected strains determined from the DIC data, whereas the thick red lines are the corrected strains (i.e., moving average over 200 mm) determined from the DIC data. The solid black lines are the strain measurements from the DFOS (i.e., 2 × <span class="html-italic">Φ</span>12 rebars, Channel 1). A heatmap is provided next to each plot, representing the uncorrected, raw DIC strain (in units of mm/m).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1745'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01745/article_deploy/html/images/sensors-23-01745-g012-550.jpg?1675679619" title=" <strong>Figure 12</strong><br/> <p>DIC and DFOS longitudinal strain profiles for the west web-flange intersection of wall unit UW2 subjected to torsion, in tension (Position O−): (<b>a</b>) <span class="html-italic">θ</span> = −15 mrad; (<b>b</b>) <span class="html-italic">θ</span> = −20 mrad; (<b>c</b>) <span class="html-italic">θ</span> = −25 mrad; (<b>d</b>) <span class="html-italic">θ</span> = −30 mrad, and in compression (Position O+); (<b>e</b>) <span class="html-italic">θ</span> = 15 mrad; (<b>f</b>) <span class="html-italic">θ</span> = 20 mrad; (<b>g</b>) <span class="html-italic">θ</span> = 25 mrad; and (<b>h</b>) <span class="html-italic">θ</span> = 30 mrad. The thin blue lines are the uncorrected strains determined from the DIC data, whereas the thick red lines are the corrected strains (i.e., moving average over 200 mm) determined from the DIC data. The solid black lines are the strain measurements from the DFOS (i.e., 2 × <span class="html-italic">Φ</span>12 rebars, Channel 1). A heatmap is provided next to each plot, representing the uncorrected, raw DIC strain (in units of mm/m).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1745'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01745/article_deploy/html/images/sensors-23-01745-g013-550.jpg?1675679614" title=" <strong>Figure 13</strong><br/> <p>DIC and DFOS longitudinal strain profiles for the west web-flange intersection of wall unit UW3 subjected to flexure and torsion, in tension (Position C+): (<b>a</b>) <span class="html-italic">δ</span> = −0.1%, <span class="html-italic">θ</span> = 1 mrad; (<b>b</b>) <span class="html-italic">δ</span> = −0.2%, <span class="html-italic">θ</span> = 2 mrad; (<b>c</b>) <span class="html-italic">δ</span> = −0.3%, <span class="html-italic">θ</span> = 3 mrad; (<b>d</b>) <span class="html-italic">δ</span> = −0.4%, <span class="html-italic">θ</span> = 4 mrad and in compression (Position D); (<b>e</b>) <span class="html-italic">δ</span> = 0.1%, <span class="html-italic">θ</span> = −1 mrad; (<b>f</b>) <span class="html-italic">δ</span> = 0.2%, <span class="html-italic">θ</span> = −2 mrad; (<b>g</b>) <span class="html-italic">δ</span> = 0.3%, <span class="html-italic">θ</span> = −3 mrad; and (<b>h</b>) <span class="html-italic">δ</span> = 0.4%, <span class="html-italic">θ</span> = −4 mrad. The blue thin lines are the uncorrected strains determined from the DIC data, whereas the red thick lines are the corrected strains (i.e., moving average over 200 mm) determined from the DIC data. The solid black lines are the strain measurements from the DFOS (i.e., 2 × <span class="html-italic">Φ</span>12 rebars, Channel 1). A heatmap is provided next to each plot, representing the uncorrected, raw DIC strain (in units of mm/m).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1745'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01745/article_deploy/html/images/sensors-23-01745-g014-550.jpg?1675679610" title=" <strong>Figure 14</strong><br/> <p>Anchorage slip (Δ<span class="html-italic"><sub>v</sub></span>) calculated from the distributed optical fiber sensor (DFOS) strain profiles compared to the micrometers for unit UW1 subjected to in-plane flexure: (<b>a</b>) DFOS Channel 2, Micrometer 5; (<b>b</b>) DFOS Channel 3, Micrometer 2; and (<b>c</b>) DFOS Channel 4, Micrometer 1.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1745'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01745/article_deploy/html/images/sensors-23-01745-g015-550.jpg?1675679624" title=" <strong>Figure 15</strong><br/> <p>Anchorage slip (Δ<span class="html-italic"><sub>v</sub></span>) calculated from the distributed optical fiber sensor (DFOS) strain profiles compared to the micrometers for unit UW2 subjected to torsion: (<b>a</b>) DFOS Channel 2, Micrometer 5; (<b>b</b>) DFOS Channel 3, Micrometer 2; and (<b>c</b>) DFOS Channel 4, Micrometer 1.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1745'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01745/article_deploy/html/images/sensors-23-01745-g016-550.jpg?1675679616" title=" <strong>Figure 16</strong><br/> <p>Anchorage slip (Δ<span class="html-italic"><sub>v</sub></span>) calculated from the distributed optical fiber sensor (DFOS) strain profiles compared to the micrometers for unit UW3 subjected to torsion and flexure: (<b>a</b>) DFOS Channel 2, Micrometer 5; (<b>b</b>) DFOS Channel 3, Micrometer 2; and (<b>c</b>) DFOS Channel 4, Micrometer 1.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1745'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1057570" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 13 pages, 1658 KiB   </span> <a href="/1424-8220/23/3/1744/pdf?version=1675843053" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Evaluating IoT-Based Services to Support Patient Empowerment in Digital Home Hospitalization Services" data-journal="sensors"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1424-8220/23/3/1744">Evaluating IoT-Based Services to Support Patient Empowerment in Digital Home Hospitalization Services</a> <div class="authors"> by <span class="inlineblock "><strong>Patricia Abril-Jiménez</strong>, </span><span class="inlineblock "><strong>Beatriz Merino-Barbancho</strong>, </span><span class="inlineblock "><strong>Giuseppe Fico</strong>, </span><span class="inlineblock "><strong>Juan Carlos Martín Guirado</strong>, </span><span class="inlineblock "><strong>Cecilia Vera-Muñoz</strong>, </span><span class="inlineblock "><strong>Irene Mallo</strong>, </span><span class="inlineblock "><strong>Ivana Lombroni</strong>, </span><span class="inlineblock "><strong>María Fernanda Cabrera Umpierrez</strong> and </span><span class="inlineblock "><strong>María Teresa Arredondo Waldmeyer</strong></span> </div> <div class="color-grey-dark"> <em>Sensors</em> <b>2023</b>, <em>23</em>(3), 1744; <a href="https://doi.org/10.3390/s23031744">https://doi.org/10.3390/s23031744</a> - 3 Feb 2023 </div> <a href="/1424-8220/23/3/1744#metrics">Cited by 5</a> | Viewed by 2652 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Hospitals need to optimize patient care, as, among other factors, life expectancy has increased due to improvements in sanitation, nutrition, and medicines. Hospitalization-at-home (HaH) could increase admission efficiency, moderate costs, and reduce the demand for beds. This study aimed to provide data on <a href="#" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1744/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Hospitals need to optimize patient care, as, among other factors, life expectancy has increased due to improvements in sanitation, nutrition, and medicines. Hospitalization-at-home (HaH) could increase admission efficiency, moderate costs, and reduce the demand for beds. This study aimed to provide data on the feasibility, acceptability, and effectiveness of the integration of IoT-based technology to support the remote monitoring and follow-up of patients admitted to HaH units, as well as the acceptability of IoT-based solutions in healthcare processes. The need for a reduction in the number of admission days, the percentage of admissions after discharge, and the actions of the emergency services during admission were the most relevant findings of this study. Furthermore, in terms of patient safety and trust perception, 98% of patients preferred this type of digitally-supported hospitalization model and up to 95% were very satisfied. On the professional side, the results showed a reduction in work overload and an increase in trust when the system was adopted. <a href="/1424-8220/23/3/1744">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/sensors/special_issues/Y52O3R6CSU ">Wearable Sensors and IoT Devices Applied in Daily Life</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1744/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1057570"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1057570"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1057570" data-cycle-prev="#prev1057570" data-cycle-progressive="#images1057570" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1057570-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/sensors/sensors-23-01744/article_deploy/html/images/sensors-23-01744-g001-550.jpg?1675843116" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1057570" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1057570-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01744/article_deploy/html/images/sensors-23-01744-g002-550.jpg?1675843118'><p>Figure 2</p></div></script></div></div><div id="article-1057570-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/sensors/sensors-23-01744/article_deploy/html/images/sensors-23-01744-g001-550.jpg?1675843116" title=" <strong>Figure 1</strong><br/> <p>Process and IoT devices flowchart.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1744'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01744/article_deploy/html/images/sensors-23-01744-g002-550.jpg?1675843118" title=" <strong>Figure 2</strong><br/> <p>Conceptual framework to generate the Better@Home assessment questionnaire.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1744'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1057546" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 11 pages, 1052 KiB   </span> <a href="/1424-8220/23/3/1743/pdf?version=1675737782" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Multi-Input Speech Emotion Recognition Model Using Mel Spectrogram and GeMAPS" data-journal="sensors"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Brief Report</span></div> <a class="title-link" href="/1424-8220/23/3/1743">Multi-Input Speech Emotion Recognition Model Using Mel Spectrogram and GeMAPS</a> <div class="authors"> by <span class="inlineblock "><strong>Itsuki Toyoshima</strong>, </span><span class="inlineblock "><strong>Yoshifumi Okada</strong>, </span><span class="inlineblock "><strong>Momoko Ishimaru</strong>, </span><span class="inlineblock "><strong>Ryunosuke Uchiyama</strong> and </span><span class="inlineblock "><strong>Mayu Tada</strong></span> </div> <div class="color-grey-dark"> <em>Sensors</em> <b>2023</b>, <em>23</em>(3), 1743; <a href="https://doi.org/10.3390/s23031743">https://doi.org/10.3390/s23031743</a> - 3 Feb 2023 </div> <a href="/1424-8220/23/3/1743#metrics">Cited by 5</a> | Viewed by 3814 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The existing research on emotion recognition commonly uses mel spectrogram (MelSpec) and Geneva minimalistic acoustic parameter set (GeMAPS) as acoustic parameters to learn the audio features. MelSpec can represent the time-series variations of each frequency but cannot manage multiple types of audio features. <a href="#" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1743/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> The existing research on emotion recognition commonly uses mel spectrogram (MelSpec) and Geneva minimalistic acoustic parameter set (GeMAPS) as acoustic parameters to learn the audio features. MelSpec can represent the time-series variations of each frequency but cannot manage multiple types of audio features. On the other hand, GeMAPS can handle multiple audio features but fails to provide information on their time-series variations. Thus, this study proposes a speech emotion recognition model based on a multi-input deep neural network that simultaneously learns these two audio features. The proposed model comprises three parts, specifically, for learning MelSpec in image format, learning GeMAPS in vector format, and integrating them to predict the emotion. Additionally, a focal loss function is introduced to address the imbalanced data problem among the emotion classes. The results of the recognition experiments demonstrate weighted and unweighted accuracies of 0.6657 and 0.6149, respectively, which are higher than or comparable to those of the existing state-of-the-art methods. Overall, the proposed model significantly improves the recognition accuracy of the emotion “happiness”, which has been difficult to identify in previous studies owing to limited data. Therefore, the proposed model can effectively recognize emotions from speech and can be applied for practical purposes with future development. <a href="/1424-8220/23/3/1743">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/sensors/special_issues/Emotion_Recognition ">Emotion Recognition Based on Sensors (Volume II)</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1743/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1057546"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1057546"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1057546" data-cycle-prev="#prev1057546" data-cycle-progressive="#images1057546" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1057546-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/sensors/sensors-23-01743/article_deploy/html/images/sensors-23-01743-g001-550.jpg?1675737855" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1057546" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1057546-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01743/article_deploy/html/images/sensors-23-01743-g002-550.jpg?1675737854'><p>Figure 2</p></div></script></div></div><div id="article-1057546-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/sensors/sensors-23-01743/article_deploy/html/images/sensors-23-01743-g001-550.jpg?1675737855" title=" <strong>Figure 1</strong><br/> <p>Architecture of proposed model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1743'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01743/article_deploy/html/images/sensors-23-01743-g002-550.jpg?1675737854" title=" <strong>Figure 2</strong><br/> <p>Confusion matrixes obtained in four experimental settings.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1743'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1057525" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 20 pages, 5948 KiB   </span> <a href="/1424-8220/23/3/1742/pdf?version=1675999558" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Implementation of the Digital QS-SVM-Based Beamformer on an FPGA Platform" data-journal="sensors"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1424-8220/23/3/1742">Implementation of the Digital QS-SVM-Based Beamformer on an FPGA Platform</a> <div class="authors"> by <span class="inlineblock "><strong>Somayeh Komeylian</strong> and </span><span class="inlineblock "><strong>Christopher Paolini</strong></span> </div> <div class="color-grey-dark"> <em>Sensors</em> <b>2023</b>, <em>23</em>(3), 1742; <a href="https://doi.org/10.3390/s23031742">https://doi.org/10.3390/s23031742</a> - 3 Feb 2023 </div> <a href="/1424-8220/23/3/1742#metrics">Cited by 3</a> | Viewed by 2172 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> To address practical challenges in establishing and maintaining robust wireless connectivity such as multi-path effects, low latency, size reduction, and high data rate, we have deployed the digital beamformer, as a spatial filter, by using the hybrid antenna array at an operating frequency <a href="#" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1742/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> To address practical challenges in establishing and maintaining robust wireless connectivity such as multi-path effects, low latency, size reduction, and high data rate, we have deployed the digital beamformer, as a spatial filter, by using the hybrid antenna array at an operating frequency of 10 GHz. The proposed digital beamformer utilizes a combination of the two well-established beamforming techniques of minimum variance distortionless response (MVDR) and linearly constrained minimum variance (LCMV). In this case, the MVDR beamforming method updates weight vectors on the FPGA board, while the LCMV beamforming technique performs nullsteering in directions of interference signals in the real environment. The most well-established machine learning technique of support vector machine (SVM) for the Direction of Arrival (DoA) estimation is limited to problems with linearly-separable datasets. To overcome the aforementioned constraint, the quadratic surface support vector machine (QS-SVM) classifier with a small regularizer has been used in the proposed beamformer for the DoA estimation in addition to the two beamforming techniques of LCMV and MVDR. In this work, we have assumed that five hybrid array antennas and three sources are available, at which one of the sources transmits the signal of interest. The QS-SVM-based beamformer has been deployed on the FPGA board for spatially filtering two signals from undesired directions and passing only one of the signals from the desired direction. The simulation results have verified the strong performance of the QS-SVM-based beamformer in suppressing interference signals, which are accompanied by placing deep nulls with powers less than −10 dB in directions of interference signals, and transferring the desired signal. Furthermore, we have verified that the performance of the QS-SVM-based beamformer yields other advantages including average latency time in the order of milliseconds, performance efficiency of more than 90%, and throughput of nearly 100%. <a href="/1424-8220/23/3/1742">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Section <a href="/journal/sensors/sections/communications">Communications</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1742/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1057525"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1057525"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1057525" data-cycle-prev="#prev1057525" data-cycle-progressive="#images1057525" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1057525-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/sensors/sensors-23-01742/article_deploy/html/images/sensors-23-01742-g001-550.jpg?1675999640" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1057525" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1057525-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01742/article_deploy/html/images/sensors-23-01742-g002-550.jpg?1675999644'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1057525-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01742/article_deploy/html/images/sensors-23-01742-g003-550.jpg?1675999642'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1057525-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01742/article_deploy/html/images/sensors-23-01742-g004-550.jpg?1675999646'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1057525-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01742/article_deploy/html/images/sensors-23-01742-g005-550.jpg?1675999631'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1057525-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01742/article_deploy/html/images/sensors-23-01742-g006-550.jpg?1675999636'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1057525-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01742/article_deploy/html/images/sensors-23-01742-g007-550.jpg?1675999632'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1057525-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01742/article_deploy/html/images/sensors-23-01742-g008-550.jpg?1675999640'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1057525-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01742/article_deploy/html/images/sensors-23-01742-g009-550.jpg?1675999638'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1057525-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01742/article_deploy/html/images/sensors-23-01742-g010-550.jpg?1675999635'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1057525-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01742/article_deploy/html/images/sensors-23-01742-g011-550.jpg?1675999643'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1057525-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01742/article_deploy/html/images/sensors-23-01742-g012-550.jpg?1675999632'><p>Figure 12</p></div> --- <div class='openpopupgallery' data-imgindex='12' data-target='article-1057525-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01742/article_deploy/html/images/sensors-23-01742-g013-550.jpg?1675999647'><p>Figure 13</p></div></script></div></div><div id="article-1057525-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/sensors/sensors-23-01742/article_deploy/html/images/sensors-23-01742-g001-550.jpg?1675999640" title=" <strong>Figure 1</strong><br/> <p>A demonstration of the 3D configuration of the hybrid antenna array with bowtie elements at the frequency of the operation of 10 GHz with bowtie elements. (<b>a</b>) The assigned design parameters of the hybrid antenna array are listed in detail in <a href="#sensors-23-01742-t001" class="html-table">Table 1</a>. In this work, each hybrid antenna array consists of three cylindrical antenna arrays [<a href="#B15-sensors-23-01742" class="html-bibr">15</a>], and one circular antenna array [<a href="#B16-sensors-23-01742" class="html-bibr">16</a>], and (<b>b</b>) a top view of the bowtie antenna; <span class="html-italic">l</span>: arm length, <math display="inline"><semantics> <mi>α</mi> </semantics></math>: flare angle, <span class="html-italic">g</span>: feed gap, <span class="html-italic">w</span>: bar width, <span class="html-italic">b</span>: bar length. In this work, the design parameters of each bowtie element are assumed to be <span class="html-italic">l</span> = 6 mm, <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>=</mo> <msup> <mn>60</mn> <mo>∘</mo> </msup> </mrow> </semantics></math>, <span class="html-italic">g</span> = 0.02 mm, <span class="html-italic">w</span> = 0.02 mm, and its thickness is equal to 0.01 mm [<a href="#B7-sensors-23-01742" class="html-bibr">7</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1742'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01742/article_deploy/html/images/sensors-23-01742-g002-550.jpg?1675999644" title=" <strong>Figure 2</strong><br/> <p>Procedures for the DoA estimation using the QS-SVM-based digital beamformer. It is assumed that there are <span class="html-italic">M</span> numbers of the proposed hybrid array antennas in <a href="#sensors-23-01742-f001" class="html-fig">Figure 1</a>b of [<a href="#B7-sensors-23-01742" class="html-bibr">7</a>]. Each hybrid antenna array consists of 140 elements whose outputs have been measured. The proposed QS-SVM algorithm for the DoA estimation measures outputs of <span class="html-italic">N</span> array elements and predicts the directions of <span class="html-italic">L</span> signals impinging on the array elements. In this figure, position vectors are supposed to be in a two-dimensional plane of <math display="inline"><semantics> <mi>θ</mi> </semantics></math> and <math display="inline"><semantics> <mi>ϕ</mi> </semantics></math>. The process of the DoA estimation is to monitor the <span class="html-italic">N</span> outputs of antenna elements and predict the angle of arrival of <span class="html-italic">L</span> signals, <math display="inline"><semantics> <mrow> <mi>l</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>…</mo> <mo>,</mo> <mi>L</mi> </mrow> </semantics></math>. <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mi>M</mi> <mo>×</mo> <mn>140</mn> </mrow> </semantics></math>. M = 5 in this work. <math display="inline"><semantics> <msup> <mi>K</mi> <mo>′</mo> </msup> </semantics></math> denotes a number of sources that we can obtain after the DoA estimator.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1742'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01742/article_deploy/html/images/sensors-23-01742-g003-550.jpg?1675999642" title=" <strong>Figure 3</strong><br/> <p>(<b>a</b>) A demonstration of the proposed QS-SVM-based digital beamformer. Here, position vectors are assumed to be in a plane of <math display="inline"><semantics> <mi>θ</mi> </semantics></math> variable and <math display="inline"><semantics> <mi>ϕ</mi> </semantics></math> constant. An angle of <math display="inline"><semantics> <msup> <mn>45</mn> <mo>∘</mo> </msup> </semantics></math> is the direction of a desired signal, and the steering angle should be identical to it. However, angles of <math display="inline"><semantics> <msup> <mn>30</mn> <mo>∘</mo> </msup> </semantics></math>, and <math display="inline"><semantics> <msup> <mn>50</mn> <mo>∘</mo> </msup> </semantics></math> represent the directions of undesired signals, and (<b>b</b>) a demonstration of the proposed QS-SVM-based digital beamformer in the real and hardware environments.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1742'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01742/article_deploy/html/images/sensors-23-01742-g004-550.jpg?1675999646" title=" <strong>Figure 4</strong><br/> <p>A demonstration of the spatial LCMV beamforming technique in the real environment integrated with the FPGA blocks. The direction of the desired source is <math display="inline"><semantics> <msup> <mn>45</mn> <mo>∘</mo> </msup> </semantics></math>. However, two other directions of <math display="inline"><semantics> <msup> <mn>50</mn> <mo>∘</mo> </msup> </semantics></math> and <math display="inline"><semantics> <msup> <mn>30</mn> <mo>∘</mo> </msup> </semantics></math> impinge on the hybrid array antennas as unwanted signals. The LCMV beamforming technique is capable of spatially filtering unwanted signals.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1742'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01742/article_deploy/html/images/sensors-23-01742-g005-550.jpg?1675999631" title=" <strong>Figure 5</strong><br/> <p>A demonstration of the complex Q-less QR forward-backward substitute on the FPGA board. In this work, Quadratic programming (QP) has been employed for solving the QS-SVM algorithm. The QP solver refers to the mathematical problem of finding weight vectors <math display="inline"><semantics> <mi mathvariant="bold">w</mi> </semantics></math> in Equation (<a href="#FD11-sensors-23-01742" class="html-disp-formula">11</a>) with respect to <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>(</mo> <mi mathvariant="bold">x</mi> <mo>)</mo> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> and under the aforementioned constraints of Equation (<a href="#FD17-sensors-23-01742" class="html-disp-formula">17</a>) and (<a href="#FD18-sensors-23-01742" class="html-disp-formula">18</a>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1742'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01742/article_deploy/html/images/sensors-23-01742-g006-550.jpg?1675999636" title=" <strong>Figure 6</strong><br/> <p>The aforementioned subsystem in <a href="#sensors-23-01742-f004" class="html-fig">Figure 4</a>, the LCMV beamforming technique, and other mathematical operations in the real environment. The Simulink demonstration of all mathematical operations in the MATLAB platform with detail.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1742'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01742/article_deploy/html/images/sensors-23-01742-g007-550.jpg?1675999632" title=" <strong>Figure 7</strong><br/> <p>Hardware implementation of the proposed digital SVM-based beamformer on the FPGA platform.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1742'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01742/article_deploy/html/images/sensors-23-01742-g008-550.jpg?1675999640" title=" <strong>Figure 8</strong><br/> <p>The quadrature programming solver for solving the QS-SVM problem in the HDL implementation.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1742'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01742/article_deploy/html/images/sensors-23-01742-g009-550.jpg?1675999638" title=" <strong>Figure 9</strong><br/> <p>(<b>a</b>) A hardware demonstration for the inner products in the QS-SVM-based beamformer using HDL, (<b>b</b>) QS-SVM inner product, and (<b>c</b>) HDL complex multiplication.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1742'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01742/article_deploy/html/images/sensors-23-01742-g010-550.jpg?1675999635" title=" <strong>Figure 10</strong><br/> <p>The hardware implementation of HDL complex multiplications of the QS-SVM optimization method.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1742'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01742/article_deploy/html/images/sensors-23-01742-g011-550.jpg?1675999643" title=" <strong>Figure 11</strong><br/> <p>The spatial filtering performance of the QS-SVM-based digital beamformer on the FPGA board. The obtained results are associated with the part of the pattern plot in <a href="#sensors-23-01742-f003" class="html-fig">Figure 3</a>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1742'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01742/article_deploy/html/images/sensors-23-01742-g012-550.jpg?1675999632" title=" <strong>Figure 12</strong><br/> <p>Variation of throughputs of the classification performance of the QS-SVM-based beamformer in terms of SNRs consistent with <a href="#sensors-23-01742-f003" class="html-fig">Figure 3</a>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1742'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01742/article_deploy/html/images/sensors-23-01742-g013-550.jpg?1675999647" title=" <strong>Figure 13</strong><br/> <p>Latency analysis for different batch sizes consistent with <a href="#sensors-23-01742-f003" class="html-fig">Figure 3</a>. Latency is presented in milliseconds and averaged over the test set of the network.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1742'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1057474" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 12 pages, 2679 KiB   </span> <a href="/1424-8220/23/3/1741/pdf?version=1675757481" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="A New Method for Training CycleGAN to Enhance Images of Cold Seeps in the Qiongdongnan Sea" data-journal="sensors"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1424-8220/23/3/1741">A New Method for Training CycleGAN to Enhance Images of Cold Seeps in the Qiongdongnan Sea</a> <div class="authors"> by <span class="inlineblock "><strong>Yuanheng Li</strong>, </span><span class="inlineblock "><strong>Shengxiong Yang</strong>, </span><span class="inlineblock "><strong>Yuehua Gong</strong>, </span><span class="inlineblock "><strong>Jingya Cao</strong>, </span><span class="inlineblock "><strong>Guang Hu</strong>, </span><span class="inlineblock "><strong>Yutian Deng</strong>, </span><span class="inlineblock "><strong>Dongmei Tian</strong> and </span><span class="inlineblock "><strong>Junming Zhou</strong></span> </div> <div class="color-grey-dark"> <em>Sensors</em> <b>2023</b>, <em>23</em>(3), 1741; <a href="https://doi.org/10.3390/s23031741">https://doi.org/10.3390/s23031741</a> - 3 Feb 2023 </div> <a href="/1424-8220/23/3/1741#metrics">Cited by 2</a> | Viewed by 1936 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Clear underwater images can help researchers detect cold seeps, gas hydrates, and biological resources. However, the quality of these images suffers from nonuniform lighting, a limited range of visibility, and unwanted signals. CycleGAN has been broadly studied in regard to underwater image enhancement, <a href="#" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1741/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Clear underwater images can help researchers detect cold seeps, gas hydrates, and biological resources. However, the quality of these images suffers from nonuniform lighting, a limited range of visibility, and unwanted signals. CycleGAN has been broadly studied in regard to underwater image enhancement, but it is difficult to apply the model for the further detection of Haima cold seeps in the South China Sea because the model can be difficult to train if the dataset used is not appropriate. In this article, we devise a new method of building a dataset using MSRCR and choose the best images based on the widely used UIQM scheme to build the dataset. The experimental results show that a good CycleGAN could be trained with the dataset using the proposed method. The model has good potential for applications in detecting the Haima cold seeps and can be applied to other cold seeps, such as the cold seeps in the North Sea. We conclude that the method used for building the dataset can be applied to train CycleGAN when enhancing images from cold seeps. <a href="/1424-8220/23/3/1741">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/sensors/special_issues/nonlinear_signal_image_processing_trends_directions ">Nonlinear Signal and Image Processing: Current Trends and Future Directions</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1741/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1057474"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1057474"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1057474" data-cycle-prev="#prev1057474" data-cycle-progressive="#images1057474" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1057474-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/sensors/sensors-23-01741/article_deploy/html/images/sensors-23-01741-g001-550.jpg?1675757553" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1057474" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1057474-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01741/article_deploy/html/images/sensors-23-01741-g002-550.jpg?1675757550'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1057474-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01741/article_deploy/html/images/sensors-23-01741-g003-550.jpg?1675757554'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1057474-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01741/article_deploy/html/images/sensors-23-01741-g004-550.jpg?1675757549'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1057474-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01741/article_deploy/html/images/sensors-23-01741-g005-550.jpg?1675757545'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1057474-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01741/article_deploy/html/images/sensors-23-01741-g006-550.jpg?1675757547'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1057474-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01741/article_deploy/html/images/sensors-23-01741-g007-550.jpg?1675757556'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1057474-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01741/article_deploy/html/images/sensors-23-01741-g008-550.jpg?1675757552'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1057474-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01741/article_deploy/html/images/sensors-23-01741-g009-550.jpg?1675757550'><p>Figure 9</p></div></script></div></div><div id="article-1057474-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/sensors/sensors-23-01741/article_deploy/html/images/sensors-23-01741-g001-550.jpg?1675757553" title=" <strong>Figure 1</strong><br/> <p>CycleGAN enhancement of an underwater image with an inappropriate dataset.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1741'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01741/article_deploy/html/images/sensors-23-01741-g002-550.jpg?1675757550" title=" <strong>Figure 2</strong><br/> <p>Basic structure of CycleGAN.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1741'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01741/article_deploy/html/images/sensors-23-01741-g003-550.jpg?1675757554" title=" <strong>Figure 3</strong><br/> <p>(<b>a</b>) Forward and (<b>b</b>) backward consistency losses.