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overflow: hidden; text-overflow: ellipsis; -webkit-line-clamp: 3; -webkit-box-orient: vertical; }</style><div class="col-xs-12 clearfix"><div class="u-floatLeft"><h1 class="PageHeader-title u-m0x u-fs30">Kalman Filter</h1><div class="u-tcGrayDark">13,388 Followers</div><div class="u-tcGrayDark u-mt2x">Recent papers in <b>Kalman Filter</b></div></div></div></div></div></div><div class="TabbedNavigation"><div class="container"><div class="row"><div class="col-xs-12 clearfix"><ul class="nav u-m0x u-p0x list-inline u-displayFlex"><li class="active"><a href="https://www.academia.edu/Documents/in/Kalman_Filter">Top Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Kalman_Filter/MostCited">Most Cited Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Kalman_Filter/MostDownloaded">Most Downloaded Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Kalman_Filter/MostRecent">Newest Papers</a></li><li><a class="" href="https://www.academia.edu/People/Kalman_Filter">People</a></li></ul></div><style type="text/css">ul.nav{flex-direction:row}@media(max-width: 567px){ul.nav{flex-direction:column}.TabbedNavigation li{max-width:100%}.TabbedNavigation li.active{background-color:var(--background-grey, #dddde2)}.TabbedNavigation li.active:before,.TabbedNavigation li.active:after{display:none}}</style></div></div></div><div class="container"><div class="row"><div class="col-xs-12"><div class="u-displayFlex"><div class="u-flexGrow1"><div class="works"><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_63488844" data-work_id="63488844" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/63488844/Fundamentals_of_Kalman_Filtering_A_Practical_Approach">Fundamentals of Kalman Filtering: A Practical Approach</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This text is a practical guide to building Kalman filters and shows how the filtering equations can be applied to real-life problems. Numerous examples are presented in detail, showing the many ways in which Kalman filters can be... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_63488844" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This text is a practical guide to building Kalman filters and shows how the filtering equations can be applied to real-life problems. Numerous examples are presented in detail, showing the many ways in which Kalman filters can be designed. Computer code written in FORTRAN, MATLAB[registered], and True BASIC accompanies all of the examples so that the interested reader can verify concepts and explore issues beyond the scope of the text. Sometimes mistakes are introduced intentionally to the initial filter designs to show the reader what happens when the filter is not working properly. The text spends a great deal of time setting up a problem before the Kalman filter is actually formulated to give the reader an intuitive feel for the problem being addressed. Real problems are seldom presented in the form of differential equations and they usually do not have unique solutions. Therefore, the authors illustrate several different filtering approaches for tackling a problem. Readers will ...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/63488844" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="36d6c7385453db7eaa6636b2b7fd295c" rel="nofollow" data-download="{"attachment_id":75903690,"asset_id":63488844,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/75903690/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="169158941" href="https://independent.academia.edu/PaulZarchan">Paul Zarchan</a><script data-card-contents-for-user="169158941" type="text/json">{"id":169158941,"first_name":"Paul","last_name":"Zarchan","domain_name":"independent","page_name":"PaulZarchan","display_name":"Paul Zarchan","profile_url":"https://independent.academia.edu/PaulZarchan?f_ri=49146","photo":"https://0.academia-photos.com/169158941/166619004/156531939/s65_paul.zarchan.png"}</script></span></span></li><li class="js-paper-rank-work_63488844 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="63488844"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 63488844, container: ".js-paper-rank-work_63488844", }); 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Numerous examples are presented in detail, showing the many ways in which Kalman filters can be designed. Computer code written in FORTRAN, MATLAB[registered], and True BASIC accompanies all of the examples so that the interested reader can verify concepts and explore issues beyond the scope of the text. Sometimes mistakes are introduced intentionally to the initial filter designs to show the reader what happens when the filter is not working properly. The text spends a great deal of time setting up a problem before the Kalman filter is actually formulated to give the reader an intuitive feel for the problem being addressed. Real problems are seldom presented in the form of differential equations and they usually do not have unique solutions. Therefore, the authors illustrate several different filtering approaches for tackling a problem. Readers will ...","downloadable_attachments":[{"id":75903690,"asset_id":63488844,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":169158941,"first_name":"Paul","last_name":"Zarchan","domain_name":"independent","page_name":"PaulZarchan","display_name":"Paul Zarchan","profile_url":"https://independent.academia.edu/PaulZarchan?f_ri=49146","photo":"https://0.academia-photos.com/169158941/166619004/156531939/s65_paul.zarchan.png"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=49146","nofollow":true},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_27157812" data-work_id="27157812" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/27157812/Onboard_Real_Time_Estimation_of_Vehicle_Lateral_Tire_and_x2013_Road_Forces_and_Sideslip_Angle">Onboard Real-Time Estimation of Vehicle Lateral Tire&#x2013;Road Forces and Sideslip Angle</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The principal concerns in driving safety with standard vehicles or cybercars are understanding and preventing risky situations. A close examination of accident data reveals that losing control of the vehicle is the main reason for most... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_27157812" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The principal concerns in driving safety with standard vehicles or cybercars are understanding and preventing risky situations. A close examination of accident data reveals that losing control of the vehicle is the main reason for most car accidents. To help to prevent such accidents, vehicle-control systems may be used, which require certain input data concerning vehicledynamic parameters and vehicle-road interaction. Unfortunately, some fundamental parameters, like tire-road forces and sideslip angle are difficult to measure in a car, for both technical and economic reasons. Therefore, this study presents a dynamic modeling and observation method to estimate these variables. One of the major contributions of this study, with respect to our previous work and to the largest literature in the field of the lateral dynamic estimation, is the fact that lateral tire force at each wheel is discussed in details. To address system nonlinearities and unmodeled dynamics, two observers derived from extended and unscented Kalman filtering techniques are proposed and compared. The estimation process method is based on the dynamic response of a vehicle instrumented with available and potentially integrable sensors. Performances are tested using an experimental car. Experimental results demonstrate the ability of this approach to provide accurate estimations, and show its practical potential as a low-cost solution for calculating lateral tire forces and sideslip angle.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/27157812" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="7f08cbce35b9664484c4e40e336688e8" rel="nofollow" data-download="{"attachment_id":47407048,"asset_id":27157812,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/47407048/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="51117377" href="https://independent.academia.edu/DLechner">Daniel Lechner</a><script data-card-contents-for-user="51117377" type="text/json">{"id":51117377,"first_name":"Daniel","last_name":"Lechner","domain_name":"independent","page_name":"DLechner","display_name":"Daniel Lechner","profile_url":"https://independent.academia.edu/DLechner?f_ri=49146","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_27157812 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="27157812"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 27157812, container: ".js-paper-rank-work_27157812", }); 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A close examination of accident data reveals that losing control of the vehicle is the main reason for most car accidents. To help to prevent such accidents, vehicle-control systems may be used, which require certain input data concerning vehicledynamic parameters and vehicle-road interaction. Unfortunately, some fundamental parameters, like tire-road forces and sideslip angle are difficult to measure in a car, for both technical and economic reasons. Therefore, this study presents a dynamic modeling and observation method to estimate these variables. One of the major contributions of this study, with respect to our previous work and to the largest literature in the field of the lateral dynamic estimation, is the fact that lateral tire force at each wheel is discussed in details. To address system nonlinearities and unmodeled dynamics, two observers derived from extended and unscented Kalman filtering techniques are proposed and compared. The estimation process method is based on the dynamic response of a vehicle instrumented with available and potentially integrable sensors. Performances are tested using an experimental car. Experimental results demonstrate the ability of this approach to provide accurate estimations, and show its practical potential as a low-cost solution for calculating lateral tire forces and sideslip angle.","downloadable_attachments":[{"id":47407048,"asset_id":27157812,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":51117377,"first_name":"Daniel","last_name":"Lechner","domain_name":"independent","page_name":"DLechner","display_name":"Daniel Lechner","profile_url":"https://independent.academia.edu/DLechner?f_ri=49146","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":60,"name":"Mechanical Engineering","url":"https://www.academia.edu/Documents/in/Mechanical_Engineering?f_ri=49146","nofollow":true},{"id":8050,"name":"Vehicle Dynamics","url":"https://www.academia.edu/Documents/in/Vehicle_Dynamics?f_ri=49146","nofollow":true},{"id":49146,"name":"Kalman 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href="https://www.academia.edu/67631845/Modelling_Study_for_Characterizing_and_Predicting_Urban_Air_Pollution">Modelling Study for Characterizing and Predicting Urban Air Pollution</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">MODELLING STUDY FOR CHARACTERIZING AND PREDICTING URBAN AIR POLLUTION Gregorio Andria, Giuseppe Cavone, Anna ML Lanzolla, Alessandro Rubino Department of Electrics and Electronics (DEE) Politecnico di Bari Viale del Turismo 8, 74100... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_67631845" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">MODELLING STUDY FOR CHARACTERIZING AND PREDICTING URBAN AIR POLLUTION Gregorio Andria, Giuseppe Cavone, Anna ML Lanzolla, Alessandro Rubino Department of Electrics and Electronics (DEE) Politecnico di Bari Viale del Turismo 8, 74100 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class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">a DIIAR c/o Polo of Como, Politecnico of Milan, Piazza Leonardo Da Vinci 32, 20133 Milano, Italy <a href="mailto:ludovico.biagi@polimi.it" rel="nofollow">ludovico.biagi@polimi.it</a> b DIMeC, University of Modena and Reggio Emilia, via Vignolese 905/B, 41100 Modena, Italy (marco.dubbini,... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_66784291" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">a DIIAR c/o Polo of Como, Politecnico of Milan, Piazza Leonardo Da Vinci 32, 20133 Milano, Italy <a href="mailto:ludovico.biagi@polimi.it" rel="nofollow">ludovico.biagi@polimi.it</a> b DIMeC, University of Modena and Reggio Emilia, via Vignolese 905/B, 41100 Modena, Italy (marco.dubbini, capra.alessandro, cristina.castagnetti)@unimore.it c Department of Mathematics, University of Modena and Reggio Emilia, 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class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" rel="nofollow" href="https://www.academia.edu/37062565/FPGA_IMPLEMENTATION_OF_DEBLOCKING_FILTER_CUSTOM_INSTRUCTION_HARDWARE_ON_NIOS_II_BASED_SOC">FPGA IMPLEMENTATION OF DEBLOCKING FILTER CUSTOM INSTRUCTION HARDWARE ON NIOS-II BASED SOC</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper presents a frame work for hardware acceleration for post video processing system implemented on FPGA. The deblocking filter algorithms ported on SOC having Altera NIOS-II soft core processor.SOC designed with the help of SOPC... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_37062565" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper presents a frame work for hardware acceleration for post video processing system implemented on FPGA. The deblocking filter algorithms ported on SOC having Altera NIOS-II soft core processor.SOC designed with the help of SOPC builder .Custom instructions are chosen by identifying the most frequently used tasks in the algorithm and the instruction set of NIOS-II processor has been extended. Deblocking filter new instruction added to the processor that are implemented in hardware and interfaced to the NIOS-II processor. New instruction added to the processor to boost the performance of the deblocking filter algorithm. Use of custom instructions the implemented tasks have been accelerated by 5.88%. 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unemployment and Okun's law: Some evidence from the G7","created_at":"2014-03-10T18:00:28.228-07:00","url":"https://www.academia.edu/6365536/Output_unemployment_and_Okuns_law_Some_evidence_from_the_G7?f_ri=49146","dom_id":"work_6365536","summary":null,"downloadable_attachments":[{"id":48900110,"asset_id":6365536,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":9951306,"first_name":"Hassan","last_name":"Molana","domain_name":"dundee","page_name":"HassanMolana","display_name":"Hassan Molana","profile_url":"https://dundee.academia.edu/HassanMolana?f_ri=49146","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":724,"name":"Economics","url":"https://www.academia.edu/Documents/in/Economics?f_ri=49146","nofollow":true},{"id":48414,"name":"Applied Economics Letters","url":"https://www.academia.edu/Documents/in/Applied_Economics_Letters?f_ri=49146","nofollow":true},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true},{"id":2523694,"name":"Unemployment rate","url":"https://www.academia.edu/Documents/in/Unemployment_rate?f_ri=49146","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_14727584" data-work_id="14727584" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/14727584/Low_Power_Distributed_Kalman_Filter_for_Wireless_Sensor_Networks">Low-Power Distributed Kalman Filter for Wireless Sensor Networks</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Distributed estimation algorithms have attracted a lot of attention in the past few years, particularly in the framework of Wireless Sensor Network (WSN). Distributed Kalman Filter (DKF) is one of the most fundamental distributed... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_14727584" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Distributed estimation algorithms have attracted a lot of attention in the past few years, particularly in the framework of Wireless Sensor Network (WSN). Distributed Kalman Filter (DKF) is one of the most fundamental distributed estimation algorithms for scalable wireless sensor fusion. Most DKF methods proposed in the literature rely on consensus filters algorithm. The convergence rate of such distributed consensus algorithms typically depends on the network topology. This paper proposes a low-power DKF. The proposed DKF is based on a fast polynomial filter. The idea is to apply a polynomial filter to the network matrix that will shape its spectrum in order to increase the convergence rate by minimizing its second largest eigenvalue. Fast convergence can contribute to significant energy saving. In order to implement the DKF in WSN, more power saving is needed. Since multiplication is the atomic operation of Kalman filter, so saving power at the multiplication level can significantly impact the energy consumption of the DKF. This paper also proposes a novel light-weight and low-power multiplication algorithm. The proposed algorithm aims to decrease the number of instruction cycles, save power, and reduce the memory storage without increasing the code complexity or sacrificing accuracy.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/14727584" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="a494836f80383225ebed4f5c0d1fee5c" rel="nofollow" data-download="{"attachment_id":43937399,"asset_id":14727584,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/43937399/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="33686732" href="https://cmich.academia.edu/AhmedAbdelgawad">Ahmed Abdelgawad</a><script data-card-contents-for-user="33686732" type="text/json">{"id":33686732,"first_name":"Ahmed","last_name":"Abdelgawad","domain_name":"cmich","page_name":"AhmedAbdelgawad","display_name":"Ahmed Abdelgawad","profile_url":"https://cmich.academia.edu/AhmedAbdelgawad?f_ri=49146","photo":"https://0.academia-photos.com/33686732/19092150/19042355/s65_ahmed.abdelgawad.png"}</script></span></span></li><li class="js-paper-rank-work_14727584 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="14727584"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 14727584, container: ".js-paper-rank-work_14727584", }); });</script></li><li class="js-percentile-work_14727584 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 14727584; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_14727584"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_14727584 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="14727584"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 14727584; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=14727584]").text(description); $(".js-view-count-work_14727584").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_14727584").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="14727584"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="49146" rel="nofollow" href="https://www.academia.edu/Documents/in/Kalman_Filter">Kalman Filter</a>, <script data-card-contents-for-ri="49146" type="text/json">{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="181287" rel="nofollow" href="https://www.academia.edu/Documents/in/Low_Power">Low Power</a>, <script data-card-contents-for-ri="181287" type="text/json">{"id":181287,"name":"Low Power","url":"https://www.academia.edu/Documents/in/Low_Power?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1211847" rel="nofollow" href="https://www.academia.edu/Documents/in/Wireless_Sensor_Network">Wireless Sensor Network</a>, <script data-card-contents-for-ri="1211847" type="text/json">{"id":1211847,"name":"Wireless Sensor Network","url":"https://www.academia.edu/Documents/in/Wireless_Sensor_Network?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1237788" rel="nofollow" href="https://www.academia.edu/Documents/in/Electrical_And_Electronic_Engineering">Electrical And Electronic Engineering</a><script data-card-contents-for-ri="1237788" type="text/json">{"id":1237788,"name":"Electrical And Electronic Engineering","url":"https://www.academia.edu/Documents/in/Electrical_And_Electronic_Engineering?f_ri=49146","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=14727584]'), work: {"id":14727584,"title":"Low-Power Distributed Kalman Filter for Wireless Sensor Networks","created_at":"2015-08-06T18:21:14.903-07:00","url":"https://www.academia.edu/14727584/Low_Power_Distributed_Kalman_Filter_for_Wireless_Sensor_Networks?f_ri=49146","dom_id":"work_14727584","summary":"Distributed estimation algorithms have attracted a lot of attention in the past few years, particularly in the framework of Wireless Sensor Network (WSN). Distributed Kalman Filter (DKF) is one of the most fundamental distributed estimation algorithms for scalable wireless sensor fusion. Most DKF methods proposed in the literature rely on consensus filters algorithm. The convergence rate of such distributed consensus algorithms typically depends on the network topology. This paper proposes a low-power DKF. The proposed DKF is based on a fast polynomial filter. The idea is to apply a polynomial filter to the network matrix that will shape its spectrum in order to increase the convergence rate by minimizing its second largest eigenvalue. Fast convergence can contribute to significant energy saving. In order to implement the DKF in WSN, more power saving is needed. Since multiplication is the atomic operation of Kalman filter, so saving power at the multiplication level can significantly impact the energy consumption of the DKF. This paper also proposes a novel light-weight and low-power multiplication algorithm. The proposed algorithm aims to decrease the number of instruction cycles, save power, and reduce the memory storage without increasing the code complexity or sacrificing