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1741'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01741/article_deploy/html/images/sensors-23-01741-g004-550.jpg?1675757549" title=" <strong>Figure 4</strong><br/> <p>Enhancement of MSRCR under different parameters.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1741'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01741/article_deploy/html/images/sensors-23-01741-g005-550.jpg?1675757545" title=" <strong>Figure 5</strong><br/> <p>Training loss for CycleGAN.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1741'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01741/article_deploy/html/images/sensors-23-01741-g006-550.jpg?1675757547" title=" <strong>Figure 6</strong><br/> <p>Enhancement results for the trained CycleGAN.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1741'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01741/article_deploy/html/images/sensors-23-01741-g007-550.jpg?1675757556" title=" <strong>Figure 7</strong><br/> <p>Results for the trained CycleGAN’s enhancement of images in the North Sea [<a href="#B39-sensors-23-01741" class="html-bibr">39</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1741'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01741/article_deploy/html/images/sensors-23-01741-g008-550.jpg?1675757552" title=" <strong>Figure 8</strong><br/> <p>Degradation of enhancement results.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1741'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01741/article_deploy/html/images/sensors-23-01741-g009-550.jpg?1675757550" title=" <strong>Figure 9</strong><br/> <p>Enhanced images using EnlightenGAN.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1741'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1057522" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 11 pages, 1831 KiB   </span> <a href="/1424-8220/23/3/1740/pdf?version=1675440104" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Assessment of a Side-Row Continuous Canopy Shaking Harvester and Its Adaptability to the Portuguese Cobrançosa Variety in High-Density Olive Orchards" data-journal="sensors"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1424-8220/23/3/1740">Assessment of a Side-Row Continuous Canopy Shaking Harvester and Its Adaptability to the Portuguese Cobrançosa Variety in High-Density Olive Orchards</a> <div class="authors"> by <span class="inlineblock "><strong>Fernando Aragon-Rodriguez</strong>, </span><span class="inlineblock "><strong>António B. Dias</strong>, </span><span class="inlineblock "><strong>Anacleto Pinheiro</strong>, </span><span class="inlineblock "><strong>José Peça</strong>, </span><span class="inlineblock "><strong>Ivo Lourenço Días</strong> and </span><span class="inlineblock "><strong>Sergio Castro-Garcia</strong></span> </div> <div class="color-grey-dark"> <em>Sensors</em> <b>2023</b>, <em>23</em>(3), 1740; <a href="https://doi.org/10.3390/s23031740">https://doi.org/10.3390/s23031740</a> - 3 Feb 2023 </div> <a href="/1424-8220/23/3/1740#metrics">Cited by 2</a> | Viewed by 1935 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The olive tree is an important crop in Portugal, where different levels of intensification coexist. The traditional olive orchards present profitability problems, mainly due to harvesting, so there has been a drastic reconversion towards high-density or super-high-density olive orchards. The latter present major <a href="#" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1740/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> The olive tree is an important crop in Portugal, where different levels of intensification coexist. The traditional olive orchards present profitability problems, mainly due to harvesting, so there has been a drastic reconversion towards high-density or super-high-density olive orchards. The latter present major constraints due to very specific needs for their use, being practically destined for new orchards. Consequently, the possibility of using systems based on canopy shakers in high-density olive orchards with local varieties is promising. The objective of this work is to evaluate a prototype canopy shaker for the harvesting of high-density olive orchards of the Portuguese variety ‘Cobrançosa’. The evaluation is based on the study of canopy shaking in order to adapt canopy training and the adaptability of the machine. For this purpose, the vibration of 72 points of the tree canopy was recorded and a qualitative assessment of the harvest was carried out. Differences were found between the different zones according to the direction of the forward movement of the harvester and the distance to the trunk. These differences were associated with the values obtained for fruit detachment, and a greater quantity of fruit was harvested in the areas of the canopy in contact with the rods. <a href="/1424-8220/23/3/1740">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Section <a href="/journal/sensors/sections/Smart_Agriculture">Smart Agriculture</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1740/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1057522"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1057522"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1057522" data-cycle-prev="#prev1057522" data-cycle-progressive="#images1057522" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1057522-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/sensors/sensors-23-01740/article_deploy/html/images/sensors-23-01740-g001-550.jpg?1675440174" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1057522" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1057522-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01740/article_deploy/html/images/sensors-23-01740-g002-550.jpg?1675440171'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1057522-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01740/article_deploy/html/images/sensors-23-01740-g003-550.jpg?1675440169'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1057522-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01740/article_deploy/html/images/sensors-23-01740-g004-550.jpg?1675440168'><p>Figure 4</p></div></script></div></div><div id="article-1057522-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/sensors/sensors-23-01740/article_deploy/html/images/sensors-23-01740-g001-550.jpg?1675440174" title=" <strong>Figure 1</strong><br/> <p>Prototype used for mechanised harvesting of high-density olive orchard. (<b>a</b>) Side view of the machine; (<b>b</b>) rear view of the machine.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1740'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01740/article_deploy/html/images/sensors-23-01740-g002-550.jpg?1675440171" title=" <strong>Figure 2</strong><br/> <p>Division of the different study areas of the canopy. (<b>a</b>) Radial division of the zones; (<b>b</b>) division of the zones according to the direction of the forward movement of the harvester in the zones.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1740'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01740/article_deploy/html/images/sensors-23-01740-g003-550.jpg?1675440169" title=" <strong>Figure 3</strong><br/> <p>Qualitative comparison of quantity of fruit before and after mechanised harvesting in the different radial zones from the tree trunk in the study area (1.1 to 3.0 m above the ground). A different letter indicates significant differences (Friedman’s test, <span class="html-italic">p</span> &lt; 0.05).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1740'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01740/article_deploy/html/images/sensors-23-01740-g004-550.jpg?1675440168" title=" <strong>Figure 4</strong><br/> <p>Qualitative comparison of fruit quantity after mechanised harvesting in the machine start and exit zone at the canopy of the tree in the study area (1.1 to 3.0 m above the ground) in different radial zones. A different letter indicates significant differences (Friedman’s test, <span class="html-italic">p</span> &lt; 0.05).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1740'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1057417" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 16 pages, 27318 KiB   </span> <a href="/1424-8220/23/3/1739/pdf?version=1675743799" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Measurement of Water Vapor Condensation on Apple Surfaces during Controlled Atmosphere Storage" data-journal="sensors"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1424-8220/23/3/1739">Measurement of Water Vapor Condensation on Apple Surfaces during Controlled Atmosphere Storage</a> <div class="authors"> by <span class="inlineblock "><strong>Manfred Linke</strong>, </span><span class="inlineblock "><strong>Ulrike Praeger</strong>, </span><span class="inlineblock "><strong>Daniel A. Neuwald</strong> and </span><span class="inlineblock "><strong>Martin Geyer</strong></span> </div> <div class="color-grey-dark"> <em>Sensors</em> <b>2023</b>, <em>23</em>(3), 1739; <a href="https://doi.org/10.3390/s23031739">https://doi.org/10.3390/s23031739</a> - 3 Feb 2023 </div> <a href="/1424-8220/23/3/1739#metrics">Cited by 6</a> | Viewed by 2934 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Apples are stored at temperatures close to 0 °C and high relative humidity (up to 95%) under controlled atmosphere conditions. Under these conditions, the cyclic operation of the refrigeration machine and the associated temperature fluctuations can lead to localized undershoots of the dew <a href="#" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1739/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Apples are stored at temperatures close to 0 °C and high relative humidity (up to 95%) under controlled atmosphere conditions. Under these conditions, the cyclic operation of the refrigeration machine and the associated temperature fluctuations can lead to localized undershoots of the dew point on fruit surfaces. The primary question for the present study was to prove that such condensation processes can be measured under practical conditions during apple storage. Using the example of a measuring point in the upper apple layer of a large bin in the supply air area, this evidence was provided. Using two independent measuring methods, a wetness sensor attached to the apple surface and determination of climatic conditions near the fruit, the phases of condensation, namely active condensation and evaporation, were measured over three weeks as a function of the operating time of the cooling system components (refrigeration machine, fans, defrosting regime). The system for measurement and continuous data acquisition in the case of an airtight CA-storage room is presented and the influence of the operation of the cooling system components in relation to condensation phenomena was evaluated. Depending on the set point specifications for ventilation and defrost control, condensed water was present on the apple surface between 33.4% and 100% of the duration of the varying cooling/re-warming cycles. <a href="/1424-8220/23/3/1739">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Collection <a href=" /journal/sensors/topical_collections/Sensors_Environmental_Food ">Sensors and Biosensors for Environmental and Food Applications</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1739/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1057417"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1057417"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1057417" data-cycle-prev="#prev1057417" data-cycle-progressive="#images1057417" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1057417-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/sensors/sensors-23-01739/article_deploy/html/images/sensors-23-01739-g001-550.jpg?1675743869" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1057417" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1057417-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01739/article_deploy/html/images/sensors-23-01739-g002-550.jpg?1675743873'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1057417-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01739/article_deploy/html/images/sensors-23-01739-g003-550.jpg?1675743876'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1057417-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01739/article_deploy/html/images/sensors-23-01739-g004-550.jpg?1675743882'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1057417-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01739/article_deploy/html/images/sensors-23-01739-g005-550.jpg?1675743877'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1057417-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01739/article_deploy/html/images/sensors-23-01739-g006-550.jpg?1675743871'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1057417-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01739/article_deploy/html/images/sensors-23-01739-g007-550.jpg?1675743883'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1057417-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01739/article_deploy/html/images/sensors-23-01739-g008-550.jpg?1675743878'><p>Figure 8</p></div></script></div></div><div id="article-1057417-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/sensors/sensors-23-01739/article_deploy/html/images/sensors-23-01739-g001-550.jpg?1675743869" title=" <strong>Figure 1</strong><br/> <p>Overall view of the cold room with storage bins, cooling unit (evaporator and fans), all bins measured (blue dots), and selected measuring point (red circle) in the supply air area.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1739'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01739/article_deploy/html/images/sensors-23-01739-g002-550.jpg?1675743873" title=" <strong>Figure 2</strong><br/> <p>Wetness sensor and infrared sensor at the measuring apple (<b>a</b>) and grouping of the sensors in the upper layer of the apple bin (bottom left, wetness sensor and infrared temperature sensor attached to the same apple; bottom right, air temperature and relative humidity sensor) (<b>b</b>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1739'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01739/article_deploy/html/images/sensors-23-01739-g003-550.jpg?1675743876" title=" <strong>Figure 3</strong><br/> <p>Data acquisition system in master-slave configuration, two digital sensors and one analog sensor with operational amplifier for each of 3 measuring points. I2C: inter-integrated circuit; SCL: serial clock; SDA: serial data; OA1: operational amplifier measuring point 1; VCC: voltage at common collector; GND: ground; A1: analog input pin 1; RTC: real time clock; T: temperature; Tsurf: surface temperature; rH: relative humidity; SPI: serial peripheral interface.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1739'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01739/article_deploy/html/images/sensors-23-01739-g004-550.jpg?1675743882" title=" <strong>Figure 4</strong><br/> <p>Tested electrode structures of the wetness sensor (<b>A</b>), infrared temperature sensor with holding device (<b>B</b>) and temperature/humidity sensor with holding device/basket (<b>C</b>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1739'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01739/article_deploy/html/images/sensors-23-01739-g005-550.jpg?1675743877" title=" <strong>Figure 5</strong><br/> <p>(<b>A</b>) Condensate on a cold apple surface during re-warming with free convection: Distribution of condensed water droplets in the sensor film area. (<b>B</b>) No sign of condensation in the sensor area after progressive re-warming of a dry apple surface.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1739'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01739/article_deploy/html/images/sensors-23-01739-g006-550.jpg?1675743871" title=" <strong>Figure 6</strong><br/> <p>Cycle 1 without defrosting process in the re-warming phase. (<b>A</b>) Parameters of the cold air (temperature, relative humidity) adjacent to the produce, operating mode of the components of the cooling system (cooling, defrost, ventilation), and total retention time of condensed water (cond/eva) on the fruit surface. (<b>B</b>) Produce surface temperature, dew point temperature of the surrounding air, and signal history of the wetness sensor. The signal history of all sensors shown here covers a period of 3 h and 20 min. All parameters were measured in the top layer of apples in the bin in middle row of tier 3.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1739'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01739/article_deploy/html/images/sensors-23-01739-g007-550.jpg?1675743883" title=" <strong>Figure 7</strong><br/> <p>Cycle 2 with defrosting process during re-warming phase. (<b>A</b>) Parameters of the cold air (temperature, relative humidity) adjacent to the produce, operating mode of the components of the cooling system (cooling, defrost, ventilation), and total retention time of condensed water (cond/eva) on the fruit surface. (<b>B</b>) Produce surface temperature, dew point temperature of the surrounding air, and signal history of the wetness sensor. The signal history of all sensors shown here covers a period of 3 h and 20 min. All parameters were measured in the top layer of apples in the bin in middle row of tier 3.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1739'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01739/article_deploy/html/images/sensors-23-01739-g008-550.jpg?1675743878" title=" <strong>Figure 8</strong><br/> <p>Cycle 3 with interruption of the cooling phase. (<b>A</b>) Parameters of the cold air (temperature, relative humidity) adjacent to the produce, operating mode of the components of the cooling system (cooling, defrost, ventilation), and total retention time of condensed water (cond/eva) on the fruit surface. (<b>B</b>) Produce surface temperature, dew point temperature of the surrounding air, and signal history of the wetness sensor. The signal history of all sensors shown here covers a period of 3 h and 20 min. All parameters were measured in the top layer of apples in the bin in middle row of tier 3.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1739'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1057556" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 13 pages, 4346 KiB   </span> <a href="/1424-8220/23/3/1738/pdf?version=1675757372" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Validity and Reliability of Inertial Measurement Unit (IMU)-Derived 3D Joint Kinematics in Persons Wearing Transtibial Prosthesis" data-journal="sensors"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label feature" data-dropdown="drop-article-label-feature" aria-expanded="false">Feature Paper</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1424-8220/23/3/1738">Validity and Reliability of Inertial Measurement Unit (IMU)-Derived 3D Joint Kinematics in Persons Wearing Transtibial Prosthesis</a> <div class="authors"> by <span class="inlineblock "><strong>Jutima Rattanakoch</strong>, </span><span class="inlineblock "><strong>Manunchaya Samala</strong>, </span><span class="inlineblock "><strong>Weerawat Limroongreungrat</strong>, </span><span class="inlineblock "><strong>Gary Guerra</strong>, </span><span class="inlineblock "><strong>Kittichai Tharawadeepimuk</strong>, </span><span class="inlineblock "><strong>Ampika Nanbancha</strong>, </span><span class="inlineblock "><strong>Wisavaporn Niamsang</strong>, </span><span class="inlineblock "><strong>Pichitpol Kerdsomnuek</strong> and </span><span class="inlineblock "><strong>Sarit Suwanmana</strong></span> </div> <div class="color-grey-dark"> <em>Sensors</em> <b>2023</b>, <em>23</em>(3), 1738; <a href="https://doi.org/10.3390/s23031738">https://doi.org/10.3390/s23031738</a> - 3 Feb 2023 </div> <a href="/1424-8220/23/3/1738#metrics">Cited by 9</a> | Viewed by 3500 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Background: A validity and reliability assessment of inertial measurement unit (IMU)-derived joint angular kinematics during walking is a necessary step for motion analysis in the lower extremity prosthesis user population. This study aimed to assess the accuracy and reliability of an inertial measurement <a href="#" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1738/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Background: A validity and reliability assessment of inertial measurement unit (IMU)-derived joint angular kinematics during walking is a necessary step for motion analysis in the lower extremity prosthesis user population. This study aimed to assess the accuracy and reliability of an inertial measurement unit (IMU) system compared to an optical motion capture (OMC) system in transtibial prosthesis (TTP) users. Methods: Thirty TTP users were recruited and underwent simultaneous motion capture from IMU and OMC systems during walking. Reliability and validity were assessed using intra- and inter-subject variability with standard deviation (S.D.), average S.D., and intraclass correlation coefficient (ICC). Results: The intra-subject S.D. for all rotations of the lower limb joints were less than 1° for both systems. The IMU system had a lower mean S.D. (<sup>o</sup>), as seen in inter-subject variability. The ICC revealed good to excellent agreement between the two systems for all sagittal kinematic parameters. Conclusion: All joint angular kinematic comparisons supported the IMU system’s results as comparable to OMC. The IMU was capable of precise sagittal plane motion data and demonstrated validity and reliability to OMC. These findings evidence that when compared to OMC, an IMU system may serve well in evaluating the gait of lower limb prosthesis users. <a href="/1424-8220/23/3/1738">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/sensors/special_issues/BSHMM ">Biomedical Sensing for Human Motion Monitoring</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1738/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1057556"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1057556"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1057556" data-cycle-prev="#prev1057556" data-cycle-progressive="#images1057556" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1057556-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/sensors/sensors-23-01738/article_deploy/html/images/sensors-23-01738-g001-550.jpg?1675757451" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1057556" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1057556-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01738/article_deploy/html/images/sensors-23-01738-g002-550.jpg?1675757453'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1057556-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01738/article_deploy/html/images/sensors-23-01738-g003-550.jpg?1675757442'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1057556-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01738/article_deploy/html/images/sensors-23-01738-g004-550.jpg?1675757443'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1057556-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01738/article_deploy/html/images/sensors-23-01738-g005-550.jpg?1675757446'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1057556-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01738/article_deploy/html/images/sensors-23-01738-g006-550.jpg?1675757448'><p>Figure 6</p></div></script></div></div><div id="article-1057556-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/sensors/sensors-23-01738/article_deploy/html/images/sensors-23-01738-g001-550.jpg?1675757451" title=" <strong>Figure 1</strong><br/> <p>Flow chart showing the experimental design of the study.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1738'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01738/article_deploy/html/images/sensors-23-01738-g002-550.jpg?1675757453" title=" <strong>Figure 2</strong><br/> <p>Image displaying a single comprehensive set for two systems, Inertial measurement unit (IMU) and cluster-based marker (CBM) set of optical motion capture system (OMC).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1738'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01738/article_deploy/html/images/sensors-23-01738-g003-550.jpg?1675757442" title=" <strong>Figure 3</strong><br/> <p>Images (<b>a</b>,<b>b</b>) are the rigid plates for IMU and CBM set of OMC (<b>c</b>). The cluster-based marker (CBM) set for an optical motion capture (OMC). Both the IMU and the cluster were built with the same coordinate system.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1738'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01738/article_deploy/html/images/sensors-23-01738-g004-550.jpg?1675757443" title=" <strong>Figure 4</strong><br/> <p>Procedure of data processing [<a href="#B26-sensors-23-01738" class="html-bibr">26</a>] for averaged intra-subject variability and average inter-subject variability across the gait cycle.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1738'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01738/article_deploy/html/images/sensors-23-01738-g005-550.jpg?1675757446" title=" <strong>Figure 5</strong><br/> <p>The visual waveform of walking kinematics on the amputated side. The black lines represent IMU and the green lines represent OMC. Note: IMU: Inertial Measurement Unit, OMC: Optical Motion Capture.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1738'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01738/article_deploy/html/images/sensors-23-01738-g006-550.jpg?1675757448" title=" <strong>Figure 6</strong><br/> <p>The visual waveform of walking kinematics on the sound side. The black lines represent IMU and the green lines represent OMC. Note: IMU: Inertial Measurement Unit, OMC: Optical Motion Capture.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1738'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1057396" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 21 pages, 23263 KiB   </span> <a href="/1424-8220/23/3/1737/pdf?version=1675846232" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Optical Panel Inspection Using Explicit Band Gaussian Filtering Methods in Discrete Cosine Domain" data-journal="sensors"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1424-8220/23/3/1737">Optical Panel Inspection Using Explicit Band Gaussian Filtering Methods in Discrete Cosine Domain</a> <div class="authors"> by <span class="inlineblock "><strong>Hong-Dar Lin</strong>, </span><span class="inlineblock "><strong>Huan-Hua Tsai</strong>, </span><span class="inlineblock "><strong>Chou-Hsien Lin</strong> and </span><span class="inlineblock "><strong>Hung-Tso Chang</strong></span> </div> <div class="color-grey-dark"> <em>Sensors</em> <b>2023</b>, <em>23</em>(3), 1737; <a href="https://doi.org/10.3390/s23031737">https://doi.org/10.3390/s23031737</a> - 3 Feb 2023 </div> <a href="/1424-8220/23/3/1737#metrics">Cited by 2</a> | Viewed by 1550 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Capacitive touch panels (CTPs) have the merits of being waterproof, antifouling, scratch resistant, and capable of rapid response, making them more popular in various touch electronic products. However, the CTP has a multilayer structure, and the background is a directional texture. The inspection <a href="#" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1737/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Capacitive touch panels (CTPs) have the merits of being waterproof, antifouling, scratch resistant, and capable of rapid response, making them more popular in various touch electronic products. However, the CTP has a multilayer structure, and the background is a directional texture. The inspection work is more difficult when the defect area is small and occurs in the textured background. This study focused mainly on the automated defect inspection of CTPs with structural texture on the surface, using the spectral attributes of the discrete cosine transform (DCT) with the proposed three-way double-band Gaussian filtering (3W-DBGF) method. With consideration to the bandwidth and angle of the high-energy region combined with the characteristics of band filtering, threshold filtering, and Gaussian distribution filtering, the frequency values with higher energy are removed, and after reversal to the spatial space, the textured background can be weakened and the defects enhanced. Finally, we use simple statistics to set binarization threshold limits that can accurately separate defects from the background. The detection outcomes showed that the flaw detection rate of the DCT-based 3W-DBGF approach was 94.21%, the false-positive rate of the normal area was 1.97%, and the correct classification rate was 98.04%. <a href="/1424-8220/23/3/1737">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/sensors/special_issues/E6TIR8G8PK ">Computer Vision and Sensing Technologies for Industrial Quality Inspection</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1737/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1057396"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1057396"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1057396" data-cycle-prev="#prev1057396" data-cycle-progressive="#images1057396" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1057396-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/sensors/sensors-23-01737/article_deploy/html/images/sensors-23-01737-g001-550.jpg?1675846315" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1057396" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1057396-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01737/article_deploy/html/images/sensors-23-01737-g002-550.jpg?1675846320'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1057396-popup'><span class="helper"></span><img 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src='https://pub.mdpi-res.com/sensors/sensors-23-01737/article_deploy/html/images/sensors-23-01737-g010-550.jpg?1675846334'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1057396-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01737/article_deploy/html/images/sensors-23-01737-g011-550.jpg?1675846327'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1057396-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01737/article_deploy/html/images/sensors-23-01737-g012-550.jpg?1675846335'><p>Figure 12</p></div> --- <div class='openpopupgallery' data-imgindex='12' data-target='article-1057396-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01737/article_deploy/html/images/sensors-23-01737-g013-550.jpg?1675846334'><p>Figure 13</p></div> --- <div class='openpopupgallery' data-imgindex='13' data-target='article-1057396-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01737/article_deploy/html/images/sensors-23-01737-g014-550.jpg?1675846313'><p>Figure 14</p></div> --- <div class='openpopupgallery' data-imgindex='14' data-target='article-1057396-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01737/article_deploy/html/images/sensors-23-01737-g015-550.jpg?1675846336'><p>Figure 15</p></div> --- <div class='openpopupgallery' data-imgindex='15' data-target='article-1057396-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01737/article_deploy/html/images/sensors-23-01737-g016-550.jpg?1675846323'><p>Figure 16</p></div> --- <div class='openpopupgallery' data-imgindex='16' data-target='article-1057396-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01737/article_deploy/html/images/sensors-23-01737-g017-550.jpg?1675846309'><p>Figure 17</p></div> --- <div class='openpopupgallery' data-imgindex='17' data-target='article-1057396-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01737/article_deploy/html/images/sensors-23-01737-g018-550.jpg?1675846312'><p>Figure 18</p></div> --- <div class='openpopupgallery' data-imgindex='18' data-target='article-1057396-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01737/article_deploy/html/images/sensors-23-01737-g019-550.jpg?1675846333'><p>Figure 19</p></div></script></div></div><div id="article-1057396-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/sensors/sensors-23-01737/article_deploy/html/images/sensors-23-01737-g001-550.jpg?1675846315" title=" <strong>Figure 1</strong><br/> <p>CTP images with directional textured backgrounds. (<b>a</b>) A testing sample and a normal image. (<b>b</b>–<b>d</b>) Defective images with three main defect types.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1737'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01737/article_deploy/html/images/sensors-23-01737-g002-550.jpg?1675846320" title=" <strong>Figure 2</strong><br/> <p>The relevant cosine spectrum images of a normal image (<b>a</b>,<b>a1</b>) and a defective image (<b>b</b>,<b>b1</b>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1737'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01737/article_deploy/html/images/sensors-23-01737-g003-550.jpg?1675846324" title=" <strong>Figure 3</strong><br/> <p>The angular relationship between the surface texture of the testing images (<b>a</b>,<b>b</b>) and the corresponding discrete cosine spectrum images (<b>a1</b>,<b>b1</b>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1737'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01737/article_deploy/html/images/sensors-23-01737-g004-550.jpg?1675846338" title=" <strong>Figure 4</strong><br/> <p>Threshold filtering (TF) method applied to the DCT domain.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1737'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01737/article_deploy/html/images/sensors-23-01737-g005-550.jpg?1675846336" title=" <strong>Figure 5</strong><br/> <p>The schematic diagram of the DCT three-way band filtering approach.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1737'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01737/article_deploy/html/images/sensors-23-01737-g006-550.jpg?1675846318" title=" <strong>Figure 6</strong><br/> <p>The filtering effects of the DCT three-way band filtering (3W-BF) method with various cutting widths.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1737'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01737/article_deploy/html/images/sensors-23-01737-g007-550.jpg?1675846308" title=" <strong>Figure 7</strong><br/> <p>The filtering effects of the DCT three-way band-threshold filtering (3W-BTF) method with various cutting widths and threshold values.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1737'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01737/article_deploy/html/images/sensors-23-01737-g008-550.jpg?1675846340" title=" <strong>Figure 8</strong><br/> <p>The DCT three-way band Gaussian filtering (3W-BGF) process for training images.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1737'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01737/article_deploy/html/images/sensors-23-01737-g009-550.jpg?1675846330" title=" <strong>Figure 9</strong><br/> <p>The filtering effects of the DCT three-way band-Gaussian filtering (3W-BGF) method with various cutting widths and energy threshold values.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1737'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01737/article_deploy/html/images/sensors-23-01737-g010-550.jpg?1675846334" title=" <strong>Figure 10</strong><br/> <p>Schematic diagram of the widths of the three-direction main bands and sub-bands of the 3W-DBGF filter.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1737'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01737/article_deploy/html/images/sensors-23-01737-g011-550.jpg?1675846327" title=" <strong>Figure 11</strong><br/> <p>The filtering effects of the DCT-based 3W-DBGF method with various main band and sub-bandwidths and energy threshold values.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1737'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01737/article_deploy/html/images/sensors-23-01737-g012-550.jpg?1675846335" title=" <strong>Figure 12</strong><br/> <p>ROC curves of the thresholds of 50, 100, and 150 using the DCT-based 3W-BTF method with a bandwidth of 1.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1737'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01737/article_deploy/html/images/sensors-23-01737-g013-550.jpg?1675846334" title=" <strong>Figure 13</strong><br/> <p>ROC curves of the <math display="inline"><semantics> <mrow> <msub> <mi>λ</mi> <mn>2</mn> </msub> </mrow> </semantics></math> values of 1, 2, and 3 using the DCT-based 3W-DBGF method with three preset fixed parameters.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1737'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01737/article_deploy/html/images/sensors-23-01737-g014-550.jpg?1675846313" title=" <strong>Figure 14</strong><br/> <p>The ROC curve of various binarization threshold <span class="html-italic">k</span> values obtained using the DCT-based 3W-DBGF method in large-sample experiments.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1737'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01737/article_deploy/html/images/sensors-23-01737-g015-550.jpg?1675846336" title=" <strong>Figure 15</strong><br/> <p>ROC curves of detection performance of the different detection methods.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1737'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01737/article_deploy/html/images/sensors-23-01737-g016-550.jpg?1675846323" title=" <strong>Figure 16</strong><br/> <p>Partial resulting images of different detection methods.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1737'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01737/article_deploy/html/images/sensors-23-01737-g017-550.jpg?1675846309" title=" <strong>Figure 17</strong><br/> <p>ROC curves of the detection benefit using the DCT-based 3W-DBGF method for (<b>a</b>) small offsets (±6°) and (<b>b</b>) large offsets (±15°) in band angles.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1737'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01737/article_deploy/html/images/sensors-23-01737-g018-550.jpg?1675846312" title=" <strong>Figure 18</strong><br/> <p>Partial detection results of using the DCT-based 3W-DBGF method for various levels of offsets in band angles.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1737'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01737/article_deploy/html/images/sensors-23-01737-g019-550.jpg?1675846333" title=" <strong>Figure 19</strong><br/> <p>Partial detection results of CTPs with three different complexity levels of background textures obtained by the proposed methods.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1737'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1057383" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 14 pages, 4558 KiB   </span> <a href="/1424-8220/23/3/1736/pdf?version=1675678852" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Deriving Multiple-Layer Information from a Motion-Sensing Mattress for Precision Care" data-journal="sensors"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1424-8220/23/3/1736">Deriving Multiple-Layer Information from a Motion-Sensing Mattress for Precision Care</a> <div class="authors"> by <span class="inlineblock "><strong>Dorothy Bai</strong>, </span><span class="inlineblock "><strong>Mu-Chieh Ho</strong>, </span><span class="inlineblock "><strong>Bhekumuzi M. Mathunjwa</strong> and </span><span class="inlineblock "><strong>Yeh-Liang Hsu</strong></span> </div> <div class="color-grey-dark"> <em>Sensors</em> <b>2023</b>, <em>23</em>(3), 1736; <a href="https://doi.org/10.3390/s23031736">https://doi.org/10.3390/s23031736</a> - 3 Feb 2023 </div> <a href="/1424-8220/23/3/1736#metrics">Cited by 7</a> | Viewed by 2412 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Bed is often the personal care unit in hospitals, nursing homes, and individuals’ homes. Rich care-related information can be derived from the sensing data from bed. Patient fall is a significant issue in hospitals, many of which are related to getting in and/or <a href="#" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1736/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Bed is often the personal care unit in hospitals, nursing homes, and individuals’ homes. Rich care-related information can be derived from the sensing data from bed. Patient fall is a significant issue in hospitals, many of which are related to getting in and/or out of bed. To prevent bed falls, a motion-sensing mattress was developed for bed-exit detection. A machine learning algorithm deployed on the chip in the control box of the mattress identified the in-bed postures based on the on/off pressure pattern of 30 sensing areas to capture the users’ bed-exit intention. This study aimed to explore how sleep-related data derived from the on/off status of 30 sensing areas of this motion-sensing mattress can be used for multiple layers of precision care information, including wellbeing status on the dashboard and big data analysis for living pattern clustering. This study describes how multiple layers of personalized care-related information are further derived from the motion-sensing mattress, including real-time in-bed/off-bed status, daily records, sleep quality, prolonged pressure areas, and long-term living patterns. Twenty-four mattresses and the smart mattress care system (SMCS) were installed in a dementia nursing home in Taiwan for a field trial. Residents’ on-bed/off-bed data were collected for 12 weeks from August to October 2021. The SMCS was developed to display care-related information via an integrated dashboard as well as sending reminders to caregivers when detecting events such as bed exits and changes in patients’ sleep and living patterns. The ultimate goal is to support caregivers with precision care, reduce their care burden, and increase the quality of care. At the end of the field trial, we interviewed four caregivers for their subjective opinions about whether and how the SMCS helped their work. The caregivers’ main responses included that the SMCS helped caregivers notice the abnormal situation for people with dementia, communicate with family members of the residents, confirm medication adjustments, and whether the standard care procedure was appropriately conducted. Future studies are suggested to focus on integrated care strategy recommendations based on users’ personalized sleep-related data. <a href="/1424-8220/23/3/1736">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/sensors/special_issues/HSP_WD ">Human Signal Processing Based on Wearable Non-invasive Device</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1736/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1057383"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1057383"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1057383" data-cycle-prev="#prev1057383" data-cycle-progressive="#images1057383" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1057383-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/sensors/sensors-23-01736/article_deploy/html/images/sensors-23-01736-g001-550.jpg?1675678934" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1057383" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1057383-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01736/article_deploy/html/images/sensors-23-01736-g002-550.jpg?1675678935'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1057383-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01736/article_deploy/html/images/sensors-23-01736-g003-550.jpg?1675678933'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1057383-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01736/article_deploy/html/images/sensors-23-01736-g004-550.jpg?1675678944'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1057383-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01736/article_deploy/html/images/sensors-23-01736-g005-550.jpg?1675678945'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1057383-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01736/article_deploy/html/images/sensors-23-01736-g006-550.jpg?1675678939'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1057383-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01736/article_deploy/html/images/sensors-23-01736-g007-550.jpg?1675678939'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1057383-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01736/article_deploy/html/images/sensors-23-01736-g008-550.jpg?1675678937'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1057383-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01736/article_deploy/html/images/sensors-23-01736-g009-550.jpg?1675678946'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1057383-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01736/article_deploy/html/images/sensors-23-01736-g010-550.jpg?1675678941'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1057383-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01736/article_deploy/html/images/sensors-23-01736-g011-550.jpg?1675678943'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1057383-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01736/article_deploy/html/images/sensors-23-01736-g012-550.jpg?1675678940'><p>Figure 12</p></div></script></div></div><div id="article-1057383-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/sensors/sensors-23-01736/article_deploy/html/images/sensors-23-01736-g001-550.jpg?1675678934" title=" <strong>Figure 1</strong><br/> <p><span class="html-italic">Whiz</span>Pad is a foam mattress capable of motion sensing.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1736'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01736/article_deploy/html/images/sensors-23-01736-g002-550.jpg?1675678935" title=" <strong>Figure 2</strong><br/> <p>Real-time display of pressure pattern and in-bed posture.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1736'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01736/article_deploy/html/images/sensors-23-01736-g003-550.jpg?1675678933" title=" <strong>Figure 3</strong><br/> <p>In-bed/off-bed record in 24 h.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1736'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01736/article_deploy/html/images/sensors-23-01736-g004-550.jpg?1675678944" title=" <strong>Figure 4</strong><br/> <p>Movement counts and sleep record in 24 h.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1736'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01736/article_deploy/html/images/sensors-23-01736-g005-550.jpg?1675678945" title=" <strong>Figure 5</strong><br/> <p>Two-week norm and norm score.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1736'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01736/article_deploy/html/images/sensors-23-01736-g006-550.jpg?1675678939" title=" <strong>Figure 6</strong><br/> <p>Dashboard for overall status.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1736'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01736/article_deploy/html/images/sensors-23-01736-g007-550.jpg?1675678939" title=" <strong>Figure 7</strong><br/> <p>Prolonged pressure record.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1736'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01736/article_deploy/html/images/sensors-23-01736-g008-550.jpg?1675678937" title=" <strong>Figure 8</strong><br/> <p>Wellbeing dashboard for seven days.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1736'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01736/article_deploy/html/images/sensors-23-01736-g009-550.jpg?1675678946" title=" <strong>Figure 9</strong><br/> <p>Daily in-bed/off-bed patterns of four types: (<b>a</b>) Regular (<b>b</b>) Free (<b>c</b>) Bedridden (<b>d</b>) Leave-home.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1736'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01736/article_deploy/html/images/sensors-23-01736-g010-550.jpg?1675678941" title=" <strong>Figure 10</strong><br/> <p>Living patterns changes of residents over 7 days.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1736'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01736/article_deploy/html/images/sensors-23-01736-g011-550.jpg?1675678943" title=" <strong>Figure 11</strong><br/> <p>The screen displayed at the nursing station for the real-time status of the residents.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1736'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01736/article_deploy/html/images/sensors-23-01736-g012-550.jpg?1675678940" title=" <strong>Figure 12</strong><br/> <p>The list of records of all residents.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1736'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1057379" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 17 pages, 3464 KiB   </span> <a href="/1424-8220/23/3/1735/pdf?version=1675834688" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Multiple In-Mold Sensors for Quality and Process Control in Injection Molding" data-journal="sensors"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1424-8220/23/3/1735">Multiple In-Mold Sensors for Quality and Process Control in Injection Molding</a> <div class="authors"> by <span class="inlineblock "><strong>Richárd Dominik Párizs</strong>, </span><span class="inlineblock "><strong>Dániel Török</strong>, </span><span class="inlineblock "><strong>Tatyana Ageyeva</strong> and </span><span class="inlineblock "><strong>József Gábor Kovács</strong></span> </div> <div class="color-grey-dark"> <em>Sensors</em> <b>2023</b>, <em>23</em>(3), 1735; <a href="https://doi.org/10.3390/s23031735">https://doi.org/10.3390/s23031735</a> - 3 Feb 2023 </div> <a href="/1424-8220/23/3/1735#metrics">Cited by 8</a> | Viewed by 3619 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The simultaneous improvement of injection molding process efficiency and product quality, as required by Industry 4.0, is a complex, non-trivial task that requires a comprehensive approach, which involves a combination of sensoring and information techniques. In this study, we investigated the suitability of <a href="#" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1735/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> The simultaneous improvement of injection molding process efficiency and product quality, as required by Industry 4.0, is a complex, non-trivial task that requires a comprehensive approach, which involves a combination of sensoring and information techniques. In this study, we investigated the suitability of in-mold pressure sensors to control the injection molding process in multi-cavity molds. We have conducted several experiments to show how to optimize the clamping force, switchover, or holding time by measuring only pressure in a multi-cavity mold. The results show that the pressure curves and the pressure integral are suitable for determining optimal clamping force. We also proved that in-channel sensors could be effectively used for a pressure-controlled SWOP. In the volume-controlled method, only the sensors in the cavity were capable of correctly detecting the end of the filling. We proposed a method to optimize the holding phase. In this method, we first determined the integration time of the area under the pressure curve and then performed a model fit using the relationship between the pressure integral and product mass. The saturation curve fitted to the pressure data can easily determine the gate freeze-off time from pressure measurements. <a href="/1424-8220/23/3/1735">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/sensors/special_issues/smart_sensing_civil_infrastructure_monitoring ">Smart Sensing and Artificial Intelligence for Civil Infrastructure Monitoring and Management</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1735/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1057379"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1057379"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1057379" data-cycle-prev="#prev1057379" data-cycle-progressive="#images1057379" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1057379-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/sensors/sensors-23-01735/article_deploy/html/images/sensors-23-01735-g001-550.jpg?1675834764" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1057379" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1057379-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01735/article_deploy/html/images/sensors-23-01735-g002-550.jpg?1675834759'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1057379-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01735/article_deploy/html/images/sensors-23-01735-g003-550.jpg?1675834764'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1057379-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01735/article_deploy/html/images/sensors-23-01735-g004-550.jpg?1675834756'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1057379-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01735/article_deploy/html/images/sensors-23-01735-g005-550.jpg?1675834761'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1057379-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01735/article_deploy/html/images/sensors-23-01735-g006-550.jpg?1675834757'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1057379-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01735/article_deploy/html/images/sensors-23-01735-g007-550.jpg?1675834761'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1057379-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01735/article_deploy/html/images/sensors-23-01735-g008-550.jpg?1675834753'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1057379-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01735/article_deploy/html/images/sensors-23-01735-g009-550.jpg?1675834754'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1057379-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01735/article_deploy/html/images/sensors-23-01735-g010-550.jpg?1675834760'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1057379-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01735/article_deploy/html/images/sensors-23-01735-g011a-550.jpg?1675834758'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1057379-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01735/article_deploy/html/images/sensors-23-01735-g011b-550.jpg?1675834762'><p>Figure 11 Cont.</p></div> --- <div class='openpopupgallery' data-imgindex='12' data-target='article-1057379-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01735/article_deploy/html/images/sensors-23-01735-g012-550.jpg?1675834755'><p>Figure 12</p></div> --- <div class='openpopupgallery' data-imgindex='13' data-target='article-1057379-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01735/article_deploy/html/images/sensors-23-01735-g013-550.jpg?1675834763'><p>Figure 13</p></div> --- <div class='openpopupgallery' data-imgindex='14' data-target='article-1057379-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01735/article_deploy/html/images/sensors-23-01735-g014-550.jpg?1675834756'><p>Figure 14</p></div> --- <div class='openpopupgallery' data-imgindex='15' data-target='article-1057379-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01735/article_deploy/html/images/sensors-23-01735-g015-550.jpg?1675834757'><p>Figure 15</p></div></script></div></div><div id="article-1057379-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/sensors/sensors-23-01735/article_deploy/html/images/sensors-23-01735-g001-550.jpg?1675834764" title=" <strong>Figure 1</strong><br/> <p>A multi-cavity mold and locations of pressure sensor locations.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1735'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01735/article_deploy/html/images/sensors-23-01735-g002-550.jpg?1675834759" title=" <strong>Figure 2</strong><br/> <p>In-mold pressure change as a function of time at different clamping forces (cavity 04, post-gate sensor).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1735'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01735/article_deploy/html/images/sensors-23-01735-g003-550.jpg?1675834764" title=" <strong>Figure 3</strong><br/> <p>The change of the pressure integral (<b>a</b>) and part weight (<b>b</b>) of the inner (07 and 08) and outer (01 and 02) cavities due to a change in the clamping force.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1735'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01735/article_deploy/html/images/sensors-23-01735-g004-550.jpg?1675834756" title=" <strong>Figure 4</strong><br/> <p>The adjusted <span class="html-italic">p</span>-values (from the Tukey–Kramer post hoc test) for each clamping force pair for the pressure integrals (below the diagonal) and part weights values (above the diagonal).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1735'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01735/article_deploy/html/images/sensors-23-01735-g005-550.jpg?1675834761" title=" <strong>Figure 5</strong><br/> <p>The effect of sensor location on switchover pressure (sensors from the flow path to cavity 02).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1735'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01735/article_deploy/html/images/sensors-23-01735-g006-550.jpg?1675834757" title=" <strong>Figure 6</strong><br/> <p>The effect of the switchover pressure threshold on in-mold pressure, measured with a sensor in a runner (tertiary channel sensor to cavity 02).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1735'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01735/article_deploy/html/images/sensors-23-01735-g007-550.jpg?1675834761" title=" <strong>Figure 7</strong><br/> <p>Change of part weight due to the injected melt volume.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1735'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01735/article_deploy/html/images/sensors-23-01735-g008-550.jpg?1675834753" title=" <strong>Figure 8</strong><br/> <p>Maximum pressure for each cavity based on: (<b>a</b>) tertiary channel sensors; (<b>b</b>) pre-gate sensors; (<b>c</b>) post-gate sensors; (<b>d</b>) end-of-cavity sensors.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1735'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01735/article_deploy/html/images/sensors-23-01735-g009-550.jpg?1675834754" title=" <strong>Figure 9</strong><br/> <p>Melt arrival times to the end-of-cavity sensors: (<b>a</b>) v<sub>inj</sub> = 10 cm<sup>3</sup>/s and for (<b>b</b>) v<sub>inj</sub> =110 cm<sup>3</sup>/s (<span class="html-italic">T</span><sub>melt</sub> = 215 °C).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1735'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01735/article_deploy/html/images/sensors-23-01735-g010-550.jpg?1675834760" title=" <strong>Figure 10</strong><br/> <p>Effect of melt temperature and injection rate on the ratio of the longest and shortest melt arrival times: (<b>a</b>) cavity 03–07; (<b>b</b>) cavity 04–08.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1735'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01735/article_deploy/html/images/sensors-23-01735-g011a-550.jpg?1675834758" title=" <strong>Figure 11</strong><br/> <p>In-mold pressure curves as a function of time and holding time measured with a: (<b>a</b>) pre-gate sensor; (<b>b</b>) post-gate sensor (cavity 01).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1735'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01735/article_deploy/html/images/sensors-23-01735-g011b-550.jpg?1675834762" title=" <strong>Figure 11 Cont.</strong><br/> <p>In-mold pressure curves as a function of time and holding time measured with a: (<b>a</b>) pre-gate sensor; (<b>b</b>) post-gate sensor (cavity 01).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1735'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01735/article_deploy/html/images/sensors-23-01735-g012-550.jpg?1675834755" title=" <strong>Figure 12</strong><br/> <p>The changing of the correlation coefficients and their 95% confidence interval between the pressure integral and mass based on integration time and the adjusted probabilities for each correlation (cavity 08).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1735'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01735/article_deploy/html/images/sensors-23-01735-g013-550.jpg?1675834763" title=" <strong>Figure 13</strong><br/> <p>Relationship between the pressure integral and sample mass (R = 0.973 with a significance level of 0.05).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1735'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01735/article_deploy/html/images/sensors-23-01735-g014-550.jpg?1675834756" title=" <strong>Figure 14</strong><br/> <p>Mass and pressure integral as a function of holding time (cavity no. 1, post-gate sensor, p<sub>hold</sub> = 600 bar, integration time 3 s).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1735'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01735/article_deploy/html/images/sensors-23-01735-g015-550.jpg?1675834757" title=" <strong>Figure 15</strong><br/> <p>Predicting gate freeze-off time from the saturation curve from the measurement of mass and pressure.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1735'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1057360" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 24 pages, 3445 KiB   </span> <a href="/1424-8220/23/3/1734/pdf?version=1675927227" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="A GPS-Referenced Wavelength Standard for High-Precision Displacement Interferometry at λ = 633 nm" data-journal="sensors"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1424-8220/23/3/1734">A GPS-Referenced Wavelength Standard for High-Precision Displacement Interferometry at λ = 633 nm</a> <div class="authors"> by <span class="inlineblock "><strong>Ulrike Blumröder</strong>, </span><span class="inlineblock "><strong>Paul Köchert</strong>, </span><span class="inlineblock "><strong>Thomas Fröhlich</strong>, </span><span class="inlineblock "><strong>Thomas Kissinger</strong>, </span><span class="inlineblock "><strong>Ingo Ortlepp</strong>, </span><span class="inlineblock "><strong>Jens Flügge</strong>, </span><span class="inlineblock "><strong>Harald Bosse</strong> and </span><span class="inlineblock "><strong>Eberhard Manske</strong></span> </div> <div class="color-grey-dark"> <em>Sensors</em> <b>2023</b>, <em>23</em>(3), 1734; <a href="https://doi.org/10.3390/s23031734">https://doi.org/10.3390/s23031734</a> - 3 Feb 2023 </div> Viewed by 2789 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Since the turn of the millennium, the development and commercial availability of optical frequency combs has led to a steadily increase of worldwide installed frequency combs and a growing interest in using them for industrial-related metrology applications. Especially, GPS-referenced frequency combs often serve <a href="#" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1734/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Since the turn of the millennium, the development and commercial availability of optical frequency combs has led to a steadily increase of worldwide installed frequency combs and a growing interest in using them for industrial-related metrology applications. Especially, GPS-referenced frequency combs often serve as a “self-calibrating” length standard for laser wavelength calibration in many national metrology institutes with uncertainties better than <i>u</i> = 1 × 10<sup>−11</sup>. In this contribution, the application of a He-Ne laser source permanently disciplined to a GPS-referenced frequency comb for the interferometric measurements in a nanopositioning machine with a measuring volume of 200 mm × 200 mm × 25 mm (NPMM-200) is discussed. For this purpose, the frequency stability of the GPS-referenced comb is characterized by heterodyning with a diode laser referenced to an ultrastable cavity. Based on this comparison, an uncertainty of <i>u</i> = 9.2 × 10<sup>−12</sup> (<i>τ</i> = 8 s, k = 2) for the GPS-referenced comb has been obtained. By stabilizing a tunable He-Ne source to a single comb line, the long-term frequency stability of the comb is transferred onto our gas lasers increasing their long-term stability by three orders of magnitude. Second, short-term fluctuations-related length measurement errors were reduced to a value that falls below the nominal resolving capabilities of our interferometers (Δ<i>L</i>/<i>L</i> = 2.9 × 10<sup>−11</sup>). Both measures make the influence of frequency distortions on the interferometric length measurement within the NPMM-200 negligible. Furthermore, this approach establishes a permanent link of interferometric length measurements to an atomic clock. <a href="/1424-8220/23/3/1734">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/sensors/special_issues/Precision_metrology_UPL_OFC ">Precision Metrology Using Ultrashort Pulse Laser and Optical Frequency Comb</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1734/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1057360"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1057360"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1057360" data-cycle-prev="#prev1057360" 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src='https://pub.mdpi-res.com/sensors/sensors-23-01734/article_deploy/html/images/sensors-23-01734-g003-550.jpg?1675927323'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1057360-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01734/article_deploy/html/images/sensors-23-01734-g004-550.jpg?1675927324'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1057360-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01734/article_deploy/html/images/sensors-23-01734-g005-550.jpg?1675927319'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1057360-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01734/article_deploy/html/images/sensors-23-01734-g006-550.jpg?1675927330'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1057360-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01734/article_deploy/html/images/sensors-23-01734-g007-550.jpg?1675927309'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1057360-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01734/article_deploy/html/images/sensors-23-01734-g008-550.jpg?1675927321'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1057360-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01734/article_deploy/html/images/sensors-23-01734-g009-550.jpg?1675927314'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1057360-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01734/article_deploy/html/images/sensors-23-01734-g010-550.jpg?1675927318'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1057360-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01734/article_deploy/html/images/sensors-23-01734-g011-550.jpg?1675927316'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1057360-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01734/article_deploy/html/images/sensors-23-01734-g012-550.jpg?1675927322'><p>Figure 12</p></div> --- <div class='openpopupgallery' data-imgindex='12' data-target='article-1057360-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01734/article_deploy/html/images/sensors-23-01734-g013a-550.jpg?1675927311'><p>Figure 13</p></div> --- <div class='openpopupgallery' data-imgindex='13' data-target='article-1057360-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01734/article_deploy/html/images/sensors-23-01734-g013b-550.jpg?1675927312'><p>Figure 13 Cont.</p></div></script></div></div><div id="article-1057360-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/sensors/sensors-23-01734/article_deploy/html/images/sensors-23-01734-g001-550.jpg?1675927327" title=" <strong>Figure 1</strong><br/> <p>Setup of the NPMM-200 and its He-Ne laser source for three interferometer axes. The abbreviations in the left picture denote: TMFS—two-mode frequency stabilization, PD—photodiode, PBS—polarizing beam splitter, FC—fiber coupling. The numbers in the right picture correspond to: 1—metrology frame, 2—Positioning stage with corner mirror plate, 3—Sensor mount with nano probe system, 4, 5—Interferometer <span class="html-italic">x</span>, <span class="html-italic">y</span>-axis, 6, 7—Autocollimators (angle sensors). The interferometer of the z-axis is not visible. The base plate below the metrology frame additionally carries the guiding and drive system of the <span class="html-italic">x</span>-, <span class="html-italic">y</span>-, and <span class="html-italic">z</span>-axis (see details in [<a href="#B32-sensors-23-01734" class="html-bibr">32</a>,<a href="#B40-sensors-23-01734" class="html-bibr">40</a>]).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1734'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01734/article_deploy/html/images/sensors-23-01734-g002-550.jpg?1675927328" title=" <strong>Figure 2</strong><br/> <p>Scheme of a possible traceability chain for laser wavelength calibration deploying iodine-stabilized He-Ne lasers. The given uncertainties were taken from the literature as given within the text.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1734'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01734/article_deploy/html/images/sensors-23-01734-g003-550.jpg?1675927323" title=" <strong>Figure 3</strong><br/> <p>Structure of the GPS-referenced frequency comb at the IPMS. The abbreviations denote: OFC—optical frequency comb, GPSDO—GPS disciplined oscillator, SDU—signal distribution unit, PD—photodiode. The numbers 1–4 correspond to the respective frequency counter channels.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1734'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01734/article_deploy/html/images/sensors-23-01734-g004-550.jpg?1675927324" title=" <strong>Figure 4</strong><br/> <p>Structure of the ORS. The abbreviations denote: ECDL—external cavity diode laser, FC—fiber-coupled, NBS—neutral beam splitter, C—collimator, PMF—polarization-maintaining fiber, M—mirror, EOM—electro-optic modulator, HWP—half wave plate, PBS—polarizing beam splitter, QWP—quarter wave plate, L—lens, DDS—direct digital synthesizer, LPF—low pass filter. The red lines indicate optical path lengths, the dashed gray lines electronic signals. The light of the ECDL is sent through a phase-modulator operating at 20 MHz to generate sidebands for the PDH-lock. The 20-MHz reference signal is provided by a DDS. The error signal is created by mixing the signal from the photodetector containing the reflected light from the cavity with the signal from the DDS and subsequent low-pass filtering. The frequency of the laser is stabilized via fast feedback to the current and slow feedback to the piezo-electric actuator controlling the external cavity length of the diode laser [<a href="#B65-sensors-23-01734" class="html-bibr">65</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1734'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01734/article_deploy/html/images/sensors-23-01734-g005-550.jpg?1675927319" title=" <strong>Figure 5</strong><br/> <p>Frequency stability of the ORS as retrieved from manufacturer data (M.d.). (<b>a</b>) Time dependent beat frequency between two optical reference systems of comparable frequency stability with linear drift removed, (<b>b</b>) corresponding relative Allan deviation (see <a href="#sec2dot4-sensors-23-01734" class="html-sec">Section 2.4</a>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1734'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01734/article_deploy/html/images/sensors-23-01734-g006-550.jpg?1675927330" title=" <strong>Figure 6</strong><br/> <p>Layout of the comb-referenced He-Ne heterodyne source. The abbreviations denote: GPSDO—GPS disciplined oscillator, SDU—signal distribution unit, APD—avalanche photodiode, PBS—polarizing beam splitter, M—mirror, BSA—beam splitter assembly, FI—Faraday isolator, FC—fiber coupling, UIC—transconductive amplifier (U/I converter), FPGA—field programmable gate array.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1734'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01734/article_deploy/html/images/sensors-23-01734-g007-550.jpg?1675927309" title=" <strong>Figure 7</strong><br/> <p>Frequency instability of the free-running comb. (<b>a</b>) Time traces of the free-running comb parameters measured with the FXM50. The left <span class="html-italic">y</span>-axis depicts the change of repetition rate from its initial value. The right <span class="html-italic">y</span>-axis the CEO. (<b>b</b>) Time trace of the beat note between the respective comb line and the ORS. Since the frequency υ<sub>ORS</sub> of the ORS lies above the respective frequency of the comb line υ<sub>n</sub>, an increase in the repetition rate <span class="html-italic">f<sub>Rep</sub></span> results in a decrease of the beat frequency <span class="html-italic">f<sub>Beat</sub></span>. To monitor the frequency changes of the beat note, the measurements were performed without the bandpass filter at 60 MHz.