accuracy.","downloadable_attachments":[{"id":43937399,"asset_id":14727584,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":33686732,"first_name":"Ahmed","last_name":"Abdelgawad","domain_name":"cmich","page_name":"AhmedAbdelgawad","display_name":"Ahmed Abdelgawad","profile_url":"https://cmich.academia.edu/AhmedAbdelgawad?f_ri=49146","photo":"https://0.academia-photos.com/33686732/19092150/19042355/s65_ahmed.abdelgawad.png"}],"research_interests":[{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true},{"id":181287,"name":"Low Power","url":"https://www.academia.edu/Documents/in/Low_Power?f_ri=49146","nofollow":true},{"id":1211847,"name":"Wireless Sensor Network","url":"https://www.academia.edu/Documents/in/Wireless_Sensor_Network?f_ri=49146","nofollow":true},{"id":1237788,"name":"Electrical And Electronic Engineering","url":"https://www.academia.edu/Documents/in/Electrical_And_Electronic_Engineering?f_ri=49146","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_15152878" data-work_id="15152878" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/15152878/Multi_robot_localization_using_relative_observations">Multi-robot localization using relative observations</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In this paper we consider the problem of simultaneously localizing all members of a team of robots. Each robot is equipped with proprioceptive sensors and exteroceptive sensors. The latter provide relative observations between the robots.... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_15152878" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this paper we consider the problem of simultaneously localizing all members of a team of robots. Each robot is equipped with proprioceptive sensors and exteroceptive sensors. The latter provide relative observations between the robots. Proprioceptive and exteroceptive data are fused with an Extended Kalman Filter. We derive the equations for this estimator for the most general relative observation between two robots. Then we consider three special cases of relative observations and we present the structure of the filter for each case. Finally, we study the performance of the approach through many accurate simulations.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/15152878" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="3a4e23674302b6561465d546e667f04d" rel="nofollow" data-download="{"attachment_id":43526985,"asset_id":15152878,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/43526985/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="34192914" href="https://ethz.academia.edu/RSiegwart">Roland Siegwart</a><script data-card-contents-for-user="34192914" type="text/json">{"id":34192914,"first_name":"Roland","last_name":"Siegwart","domain_name":"ethz","page_name":"RSiegwart","display_name":"Roland Siegwart","profile_url":"https://ethz.academia.edu/RSiegwart?f_ri=49146","photo":"https://0.academia-photos.com/34192914/10027606/11184961/s65_roland.siegwart.jpg"}</script></span></span></li><li class="js-paper-rank-work_15152878 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="15152878"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 15152878, container: ".js-paper-rank-work_15152878", }); });</script></li><li class="js-percentile-work_15152878 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 15152878; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_15152878"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_15152878 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="15152878"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 15152878; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=15152878]").text(description); $(".js-view-count-work_15152878").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_15152878").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="15152878"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">7</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="3504" rel="nofollow" href="https://www.academia.edu/Documents/in/General_Relativity">General Relativity</a>, <script data-card-contents-for-ri="3504" type="text/json">{"id":3504,"name":"General Relativity","url":"https://www.academia.edu/Documents/in/General_Relativity?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="49146" rel="nofollow" href="https://www.academia.edu/Documents/in/Kalman_Filter">Kalman Filter</a>, <script data-card-contents-for-ri="49146" type="text/json">{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="143115" rel="nofollow" href="https://www.academia.edu/Documents/in/Sensor_Fusion">Sensor Fusion</a>, <script data-card-contents-for-ri="143115" type="text/json">{"id":143115,"name":"Sensor Fusion","url":"https://www.academia.edu/Documents/in/Sensor_Fusion?