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1734'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01734/article_deploy/html/images/sensors-23-01734-g008-550.jpg?1675927321" title=" <strong>Figure 8</strong><br/> <p>In-loop comb parameters of the GPS-referenced frequency comb. (<b>a</b>) Time trace of the locked repetition rate. (<b>b</b>) Time trace of the locked CEO. Both plots depict the frequency deviations from the mean value over a measurement time of 24 h. (<b>c</b>) Calculated Allan deviations. The gray and blue lines present a linear fit to the data as described within the text.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1734'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01734/article_deploy/html/images/sensors-23-01734-g009-550.jpg?1675927314" title=" <strong>Figure 9</strong><br/> <p>Frequency instability of an in-loop GPS-referenced frequency comb. (<b>a</b>) Time trace of the heterodyne signal between the OFC and the ORS. The red line indicates a linear fit to the data with <span class="html-italic">f</span> = 62.384 MHz–0.067 Hz/s. (<b>b</b>) A 1 h section of the frequency fluctuations of the heterodyne signal after the linear drift was removed from the measured data. (<b>c</b>) The respective relative Allan deviation of the heterodyne signal obtained by dividing the Allan deviation by <span class="html-italic">f</span><sub>HeNe</sub> ≈ <span class="html-italic">f</span><sub>Comb</sub> ≈ 474 THz. The abbreviation M.d. denotes “manufacturer data”.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1734'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01734/article_deploy/html/images/sensors-23-01734-g010-550.jpg?1675927318" title=" <strong>Figure 10</strong><br/> <p>Frequency instability of the He-Ne heterodyne source used as a starting point for the realization of a comb-referenced laser module. (<b>a</b>) A 24 h-measurement series of the secondary standard laser (SSL) against a comb line, (<b>b</b>) 24 h measurement of the metrology laser (ML). In each of the plots, the frequency deviations from the mean value derived over the whole measurement series of 24 h are shown. (<b>c</b>) Relative Allan deviations obtained by dividing the frequency fluctuations by a nominal value of 474 THz.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1734'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01734/article_deploy/html/images/sensors-23-01734-g011-550.jpg?1675927316" title=" <strong>Figure 11</strong><br/> <p>Frequency instability of the comb-referenced-He-Ne heterodyne source. (<b>a</b>) 24-h measurement series of the secondary standard laser (SSL) prior and after closing the control loop (closed-loop operation reproduced from [<a href="#B35-sensors-23-01734" class="html-bibr">35</a>]). (<b>b</b>) Relative Allan deviations. The Allan deviation of the OFC is obtained by combining the Allan deviation as calculated from the measurement of the beat signal between the ORS and the OFC for integration times below 4000 s and the Allan deviation given by the manufacturer for integration times above 4000 s. (<b>c</b>) The 1 h measurement sections of the 24-h timetrace. The upper graph depicts the SSL locked to the comb and the frequency fluctuations of the drift-removed beat signal between a comb line and the ORS (taken from <a href="#sensors-23-01734-f009" class="html-fig">Figure 9</a>b) for comparison. Please note that in contrast to the in-loop measurements of the heterodyne source, those measurements were not taken simultaneously. The graph in the middle enlarges the frequency scale to illustrate the frequency fluctuations of the SSL when locked to the comb line. The lower graph further enlarges the frequency scale to depict the fluctuations of the beat signal between the ML and the SSL, when locked to the comb line. All three graphs show the same 1 h section and present the frequency deviations from the respective mean value of the whole measurement series.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1734'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01734/article_deploy/html/images/sensors-23-01734-g012-550.jpg?1675927322" title=" <strong>Figure 12</strong><br/> <p>Scheme of the reduced traceability chain for laser wavelength calibration of the metrology lasers of the NPMM-200.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1734'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01734/article_deploy/html/images/sensors-23-01734-g013a-550.jpg?1675927311" title=" <strong>Figure 13</strong><br/> <p>Influence of frequency distortions on the interferometric length measurement within the NPMM-200. (<b>a</b>,<b>b</b>) transform the frequency fluctuations and uncertainty of the comb line into a theoretical length measurement error for a maximum measurement range of <span class="html-italic">L</span> = 200 mm. (<b>c</b>,<b>d</b>) experimentally demonstrate the influence of frequency changes on the interferometric length measurement within the NPMM-200 at a constant distance of <span class="html-italic">L</span> = 100 mm using the comb-referenced He-Ne laser as a tunable laser source, (<b>c</b>) equidistant frequency steps of Δ<span class="html-italic">f</span> = 1.25 MHz and (<b>d</b>) repeated frequency jumps of Δ<span class="html-italic">f</span> = 0.625 MHz height. Integration time 0.5 s [<a href="#B35-sensors-23-01734" class="html-bibr">35</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1734'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01734/article_deploy/html/images/sensors-23-01734-g013b-550.jpg?1675927312" title=" <strong>Figure 13 Cont.</strong><br/> <p>Influence of frequency distortions on the interferometric length measurement within the NPMM-200. (<b>a</b>,<b>b</b>) transform the frequency fluctuations and uncertainty of the comb line into a theoretical length measurement error for a maximum measurement range of <span class="html-italic">L</span> = 200 mm. (<b>c</b>,<b>d</b>) experimentally demonstrate the influence of frequency changes on the interferometric length measurement within the NPMM-200 at a constant distance of <span class="html-italic">L</span> = 100 mm using the comb-referenced He-Ne laser as a tunable laser source, (<b>c</b>) equidistant frequency steps of Δ<span class="html-italic">f</span> = 1.25 MHz and (<b>d</b>) repeated frequency jumps of Δ<span class="html-italic">f</span> = 0.625 MHz height. Integration time 0.5 s [<a href="#B35-sensors-23-01734" class="html-bibr">35</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1734'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1057328" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 20 pages, 8977 KiB   </span> <a href="/1424-8220/23/3/1733/pdf?version=1675432907" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Variable Thickness Strain Pre-Extrapolation for the Inverse Finite Element Method" data-journal="sensors"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1424-8220/23/3/1733">Variable Thickness Strain Pre-Extrapolation for the Inverse Finite Element Method</a> <div class="authors"> by <span class="inlineblock "><strong>Dario Poloni</strong>, </span><span class="inlineblock "><strong>Daniele Oboe</strong>, </span><span class="inlineblock "><strong>Claudio Sbarufatti</strong> and </span><span class="inlineblock "><strong>Marco Giglio</strong></span> </div> <div class="color-grey-dark"> <em>Sensors</em> <b>2023</b>, <em>23</em>(3), 1733; <a href="https://doi.org/10.3390/s23031733">https://doi.org/10.3390/s23031733</a> - 3 Feb 2023 </div> Viewed by 2549 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The inverse Finite Element Method (iFEM) has recently gained much popularity within the Structural Health Monitoring (SHM) field since, given sparse strain measurements, it reconstructs the displacement field of any beam or shell structure independently of the external loading conditions and of the <a href="#" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1733/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> The inverse Finite Element Method (iFEM) has recently gained much popularity within the Structural Health Monitoring (SHM) field since, given sparse strain measurements, it reconstructs the displacement field of any beam or shell structure independently of the external loading conditions and of the material properties. However, in principle, the iFEM requires a triaxial strain measurement for each inverse finite element, which is seldom feasible in practical applications due to both costs and cabling-related limitations. To alleviate this problem several techniques to pre-extrapolate the measured strains have been developed, so that interpolated or extrapolated strain values are inputted to elements without physical sensors: the benefit is that the required number of sensors can be reduced. Nevertheless, whenever the monitored components comprise regions of different thicknesses, each region of constant thickness must be extrapolated separately, due to thickness-induced discontinuities in the strain field. This is the case in many practical applications, especially those concerning fiber-reinforced composite laminates. This paper proposes to extrapolate the measured strain field in a thickness-normalized space, where the thickness-induced trends are removed; this novel method can significantly decrease the number of required sensors, effectively reducing the costs of iFEM-based SHM systems. The method is validated in a simple but informative numerical case study, highlighting the potentialities and benefits of the proposed approach for more complex application scenarios. <a href="/1424-8220/23/3/1733">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/sensors/special_issues/shapesensing20212022 ">Shape Sensing 2021-2024</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1733/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1057328"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1057328"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1057328" data-cycle-prev="#prev1057328" data-cycle-progressive="#images1057328" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1057328-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g001-550.jpg?1675432992" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1057328" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1057328-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g002-550.jpg?1675433005'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1057328-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g003-550.jpg?1675433000'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1057328-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g004-550.jpg?1675433007'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1057328-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g005-550.jpg?1675432998'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1057328-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g006-550.jpg?1675433006'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1057328-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g007-550.jpg?1675432996'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1057328-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g008-550.jpg?1675433002'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1057328-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g009-550.jpg?1675432994'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1057328-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g010-550.jpg?1675432997'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1057328-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g011-550.jpg?1675432995'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1057328-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g012-550.jpg?1675433011'><p>Figure 12</p></div> --- <div class='openpopupgallery' data-imgindex='12' data-target='article-1057328-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g013-550.jpg?1675432993'><p>Figure 13</p></div> --- <div class='openpopupgallery' data-imgindex='13' data-target='article-1057328-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g014-550.jpg?1675433010'><p>Figure 14</p></div> --- <div class='openpopupgallery' data-imgindex='14' data-target='article-1057328-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g015-550.jpg?1675433008'><p>Figure 15</p></div> --- <div class='openpopupgallery' data-imgindex='15' data-target='article-1057328-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g016-550.jpg?1675433009'><p>Figure 16</p></div> --- <div class='openpopupgallery' data-imgindex='16' data-target='article-1057328-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g017-550.jpg?1675433003'><p>Figure 17</p></div> --- <div class='openpopupgallery' data-imgindex='17' data-target='article-1057328-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g018-550.jpg?1675432999'><p>Figure 18</p></div> --- <div class='openpopupgallery' data-imgindex='18' data-target='article-1057328-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g019-550.jpg?1675433005'><p>Figure 19</p></div></script></div></div><div id="article-1057328-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g001-550.jpg?1675432992" title=" <strong>Figure 1</strong><br/> <p>Local (x, y, z) and global (X, Y, Z) reference systems for the iQS4 element; numbers 1 to 4 represent the local node labels.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1733'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g002-550.jpg?1675433005" title=" <strong>Figure 2</strong><br/> <p>Discrete sensor location on the shell structure.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1733'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g003-550.jpg?1675433000" title=" <strong>Figure 3</strong><br/> <p>(<b>a</b>) Composite plate dimensions and (<b>b</b>) layup sequence.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1733'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g004-550.jpg?1675433007" title=" <strong>Figure 4</strong><br/> <p>(<b>a</b>) iFEM mesh and (<b>b</b>) direct FEM mesh: Triaxial strain sensors are placed on both the bottom and top sides at each blue-filled circle.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1733'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g005-550.jpg?1675432998" title=" <strong>Figure 5</strong><br/> <p>Traction: loading and boundary conditions.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1733'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g006-550.jpg?1675433006" title=" <strong>Figure 6</strong><br/> <p>Traction: normalized membrane strain <math display="inline"><semantics> <mrow> <msub> <mi>η</mi> <mrow> <mi>X</mi> <mi>X</mi> </mrow> </msub> </mrow> </semantics></math> pre-extrapolation.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1733'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g007-550.jpg?1675432996" title=" <strong>Figure 7</strong><br/> <p>Traction: (<b>a</b>) membrane strain <math display="inline"><semantics> <mrow> <msub> <mi>e</mi> <mrow> <mi>X</mi> <mi>X</mi> </mrow> </msub> </mrow> </semantics></math> and (<b>b</b>) normalized membrane strain <math display="inline"><semantics> <mrow> <msub> <mi>η</mi> <mrow> <mi>X</mi> <mi>X</mi> </mrow> </msub> </mrow> </semantics></math>, slice at Y = 250 mm.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1733'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g008-550.jpg?1675433002" title=" <strong>Figure 8</strong><br/> <p>Traction: percentage error of the <math display="inline"><semantics> <mrow> <msub> <mi>U</mi> <mi>X</mi> </msub> </mrow> </semantics></math> displacement.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1733'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g009-550.jpg?1675432994" title=" <strong>Figure 9</strong><br/> <p>Out-of-plane tip loading: loading and boundary conditions.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1733'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g010-550.jpg?1675432997" title=" <strong>Figure 10</strong><br/> <p>Out-of-plane tip loading: normalized curvature <math display="inline"><semantics> <mrow> <msub> <mi>ξ</mi> <mrow> <mi>X</mi> <mi>X</mi> </mrow> </msub> </mrow> </semantics></math> pre-extrapolation.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1733'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g011-550.jpg?1675432995" title=" <strong>Figure 11</strong><br/> <p>(<b>a</b>) out-of-plane tip loading: curvature <math display="inline"><semantics> <mrow> <msub> <mi>κ</mi> <mrow> <mi>X</mi> <mi>X</mi> </mrow> </msub> </mrow> </semantics></math> and (<b>b</b>) normalized curvature <math display="inline"><semantics> <mrow> <msub> <mi>ξ</mi> <mrow> <mi>X</mi> <mi>X</mi> </mrow> </msub> </mrow> </semantics></math>, slice at Y = 250 mm.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1733'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g012-550.jpg?1675433011" title=" <strong>Figure 12</strong><br/> <p>Out-of-plane tip loading: percentage error of the <math display="inline"><semantics> <mrow> <msub> <mi>U</mi> <mi>Z</mi> </msub> </mrow> </semantics></math> displacement.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1733'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g013-550.jpg?1675432993" title=" <strong>Figure 13</strong><br/> <p>Uniform pressure: loading and boundary conditions.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1733'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g014-550.jpg?1675433010" title=" <strong>Figure 14</strong><br/> <p>Uniform pressure: normalized curvature <math display="inline"><semantics> <mrow> <msub> <mi>ξ</mi> <mrow> <mi>X</mi> <mi>X</mi> </mrow> </msub> </mrow> </semantics></math> pre-extrapolation.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1733'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g015-550.jpg?1675433008" title=" <strong>Figure 15</strong><br/> <p>Uniform pressure: (<b>a</b>) curvature <math display="inline"><semantics> <mrow> <msub> <mi>κ</mi> <mrow> <mi>X</mi> <mi>X</mi> </mrow> </msub> </mrow> </semantics></math> and (<b>b</b>) normalized curvature <math display="inline"><semantics> <mrow> <msub> <mi>ξ</mi> <mrow> <mi>X</mi> <mi>X</mi> </mrow> </msub> </mrow> </semantics></math>, slice at Y = 250 mm.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1733'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g016-550.jpg?1675433009" title=" <strong>Figure 16</strong><br/> <p>Uniform pressure: percentage error of the <math display="inline"><semantics> <mrow> <msub> <mi>U</mi> <mi>Z</mi> </msub> </mrow> </semantics></math> displacement.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1733'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g017-550.jpg?1675433003" title=" <strong>Figure 17</strong><br/> <p>Shear loading: loading and boundary conditions.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1733'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g018-550.jpg?1675432999" title=" <strong>Figure 18</strong><br/> <p>Shear loading: (<b>a</b>) membrane strain <math display="inline"><semantics> <mrow> <msub> <mi>e</mi> <mrow> <mi>X</mi> <mi>Y</mi> </mrow> </msub> </mrow> </semantics></math> and (<b>b</b>) normalized membrane strain <math display="inline"><semantics> <mrow> <msub> <mi>η</mi> <mrow> <mi>X</mi> <mi>Y</mi> </mrow> </msub> </mrow> </semantics></math>, slice at Y = 250 mm.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1733'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01733/article_deploy/html/images/sensors-23-01733-g019-550.jpg?1675433005" title=" <strong>Figure 19</strong><br/> <p>Shear loading: percentage error of the <math display="inline"><semantics> <mrow> <msub> <mi>U</mi> <mi>Y</mi> </msub> </mrow> </semantics></math> displacement.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1733'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1057318" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 20 pages, 3026 KiB   </span> <a href="/1424-8220/23/3/1732/pdf?version=1675840732" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Deep Reinforcement Learning for Edge Caching with Mobility Prediction in Vehicular Networks" data-journal="sensors"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1424-8220/23/3/1732">Deep Reinforcement Learning for Edge Caching with Mobility Prediction in Vehicular Networks</a> <div class="authors"> by <span class="inlineblock "><strong>Yoonjeong Choi</strong> and </span><span class="inlineblock "><strong>Yujin Lim</strong></span> </div> <div class="color-grey-dark"> <em>Sensors</em> <b>2023</b>, <em>23</em>(3), 1732; <a href="https://doi.org/10.3390/s23031732">https://doi.org/10.3390/s23031732</a> - 3 Feb 2023 </div> <a href="/1424-8220/23/3/1732#metrics">Cited by 3</a> | Viewed by 2305 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> As vehicles are connected to the Internet, various services can be provided to users. However, if the requests of vehicle users are concentrated on the remote server, the transmission delay increases, and there is a high possibility that the delay constraint cannot be <a href="#" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1732/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> As vehicles are connected to the Internet, various services can be provided to users. However, if the requests of vehicle users are concentrated on the remote server, the transmission delay increases, and there is a high possibility that the delay constraint cannot be satisfied. To solve this problem, caching can be performed at a closer proximity to the user which in turn would reduce the latency by distributing requests. The road side unit (RSU) and vehicle can serve as caching nodes by providing storage space closer to users through a mobile edge computing (MEC) server and an on-board unit (OBU), respectively. In this paper, we propose a caching strategy for both RSUs and vehicles with the goal of maximizing the caching node throughput. The vehicles move at a greater speed; thus, if positions of the vehicles are predictable in advance, this helps to determine the location and type of content that has to be cached. By using the temporal and spatial characteristics of vehicles, we adopted a long short-term memory (LSTM) to predict the locations of the vehicles. To respond to time-varying content popularity, a deep deterministic policy gradient (DDPG) was used to determine the size of each piece of content to be stored in the caching nodes. Experiments in various environments have proven that the proposed algorithm performs better when compared to other caching methods in terms of the throughput of caching nodes, delay constraint satisfaction, and update cost. <a href="/1424-8220/23/3/1732">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Topic <a href="/topics/IOT_Communication_Engineering">IOT, Communication and Engineering</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1732/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1057318"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1057318"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1057318" data-cycle-prev="#prev1057318" data-cycle-progressive="#images1057318" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1057318-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/sensors/sensors-23-01732/article_deploy/html/images/sensors-23-01732-g001-550.jpg?1675840805" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1057318" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1057318-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01732/article_deploy/html/images/sensors-23-01732-g002-550.jpg?1675840807'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1057318-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01732/article_deploy/html/images/sensors-23-01732-g003-550.jpg?1675840808'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1057318-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01732/article_deploy/html/images/sensors-23-01732-g004-550.jpg?1675840801'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1057318-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01732/article_deploy/html/images/sensors-23-01732-g005-550.jpg?1675840802'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1057318-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01732/article_deploy/html/images/sensors-23-01732-g006-550.jpg?1675840808'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1057318-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01732/article_deploy/html/images/sensors-23-01732-g007-550.jpg?1675840802'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1057318-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01732/article_deploy/html/images/sensors-23-01732-g008-550.jpg?1675840806'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1057318-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01732/article_deploy/html/images/sensors-23-01732-g009-550.jpg?1675840804'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1057318-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01732/article_deploy/html/images/sensors-23-01732-g010-550.jpg?1675840803'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1057318-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01732/article_deploy/html/images/sensors-23-01732-g011-550.jpg?1675840806'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1057318-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01732/article_deploy/html/images/sensors-23-01732-g012-550.jpg?1675840804'><p>Figure 12</p></div></script></div></div><div id="article-1057318-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/sensors/sensors-23-01732/article_deploy/html/images/sensors-23-01732-g001-550.jpg?1675840805" title=" <strong>Figure 1</strong><br/> <p>Caching framework in the vehicular network with different types of caching nodes.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1732'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01732/article_deploy/html/images/sensors-23-01732-g002-550.jpg?1675840807" title=" <strong>Figure 2</strong><br/> <p>Architecture of LSTM.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1732'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01732/article_deploy/html/images/sensors-23-01732-g003-550.jpg?1675840808" title=" <strong>Figure 3</strong><br/> <p>Architecture of DDPG.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1732'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01732/article_deploy/html/images/sensors-23-01732-g004-550.jpg?1675840801" title=" <strong>Figure 4</strong><br/> <p>Throughput according to the increase in the number of vehicles.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1732'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01732/article_deploy/html/images/sensors-23-01732-g005-550.jpg?1675840802" title=" <strong>Figure 5</strong><br/> <p>Delay constraint satisfaction with respect to the increase in the number of vehicles.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1732'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01732/article_deploy/html/images/sensors-23-01732-g006-550.jpg?1675840808" title=" <strong>Figure 6</strong><br/> <p>Update cost according to the increase in the number of vehicles.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1732'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01732/article_deploy/html/images/sensors-23-01732-g007-550.jpg?1675840802" title=" <strong>Figure 7</strong><br/> <p>Throughput with respect to the increase in the number of RSUs.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1732'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01732/article_deploy/html/images/sensors-23-01732-g008-550.jpg?1675840806" title=" <strong>Figure 8</strong><br/> <p>Delay constraint satisfaction with respect to the increase in the number of RSUs.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1732'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01732/article_deploy/html/images/sensors-23-01732-g009-550.jpg?1675840804" title=" <strong>Figure 9</strong><br/> <p>Update cost with respect to the increase in the number of RSUs.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1732'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01732/article_deploy/html/images/sensors-23-01732-g010-550.jpg?1675840803" title=" <strong>Figure 10</strong><br/> <p>Edge throughput with respect to the increase in the capacity of RSUs.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1732'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01732/article_deploy/html/images/sensors-23-01732-g011-550.jpg?1675840806" title=" <strong>Figure 11</strong><br/> <p>Delay constraint satisfaction according to the increase in the capacity of RSUs.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1732'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01732/article_deploy/html/images/sensors-23-01732-g012-550.jpg?1675840804" title=" <strong>Figure 12</strong><br/> <p>Update cost according to the increase in the capacity of RSUs.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1732'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1057252" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 15 pages, 4253 KiB   </span> <a href="/1424-8220/23/3/1731/pdf?version=1675750469" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Vehicle Detection and Recognition Approach in Multi-Scale Traffic Monitoring System via Graph-Based Data Optimization" data-journal="sensors"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1424-8220/23/3/1731">Vehicle Detection and Recognition Approach in Multi-Scale Traffic Monitoring System via Graph-Based Data Optimization</a> <div class="authors"> by <span class="inlineblock "><strong>Grzegorz Wieczorek</strong>, </span><span class="inlineblock "><strong>Sheikh Badar ud din Tahir</strong>, </span><span class="inlineblock "><strong>Israr Akhter</strong> and </span><span class="inlineblock "><strong>Jaroslaw Kurek</strong></span> </div> <div class="color-grey-dark"> <em>Sensors</em> <b>2023</b>, <em>23</em>(3), 1731; <a href="https://doi.org/10.3390/s23031731">https://doi.org/10.3390/s23031731</a> - 3 Feb 2023 </div> <a href="/1424-8220/23/3/1731#metrics">Cited by 7</a> | Viewed by 3835 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Over the past few years, significant investments in smart traffic monitoring systems have been made. The most important step in machine learning is detecting and recognizing objects relative to vehicles. Due to variations in vision and different lighting conditions, the recognition and tracking <a href="#" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1731/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Over the past few years, significant investments in smart traffic monitoring systems have been made. The most important step in machine learning is detecting and recognizing objects relative to vehicles. Due to variations in vision and different lighting conditions, the recognition and tracking of vehicles under varying extreme conditions has become one of the most challenging tasks. To deal with this, our proposed system presents an adaptive method for robustly recognizing several existing automobiles in dense traffic settings. Additionally, this research presents a broad framework for effective on-road vehicle recognition and detection. Furthermore, the proposed system focuses on challenges typically noticed in analyzing traffic scenes captured by in-vehicle cameras, such as consistent extraction of features. First, we performed frame conversion, background subtraction, and object shape optimization as preprocessing steps. Next, two important features (energy and deep optical flow) were extracted. The incorporation of energy and dense optical flow features in distance-adaptive window areas and subsequent processing over the fused features resulted in a greater capacity for discrimination. Next, a graph-mining-based approach was applied to select optimal features. Finally, the artificial neural network was adopted for detection and classification. The experimental results show significant performance in two benchmark datasets, including the LISA and KITTI 7 databases. The LISA dataset achieved a mean recognition rate of 93.75% on the LDB1 and LDB2 databases, whereas KITTI attained 82.85% accuracy on separate training of ANN. <a href="/1424-8220/23/3/1731">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/sensors/special_issues/IY2M5H8681 ">Engineering Applications of Artificial Intelligence for Sensors</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1731/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1057252"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1057252"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1057252" data-cycle-prev="#prev1057252" data-cycle-progressive="#images1057252" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1057252-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/sensors/sensors-23-01731/article_deploy/html/images/sensors-23-01731-g001-550.jpg?1675750542" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1057252" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1057252-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01731/article_deploy/html/images/sensors-23-01731-g002-550.jpg?1675750540'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1057252-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01731/article_deploy/html/images/sensors-23-01731-g003-550.jpg?1675750539'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1057252-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01731/article_deploy/html/images/sensors-23-01731-g004-550.jpg?1675750546'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1057252-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01731/article_deploy/html/images/sensors-23-01731-g005-550.jpg?1675750544'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1057252-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01731/article_deploy/html/images/sensors-23-01731-g006-550.jpg?1675750543'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1057252-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01731/article_deploy/html/images/sensors-23-01731-g007-550.jpg?1675750541'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1057252-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01731/article_deploy/html/images/sensors-23-01731-g008-550.jpg?1675750545'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1057252-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01731/article_deploy/html/images/sensors-23-01731-g009-550.jpg?1675750538'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1057252-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01731/article_deploy/html/images/sensors-23-01731-g010-550.jpg?1675750549'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1057252-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01731/article_deploy/html/images/sensors-23-01731-g011-550.jpg?1675750550'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1057252-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01731/article_deploy/html/images/sensors-23-01731-g012-550.jpg?1675750537'><p>Figure 12</p></div></script></div></div><div id="article-1057252-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/sensors/sensors-23-01731/article_deploy/html/images/sensors-23-01731-g001-550.jpg?1675750542" title=" <strong>Figure 1</strong><br/> <p>The suggested vehicle detection framework process diagram.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1731'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01731/article_deploy/html/images/sensors-23-01731-g002-550.jpg?1675750540" title=" <strong>Figure 2</strong><br/> <p>Example results of pre-processing.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1731'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01731/article_deploy/html/images/sensors-23-01731-g003-550.jpg?1675750539" title=" <strong>Figure 3</strong><br/> <p>Example results of preprocessing: (<b>a</b>) denoised image and (<b>b</b>) background-subtracted image.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1731'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01731/article_deploy/html/images/sensors-23-01731-g004-550.jpg?1675750546" title=" <strong>Figure 4</strong><br/> <p>Example results of object shape optimization: (<b>a</b>) background-subtracted image and (<b>b</b>) optimized shape in binary format.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1731'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01731/article_deploy/html/images/sensors-23-01731-g005-550.jpg?1675750544" title=" <strong>Figure 5</strong><br/> <p>Example results of extracting region of interest. (<b>a</b>) front (<b>b</b>) back.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1731'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01731/article_deploy/html/images/sensors-23-01731-g006-550.jpg?1675750543" title=" <strong>Figure 6</strong><br/> <p>Example results of energy features: (<b>a</b>) background-subtracted image and (<b>b</b>) energy features.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1731'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01731/article_deploy/html/images/sensors-23-01731-g007-550.jpg?1675750541" title=" <strong>Figure 7</strong><br/> <p>Example results of dense optical flow features: (<b>a</b>) background-subtracted image and (<b>b</b>) example results of dense optical flow.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1731'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01731/article_deploy/html/images/sensors-23-01731-g008-550.jpg?1675750545" title=" <strong>Figure 8</strong><br/> <p>The model overview and diagram of artificial neural network.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1731'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01731/article_deploy/html/images/sensors-23-01731-g009-550.jpg?1675750538" title=" <strong>Figure 9</strong><br/> <p>The example images of LISA dataset.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1731'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01731/article_deploy/html/images/sensors-23-01731-g010-550.jpg?1675750549" title=" <strong>Figure 10</strong><br/> <p>The example images of KTTI dataset.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1731'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01731/article_deploy/html/images/sensors-23-01731-g011-550.jpg?1675750550" title=" <strong>Figure 11</strong><br/> <p>Confusion matrix of 7 classes on the KITTI database via ANN.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1731'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01731/article_deploy/html/images/sensors-23-01731-g012-550.jpg?1675750537" title=" <strong>Figure 12</strong><br/> <p>Comparison of our proposed MTMS with PointNet [<a href="#B39-sensors-23-01731" class="html-bibr">39</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1731'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1057230" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 33 pages, 12498 KiB   </span> <a href="/1424-8220/23/3/1730/pdf?version=1676019895" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Evaluation of Geometric Data Registration of Small Objects from Non-Invasive Techniques: Applicability to the HBIM Field" data-journal="sensors"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1424-8220/23/3/1730">Evaluation of Geometric Data Registration of Small Objects from Non-Invasive Techniques: Applicability to the HBIM Field</a> <div class="authors"> by <span class="inlineblock "><strong>Juan Moyano</strong>, </span><span class="inlineblock "><strong>Elena Cabrera-Revuelta</strong>, </span><span class="inlineblock "><strong>Juan E. Nieto-Julián</strong>, </span><span class="inlineblock "><strong>María Fernández-Alconchel</strong> and </span><span class="inlineblock "><strong>Pedro Fernández-Valderrama</strong></span> </div> <div class="color-grey-dark"> <em>Sensors</em> <b>2023</b>, <em>23</em>(3), 1730; <a href="https://doi.org/10.3390/s23031730">https://doi.org/10.3390/s23031730</a> - 3 Feb 2023 </div> <a href="/1424-8220/23/3/1730#metrics">Cited by 11</a> | Viewed by 2428 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Reverse engineering and the creation of digital twins are advantageous for documenting, cataloging, and maintenance control tracking in the cultural heritage field. Digital copies of the objects into Building Information Models (BIM) add cultural interest to every artistic work. Low-cost 3D sensors, particularly <a href="#" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1730/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Reverse engineering and the creation of digital twins are advantageous for documenting, cataloging, and maintenance control tracking in the cultural heritage field. Digital copies of the objects into Building Information Models (BIM) add cultural interest to every artistic work. Low-cost 3D sensors, particularly structured-light scanners, have evolved towards multiple uses in the entertainment market but also as data acquisition and processing techniques for research purposes. Nowadays, with the development of structured-light data capture technologies, the geometry of objects can be recorded in high-resolution 3D datasets at a very low cost. On this basis, this research addresses a small artifact with geometric singularities that is representative of small museum objects. For this, the precision of two structured-light scanners is compared with that of the photogrammetric technique based on short-range image capture: a high-cost Artec Spider 3D scanner, and the low-cost Revopoint POP 3D scanner. Data capture accuracy is evaluated through a mathematical algorithm and point set segmentation to verify the spatial resolution. In addition, the precision of the 3D model is studied through a vector analysis in a BIM environment, an unprecedented analysis until now. The work evaluates the accuracy of the devices through algorithms and the study of point density at the submillimeter scale. Although the results of the 3D geometry may vary in a morphometric analysis depending on the device records, the results demonstrate similar accuracies in that submillimeter range. Photogrammetry achieved an accuracy of 0.70 mm versus the Artec Spider and 0.57 mm against the Revopoint POP 3D scanner. <a href="/1424-8220/23/3/1730">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Section <a href="/journal/sensors/sections/physicalsensors">Physical Sensors</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1730/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1057230"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1057230"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1057230" data-cycle-prev="#prev1057230" data-cycle-progressive="#images1057230" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1057230-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g001-550.jpg?1676019977" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1057230" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1057230-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g002-550.jpg?1676019992'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1057230-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g003-550.jpg?1676019972'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1057230-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g004-550.jpg?1676019985'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1057230-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g005-550.jpg?1676019999'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1057230-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g006-550.jpg?1676019978'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1057230-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g007-550.jpg?1676019975'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1057230-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g008-550.jpg?1676019984'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1057230-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g009-550.jpg?1676019997'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1057230-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g010-550.jpg?1676020007'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1057230-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g011-550.jpg?1676019970'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1057230-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g012-550.jpg?1676019991'><p>Figure 12</p></div> --- <div class='openpopupgallery' data-imgindex='12' data-target='article-1057230-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g013-550.jpg?1676020000'><p>Figure 13</p></div> --- <div class='openpopupgallery' data-imgindex='13' data-target='article-1057230-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g014-550.jpg?1676020005'><p>Figure 14</p></div> --- <div class='openpopupgallery' data-imgindex='14' data-target='article-1057230-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g015-550.jpg?1676020010'><p>Figure 15</p></div> --- <div class='openpopupgallery' data-imgindex='15' data-target='article-1057230-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g016-550.jpg?1676019988'><p>Figure 16</p></div> --- <div class='openpopupgallery' data-imgindex='16' data-target='article-1057230-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g017-550.jpg?1676019990'><p>Figure 17</p></div> --- <div class='openpopupgallery' data-imgindex='17' data-target='article-1057230-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g018-550.jpg?1676019973'><p>Figure 18</p></div> --- <div class='openpopupgallery' data-imgindex='18' data-target='article-1057230-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g019-550.jpg?1676020008'><p>Figure 19</p></div> --- <div class='openpopupgallery' data-imgindex='19' data-target='article-1057230-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g020-550.jpg?1676019995'><p>Figure 20</p></div> --- <div class='openpopupgallery' data-imgindex='20' data-target='article-1057230-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g021-550.jpg?1676020011'><p>Figure 21</p></div> --- <div class='openpopupgallery' data-imgindex='21' data-target='article-1057230-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g022-550.jpg?1676020002'><p>Figure 22</p></div> --- <div class='openpopupgallery' data-imgindex='22' data-target='article-1057230-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g023-550.jpg?1676019980'><p>Figure 23</p></div> --- <div class='openpopupgallery' data-imgindex='23' data-target='article-1057230-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g024-550.jpg?1676020003'><p>Figure 24</p></div> --- <div class='openpopupgallery' data-imgindex='24' data-target='article-1057230-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g025-550.jpg?1676019987'><p>Figure 25</p></div> --- <div class='openpopupgallery' data-imgindex='25' data-target='article-1057230-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g026-550.jpg?1676019994'><p>Figure 26</p></div> --- <div class='openpopupgallery' data-imgindex='26' data-target='article-1057230-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g027-550.jpg?1676019982'><p>Figure 27</p></div> --- <div class='openpopupgallery' data-imgindex='27' data-target='article-1057230-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g028-550.jpg?1676020004'><p>Figure 28</p></div> --- <div class='openpopupgallery' data-imgindex='28' data-target='article-1057230-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g029-550.jpg?1676019968'><p>Figure 29</p></div> --- <div class='openpopupgallery' data-imgindex='29' data-target='article-1057230-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g030-550.jpg?1676019966'><p>Figure 30</p></div> --- <div class='openpopupgallery' data-imgindex='30' data-target='article-1057230-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g031-550.jpg?1676019979'><p>Figure 31</p></div> --- <div class='openpopupgallery' data-imgindex='31' data-target='article-1057230-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g032-550.jpg?1676019971'><p>Figure 32</p></div> --- <div class='openpopupgallery' data-imgindex='32' data-target='article-1057230-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g033-550.jpg?1676019967'><p>Figure 33</p></div></script></div></div><div id="article-1057230-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g001-550.jpg?1676019977" title=" <strong>Figure 1</strong><br/> <p>Experimentation workflow description.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g002-550.jpg?1676019992" title=" <strong>Figure 2</strong><br/> <p>Capture process using Artec Studio Professional v.12 software.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g003-550.jpg?1676019972" title=" <strong>Figure 3</strong><br/> <p>Capture process using HandySCAN software.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g004-550.jpg?1676019985" title=" <strong>Figure 4</strong><br/> <p>Process of capturing images in photogrammetry.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g005-550.jpg?1676019999" title=" <strong>Figure 5</strong><br/> <p>Analysis of the difference between the point cloud of the scanners Artec Spider and Revopoint POP in evaluation 2. Result of applying the C2C algorithm. Color map of the distribution of distances. On the color scale, red represents the maximum distance expressed in millimeters.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g006-550.jpg?1676019978" title=" <strong>Figure 6</strong><br/> <p>Histogram of <a href="#sensors-23-01730-f005" class="html-fig">Figure 5</a>. Histogram units: millimeters (X axis) and number of points (Y axis).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g007-550.jpg?1676019975" title=" <strong>Figure 7</strong><br/> <p>Analysis of the difference between the point cloud of the scanner Artec Spider and SfM in evaluation 3. Result of applying the C2C algorithm. Color map of the distribution of distances. On the color scale, red represents the maximum distance expressed in millimeters.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g008-550.jpg?1676019984" title=" <strong>Figure 8</strong><br/> <p>Histogram of <a href="#sensors-23-01730-f007" class="html-fig">Figure 7</a>. Histogram units: millimeters (X axis) and number of points (Y axis).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g009-550.jpg?1676019997" title=" <strong>Figure 9</strong><br/> <p>Analysis of the difference between the point cloud (Phase 1) between photogrammetry technique and the scanner Revopoint POP in evaluation 3. Result of applying the C2C algorithm. Color map of the distribution of distances. On the color scale, red represents the maximum distance expressed in millimeters.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g010-550.jpg?1676020007" title=" <strong>Figure 10</strong><br/> <p>Histogram of <a href="#sensors-23-01730-f009" class="html-fig">Figure 9</a>. Histogram units: millimeters (X axis) and number of points (Y axis).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g011-550.jpg?1676019970" title=" <strong>Figure 11</strong><br/> <p>Analysis of the difference between the point cloud (Phase 2) between photogrammetry technique and the scanner Revopoint POP in evaluation 3. Result of applying the C2C algorithm. Color map of the distribution of distances. On the color scale, red represents the maximum distance expressed in millimeters.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g012-550.jpg?1676019991" title=" <strong>Figure 12</strong><br/> <p>Histogram of <a href="#sensors-23-01730-f011" class="html-fig">Figure 11</a>. Histogram units: millimeters (X axis) and number of points (Y axis).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g013-550.jpg?1676020000" title=" <strong>Figure 13</strong><br/> <p>Woman bust image.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g014-550.jpg?1676020005" title=" <strong>Figure 14</strong><br/> <p>Process of capturing images in photogrammetry of <a href="#sensors-23-01730-f013" class="html-fig">Figure 13</a>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g015-550.jpg?1676020010" title=" <strong>Figure 15</strong><br/> <p>Analysis of the difference between the point cloud of the scanner Artec Spider and the scanner Revopoint POP. Result of applying the C2C algorithm. Color map of the distribution of distances. On the color scale, red represents the maximum distance expressed in millimeters.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g016-550.jpg?1676019988" title=" <strong>Figure 16</strong><br/> <p>Histogram of <a href="#sensors-23-01730-f015" class="html-fig">Figure 15</a>. Histogram units: millimeters (X axis) and number of points (Y axis).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g017-550.jpg?1676019990" title=" <strong>Figure 17</strong><br/> <p>Analysis of the difference between the point cloud of the scanner Artec Spider and SfM. Result of applying the C2C algorithm. Color map of the distribution of distances. On the color scale, red represents the maximum distance expressed in millimeters.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g018-550.jpg?1676019973" title=" <strong>Figure 18</strong><br/> <p>Histogram of <a href="#sensors-23-01730-f017" class="html-fig">Figure 17</a>. Histogram units: millimeters (X axis) and number of points (Y axis).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g019-550.jpg?1676020008" title=" <strong>Figure 19</strong><br/> <p>Analysis of the difference between the point cloud between photogrammetry technique and the scanner Revopoint POP. Result of applying the C2C algorithm. Color map of the distribution of distances. On the color scale, red represents the maximum distance expressed in millimeters.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g020-550.jpg?1676019995" title=" <strong>Figure 20</strong><br/> <p>Histogram of <a href="#sensors-23-01730-f019" class="html-fig">Figure 19</a>. Histogram units: millimeters (X axis) and number of points (Y axis).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g021-550.jpg?1676020011" title=" <strong>Figure 21</strong><br/> <p>20 × 20 mm segment, it is a subset of points coming from the edge of the vessel.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g022-550.jpg?1676020002" title=" <strong>Figure 22</strong><br/> <p>Distribution of subsets of representative points in the rim plane of the vessel. (<b>a</b>) Points registered with the scanner Artec Spider, (<b>b</b>) points registered with the scanner Revopoint POP and (<b>c</b>) points registered by photogrammetry.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g023-550.jpg?1676019980" title=" <strong>Figure 23</strong><br/> <p>Box plots of the density of survey points in the segmentation of the subset of representative points of the plane of the edge of the vessel.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g024-550.jpg?1676020003" title=" <strong>Figure 24</strong><br/> <p>Point cloud model using BIM geometry.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g025-550.jpg?1676019987" title=" <strong>Figure 25</strong><br/> <p>Image of the apostolic vessel in the BIM space with the insertion of SLS<sub>VP</sub>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g026-550.jpg?1676019994" title=" <strong>Figure 26</strong><br/> <p>Maximum and minimum deviation dispersion expressed in millimeters of the section plane vectors in the BIM platform.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g027-550.jpg?1676019982" title=" <strong>Figure 27</strong><br/> <p>Representation of the subsets of points in the OYZ projection plane, (<b>a</b>) SLS<sub>VS</sub>, (<b>b</b>) SLS<sub>VP</sub> and (<b>c</b>) SfM<sub>V</sub>. The abscissa axis represents the y-axis, and the ordinate axis represents the z-axis.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g028-550.jpg?1676020004" title=" <strong>Figure 28</strong><br/> <p>Three-dimensional mesh representation of the surface of the SLS<sub>VS</sub>, SLS<sub>VP</sub> and SfM<sub>V</sub> points subsets.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g029-550.jpg?1676019968" title=" <strong>Figure 29</strong><br/> <p>Three-dimensional mesh rendering in Rhinoceros 7.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g030-550.jpg?1676019966" title=" <strong>Figure 30</strong><br/> <p>Representation of a digital surface model of the results of the three subsets of segmentation points of the SLS<sub>VS</sub>, SLS<sub>VP</sub> and SfM<sub>V</sub>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g031-550.jpg?1676019979" title=" <strong>Figure 31</strong><br/> <p>Results of the two consecutive evaluations of segmentation in the set of points between SLS<sub>VS</sub> and SLS<sub>VP</sub>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g032-550.jpg?1676019971" title=" <strong>Figure 32</strong><br/> <p>Results of the three consecutive evaluations of segmentation in the set of SLS<sub>VS</sub> and SfM<sub>V</sub> points.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01730/article_deploy/html/images/sensors-23-01730-g033-550.jpg?1676019967" title=" <strong>Figure 33</strong><br/> <p>Results of the three consecutive evaluations of segmentation in the set of points between SfM<sub>V</sub> and SLS<sub>VP</sub>, (<b>A</b>) alienation automatic (<b>B</b>) alienation manual.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1730'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1057236" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 19 pages, 11988 KiB   </span> <a href="/1424-8220/23/3/1729/pdf?version=1675683502" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Interaction with Industrial Digital Twin Using Neuro-Symbolic Reasoning" data-journal="sensors"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1424-8220/23/3/1729">Interaction with Industrial Digital Twin Using Neuro-Symbolic Reasoning</a> <div class="authors"> by <span class="inlineblock "><strong>Aziz Siyaev</strong>, </span><span class="inlineblock "><strong>Dilmurod Valiev</strong> and </span><span class="inlineblock "><strong>Geun-Sik Jo</strong></span> </div> <div class="color-grey-dark"> <em>Sensors</em> <b>2023</b>, <em>23</em>(3), 1729; <a href="https://doi.org/10.3390/s23031729">https://doi.org/10.3390/s23031729</a> - 3 Feb 2023 </div> <a href="/1424-8220/23/3/1729#metrics">Cited by 11</a> | Viewed by 4485 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Digital twins have revolutionized manufacturing and maintenance, allowing us to interact with virtual yet realistic representations of the physical world in simulations to identify potential problems or opportunities for improvement. However, traditional digital twins do not have the ability to communicate with humans <a href="#" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1729/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Digital twins have revolutionized manufacturing and maintenance, allowing us to interact with virtual yet realistic representations of the physical world in simulations to identify potential problems or opportunities for improvement. However, traditional digital twins do not have the ability to communicate with humans using natural language, which limits their potential usefulness. Although conventional natural language processing methods have proven to be effective in solving certain tasks, neuro-symbolic AI offers a new approach that leads to more robust and versatile solutions. In this paper, we propose neuro-symbolic reasoning (NSR)—a fundamental method for interacting with 3D digital twins using natural language. The method understands user requests and contexts to manipulate 3D components of digital twins and is able to read maintenance manuals and implement installations and removal procedures autonomously. A practical neuro-symbolic dataset of machine-understandable manuals, 3D models, and user queries is collected to train the neuro-symbolic reasoning interaction mechanism. The evaluation demonstrates that NSR can execute user commands accurately, achieving 96.2% accuracy on test data. The proposed method has industrial importance since it provides the technology to perform maintenance procedures, request information from manuals, and serve as a tool to interact with complex virtual machinery using natural language. <a href="/1424-8220/23/3/1729">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/sensors/special_issues/AIDT ">IoT, AI, and Digital Twin for Smart Manufacturing</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1729/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1057236"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1057236"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1057236" data-cycle-prev="#prev1057236" data-cycle-progressive="#images1057236" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1057236-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/sensors/sensors-23-01729/article_deploy/html/images/sensors-23-01729-g001-550.jpg?1675683595" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1057236" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1057236-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01729/article_deploy/html/images/sensors-23-01729-g002-550.jpg?1675683597'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1057236-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01729/article_deploy/html/images/sensors-23-01729-g003-550.jpg?1675683589'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1057236-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01729/article_deploy/html/images/sensors-23-01729-g004-550.jpg?1675683599'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1057236-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01729/article_deploy/html/images/sensors-23-01729-g005-550.jpg?1675683591'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1057236-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01729/article_deploy/html/images/sensors-23-01729-g006-550.jpg?1675683596'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1057236-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01729/article_deploy/html/images/sensors-23-01729-g007-550.jpg?1675683593'><p>Figure 7</p></div></script></div></div><div id="article-1057236-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/sensors/sensors-23-01729/article_deploy/html/images/sensors-23-01729-g001-550.jpg?1675683595" title=" <strong>Figure 1</strong><br/> <p>Neuro-symbolic reasoning for interaction with digital twins.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1729'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01729/article_deploy/html/images/sensors-23-01729-g002-550.jpg?1675683597" title=" <strong>Figure 2</strong><br/> <p>Digital twin content creation.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1729'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01729/article_deploy/html/images/sensors-23-01729-g003-550.jpg?1675683589" title=" <strong>Figure 3</strong><br/> <p>Digital twin interaction symbolic vocabulary.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1729'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01729/article_deploy/html/images/sensors-23-01729-g004-550.jpg?1675683599" title=" <strong>Figure 4</strong><br/> <p>Neuro-symbolic dataset of queries.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1729'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01729/article_deploy/html/images/sensors-23-01729-g005-550.jpg?1675683591" title=" <strong>Figure 5</strong><br/> <p>Neuro-symbolic reasoning pipeline.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1729'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01729/article_deploy/html/images/sensors-23-01729-g006-550.jpg?1675683596" title=" <strong>Figure 6</strong><br/> <p>Example of execution of symbolic programs.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1729'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01729/article_deploy/html/images/sensors-23-01729-g007-550.jpg?1675683593" title=" <strong>Figure 7</strong><br/> <p>Digital twin interaction software.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1729'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1057228" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 19 pages, 4815 KiB   </span> <a href="/1424-8220/23/3/1728/pdf?version=1675863335" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="An Affordable NIR Spectroscopic System for Fraud Detection in Olive Oil" data-journal="sensors"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1424-8220/23/3/1728">An Affordable NIR Spectroscopic System for Fraud Detection in Olive Oil</a> <div class="authors"> by <span class="inlineblock "><strong>Candela Melendreras</strong>, </span><span class="inlineblock "><strong>Ana Soldado</strong>, </span><span class="inlineblock "><strong>José M. Costa-Fernández</strong>, </span><span class="inlineblock "><strong>Alberto López</strong>, </span><span class="inlineblock "><strong>Marta Valledor</strong>, </span><span class="inlineblock "><strong>Juan Carlos Campo</strong> and </span><span class="inlineblock "><strong>Francisco Ferrero</strong></span> </div> <div class="color-grey-dark"> <em>Sensors</em> <b>2023</b>, <em>23</em>(3), 1728; <a href="https://doi.org/10.3390/s23031728">https://doi.org/10.3390/s23031728</a> - 3 Feb 2023 </div> <a href="/1424-8220/23/3/1728#metrics">Cited by 13</a> | Viewed by 3407 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Adulterations of olive oil are performed by adding seed oils to this high-quality product, which are cheaper than olive oils. Food safety controls have been established by the European Union to avoid these episodes. Most of these methodologies require expensive equipment, time-consuming procedures, <a href="#" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1728/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Adulterations of olive oil are performed by adding seed oils to this high-quality product, which are cheaper than olive oils. Food safety controls have been established by the European Union to avoid these episodes. Most of these methodologies require expensive equipment, time-consuming procedures, and expert personnel to execute. Near-infrared spectroscopy (NIRS) technology has many applications in the food processing industry. It analyzes food safety and quality parameters along the food chain. Using principal component analysis (PCA), the differences and similarities between olive oil and seed oils (sesame, sunflower, and flax oil) have been evaluated. To quantify the percentage of adulterated seed oil in olive oils, partial least squares (PLS) have been employed. A total of 96 samples of olive oil adulterated with seed oils were prepared. These samples were used to build a spectra library covering various mixtures containing seed oils and olive oil contents. Eighteen chemometric models were developed by combining the first and second derivatives with Standard Normal Variable (SNV) for scatter correction to classify and quantify seed oil adulteration and percentage. The results obtained for all seed oils show excellent coefficients of determination for calibration higher than 0.80. Because the instrumental aspects are not generally sufficiently addressed in the articles, we include a specific section on some key aspects of developing a high-performance and cost-effective NIR spectroscopy solution for fraud detection in olive oil. First, spectroscopy architectures are introduced, especially the Texas Instruments Digital Light Processing (DLP) technology for spectroscopy that has been used in this work. These results demonstrate that the portable prototype can be used as an effective tool to detect food fraud in liquid samples. <a href="/1424-8220/23/3/1728">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/sensors/special_issues/6Z1W5TAHL8 ">Innovative Sensors and Embedded Sensor Systems for Food Analysis</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1728/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1057228"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1057228"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1057228" data-cycle-prev="#prev1057228" data-cycle-progressive="#images1057228" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1057228-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g001-550.jpg?1675863425" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1057228" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1057228-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g002-550.jpg?1675863419'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1057228-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g003-550.jpg?1675863409'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1057228-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g004-550.jpg?1675863421'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1057228-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g005-550.jpg?1675863415'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1057228-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g006-550.jpg?1675863410'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1057228-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g007-550.jpg?1675863406'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1057228-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g008-550.jpg?1675863405'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1057228-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g009-550.jpg?1675863411'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1057228-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g010-550.jpg?1675863414'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1057228-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g011a-550.jpg?1675863413'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1057228-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g011b-550.jpg?1675863412'><p>Figure 11 Cont.</p></div> --- <div class='openpopupgallery' data-imgindex='12' data-target='article-1057228-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g012a-550.jpg?1675863422'><p>Figure 12</p></div> --- <div class='openpopupgallery' data-imgindex='13' data-target='article-1057228-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g012b-550.jpg?1675863418'><p>Figure 12 Cont.</p></div> --- <div class='openpopupgallery' data-imgindex='14' data-target='article-1057228-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g013a-550.jpg?1675863424'><p>Figure 13</p></div> --- <div class='openpopupgallery' data-imgindex='15' data-target='article-1057228-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g013b-550.jpg?1675863417'><p>Figure 13 Cont.</p></div> --- <div class='openpopupgallery' data-imgindex='16' data-target='article-1057228-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g014-550.jpg?1675863408'><p>Figure 14</p></div></script></div></div><div id="article-1057228-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g001-550.jpg?1675863425" title=" <strong>Figure 1</strong><br/> <p>Sample preparation scheme (N = 96). Three pure olive oils, 12 samples of olive oil (4 samples of extra virgin olive oil, 4 samples of virgin olive oil and 4 samples of olive oil) adulterated with flax oil, 12 samples of olive oil adulterated with sesame oil, 12 samples of olive oil adulterated with sunflower oil, and 57 samples of olive oil adulterated with two of the selected adulterant oils.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1728'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g002-550.jpg?1675863419" title=" <strong>Figure 2</strong><br/> <p>Transmittance measurement setup: (<b>a</b>) DLP NIRscan Nano EVM, (<b>b</b>) Graphical User Interface (GUI). <a href="#sec3dot4-sensors-23-01728" class="html-sec">Section 3.4</a> describes the GUI.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1728'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g003-550.jpg?1675863409" title=" <strong>Figure 3</strong><br/> <p>Spectroscopy architectures: (<b>a</b>) traditional (<b>b</b>) digital light processing (DLP).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1728'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g004-550.jpg?1675863421" title=" <strong>Figure 4</strong><br/> <p>DLP NIRscan Nano EVM with (<b>a</b>) reflective module and (<b>b</b>) transmittance module.