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="872410" rel="nofollow" href="https://www.academia.edu/Documents/in/Extended_Kalman_Filter">Extended Kalman Filter</a><script data-card-contents-for-ri="872410" type="text/json">{"id":872410,"name":"Extended Kalman Filter","url":"https://www.academia.edu/Documents/in/Extended_Kalman_Filter?f_ri=49146","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=15152878]'), work: {"id":15152878,"title":"Multi-robot localization using relative observations","created_at":"2015-08-24T12:33:42.588-07:00","url":"https://www.academia.edu/15152878/Multi_robot_localization_using_relative_observations?f_ri=49146","dom_id":"work_15152878","summary":"In this paper we consider the problem of simultaneously localizing all members of a team of robots. Each robot is equipped with proprioceptive sensors and exteroceptive sensors. The latter provide relative observations between the robots. Proprioceptive and exteroceptive data are fused with an Extended Kalman Filter. We derive the equations for this estimator for the most general relative observation between two robots. Then we consider three special cases of relative observations and we present the structure of the filter for each case. Finally, we study the performance of the approach through many accurate simulations.","downloadable_attachments":[{"id":43526985,"asset_id":15152878,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":34192914,"first_name":"Roland","last_name":"Siegwart","domain_name":"ethz","page_name":"RSiegwart","display_name":"Roland Siegwart","profile_url":"https://ethz.academia.edu/RSiegwart?f_ri=49146","photo":"https://0.academia-photos.com/34192914/10027606/11184961/s65_roland.siegwart.jpg"}],"research_interests":[{"id":3504,"name":"General Relativity","url":"https://www.academia.edu/Documents/in/General_Relativity?f_ri=49146","nofollow":true},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true},{"id":143115,"name":"Sensor Fusion","url":"https://www.academia.edu/Documents/in/Sensor_Fusion?f_ri=49146","nofollow":true},{"id":872410,"name":"Extended Kalman Filter","url":"https://www.academia.edu/Documents/in/Extended_Kalman_Filter?f_ri=49146","nofollow":true},{"id":1268716,"name":"Robot Localization","url":"https://www.academia.edu/Documents/in/Robot_Localization?f_ri=49146"},{"id":1461519,"name":"Robotics Automation","url":"https://www.academia.edu/Documents/in/Robotics_Automation?f_ri=49146"},{"id":1772724,"name":"Robot Navigation","url":"https://www.academia.edu/Documents/in/Robot_Navigation?f_ri=49146"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_79583679" data-work_id="79583679" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/79583679/Computational_Complexity_Comparison_of_Multi_Sensor_Single_Target_Data_Fusion_Methods_by_MATLAB">Computational Complexity Comparison of Multi-Sensor Single Target Data Fusion Methods by MATLAB</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Target tracking using observations from multiple sensors can achieve better estimation performance than a single sensor. The most famous estimation tool in target tracking is Kalman filter. There are several mathematical approaches to... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_79583679" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Target tracking using observations from multiple sensors can achieve better estimation performance than a single sensor. The most famous estimation tool in target tracking is Kalman filter. There are several mathematical approaches to combine the observations of multiple sensors by use of Kalman filter. An important issue in applying a proper approach is computational complexity. In this paper, four data fusion algorithms based on Kalman filter are considered including three centralized and one decentralized methods. Using MATLAB, computational loads of these methods are compared while number of sensors increases. The results show that inverse covariance method has the best computational performance if the number of sensors is above 20. For a smaller number of sensors, other methods, especially group sensors, are more appropriate..</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/79583679" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="2d12cf7d40eb4231df8e6fdedb4f6f6c" rel="nofollow" data-download="{"attachment_id":86248542,"asset_id":79583679,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/86248542/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="171121631" href="https://buitms.academia.edu/MohammadAdnanAshraf">Mohammad Adnan Ashraf</a><script data-card-contents-for-user="171121631" type="text/json">{"id":171121631,"first_name":"Mohammad Adnan","last_name":"Ashraf","domain_name":"buitms","page_name":"MohammadAdnanAshraf","display_name":"Mohammad Adnan Ashraf","profile_url":"https://buitms.academia.edu/MohammadAdnanAshraf?f_ri=49146","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_79583679 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="79583679"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 79583679, container: ".js-paper-rank-work_79583679", }); 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The most famous estimation tool in target tracking is Kalman filter. There are several mathematical approaches to combine the observations of multiple sensors by use of Kalman filter. An important issue in applying a proper approach is computational complexity. In this paper, four data fusion algorithms based on Kalman filter are considered including three centralized and one decentralized methods. Using MATLAB, computational loads of these methods are compared while number of sensors increases. The results show that inverse covariance method has the best computational performance if the number of sensors is above 20. For a smaller number of sensors, other methods, especially group sensors, are more appropriate..","downloadable_attachments":[{"id":86248542,"asset_id":79583679,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":171121631,"first_name":"Mohammad Adnan","last_name":"Ashraf","domain_name":"buitms","page_name":"MohammadAdnanAshraf","display_name":"Mohammad