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1728'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g005-550.jpg?1675863415" title=" <strong>Figure 5</strong><br/> <p>Interior view of the DLP NIRscan Nano-optical architecture.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1728'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g006-550.jpg?1675863410" title=" <strong>Figure 6</strong><br/> <p>Basic block diagram of the DLP NIRscan Nano hardware.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1728'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g007-550.jpg?1675863406" title=" <strong>Figure 7</strong><br/> <p>DLP NIRscan Nano lamp driver.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1728'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g008-550.jpg?1675863405" title=" <strong>Figure 8</strong><br/> <p>Transimpedance amplifier circuit.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1728'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g009-550.jpg?1675863411" title=" <strong>Figure 9</strong><br/> <p>DLP NIRscan Nano GUI Scan Screen.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1728'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g010-550.jpg?1675863414" title=" <strong>Figure 10</strong><br/> <p>SNR of the Column scan as a function of wavelength.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1728'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g011a-550.jpg?1675863413" title=" <strong>Figure 11</strong><br/> <p>Heatmap showing the Root Mean Square (RMS) values for each number of scans to be averaged. (<b>a</b>) Heatmap with the RMS values using the Column scan model; (<b>b</b>) heatmap with the RMS values using the Hadamard scan model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1728'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g011b-550.jpg?1675863412" title=" <strong>Figure 11 Cont.</strong><br/> <p>Heatmap showing the Root Mean Square (RMS) values for each number of scans to be averaged. (<b>a</b>) Heatmap with the RMS values using the Column scan model; (<b>b</b>) heatmap with the RMS values using the Hadamard scan model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1728'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g012a-550.jpg?1675863422" title=" <strong>Figure 12</strong><br/> <p>Raw spectra of all pure oils: (<b>a</b>) raw spectra of all mixtures, (<b>b</b>) seed oils and average spectrum of all adulterated samples, and (<b>c</b>) olive oils spectra.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1728'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g012b-550.jpg?1675863418" title=" <strong>Figure 12 Cont.</strong><br/> <p>Raw spectra of all pure oils: (<b>a</b>) raw spectra of all mixtures, (<b>b</b>) seed oils and average spectrum of all adulterated samples, and (<b>c</b>) olive oils spectra.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1728'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g013a-550.jpg?1675863424" title=" <strong>Figure 13</strong><br/> <p>First-derivative Savitzky–Golay spectra plus Standard Normal Variate (SNV) of all pure oils: (<b>a</b>) all mixtures, (<b>b</b>) olive oils, and (<b>c</b>) seed oils and average spectrum of all adulterated samples.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1728'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g013b-550.jpg?1675863417" title=" <strong>Figure 13 Cont.</strong><br/> <p>First-derivative Savitzky–Golay spectra plus Standard Normal Variate (SNV) of all pure oils: (<b>a</b>) all mixtures, (<b>b</b>) olive oils, and (<b>c</b>) seed oils and average spectrum of all adulterated samples.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1728'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01728/article_deploy/html/images/sensors-23-01728-g014-550.jpg?1675863408" title=" <strong>Figure 14</strong><br/> <p>Principal component analysis of three olive oils and their corresponding mixtures: (<b>a</b>) extra virgin olive oil (EVOO) and mixtures of EVOO and other seed oils, (<b>b</b>) virgin olive oil (VOO) and mixtures of VOO and other seed oils, (<b>c</b>) olive oil (OO) and mixtures of OO and other seed oils, and (<b>b</b>,<b>d</b>) all the samples pure and adulterated in the sample set.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1728'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1057212" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 15 pages, 4550 KiB   </span> <a href="/1424-8220/23/3/1727/pdf?version=1675732898" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Deposition of Thick SiO2 Coatings to Carbonyl Iron Microparticles for Thermal Stability and Microwave Performance" data-journal="sensors"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1424-8220/23/3/1727">Deposition of Thick SiO<sub>2</sub> Coatings to Carbonyl Iron Microparticles for Thermal Stability and Microwave Performance</a> <div class="authors"> by <span class="inlineblock "><strong>Arthur V. Dolmatov</strong>, </span><span class="inlineblock "><strong>Sergey S. Maklakov</strong>, </span><span class="inlineblock "><strong>Anastasia V. Artemova</strong>, </span><span class="inlineblock "><strong>Dmitry A. Petrov</strong>, </span><span class="inlineblock "><strong>Artem O. Shiryaev</strong> and </span><span class="inlineblock "><strong>Andrey N. Lagarkov</strong></span> </div> <div class="color-grey-dark"> <em>Sensors</em> <b>2023</b>, <em>23</em>(3), 1727; <a href="https://doi.org/10.3390/s23031727">https://doi.org/10.3390/s23031727</a> - 3 Feb 2023 </div> <a href="/1424-8220/23/3/1727#metrics">Cited by 5</a> | Viewed by 2220 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Thick dielectric SiO<sub>2</sub> shells on the surface of iron particles enhance the thermal and electrodynamic parameters of the iron. A technique to deposit thick, 500-nm, SiO<sub>2</sub> shell to the surface of carbonyl iron (CI) particles was developed. The method consists of <a href="#" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1727/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Thick dielectric SiO<sub>2</sub> shells on the surface of iron particles enhance the thermal and electrodynamic parameters of the iron. A technique to deposit thick, 500-nm, SiO<sub>2</sub> shell to the surface of carbonyl iron (CI) particles was developed. The method consists of repeated deposition of SiO<sub>2</sub> particles with air drying between iterations. This method allows to obtain thick dielectric shells up to 475 nm on individual CI particles. The paper shows that a thick SiO<sub>2</sub> protective layer reduces the permittivity of the ‘Fe-SiO<sub>2</sub>—paraffin’ composite in accordance with the Maxwell Garnett medium theory. The protective shell increases the thermal stability of iron, when heated in air, by shifting the transition temperature to the higher oxide. The particle size, the thickness of the SiO<sub>2</sub> shells, and the elemental analysis of the samples were studied using a scanning electron microscope. A coaxial waveguide and the Nicholson–Ross technique were used to measure microwave permeability and permittivity of the samples. A vibrating-sample magnetometer (VSM) was used to measure the magnetostatic data. A synchronous thermal analysis was applied to measure the thermal stability of the coated iron particles. The developed samples can be applied for electromagnetic compatibility problems, as well as the active material for various types of sensors. <a href="/1424-8220/23/3/1727">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Section <a href="/journal/sensors/sections/sensor_materials">Sensor Materials</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1727/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1057212"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1057212"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1057212" data-cycle-prev="#prev1057212" data-cycle-progressive="#images1057212" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1057212-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/sensors/sensors-23-01727/article_deploy/html/images/sensors-23-01727-g001-550.jpg?1675732975" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1057212" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1057212-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01727/article_deploy/html/images/sensors-23-01727-g002-550.jpg?1675732980'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1057212-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01727/article_deploy/html/images/sensors-23-01727-g003-550.jpg?1675732963'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1057212-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01727/article_deploy/html/images/sensors-23-01727-g004-550.jpg?1675732966'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1057212-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01727/article_deploy/html/images/sensors-23-01727-g005-550.jpg?1675732977'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1057212-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01727/article_deploy/html/images/sensors-23-01727-g006-550.jpg?1675732976'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1057212-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01727/article_deploy/html/images/sensors-23-01727-g007-550.jpg?1675732981'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1057212-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01727/article_deploy/html/images/sensors-23-01727-g008-550.jpg?1675732978'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1057212-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01727/article_deploy/html/images/sensors-23-01727-g009-550.jpg?1675732964'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1057212-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01727/article_deploy/html/images/sensors-23-01727-g010-550.jpg?1675732972'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1057212-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01727/article_deploy/html/images/sensors-23-01727-g011-550.jpg?1675732965'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1057212-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01727/article_deploy/html/images/sensors-23-01727-g012-550.jpg?1675732974'><p>Figure 12</p></div></script></div></div><div id="article-1057212-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/sensors/sensors-23-01727/article_deploy/html/images/sensors-23-01727-g001-550.jpg?1675732975" title=" <strong>Figure 1</strong><br/> <p>(<b>a</b>) Dependence of ε eff (Fe-SiO<sub>2</sub>) on the SiO<sub>2</sub> shell thickness. (<b>b</b>). Dependence of ε eff (Fe-SiO<sub>2</sub> + wax) on the SiO<sub>2</sub> shell thickness.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1727'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01727/article_deploy/html/images/sensors-23-01727-g002-550.jpg?1675732980" title=" <strong>Figure 2</strong><br/> <p>SEM images of Fe-SiO<sub>2</sub> particles with a different number of iterations.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1727'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01727/article_deploy/html/images/sensors-23-01727-g003-550.jpg?1675732963" title=" <strong>Figure 3</strong><br/> <p>Images of individual SiO<sub>2</sub> particles.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1727'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01727/article_deploy/html/images/sensors-23-01727-g004-550.jpg?1675732966" title=" <strong>Figure 4</strong><br/> <p>Particle size distribution measured for pure carbonyl iron,; one, two, and three iterations.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1727'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01727/article_deploy/html/images/sensors-23-01727-g005-550.jpg?1675732977" title=" <strong>Figure 5</strong><br/> <p>Cumulative particle size curve for uncoated CI; one, two, and three iterations.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1727'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01727/article_deploy/html/images/sensors-23-01727-g006-550.jpg?1675732976" title=" <strong>Figure 6</strong><br/> <p>(<b>a</b>) Fe:Si ratio and (<b>b</b>) Si:O ratio with a variation in the number of iterations.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1727'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01727/article_deploy/html/images/sensors-23-01727-g007-550.jpg?1675732981" title=" <strong>Figure 7</strong><br/> <p>Magnetization curves in an iterative series of experiments.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1727'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01727/article_deploy/html/images/sensors-23-01727-g008-550.jpg?1675732978" title=" <strong>Figure 8</strong><br/> <p>Saturation magnetization for one, two, and three iterations of deposition of the dielectric shell.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1727'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01727/article_deploy/html/images/sensors-23-01727-g009-550.jpg?1675732964" title=" <strong>Figure 9</strong><br/> <p>Comparison of the SEM and VSM methods.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1727'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01727/article_deploy/html/images/sensors-23-01727-g010-550.jpg?1675732972" title=" <strong>Figure 10</strong><br/> <p>Synchronous thermal analysis results. Comparison of the curves for pure iron (<b>A</b>) and Fe-SiO<sub>2</sub> particles in cases of one, (<b>B</b>) two (<b>C</b>) and three (<b>D</b>) iterations.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1727'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01727/article_deploy/html/images/sensors-23-01727-g011-550.jpg?1675732965" title=" <strong>Figure 11</strong><br/> <p>Synchronous thermal analysis results. Detailed 600–720 C temperature interval.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1727'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01727/article_deploy/html/images/sensors-23-01727-g012-550.jpg?1675732974" title=" <strong>Figure 12</strong><br/> <p>Frequency dispersion of permittivity (<b>A</b>,<b>B</b>) and permeability (<b>C</b>,<b>D</b>) in the experiments with a different number of iterations of the SiO<sub>2</sub> shell deposition.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1727'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1057191" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 29 pages, 12829 KiB   </span> <a href="/1424-8220/23/3/1726/pdf?version=1675427966" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Table Tennis Track Detection Based on Temporal Feature Multiplexing Network" data-journal="sensors"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1424-8220/23/3/1726">Table Tennis Track Detection Based on Temporal Feature Multiplexing Network</a> <div class="authors"> by <span class="inlineblock "><strong>Wenjie Li</strong>, </span><span class="inlineblock "><strong>Xiangpeng Liu</strong>, </span><span class="inlineblock "><strong>Kang An</strong>, </span><span class="inlineblock "><strong>Chengjin Qin</strong> and </span><span class="inlineblock "><strong>Yuhua Cheng</strong></span> </div> <div class="color-grey-dark"> <em>Sensors</em> <b>2023</b>, <em>23</em>(3), 1726; <a href="https://doi.org/10.3390/s23031726">https://doi.org/10.3390/s23031726</a> - 3 Feb 2023 </div> <a href="/1424-8220/23/3/1726#metrics">Cited by 8</a> | Viewed by 4249 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Recording the trajectory of table tennis balls in real-time enables the analysis of the opponent’s attacking characteristics and weaknesses. The current analysis of the ball paths mainly relied on human viewing, which lacked certain theoretical data support. In order to solve the problem <a href="#" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1726/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Recording the trajectory of table tennis balls in real-time enables the analysis of the opponent’s attacking characteristics and weaknesses. The current analysis of the ball paths mainly relied on human viewing, which lacked certain theoretical data support. In order to solve the problem of the lack of objective data analysis in the research of table tennis competition, a target detection algorithm-based table tennis trajectory extraction network was proposed to record the trajectory of the table tennis movement in video. The network improved the feature reuse rate in order to achieve a lightweight network and enhance the detection accuracy. The core of the network was the “feature store & return” module, which could store the output of the current network layer and pass the features to the input of the network layer at the next moment to achieve efficient reuse of the features. In this module, the Transformer model was used to secondarily process the features, build the global association information, and enhance the feature richness of the feature map. According to the designed experiments, the detection accuracy of the network was 96.8% for table tennis and 89.1% for target localization. Moreover, the parameter size of the model was only 7.68 MB, and the detection frame rate could reach 634.19 FPS using the hardware for the tests. In summary, the network designed in this paper has the characteristics of both lightweight and high precision in table tennis detection, and the performance of the proposed model significantly outperforms that of the existing models. <a href="/1424-8220/23/3/1726">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Section <a href="/journal/sensors/sections/Intelligent_Sensors">Intelligent Sensors</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1726/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1057191"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1057191"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1057191" 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data-imgindex='20' data-target='article-1057191-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01726/article_deploy/html/images/sensors-23-01726-g019-550.jpg?1675428046'><p>Figure 19</p></div></script></div></div><div id="article-1057191-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/sensors/sensors-23-01726/article_deploy/html/images/sensors-23-01726-g001-550.jpg?1675428054" title=" <strong>Figure 1</strong><br/> <p>Sample image frames: (<b>a</b>) 1100 FPS, (<b>b</b>) 800 FPS, (<b>c</b>) 600 FPS, (<b>d</b>) 400 FPS, (<b>e</b>) 200 FPS, (<b>f</b>) 120 FPS.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1726'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01726/article_deploy/html/images/sensors-23-01726-g002-550.jpg?1675428033" title=" <strong>Figure 2</strong><br/> <p>The structure of Transformer model.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1726'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01726/article_deploy/html/images/sensors-23-01726-g003-550.jpg?1675428035" title=" <strong>Figure 3</strong><br/> <p>Temporal feature multiplexing network.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1726'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01726/article_deploy/html/images/sensors-23-01726-g004-550.jpg?1675428074" title=" <strong>Figure 4</strong><br/> <p>TokenLearner calculation flow.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1726'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01726/article_deploy/html/images/sensors-23-01726-g005-550.jpg?1675428045" title=" <strong>Figure 5</strong><br/> <p>Feature storage and return module.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1726'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01726/article_deploy/html/images/sensors-23-01726-g006-550.jpg?1675428077" title=" <strong>Figure 6</strong><br/> <p>Detailed network structure (partial).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1726'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01726/article_deploy/html/images/sensors-23-01726-g007-550.jpg?1675428039" title=" <strong>Figure 7</strong><br/> <p>Example of a dataset: (<b>a</b>) sample data taken horizontally to the desktop and (<b>b</b>) sample data taken perpendicular to the desktop.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1726'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01726/article_deploy/html/images/sensors-23-01726-g008-550.jpg?1675428059" title=" <strong>Figure 8</strong><br/> <p>Network 1: (<b>a</b>) configuration of Network 1 and (<b>b</b>) test results for each parameter of Network 1.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1726'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01726/article_deploy/html/images/sensors-23-01726-g009-550.jpg?1675428064" title=" <strong>Figure 9</strong><br/> <p>Network 2: (<b>a</b>) configuration of Network 2 and (<b>b</b>) test results for each parameter of Network 2.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1726'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01726/article_deploy/html/images/sensors-23-01726-g010-550.jpg?1675428037" title=" <strong>Figure 10</strong><br/> <p>Network 3: (<b>a</b>) configuration of Network 3 and (<b>b</b>) test results for each parameter of Network 3.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1726'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01726/article_deploy/html/images/sensors-23-01726-g011-550.jpg?1675428056" title=" <strong>Figure 11</strong><br/> <p>Network 4: (<b>a</b>) configuration of Network 4 and (<b>b</b>) test results for each parameter of Network 4.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1726'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01726/article_deploy/html/images/sensors-23-01726-g012-550.jpg?1675428062" title=" <strong>Figure 12</strong><br/> <p>Network 5: (<b>a</b>) configuration of Network 5 and (<b>b</b>) test results for each parameter of Network 5.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1726'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01726/article_deploy/html/images/sensors-23-01726-g013-550.jpg?1675428040" title=" <strong>Figure 13</strong><br/> <p>Network 6: (<b>a</b>) configuration of Network 6 and (<b>b</b>) test results for each parameter of Network 6.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1726'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01726/article_deploy/html/images/sensors-23-01726-g014-550.jpg?1675428061" title=" <strong>Figure 14</strong><br/> <p>Comparative results of ablation experiments.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1726'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01726/article_deploy/html/images/sensors-23-01726-g015a-550.jpg?1675428072" title=" <strong>Figure 15</strong><br/> <p>Comparison of target detection results using consecutive frames: (<b>a</b>) GroundTruth, (<b>b</b>) Network 1, (<b>c</b>) Network 2, (<b>d</b>) Network 3, (<b>e</b>) Network 4, (<b>f</b>) Network 5, (<b>g</b>) Network 6.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1726'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01726/article_deploy/html/images/sensors-23-01726-g015b-550.jpg?1675428044" title=" <strong>Figure 15 Cont.</strong><br/> <p>Comparison of target detection results using consecutive frames: (<b>a</b>) GroundTruth, (<b>b</b>) Network 1, (<b>c</b>) Network 2, (<b>d</b>) Network 3, (<b>e</b>) Network 4, (<b>f</b>) Network 5, (<b>g</b>) Network 6.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1726'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01726/article_deploy/html/images/sensors-23-01726-g016a-550.jpg?1675428057" title=" <strong>Figure 16</strong><br/> <p>Comparison results of different networks: (<b>a</b>) 3D trajectory diagram of the sphere movement, (<b>b</b>) trajectory of the sphere’s motion in x-coordinates, (<b>c</b>) trajectory of the sphere in y-coordinate motion, (<b>d</b>) trajectory diagram of the z-coordinates of the sphere’s motion.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1726'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01726/article_deploy/html/images/sensors-23-01726-g016b-550.jpg?1675428075" title=" <strong>Figure 16 Cont.</strong><br/> <p>Comparison results of different networks: (<b>a</b>) 3D trajectory diagram of the sphere movement, (<b>b</b>) trajectory of the sphere’s motion in x-coordinates, (<b>c</b>) trajectory of the sphere in y-coordinate motion, (<b>d</b>) trajectory diagram of the z-coordinates of the sphere’s motion.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1726'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01726/article_deploy/html/images/sensors-23-01726-g017-550.jpg?1675428078" title=" <strong>Figure 17</strong><br/> <p>Experimental results of horizontal table capturing.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1726'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01726/article_deploy/html/images/sensors-23-01726-g018-550.jpg?1675428060" title=" <strong>Figure 18</strong><br/> <p>Experimental results of vertical table capturing.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1726'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01726/article_deploy/html/images/sensors-23-01726-g019-550.jpg?1675428046" title=" <strong>Figure 19</strong><br/> <p>Comparison of detection speed of different networks.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1726'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1057232" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 16 pages, 3439 KiB   </span> <a href="/1424-8220/23/3/1725/pdf?version=1675429522" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Ultrasensitive Optical Fiber Sensors Working at Dispersion Turning Point: Review" data-journal="sensors"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Review</span></div> <a class="title-link" href="/1424-8220/23/3/1725">Ultrasensitive Optical Fiber Sensors Working at Dispersion Turning Point: Review</a> <div class="authors"> by <span class="inlineblock "><strong>Shengyao Xu</strong>, </span><span class="inlineblock "><strong>Peng Kang</strong>, </span><span class="inlineblock "><strong>Zhijie Hu</strong>, </span><span class="inlineblock "><strong>Weijie Chang</strong> and </span><span class="inlineblock "><strong>Feng Huang</strong></span> </div> <div class="color-grey-dark"> <em>Sensors</em> <b>2023</b>, <em>23</em>(3), 1725; <a href="https://doi.org/10.3390/s23031725">https://doi.org/10.3390/s23031725</a> - 3 Feb 2023 </div> <a href="/1424-8220/23/3/1725#metrics">Cited by 4</a> | Viewed by 2844 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Optical fiber sensors working at the dispersion turning point (DTP) have served as promising candidates for various sensing applications due to their ultrahigh sensitivity. In this review, recently developed ultrasensitive fiber sensors at the DTP, including fiber couplers, fiber gratings, and interferometers, are <a href="#" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1725/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Optical fiber sensors working at the dispersion turning point (DTP) have served as promising candidates for various sensing applications due to their ultrahigh sensitivity. In this review, recently developed ultrasensitive fiber sensors at the DTP, including fiber couplers, fiber gratings, and interferometers, are comprehensively analyzed. These three schemes are outlined in terms of operation principles, device structures, and sensing applications. We focus on sensitivity enhancement and optical transducers, we evaluate each sensing scheme based on the DTP principle, and we discuss relevant challenges, aiming to provide some clues for future research. <a href="/1424-8220/23/3/1725">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/sensors/special_issues/1BO3248F11 ">Advanced Infocomm Technology including Selected Papers from 14th ICAIT 2022</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1725/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1057232"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1057232"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1057232" data-cycle-prev="#prev1057232" data-cycle-progressive="#images1057232" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1057232-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/sensors/sensors-23-01725/article_deploy/html/images/sensors-23-01725-g001-550.jpg?1675429590" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1057232" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1057232-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01725/article_deploy/html/images/sensors-23-01725-g002-550.jpg?1675429587'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1057232-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01725/article_deploy/html/images/sensors-23-01725-g003-550.jpg?1675429589'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1057232-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01725/article_deploy/html/images/sensors-23-01725-g004-550.jpg?1675429592'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1057232-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01725/article_deploy/html/images/sensors-23-01725-g005-550.jpg?1675429588'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1057232-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01725/article_deploy/html/images/sensors-23-01725-g006-550.jpg?1675429591'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1057232-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01725/article_deploy/html/images/sensors-23-01725-g007-550.jpg?1675429589'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1057232-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01725/article_deploy/html/images/sensors-23-01725-g008-550.jpg?1675429593'><p>Figure 8</p></div></script></div></div><div id="article-1057232-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/sensors/sensors-23-01725/article_deploy/html/images/sensors-23-01725-g001-550.jpg?1675429590" title=" <strong>Figure 1</strong><br/> <p>Typical structure of microfiber coupler.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1725'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01725/article_deploy/html/images/sensors-23-01725-g002-550.jpg?1675429587" title=" <strong>Figure 2</strong><br/> <p>(<b>a</b>) Group-effective RI difference versus wavelength for microfiber couplers. (<b>b</b>) Calculated sensitivities as a function of wavelength. (<b>c</b>) Simulated reflective spectra with different surrounding RIs [<a href="#B13-sensors-23-01725" class="html-bibr">13</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1725'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01725/article_deploy/html/images/sensors-23-01725-g003-550.jpg?1675429589" title=" <strong>Figure 3</strong><br/> <p>(<b>a</b>) Illustrative diagram of femtosecond laser processing. Schematics of the femtosecond-laser-induced grating and transmission principles: (<b>b</b>) FBG, (<b>c</b>) TFBG, and (<b>d</b>) LPG.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1725'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01725/article_deploy/html/images/sensors-23-01725-g004-550.jpg?1675429592" title=" <strong>Figure 4</strong><br/> <p>(<b>a</b>) Schematic illustration of the fabrication of a graphene oxide-coated LPG immunosensor. (<b>b</b>,<b>c</b>) Functional material integration into the optical fiber waveguide platform.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1725'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01725/article_deploy/html/images/sensors-23-01725-g005-550.jpg?1675429588" title=" <strong>Figure 5</strong><br/> <p>(<b>a</b>) Schematic of in-fiber interferometric structure. (<b>b</b>) Typical experimental setup of an in-fiber interferometer.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1725'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01725/article_deploy/html/images/sensors-23-01725-g006-550.jpg?1675429591" title=" <strong>Figure 6</strong><br/> <p>(<b>a</b>) Group birefringence G and (<b>b</b>) calculated sensitivity for PAHF and SMF microfibers with different diameters. (<b>c</b>,<b>d</b>) RI sensitivity and interference cutoff regions of PAHF and SMF microfibers, respectively.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1725'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01725/article_deploy/html/images/sensors-23-01725-g007-550.jpg?1675429589" title=" <strong>Figure 7</strong><br/> <p>Schematic diagram of the advanced design of optical fiber microstructures.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1725'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01725/article_deploy/html/images/sensors-23-01725-g008-550.jpg?1675429593" title=" <strong>Figure 8</strong><br/> <p>Schematic diagrams of fiber sensor package. (<b>a</b>,<b>b</b>) optical fiber integration to microfluidic channel for sensing; (<b>c</b>) optical fiber probe for sensing.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1725'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1057184" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 33 pages, 3375 KiB   </span> <a href="/1424-8220/23/3/1724/pdf?version=1675427657" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="A Modular In-Vehicle C-ITS Architecture for Sensor Data Collection, Vehicular Communications and Cloud Connectivity" data-journal="sensors"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class='label choice' data-dropdown='drop-article-label-choice' aria-expanded='false' data-editorschoiceaddition='<a href="/journal/sensors/editors_choice">More Editor’s choice articles in journal <em>Sensors</em>.</a>'>Editor’s Choice</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1424-8220/23/3/1724">A Modular In-Vehicle C-ITS Architecture for Sensor Data Collection, Vehicular Communications and Cloud Connectivity</a> <div class="authors"> by <span class="inlineblock "><strong>David Rocha</strong>, </span><span class="inlineblock "><strong>Gil Teixeira</strong>, </span><span class="inlineblock "><strong>Emanuel Vieira</strong>, </span><span class="inlineblock "><strong>João Almeida</strong> and </span><span class="inlineblock "><strong>Joaquim Ferreira</strong></span> </div> <div class="color-grey-dark"> <em>Sensors</em> <b>2023</b>, <em>23</em>(3), 1724; <a href="https://doi.org/10.3390/s23031724">https://doi.org/10.3390/s23031724</a> - 3 Feb 2023 </div> <a href="/1424-8220/23/3/1724#metrics">Cited by 16</a> | Viewed by 4312 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The growth of the automobile industry in recent decades and the overuse of personal vehicles have amplified problems directly related to road safety, such as the increase in traffic congestion and number of accidents, as well as the degradation of the quality of <a href="#" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1724/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> The growth of the automobile industry in recent decades and the overuse of personal vehicles have amplified problems directly related to road safety, such as the increase in traffic congestion and number of accidents, as well as the degradation of the quality of roads. At the same time, and with the contribution of climate change effects, dangerous weather events have become more common on road infrastructure. In this context, Cooperative Intelligent Transport Systems (C-ITS) and Internet of Things (IoT) solutions emerge to overcome the limitations of human and local sensory systems, through the collection and distribution of relevant data to Connected and Automated Vehicles (CAVs). In this paper, an intra- and inter-vehicle sensory data collection system is presented, starting with the acquisition of relevant data present on the Controller Area Network (CAN) bus, collected through the vehicle’s On-Board-Diagnostics II (OBD-II) port, as well as on an on-board smartphone device and possibly other additional sensors. Short-range communication technologies, such as Bluetooth Low Energy (BLE), Wi-Fi, and ITS-G5, are employed in conjunction with long-range cellular networks for data dissemination and remote cloud monitoring. The results of the experimental tests allow the analysis of the road environment, as well as the notification in near real-time of adverse road conditions to drivers. The developed data collection system reveals itself as a potentially valuable tool for improving road safety and to iterate on the current Road Weather Models (RWMs). <a href="/1424-8220/23/3/1724">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/sensors/special_issues/sensor_networks_vehicular_communications ">Sensor Networks for Vehicular Communications</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1724/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1057184"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1057184"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1057184" data-cycle-prev="#prev1057184" data-cycle-progressive="#images1057184" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1057184-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g001-550.jpg?1675427753" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1057184" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1057184-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g002-550.jpg?1675427745'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1057184-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g003-550.jpg?1675427758'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1057184-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g004-550.jpg?1675427739'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1057184-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g005-550.jpg?1675427738'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1057184-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g006-550.jpg?1675427742'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1057184-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g007-550.jpg?1675427741'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1057184-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g008-550.jpg?1675427757'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1057184-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g009-550.jpg?1675427754'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1057184-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g010-550.jpg?1675427744'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1057184-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g011-550.jpg?1675427735'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1057184-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g012-550.jpg?1675427775'><p>Figure 12</p></div> --- <div class='openpopupgallery' data-imgindex='12' data-target='article-1057184-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g013-550.jpg?1675427733'><p>Figure 13</p></div> --- <div class='openpopupgallery' data-imgindex='13' data-target='article-1057184-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g014-550.jpg?1675427760'><p>Figure 14</p></div> --- <div class='openpopupgallery' data-imgindex='14' data-target='article-1057184-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g015-550.jpg?1675427738'><p>Figure 15</p></div> --- <div class='openpopupgallery' data-imgindex='15' data-target='article-1057184-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g016-550.jpg?1675427770'><p>Figure 16</p></div> --- <div class='openpopupgallery' data-imgindex='16' data-target='article-1057184-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g017-550.jpg?1675427748'><p>Figure 17</p></div> --- <div class='openpopupgallery' data-imgindex='17' data-target='article-1057184-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g018-550.jpg?1675427737'><p>Figure 18</p></div> --- <div class='openpopupgallery' data-imgindex='18' data-target='article-1057184-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g019-550.jpg?1675427747'><p>Figure 19</p></div> --- <div class='openpopupgallery' data-imgindex='19' data-target='article-1057184-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g020-550.jpg?1675427730'><p>Figure 20</p></div> --- <div class='openpopupgallery' data-imgindex='20' data-target='article-1057184-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g021-550.jpg?1675427769'><p>Figure 21</p></div> --- <div class='openpopupgallery' data-imgindex='21' data-target='article-1057184-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g022-550.jpg?1675427755'><p>Figure 22</p></div> --- <div class='openpopupgallery' data-imgindex='22' data-target='article-1057184-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g023-550.jpg?1675427746'><p>Figure 23</p></div> --- <div class='openpopupgallery' data-imgindex='23' data-target='article-1057184-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g024-550.jpg?1675427761'><p>Figure 24</p></div> --- <div class='openpopupgallery' data-imgindex='24' data-target='article-1057184-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g0A1-550.jpg?1675427767'><p>Figure A1</p></div> --- <div class='openpopupgallery' data-imgindex='25' data-target='article-1057184-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g0A2-550.jpg?1675427752'><p>Figure A2</p></div> --- <div class='openpopupgallery' data-imgindex='26' data-target='article-1057184-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g0A3-550.jpg?1675427764'><p>Figure A3</p></div></script></div></div><div id="article-1057184-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g001-550.jpg?1675427753" title=" <strong>Figure 1</strong><br/> <p>CAN transceiver and controller.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1724'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g002-550.jpg?1675427745" title=" <strong>Figure 2</strong><br/> <p>OBU with GPS receiver.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1724'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g003-550.jpg?1675427758" title=" <strong>Figure 3</strong><br/> <p>Representation of the proposed algorithm for CAN parameters’ identification.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1724'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g004-550.jpg?1675427739" title=" <strong>Figure 4</strong><br/> <p>Representation of the devices installed in the vehicle.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1724'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g005-550.jpg?1675427738" title=" <strong>Figure 5</strong><br/> <p>Representation of the vehicular data collection mechanism.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1724'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g006-550.jpg?1675427742" title=" <strong>Figure 6</strong><br/> <p>Diagram of the mobile application architecture.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1724'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g007-550.jpg?1675427741" title=" <strong>Figure 7</strong><br/> <p>OBU’s internal architecture and interfaces.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1724'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g008-550.jpg?1675427757" title=" <strong>Figure 8</strong><br/> <p>CAM and CPM message representation in the mobile app.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1724'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g009-550.jpg?1675427754" title=" <strong>Figure 9</strong><br/> <p>IVIM message representation in the mobile app.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1724'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g010-550.jpg?1675427744" title=" <strong>Figure 10</strong><br/> <p>HD-Map message representation in the mobile app.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1724'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g011-550.jpg?1675427735" title=" <strong>Figure 11</strong><br/> <p>DENM message generation in the mobile app.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1724'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g012-550.jpg?1675427775" title=" <strong>Figure 12</strong><br/> <p>CAN/OBD-II’s sensor data representation in the mobile app.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1724'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g013-550.jpg?1675427733" title=" <strong>Figure 13</strong><br/> <p>Smartphone’s sensor data representation in the mobile app.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1724'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g014-550.jpg?1675427760" title=" <strong>Figure 14</strong><br/> <p>Dataflows between OBU’s services and peripheral devices (OBD-II reader and smartphone).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1724'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g015-550.jpg?1675427738" title=" <strong>Figure 15</strong><br/> <p>Second byte of OBD-II data for engine speed (<b>top</b>) and the best CAN candidate byte (<b>bottom</b>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1724'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g016-550.jpg?1675427770" title=" <strong>Figure 16</strong><br/> <p>OBD-II data for throttle position (<b>top</b>) and the best CAN candidate byte (<b>bottom</b>).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1724'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g017-550.jpg?1675427748" title=" <strong>Figure 17</strong><br/> <p>Variation of each wheel speed through a sharp turn.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1724'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g018-550.jpg?1675427737" title=" <strong>Figure 18</strong><br/> <p>Illuminance values recorded during day hours (orange), and during the night (blue).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1724'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g019-550.jpg?1675427747" title=" <strong>Figure 19</strong><br/> <p>Orientation of the smartphone in the 3D reference space.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1724'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g020-550.jpg?1675427730" title=" <strong>Figure 20</strong><br/> <p>Acceleration of the smartphone in each axis (x, y, and z).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1724'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g021-550.jpg?1675427769" title=" <strong>Figure 21</strong><br/> <p>Rotation of the smartphone in each axis (x, y, and z).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1724'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g022-550.jpg?1675427755" title=" <strong>Figure 22</strong><br/> <p>Rotation of the smartphone in the x-axis.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1724'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g023-550.jpg?1675427746" title=" <strong>Figure 23</strong><br/> <p>Barometric pressure measured from the OBD-II reader (blue) and the smartphone (orange).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1724'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g024-550.jpg?1675427761" title=" <strong>Figure 24</strong><br/> <p>Web interface displaying the collected data.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1724'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g0A1-550.jpg?1675427767" title=" <strong>Figure A1</strong><br/> <p>OVSM ASN.1 message format.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1724'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g0A2-550.jpg?1675427752" title=" <strong>Figure A2</strong><br/> <p>SPVSM ASN.1 message format.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1724'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01724/article_deploy/html/images/sensors-23-01724-g0A3-550.jpg?1675427764" title=" <strong>Figure A3</strong><br/> <p>VSM ASN.1 message format.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1724'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1057161" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 24 pages, 11771 KiB   </span> <a href="/1424-8220/23/3/1723/pdf?version=1675676868" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Map Space Modeling Method Reflecting Safety Margin in Coastal Water Based on Electronic Chart for Path Planning" data-journal="sensors"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1424-8220/23/3/1723">Map Space Modeling Method Reflecting Safety Margin in Coastal Water Based on Electronic Chart for Path Planning</a> <div class="authors"> by <span class="inlineblock "><strong>Da-un Jang</strong> and </span><span class="inlineblock "><strong>Joo-sung Kim</strong></span> </div> <div class="color-grey-dark"> <em>Sensors</em> <b>2023</b>, <em>23</em>(3), 1723; <a href="https://doi.org/10.3390/s23031723">https://doi.org/10.3390/s23031723</a> - 3 Feb 2023 </div> Viewed by 2078 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Map space composition is the first step in ship route planning. In this study, a map modeling method for path planning is proposed. This method incorporates the safety margin based on the theory of geographic space existing in coastal waters, maneuvering space according <a href="#" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1723/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Map space composition is the first step in ship route planning. In this study, a map modeling method for path planning is proposed. This method incorporates the safety margin based on the theory of geographic space existing in coastal waters, maneuvering space according to ship characteristics, and the psychological buffer space of a ship navigator. First, the obstacle area was segmented using the binary method—a segmentation method—based on the international standard electronic chart image. Next, the margin space was incorporated through the morphological algorithm for the obstacle area. Finally, to minimize the space lost during the route search, the boundary simplification of the obstacle area was performed through the concave hull method. The experimental results of the proposed method resulted in a map that minimized the area lost due to obstacles. In addition, it was found that the distance and path-finding time were reduced compared to the conventional convex hull method. The study shows that the map modeling method is feasible, and that it can be applied to path planning. <a href="/1424-8220/23/3/1723">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/sensors/special_issues/intelligent_sensing_transportation ">The Intelligent Sensing Technology of Transportation System</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1723/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1057161"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1057161"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1057161" data-cycle-prev="#prev1057161" 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src='https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g010-550.jpg?1675676949'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1057161-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g011-550.jpg?1675676948'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1057161-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g012-550.jpg?1675676962'><p>Figure 12</p></div> --- <div class='openpopupgallery' data-imgindex='12' data-target='article-1057161-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g013-550.jpg?1675676971'><p>Figure 13</p></div> --- <div class='openpopupgallery' 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src='https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g017-550.jpg?1675676947'><p>Figure 17</p></div> --- <div class='openpopupgallery' data-imgindex='17' data-target='article-1057161-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g018-550.jpg?1675676963'><p>Figure 18</p></div> --- <div class='openpopupgallery' data-imgindex='18' data-target='article-1057161-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g019-550.jpg?1675676951'><p>Figure 19</p></div> --- <div class='openpopupgallery' data-imgindex='19' data-target='article-1057161-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g020-550.jpg?1675676957'><p>Figure 20</p></div> --- <div class='openpopupgallery' data-imgindex='20' data-target='article-1057161-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g021-550.jpg?1675676960'><p>Figure 21</p></div> --- <div class='openpopupgallery' data-imgindex='21' data-target='article-1057161-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g022-550.jpg?1675676954'><p>Figure 22</p></div> --- <div class='openpopupgallery' data-imgindex='22' data-target='article-1057161-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g023-550.jpg?1675676938'><p>Figure 23</p></div></script></div></div><div id="article-1057161-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g001-550.jpg?1675676955" title=" <strong>Figure 1</strong><br/> <p>Chart showing the Safety Depth. The safe water depth indicates a value that includes the under keel clearance (UKC), which considers the navigation features of the vessel and marine environment to the maximum draught of a normal vessel.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1723'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g002-550.jpg?1675676965" title=" <strong>Figure 2</strong><br/> <p>Turning circle components.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1723'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g003-550.jpg?1675676942" title=" <strong>Figure 3</strong><br/> <p>Ship’s bumper area. (<b>a</b>) In the bumper model, the water area that is navigable with a maximum speed of 10–16 knots and above is distinguished as the open sea. (<b>b</b>) The narrow waterway, in which the speed is reduced to 6–8 knots, is distinguished as the harbor.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1723'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g004-550.jpg?1675676945" title=" <strong>Figure 4</strong><br/> <p>Description of margin space.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1723'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g005-550.jpg?1675676968" title=" <strong>Figure 5</strong><br/> <p>Process flow of the proposed map space method.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1723'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g006-550.jpg?1675676943" title=" <strong>Figure 6</strong><br/> <p>Apply safety depth on the electronic navigational chart image. (<b>a</b>) Set of the electronic navigational chart. (<b>b</b>) Set the safety depth to 10 m (<b>c</b>) and set the safety depth to 5 m.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1723'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g007-550.jpg?1675676940" title=" <strong>Figure 7</strong><br/> <p>Convert chart image to grayscale image. (<b>a</b>) Extracted electronic chart image, (<b>b</b>) Convert to a grayscale image.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1723'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g008-550.jpg?1675676970" title=" <strong>Figure 8</strong><br/> <p>Apply binary segmentation by the Otsu algorithm. (<b>a</b>) Set <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>, (<b>b</b>) Set <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math>. (<b>c</b>) and set <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1723'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g009-550.jpg?1675676939" title=" <strong>Figure 9</strong><br/> <p>Contour following the sequence of the MNT. (<b>a</b>) Boundary tracking method. (<b>b</b>) Tracking the boundary of the obstacle area in the binary image.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1723'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g010-550.jpg?1675676949" title=" <strong>Figure 10</strong><br/> <p>Effect of direction parameters on boundary tracking. In this figure, red color arrow means direction of tracing.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1723'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g011-550.jpg?1675676948" title=" <strong>Figure 11</strong><br/> <p>(<b>a</b>) Convex and (<b>b</b>) concave polygons.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1723'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g012-550.jpg?1675676962" title=" <strong>Figure 12</strong><br/> <p>Convex and concave hull algorithms. (<b>a</b>) A set of random points. (<b>b</b>) The convex hull is connected and shows a polygon (<b>c</b>) The concave hull shows two polygons.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1723'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g013-550.jpg?1675676971" title=" <strong>Figure 13</strong><br/> <p>Construction of the obstacles area boundary. (<b>a</b>) Binary image from electronic chart image. (<b>b</b>) Tracking of obstacles boundaries. (<b>c</b>) Shape of the boundaries using the convex hull (<b>d</b>) Shape of the boundaries using concave hull through the alpha shape.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1723'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g014-550.jpg?1675676972" title=" <strong>Figure 14</strong><br/> <p>Erosion process on image data. (<b>a</b>) Blue letter A is original pixel. And it shows the black pixel represents zero and the white pixel represents one. (<b>b</b>) Blue letter B is cross-shape structuring element kernel. (<b>c</b>) Blue letter C is result of morphological erosion.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1723'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g015-550.jpg?1675676966" title=" <strong>Figure 15</strong><br/> <p>Dilation process on image data. (<b>a</b>) Blue letter A is original pixel image. (<b>b</b>) Blue letter B is cross-shape structuring element kernel. (<b>c</b>) Blue letter C is result of morphological dilation.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1723'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g016-550.jpg?1675676935" title=" <strong>Figure 16</strong><br/> <p>Applied dilation method in obstacles boundaries.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1723'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g017-550.jpg?1675676947" title=" <strong>Figure 17</strong><br/> <p>Description of simulation.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1723'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g018-550.jpg?1675676963" title=" <strong>Figure 18</strong><br/> <p>Binarization of the chart image including the safety depth (<b>a</b>). The original chart image for simulation (<b>b</b>) Set No-Go Area boundaries using the safety depth and obstacles data (<b>c</b>) Binarization of the chart image for simulation.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1723'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g019-550.jpg?1675676951" title=" <strong>Figure 19</strong><br/> <p>Simplified obstacle boundary process (<b>a</b>) Binarization map including the safety depth data. (<b>b</b>) Detection of obstacle boundaries. (<b>c</b>) Concave hull method (Shrink Factor: 0.9). (<b>d</b>) Simplified obstacle boundaries.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1723'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g020-550.jpg?1675676957" title=" <strong>Figure 20</strong><br/> <p>Comparison of obstacles’ dilation results on the concave map. (<b>a</b>) Binary map of the simulation area. (<b>b</b>) Results of dilation on the concave map <math display="inline"><semantics> <mrow> <mfenced> <mrow> <msub> <mi>D</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>574</mn> <mo> </mo> <mi mathvariant="normal">m</mi> </mrow> </mfenced> </mrow> </semantics></math>. (<b>c</b>) Results of dilation on the convex map <math display="inline"><semantics> <mrow> <mfenced> <mrow> <msub> <mi>D</mi> <mi>r</mi> </msub> <mo>=</mo> <mn>1074</mn> <mo> </mo> <mi mathvariant="normal">m</mi> </mrow> </mfenced> </mrow> </semantics></math>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1723'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g021-550.jpg?1675676960" title=" <strong>Figure 21</strong><br/> <p>Route generation results when (<b>a</b>) the safety factor was not considered and (<b>b</b>) when the safety factor was considered.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1723'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g022-550.jpg?1675676954" title=" <strong>Figure 22</strong><br/> <p>Comparison of the binary occupancy maps. (<b>a</b>) Concave hull map. (<b>b</b>) Convex hull map.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1723'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01723/article_deploy/html/images/sensors-23-01723-g023-550.jpg?1675676938" title=" <strong>Figure 23</strong><br/> <p>Route generation results. (<b>a</b>) Concave hull method. (<b>b</b>) Convex hull method.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1723'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1057339" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 15 pages, 2229 KiB   </span> <a href="/1424-8220/23/3/1722/pdf?version=1675433510" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Comparison of Heuristic Algorithms in Identification of Parameters of Anomalous Diffusion Model Based on Measurements from Sensors" data-journal="sensors"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1424-8220/23/3/1722">Comparison of Heuristic Algorithms in Identification of Parameters of Anomalous Diffusion Model Based on Measurements from Sensors</a> <div class="authors"> by <span class="inlineblock "><strong>Rafał Brociek </strong>, </span><span class="inlineblock "><strong>Agata Wajda</strong> and </span><span class="inlineblock "><strong>Damian Słota</strong></span> </div> <div class="color-grey-dark"> <em>Sensors</em> <b>2023</b>, <em>23</em>(3), 1722; <a href="https://doi.org/10.3390/s23031722">https://doi.org/10.3390/s23031722</a> - 3 Feb 2023 </div> <a href="/1424-8220/23/3/1722#metrics">Cited by 5</a> | Viewed by 1951 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> In recent times, fractional calculus has gained popularity in various types of engineering applications. Very often, the mathematical model describing a given phenomenon consists of a differential equation with a fractional derivative. As numerous studies present, the use of the fractional derivative instead <a href="#" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1722/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> In recent times, fractional calculus has gained popularity in various types of engineering applications. Very often, the mathematical model describing a given phenomenon consists of a differential equation with a fractional derivative. As numerous studies present, the use of the fractional derivative instead of the classical derivative allows for more accurate modeling of some processes. A numerical solution of anomalous heat conduction equation with Riemann-Liouville fractional derivative over space is presented in this paper. First, a differential scheme is provided to solve the direct problem. Then, the inverse problem is considered, which consists in identifying model parameters such as: thermal conductivity, order of derivative and heat transfer. Data on the basis of which the inverse problem is solved are the temperature values on the right boundary of the considered space. To solve the problem a functional describing the error of the solution is created. By determining the minimum of this functional, unknown parameters of the model are identified. In order to find a solution, selected heuristic algorithms are presented and compared. The following meta-heuristic algorithms are described and used in the paper: Ant Colony Optimization (ACO) for continous function, Butterfly Optimization Algorithm (BOA), Dynamic Butterfly Optimization Algorithm (DBOA) and Aquila Optimize (AO). The accuracy of the presented algorithms is illustrated by examples. <a href="/1424-8220/23/3/1722">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/sensors/special_issues/architectures_protocols_algorithms_wsn ">Architectures, Protocols and Algorithms of Sensor Networks</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1722/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1057339"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1057339"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1057339" data-cycle-prev="#prev1057339" data-cycle-progressive="#images1057339" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1057339-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/sensors/sensors-23-01722/article_deploy/html/images/sensors-23-01722-g001-550.jpg?1675433587" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1057339" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1057339-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01722/article_deploy/html/images/sensors-23-01722-g002-550.jpg?1675433584'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1057339-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01722/article_deploy/html/images/sensors-23-01722-g003-550.jpg?1675433583'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1057339-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01722/article_deploy/html/images/sensors-23-01722-g004-550.jpg?1675433588'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1057339-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01722/article_deploy/html/images/sensors-23-01722-g005-550.jpg?1675433582'><p>Figure 5</p></div></script></div></div><div id="article-1057339-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/sensors/sensors-23-01722/article_deploy/html/images/sensors-23-01722-g001-550.jpg?1675433587" title=" <strong>Figure 1</strong><br/> <p>Considered area with marked measuring points (a fragment of the boundary with Neumann boundary condition is marked in red, a fragment of the boundary with Robin boundary condition is marked in blue, and a fragment of the boundary with initial condition is marked in green).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1722'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01722/article_deploy/html/images/sensors-23-01722-g002-550.jpg?1675433584" title=" <strong>Figure 2</strong><br/> <p>The exact heat transfer <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> (blue line) and approximate heat transfer (dashed green line) for (<b>a</b>) ACO and (<b>b</b>) DBOA.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1722'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01722/article_deploy/html/images/sensors-23-01722-g003-550.jpg?1675433583" title=" <strong>Figure 3</strong><br/> <p>The exact heat transfer <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> (blue line) and approximate heat transfer (dashed green line) for (<b>a</b>) AO and (<b>b</b>) BOA.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1722'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01722/article_deploy/html/images/sensors-23-01722-g004-550.jpg?1675433588" title=" <strong>Figure 4</strong><br/> <p>The exact temperature <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>(</mo> <msub> <mi>x</mi> <mi>R</mi> </msub> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> in measurement point (blue line) and reconstructed temperature (green line) for (<b>a</b>) ACO and (<b>b</b>) DBOA.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1722'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01722/article_deploy/html/images/sensors-23-01722-g005-550.jpg?1675433582" title=" <strong>Figure 5</strong><br/> <p>The exact temperature <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>(</mo> <msub> <mi>x</mi> <mi>R</mi> </msub> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> in measurement point (blue line) and reconstructed temperature (green line) for (<b>a</b>) AO and (<b>b</b>) BOA.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1722'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1057154" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 16 pages, 5686 KiB   </span> <a href="/1424-8220/23/3/1721/pdf?version=1675426693" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Technology Acceptance Model for Exoskeletons for Rehabilitation of the Upper Limbs from Therapists’ Perspectives" data-journal="sensors"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1424-8220/23/3/1721">Technology Acceptance Model for Exoskeletons for Rehabilitation of the Upper Limbs from Therapists’ Perspectives</a> <div class="authors"> by <span class="inlineblock "><strong>Beatrice Luciani</strong>, </span><span class="inlineblock "><strong>Francesco Braghin</strong>, </span><span class="inlineblock "><strong>Alessandra Laura Giulia Pedrocchi</strong> and </span><span class="inlineblock "><strong>Marta Gandolla</strong></span> </div> <div class="color-grey-dark"> <em>Sensors</em> <b>2023</b>, <em>23</em>(3), 1721; <a href="https://doi.org/10.3390/s23031721">https://doi.org/10.3390/s23031721</a> - 3 Feb 2023 </div> <a href="/1424-8220/23/3/1721#metrics">Cited by 12</a> | Viewed by 3728 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Over the last few years, exoskeletons have been demonstrated to be useful tools for supporting the execution of neuromotor rehabilitation sessions. However, they are still not very present in hospitals. Therapists tend to be wary of this type of technology, thus reducing its <a href="#" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1721/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Over the last few years, exoskeletons have been demonstrated to be useful tools for supporting the execution of neuromotor rehabilitation sessions. However, they are still not very present in hospitals. Therapists tend to be wary of this type of technology, thus reducing its acceptability and, therefore, its everyday use in clinical practice. The work presented in this paper investigates a novel point of view that is different from that of patients, which is normally what is considered for similar analyses. Through the realization of a technology acceptance model, we investigate the factors that influence the acceptability level of exoskeletons for rehabilitation of the upper limbs from therapists’ perspectives. We analyzed the data collected from a pool of 55 physiotherapists and physiatrists through the distribution of a questionnaire. Pearson’s correlation and multiple linear regression were used for the analysis. The relations between the variables of interest were also investigated depending on participants’ age and experience with technology. The model built from these data demonstrated that the perceived usefulness of a robotic system, in terms of time and effort savings, was the first factor influencing therapists’ willingness to use it. Physiotherapists’ perception of the importance of interacting with an exoskeleton when carrying out an enhanced therapy session increased if survey participants already had experience with this type of rehabilitation technology, while their distrust and the consideration of others’ opinions decreased. The conclusions drawn from our analyses show that we need to invest in making this technology better known to the public—in terms of education and training—if we aim to make exoskeletons genuinely accepted and usable by therapists. In addition, integrating exoskeletons with multi-sensor feedback systems would help provide comprehensive information about the patients’ condition and progress. This can help overcome the gap that a robot creates between a therapist and the patient’s human body, reducing the fear that specialists have of this technology, and this can demonstrate exoskeletons’ utility, thus increasing their perceived level of usefulness. <a href="/1424-8220/23/3/1721">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/sensors/special_issues/bm-sensors ">Biomedical Sensors for Functional Mapping: Techniques, Methods, Experimental and Medical Applications</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1721/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1057154"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1057154"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1057154" data-cycle-prev="#prev1057154" data-cycle-progressive="#images1057154" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1057154-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/sensors/sensors-23-01721/article_deploy/html/images/sensors-23-01721-g001-550.jpg?1675426766" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1057154" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1057154-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01721/article_deploy/html/images/sensors-23-01721-g002-550.jpg?1675426765'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1057154-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01721/article_deploy/html/images/sensors-23-01721-g003-550.jpg?1675426768'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1057154-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01721/article_deploy/html/images/sensors-23-01721-g004-550.jpg?1675426764'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1057154-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01721/article_deploy/html/images/sensors-23-01721-g005-550.jpg?1675426763'><p>Figure 5</p></div></script></div></div><div id="article-1057154-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/sensors/sensors-23-01721/article_deploy/html/images/sensors-23-01721-g001-550.jpg?1675426766" title=" <strong>Figure 1</strong><br/> <p>Some examples of upper-limb rehabilitation exoskeletons. The top-left one is ANYexo by ETH Zurich (©2019, Zimmerman et al. from Ref. [<a href="#B2-sensors-23-01721" class="html-bibr">2</a>]), the bottom-left one is ARMin (©2010, Nef et al., from Ref. [<a href="#B3-sensors-23-01721" class="html-bibr">3</a>]), and the one on the right is AGREE, the prototype from our research group at Politecnico di Milano [<a href="#B4-sensors-23-01721" class="html-bibr">4</a>].</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1721'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01721/article_deploy/html/images/sensors-23-01721-g002-550.jpg?1675426765" title=" <strong>Figure 2</strong><br/> <p>Structure of the TAM. Light blue variables are those of the core of the model, and gray variables are those that we added for our specific study. The dark-blue box represents the output (i.e., the predicted variable).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1721'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01721/article_deploy/html/images/sensors-23-01721-g003-550.jpg?1675426768" title=" <strong>Figure 3</strong><br/> <p>Statistical analysis of the scores given by the 55 users to the questions. Question numbers correspond to those indicated in <a href="#secAdot1-sensors-23-01721" class="html-sec">Appendix A.1</a>. We gathered the questions by category. In each box plot, the central mark indicates the median, and the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1721'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01721/article_deploy/html/images/sensors-23-01721-g004-550.jpg?1675426764" title=" <strong>Figure 4</strong><br/> <p>Structure of the TAM with references of the correlations between the various variables involved in the study.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1721'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01721/article_deploy/html/images/sensors-23-01721-g005-550.jpg?1675426763" title=" <strong>Figure 5</strong><br/> <p>Statistics of the scores given to questions related to participants’ attitudes towards technology, divided according to the three age ranges that we identified.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1721'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1057077" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 18 pages, 8043 KiB   </span> <a href="/1424-8220/23/3/1720/pdf?version=1675423726" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Identification and Quantification of Activities Common to Intensive Care Patients; Development and Validation of a Dual-Accelerometer-Based Algorithm" data-journal="sensors"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1424-8220/23/3/1720">Identification and Quantification of Activities Common to Intensive Care Patients; Development and Validation of a Dual-Accelerometer-Based Algorithm</a> <div class="authors"> by <span class="inlineblock "><strong>Yvonne Dikkema</strong>, </span><span class="inlineblock "><strong>Noor Mouton</strong>, </span><span class="inlineblock "><strong>Koen Gerrits</strong>, </span><span class="inlineblock "><strong>Tim Valk</strong>, </span><span class="inlineblock "><strong>Mariëlle van der Steen-Diepenrink</strong>, </span><span class="inlineblock "><strong>Hans Eshuis</strong>, </span><span class="inlineblock "><strong>Han Houdijk</strong>, </span><span class="inlineblock "><strong>Cees van der Schans</strong>, </span><span class="inlineblock "><strong>Anuschka Niemeijer</strong> and </span><span class="inlineblock "><strong>Marianne Nieuwenhuis</strong></span> </div> <div class="color-grey-dark"> <em>Sensors</em> <b>2023</b>, <em>23</em>(3), 1720; <a href="https://doi.org/10.3390/s23031720">https://doi.org/10.3390/s23031720</a> - 3 Feb 2023 </div> Viewed by 1825 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> The aim of this study was to develop and validate an algorithm that can identify the type, frequency, and duration of activities common to intensive care (IC) patients. Ten healthy participants wore two accelerometers on their chest and leg while performing 14 activities <a href="#" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1720/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> The aim of this study was to develop and validate an algorithm that can identify the type, frequency, and duration of activities common to intensive care (IC) patients. Ten healthy participants wore two accelerometers on their chest and leg while performing 14 activities clustered into four protocols (i.e., natural, strict, healthcare provider, and bed cycling). A video served as the reference standard, with two raters classifying the type and duration of all activities. This classification was reliable as intraclass correlations were all above 0.76 except for walking in the healthcare provider protocol, (0.29). The data of four participants were used to develop and optimize the algorithm by adjusting body-segment angles and rest-activity-threshold values based on percentage agreement (%Agr) with the reference. The validity of the algorithm was subsequently assessed using the data from the remaining six participants. %Agr of the algorithm versus the reference standard regarding lying, sitting activities, and transitions was 95%, 74%, and 80%, respectively, for all protocols except transitions with the help of a healthcare provider, which was 14–18%. For bed cycling, %Agr was 57–76%. This study demonstrated that the developed algorithm is suitable for identifying and quantifying activities common for intensive care patients. Knowledge on the (in)activity of these patients and their impact will optimize mobilization. <a href="/1424-8220/23/3/1720">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Section <a href="/journal/sensors/sections/Wearables">Wearables</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1720/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1057077"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1057077"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1057077" data-cycle-prev="#prev1057077" data-cycle-progressive="#images1057077" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1057077-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/sensors/sensors-23-01720/article_deploy/html/images/sensors-23-01720-g001-550.jpg?1675423801" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1057077" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1057077-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01720/article_deploy/html/images/sensors-23-01720-g002-550.jpg?1675423803'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1057077-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01720/article_deploy/html/images/sensors-23-01720-g003-550.jpg?1675423792'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1057077-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01720/article_deploy/html/images/sensors-23-01720-g004-550.jpg?1675423798'><p>Figure 4</p></div></script></div></div><div id="article-1057077-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/sensors/sensors-23-01720/article_deploy/html/images/sensors-23-01720-g001-550.jpg?1675423801" title=" <strong>Figure 1</strong><br/> <p>Pictures showing the accelerometer locations, with the blue arrows indicating the midclavicular line right-thigh left (MCR-TL) and green (dotted) arrows indicating the midclavicular line left-thigh right (MCL-TR) configuration used in the present study.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1720'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01720/article_deploy/html/images/sensors-23-01720-g002-550.jpg?1675423803" title=" <strong>Figure 2</strong><br/> <p>Signal magnitude area (SMA) of one participant during the performance of activities of three protocols (natural, green; healthcare provider, blue; and strict, red). The black line is used to determine the cut-off point to identify dynamic activities during the development of the algorithm. Note that the SMA value in the healthcare provider protocol (blue) is much lower compared to the other protocols.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1720'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01720/article_deploy/html/images/sensors-23-01720-g003-550.jpg?1675423792" title=" <strong>Figure 3</strong><br/> <p>Flow chart of the developed algorithm for classification of the different activities based on body-segment-angles and SMA values of the different accelerometers. Activities in yellow and red are related to smaller and larger bodily movements, respectively. Abbreviations: SMA = signal magnitude area, MC = midclavicular accelerometer, T = thigh accelerometer, θ = theta, ψ = psi, φ = phi.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1720'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01720/article_deploy/html/images/sensors-23-01720-g004-550.jpg?1675423798" title=" <strong>Figure 4</strong><br/> <p>Validation of the developed algorithm showing percentage agreement of correct coded activity by the algorithm on the validation group (<span class="html-italic">n</span> = 6) compared to target scores based on the percentage of agreement between both video raters for all four protocols. The dotted line shows the chosen validity value of 80%. Abbreviations: %Agr, percentage agreement; MCL, midclavicular line left, MCR, midclavicular line right, TL, thigh left, TR, thigh right.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1720'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1057076" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 14 pages, 4657 KiB   </span> <a href="/1424-8220/23/3/1719/pdf?version=1675423721" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Rational Resampling Ratio as Enhancement to Shaft Imbalance Detection" data-journal="sensors"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1424-8220/23/3/1719">Rational Resampling Ratio as Enhancement to Shaft Imbalance Detection</a> <div class="authors"> by <span class="inlineblock "><strong>Adam Jablonski</strong></span> </div> <div class="color-grey-dark"> <em>Sensors</em> <b>2023</b>, <em>23</em>(3), 1719; <a href="https://doi.org/10.3390/s23031719">https://doi.org/10.3390/s23031719</a> - 3 Feb 2023 </div> Viewed by 1525 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Trend analysis is one of the most powerful techniques for monitoring the technical condition of individual mechanical components of rotating machinery. It is based on extraction of characteristic signal components according to kinetostatic configuration of the machine drivetrain. It has been used for <a href="#" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1719/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Trend analysis is one of the most powerful techniques for monitoring the technical condition of individual mechanical components of rotating machinery. It is based on extraction of characteristic signal components according to kinetostatic configuration of the machine drivetrain. It has been used for decades and is well-understood. However, classical trend analysis is based on some assumptions which have resulted from the limited computational power of embedded systems years ago. This paper tries to answer a question on whether the assumption of a single signal resampling path for calculation of signal components generated by shafts with rational transmission ratio is valid. The study was conducted using an extensive imbalance test on a medium-power test rig. The paper originally demonstrates that application of an advanced resampling algorithm does not significantly influence the overall trend increase, but it is of utmost importance when trend variance is of interest. <a href="/1424-8220/23/3/1719">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/sensors/special_issues/MMDPCES ">Modeling, Monitoring, Diagnosis, Prognosis and Control in Electromechanical Systems</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1719/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1057076"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1057076"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1057076" data-cycle-prev="#prev1057076" data-cycle-progressive="#images1057076" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1057076-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/sensors/sensors-23-01719/article_deploy/html/images/sensors-23-01719-g001-550.jpg?1675423792" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1057076" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1057076-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01719/article_deploy/html/images/sensors-23-01719-g002-550.jpg?1675423801'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1057076-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01719/article_deploy/html/images/sensors-23-01719-g003-550.jpg?1675423793'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1057076-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01719/article_deploy/html/images/sensors-23-01719-g004-550.jpg?1675423809'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1057076-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01719/article_deploy/html/images/sensors-23-01719-g005-550.jpg?1675423799'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1057076-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01719/article_deploy/html/images/sensors-23-01719-g006-550.jpg?1675423797'><p>Figure 6</p></div> --- <div class='openpopupgallery' data-imgindex='6' data-target='article-1057076-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01719/article_deploy/html/images/sensors-23-01719-g007-550.jpg?1675423794'><p>Figure 7</p></div> --- <div class='openpopupgallery' data-imgindex='7' data-target='article-1057076-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01719/article_deploy/html/images/sensors-23-01719-g008-550.jpg?1675423810'><p>Figure 8</p></div> --- <div class='openpopupgallery' data-imgindex='8' data-target='article-1057076-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01719/article_deploy/html/images/sensors-23-01719-g009-550.jpg?1675423796'><p>Figure 9</p></div> --- <div class='openpopupgallery' data-imgindex='9' data-target='article-1057076-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01719/article_deploy/html/images/sensors-23-01719-g010-550.jpg?1675423803'><p>Figure 10</p></div> --- <div class='openpopupgallery' data-imgindex='10' data-target='article-1057076-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01719/article_deploy/html/images/sensors-23-01719-g011-550.jpg?1675423807'><p>Figure 11</p></div> --- <div class='openpopupgallery' data-imgindex='11' data-target='article-1057076-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01719/article_deploy/html/images/sensors-23-01719-g012-550.jpg?1675423792'><p>Figure 12</p></div></script></div></div><div id="article-1057076-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/sensors/sensors-23-01719/article_deploy/html/images/sensors-23-01719-g001-550.jpg?1675423792" title=" <strong>Figure 1</strong><br/> <p>Calculation paths of scalar machine health indicators.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1719'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01719/article_deploy/html/images/sensors-23-01719-g002-550.jpg?1675423801" title=" <strong>Figure 2</strong><br/> <p>Placement of analyzed scalar machine lar HI-s (health indicators).</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1719'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01719/article_deploy/html/images/sensors-23-01719-g003-550.jpg?1675423793" title=" <strong>Figure 3</strong><br/> <p>Idea of the 1-per-rotation PM signal.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1719'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01719/article_deploy/html/images/sensors-23-01719-g004-550.jpg?1675423809" title=" <strong>Figure 4</strong><br/> <p>Top: signal sampled with constant sampling frequency, bottom: signal sampled with a constant number of samples per rotation.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1719'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01719/article_deploy/html/images/sensors-23-01719-g005-550.jpg?1675423799" title=" <strong>Figure 5</strong><br/> <p>Demonstration of resampling ability to reduce spectral smearing.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1719'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01719/article_deploy/html/images/sensors-23-01719-g006-550.jpg?1675423797" title=" <strong>Figure 6</strong><br/> <p>Test bench.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1719'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01719/article_deploy/html/images/sensors-23-01719-g007-550.jpg?1675423794" title=" <strong>Figure 7</strong><br/> <p>Kinetostatic diagram of the rotating machinery used in the experiment.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1719'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01719/article_deploy/html/images/sensors-23-01719-g008-550.jpg?1675423810" title=" <strong>Figure 8</strong><br/> <p>From the left: initial and two subsequent intermediate imbalance states.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1719'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01719/article_deploy/html/images/sensors-23-01719-g009-550.jpg?1675423796" title=" <strong>Figure 9</strong><br/> <p>Broadband and narrowband HIs in detection of imbalance state. Signal index represents subsequent instances of vibration signals.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1719'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01719/article_deploy/html/images/sensors-23-01719-g010-550.jpg?1675423803" title=" <strong>Figure 10</strong><br/> <p>Narrowband output_shaft_x1 HIs in detection of imbalance state. Abscissa is the same as in <a href="#sensors-23-01719-f009" class="html-fig">Figure 9</a>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1719'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01719/article_deploy/html/images/sensors-23-01719-g011-550.jpg?1675423807" title=" <strong>Figure 11</strong><br/> <p>Graphical illustration of moving average value for individual scalar health indicators. Abscissa is the same as in <a href="#sensors-23-01719-f009" class="html-fig">Figure 9</a> and <a href="#sensors-23-01719-f010" class="html-fig">Figure 10</a>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1719'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01719/article_deploy/html/images/sensors-23-01719-g012-550.jpg?1675423792" title=" <strong>Figure 12</strong><br/> <p>Ratios of cycles for the first 67 rotations of the output shaft.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1719'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1057022" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 10 pages, 3127 KiB   </span> <a href="/1424-8220/23/3/1718/pdf?version=1675422015" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Sagnac Effect Compensations and Locked States in a Ring Laser Gyroscope" data-journal="sensors"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Article</span></div> <a class="title-link" href="/1424-8220/23/3/1718">Sagnac Effect Compensations and Locked States in a Ring Laser Gyroscope</a> <div class="authors"> by <span class="inlineblock "><strong>Woo-Seok Choi</strong>, </span><span class="inlineblock "><strong>Kyu-Min Shim</strong>, </span><span class="inlineblock "><strong>Kyung-Ho Chong</strong>, </span><span class="inlineblock "><strong>Jun-Eon An</strong>, </span><span class="inlineblock "><strong>Cheon-Joong Kim</strong> and </span><span class="inlineblock "><strong>Byung-Yoon Park</strong></span> </div> <div class="color-grey-dark"> <em>Sensors</em> <b>2023</b>, <em>23</em>(3), 1718; <a href="https://doi.org/10.3390/s23031718">https://doi.org/10.3390/s23031718</a> - 3 Feb 2023 </div> <a href="/1424-8220/23/3/1718#metrics">Cited by 4</a> | Viewed by 3202 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Frequency lock-in-induced deadband phenomena are major problems of ring laser gyroscopes (RLGs), which deteriorate linear responses to changes in the applied rotation rate. In this work, the frequency lock-in phenomenon occurring in the RLG was successfully investigated by compensating for the Sagnac effect <a href="#" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1718/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Frequency lock-in-induced deadband phenomena are major problems of ring laser gyroscopes (RLGs), which deteriorate linear responses to changes in the applied rotation rate. In this work, the frequency lock-in phenomenon occurring in the RLG was successfully investigated by compensating for the Sagnac effect through frequency analysis using a newly defined error function. Integrative and generalized viewpoints from the analyzed results provide new possibilities for relevant performance improvements of optical gyroscopes, as well as a deeper understanding of locked states in principle aspects. <a href="/1424-8220/23/3/1718">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Section <a href="/journal/sensors/sections/optical_sensors">Optical Sensors</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1718/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1057022"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1057022"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1057022" data-cycle-prev="#prev1057022" data-cycle-progressive="#images1057022" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1057022-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/sensors/sensors-23-01718/article_deploy/html/images/sensors-23-01718-g001-550.jpg?1675422086" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1057022" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1057022-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01718/article_deploy/html/images/sensors-23-01718-g002-550.jpg?1675422087'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1057022-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01718/article_deploy/html/images/sensors-23-01718-g003-550.jpg?1675422084'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1057022-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01718/article_deploy/html/images/sensors-23-01718-g004-550.jpg?1675422083'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1057022-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01718/article_deploy/html/images/sensors-23-01718-g005-550.jpg?1675422086'><p>Figure 5</p></div></script></div></div><div id="article-1057022-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/sensors/sensors-23-01718/article_deploy/html/images/sensors-23-01718-g001-550.jpg?1675422086" title=" <strong>Figure 1</strong><br/> <p>Numerically calculated RLG outputs <math display="inline"><semantics> <mrow> <mrow> <mo stretchy="false">⟨</mo> <msub> <mi mathvariant="bold-italic">f</mi> <mrow> <mi mathvariant="bold-italic">o</mi> <mi mathvariant="bold-italic">u</mi> <mi mathvariant="bold-italic">t</mi> </mrow> </msub> <mo stretchy="false">⟩</mo> </mrow> <msub> <mrow/> <mrow> <mstyle mathvariant="bold" mathsize="normal"> <mo>Δ</mo> </mstyle> <mi mathvariant="bold-italic">t</mi> </mrow> </msub> </mrow> </semantics></math> with sinusoidal dithering as a function of <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">f</mi> <mi mathvariant="bold-italic">a</mi> </msub> <mo>=</mo> <mi mathvariant="bold-italic">a</mi> <mo>/</mo> <mstyle mathvariant="bold" mathsize="normal"> <mn>2</mn> </mstyle> <mi mathvariant="bold-italic">π</mi> </mrow> </semantics></math>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1718'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01718/article_deploy/html/images/sensors-23-01718-g002-550.jpg?1675422087" title=" <strong>Figure 2</strong><br/> <p><math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">R</mi> <mi mathvariant="bold-italic">a</mi> </msub> </mrow> </semantics></math> within the lock-in area.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1718'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01718/article_deploy/html/images/sensors-23-01718-g003-550.jpg?1675422084" title=" <strong>Figure 3</strong><br/> <p><math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">R</mi> <mi mathvariant="bold-italic">a</mi> </msub> </mrow> </semantics></math> outside the lock-in area.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1718'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01718/article_deploy/html/images/sensors-23-01718-g004-550.jpg?1675422083" title=" <strong>Figure 4</strong><br/> <p>Sagnac effect compensations with <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">f</mi> <mi mathvariant="bold-italic">m</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">f</mi> <mi mathvariant="bold-italic">e</mi> </msub> </mrow> </semantics></math> as a function of <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">f</mi> <mi mathvariant="bold-italic">a</mi> </msub> <mo>=</mo> <mi mathvariant="bold-italic">a</mi> <mo>/</mo> <mstyle mathvariant="bold" mathsize="normal"> <mn>2</mn> </mstyle> <mi mathvariant="bold-italic">π</mi> </mrow> </semantics></math>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1718'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01718/article_deploy/html/images/sensors-23-01718-g005-550.jpg?1675422086" title=" <strong>Figure 5</strong><br/> <p>Analyzed frequency components of <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">R</mi> <mi mathvariant="bold-italic">a</mi> </msub> </mrow> </semantics></math> with changes in <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="bold-italic">f</mi> <mi mathvariant="bold-italic">a</mi> </msub> <mo>=</mo> <mi mathvariant="bold-italic">a</mi> <mo>/</mo> <mstyle mathvariant="bold" mathsize="normal"> <mn>2</mn> </mstyle> <mi mathvariant="bold-italic">π</mi> </mrow> </semantics></math>.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1718'>Full article</a></strong> "></a></div> </div> </div> <div class="generic-item article-item"> <input class="article-list-checkbox export-element" type="checkbox" name="articles_ids[]" value="1057282" data-select-all-name="article-listing"> <div class="article-content"> <div class="label right label__btn"> <span style="font-size: 12px; color: #1a1a1a;"> 43 pages, 2386 KiB   </span> <a href="/1424-8220/23/3/1717/pdf?version=1675431348" class="UD_Listings_ArticlePDF" title="Article PDF" data-name="Low-Cost Sensors for Monitoring Coastal Climate Hazards: A Systematic Review and Meta-Analysis" data-journal="sensors"> <i class="material-icons custom-download"></i> </a> </div> <div class="article-icons"><span class="label openaccess" data-dropdown="drop-article-label-openaccess" aria-expanded="false">Open Access</span><span class="label articletype">Systematic Review</span></div> <a class="title-link" href="/1424-8220/23/3/1717">Low-Cost Sensors for Monitoring Coastal Climate Hazards: A Systematic Review and Meta-Analysis</a> <div class="authors"> by <span class="inlineblock "><strong>Tasneem Ahmed</strong>, </span><span class="inlineblock "><strong>Leo Creedon</strong> and </span><span class="inlineblock "><strong>Salem S. Gharbia</strong></span> </div> <div class="color-grey-dark"> <em>Sensors</em> <b>2023</b>, <em>23</em>(3), 1717; <a href="https://doi.org/10.3390/s23031717">https://doi.org/10.3390/s23031717</a> - 3 Feb 2023 </div> <a href="/1424-8220/23/3/1717#metrics">Cited by 5</a> | Viewed by 4616 <div class="abstract-div"> <a href="#" onclick="$(this).next('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> <strong>Abstract </strong> </a> <div class="abstract-cropped inline"> Unequivocal change in the climate system has put coastal regions around the world at increasing risk from climate-related hazards. Monitoring the coast is often difficult and expensive, resulting in sparse monitoring equipment lacking in sufficient temporal and spatial coverage. Thus, low-cost methods to <a href="#" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1717/more" onclick="$(this).parents('.abstract-cropped').toggleClass('inline').next('.abstract-full').toggleClass('inline'); return false;"> [...] Read more.</a> </div> <div class="abstract-full "> Unequivocal change in the climate system has put coastal regions around the world at increasing risk from climate-related hazards. Monitoring the coast is often difficult and expensive, resulting in sparse monitoring equipment lacking in sufficient temporal and spatial coverage. Thus, low-cost methods to monitor the coast at finer temporal and spatial resolution are imperative for climate resilience along the world’s coasts. Exploiting such low-cost methods for the development of early warning support could be invaluable to coastal settlements. This paper aims to provide the most up-to-date low-cost techniques developed and used in the last decade for monitoring coastal hazards and their forcing agents via systematic review of the peer-reviewed literature in three scientific databases: Scopus, Web of Science and ScienceDirect. A total of 60 papers retrieved from these databases through the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol were analysed in detail to yield different categories of low-cost sensors. These sensors span the entire domain for monitoring coastal hazards, as they focus on monitoring coastal zone characteristics (e.g., topography), forcing agents (e.g., water levels), and the hazards themselves (e.g., coastal flooding). It was found from the meta-analysis of the retrieved papers that terrestrial photogrammetry, followed by aerial photogrammetry, was the most widely used technique for monitoring different coastal hazards, mainly coastal erosion and shoreline change. Different monitoring techniques are available to monitor the same hazard/forcing agent, for instance, unmanned aerial vehicles (UAVs), time-lapse cameras, and wireless sensor networks (WSNs) for monitoring coastal morphological changes such as beach erosion, creating opportunities to not only select but also combine different techniques to meet specific monitoring objectives. The sensors considered in this paper are useful for monitoring the most pressing challenges in coastal zones due to the changing climate. Such a review could be extended to encompass more sensors and variables in the future due to the systematic approach of this review. This study is the first to systematically review a wide range of low-cost sensors available for the monitoring of coastal zones in the context of changing climate and is expected to benefit coastal researchers and managers to choose suitable low-cost sensors to meet their desired objectives for the regular monitoring of the coast to increase climate resilience. <a href="/1424-8220/23/3/1717">Full article</a> </div> </div> <div class="belongsTo" style="margin-bottom: 10px;"> (This article belongs to the Special Issue <a href=" /journal/sensors/special_issues/552DDDN525 "><em>Sensors</em> in 2023</a>)<br/> </div> <a href="#" class="abstract-figures-show" data-counterslink = "https://www.mdpi.com/1424-8220/23/3/1717/show" ><span >►</span><span style=" display: none;">▼</span> Show Figures </a><div class="abstract-image-preview "><div class="arrow left-arrow" id="prev1057282"><i class="fa fa-caret-left"></i></div><div class="arrow right-arrow" id="next1057282"><i class="fa fa-caret-right"></i></div><div class="absgraph cycle-slideshow manual" data-cycle-fx="scrollHorz" data-cycle-timeout="0" data-cycle-next="#next1057282" data-cycle-prev="#prev1057282" data-cycle-progressive="#images1057282" data-cycle-slides=">div" data-cycle-log="false"><div class='openpopupgallery cycle-slide' data-imgindex='0' data-target='article-1057282-popup'><span class="helper"></span><img src="data:image/gif;base64,R0lGODlhAQABAAD/ACwAAAAAAQABAAACADs=" data-src="https://pub.mdpi-res.com/sensors/sensors-23-01717/article_deploy/html/images/sensors-23-01717-g001-550.jpg?1675431425" alt="" style="border: 0;"><p>Figure 1</p></div><script id="images1057282" type="text/cycle" data-cycle-split="---"><div class='openpopupgallery' data-imgindex='1' data-target='article-1057282-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01717/article_deploy/html/images/sensors-23-01717-g002-550.jpg?1675431421'><p>Figure 2</p></div> --- <div class='openpopupgallery' data-imgindex='2' data-target='article-1057282-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01717/article_deploy/html/images/sensors-23-01717-g003-550.jpg?1675431426'><p>Figure 3</p></div> --- <div class='openpopupgallery' data-imgindex='3' data-target='article-1057282-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01717/article_deploy/html/images/sensors-23-01717-g004-550.jpg?1675431420'><p>Figure 4</p></div> --- <div class='openpopupgallery' data-imgindex='4' data-target='article-1057282-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01717/article_deploy/html/images/sensors-23-01717-g005-550.jpg?1675431422'><p>Figure 5</p></div> --- <div class='openpopupgallery' data-imgindex='5' data-target='article-1057282-popup'><span class="helper"></span><img src='https://pub.mdpi-res.com/sensors/sensors-23-01717/article_deploy/html/images/sensors-23-01717-g006-550.jpg?1675431423'><p>Figure 6</p></div></script></div></div><div id="article-1057282-popup" class="popupgallery" style="display: inline; line-height: 200%"><a href="https://pub.mdpi-res.com/sensors/sensors-23-01717/article_deploy/html/images/sensors-23-01717-g001-550.jpg?1675431425" title=" <strong>Figure 1</strong><br/> <p>PRISMA diagram for the systematic literature review on low-cost sensors for monitoring coastal climate hazards.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1717'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01717/article_deploy/html/images/sensors-23-01717-g002-550.jpg?1675431421" title=" <strong>Figure 2</strong><br/> <p>Percentage of the low-cost sensors/sensing methods.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1717'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01717/article_deploy/html/images/sensors-23-01717-g003-550.jpg?1675431426" title=" <strong>Figure 3</strong><br/> <p>The coastal zone characteristics, metocean variables, and coastal hazards monitored by the sensors reviewed in this paper.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1717'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01717/article_deploy/html/images/sensors-23-01717-g004-550.jpg?1675431420" title=" <strong>Figure 4</strong><br/> <p>The different variables obtained from the sensors/sensing methods; variables have been colour coded, with similar variables being coloured the same.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1717'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01717/article_deploy/html/images/sensors-23-01717-g005-550.jpg?1675431422" title=" <strong>Figure 5</strong><br/> <p>Photogrammetric software employed for the extraction of DEM/DSM/point clouds/orthophoto/orthomosaics.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1717'>Full article</a></strong> "></a><a href="https://pub.mdpi-res.com/sensors/sensors-23-01717/article_deploy/html/images/sensors-23-01717-g006-550.jpg?1675431423" title=" <strong>Figure 6</strong><br/> <p>Distribution of the 60 papers during 2010–2021.</p> <strong style='display: block; margin-top: 10px; font-size: 18px;'><a style='color: #fff' href='/1424-8220/23/3/1717'>Full article</a></strong> "></a></div> </div> </div> <span class="more" style="display: none;"></span> </div> <div class="row footer"> <div class="listing-select-options"> <div class="columns small-12"> <div class="select 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