Adnan Ashraf","profile_url":"https://buitms.academia.edu/MohammadAdnanAshraf?f_ri=49146","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science?f_ri=49146","nofollow":true},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=49146","nofollow":true},{"id":14852,"name":"Data Fusion (Engineering)","url":"https://www.academia.edu/Documents/in/Data_Fusion_Engineering_?f_ri=49146","nofollow":true},{"id":25395,"name":"Matlab","url":"https://www.academia.edu/Documents/in/Matlab?f_ri=49146","nofollow":true},{"id":38111,"name":"Target Tracking","url":"https://www.academia.edu/Documents/in/Target_Tracking?f_ri=49146"},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146"},{"id":64561,"name":"Computer Software","url":"https://www.academia.edu/Documents/in/Computer_Software?f_ri=49146"},{"id":134703,"name":"Data Fusion","url":"https://www.academia.edu/Documents/in/Data_Fusion?f_ri=49146"},{"id":143115,"name":"Sensor Fusion","url":"https://www.academia.edu/Documents/in/Sensor_Fusion?f_ri=49146"},{"id":170496,"name":"Multi Sensor Data Fusion","url":"https://www.academia.edu/Documents/in/Multi_Sensor_Data_Fusion?f_ri=49146"},{"id":215744,"name":"Nonlinear Kalman Filter","url":"https://www.academia.edu/Documents/in/Nonlinear_Kalman_Filter?f_ri=49146"},{"id":230873,"name":"Multiple Target Tracking","url":"https://www.academia.edu/Documents/in/Multiple_Target_Tracking?f_ri=49146"},{"id":3193313,"name":"arXiv","url":"https://www.academia.edu/Documents/in/arXiv?f_ri=49146"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_35751914" data-work_id="35751914" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/35751914/A_Robust_Null_Space_Method_for_Linear_Equality_Constrained_State_Estimation">A Robust Null Space Method for Linear Equality Constrained State Estimation</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We present a robust null space method for linear equality constrained state space estimation. Exploiting a degeneracy in the estimator statistics, an orthogonal factorization is used to decompose the problem into stochastic and... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_35751914" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We present a robust null space method for linear equality constrained state space estimation. Exploiting a degeneracy in the estimator statistics, an orthogonal factorization is used to decompose the problem into stochastic and deterministic components, which are then solved separately. The resulting dimension reduction algorithm has enhanced numerical stability, solves the constrained problem completely, and can reduce computational load by reducing the problem size. The new method addresses deficiencies in commonly used pseudo-observation or projection methods, which either do not solve the constrained problem completely or have unstable numerical implementations, due in part to the degeneracy in the estimator statistics. We present a numerical example demonstrating the effectiveness of the new method compared to other current methods.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/35751914" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="71efb3c2cfb7e66c9eac2ba5fc8a110f" rel="nofollow" data-download="{"attachment_id":55627284,"asset_id":35751914,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/55627284/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="41807546" href="https://illinois.academia.edu/MarkButala">Mark Butala</a><script data-card-contents-for-user="41807546" type="text/json">{"id":41807546,"first_name":"Mark","last_name":"Butala","domain_name":"illinois","page_name":"MarkButala","display_name":"Mark Butala","profile_url":"https://illinois.academia.edu/MarkButala?f_ri=49146","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_35751914 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="35751914"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 35751914, container: ".js-paper-rank-work_35751914", }); });</script></li><li class="js-percentile-work_35751914 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 35751914; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_35751914"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_35751914 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="35751914"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 35751914; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=35751914]").text(description); $(".js-view-count-work_35751914").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_35751914").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="35751914"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">16</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="2141" rel="nofollow" href="https://www.academia.edu/Documents/in/Signal_Processing">Signal Processing</a>, <script data-card-contents-for-ri="2141" type="text/json">{"id":2141,"name":"Signal Processing","url":"https://www.academia.edu/Documents/in/Signal_Processing?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="26817" rel="nofollow" href="https://www.academia.edu/Documents/in/Algorithm">Algorithm</a>, <script data-card-contents-for-ri="26817" type="text/json">{"id":26817,"name":"Algorithm","url":"https://www.academia.edu/Documents/in/Algorithm?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="28235" rel="nofollow" href="https://www.academia.edu/Documents/in/Multidisciplinary">Multidisciplinary</a>, <script data-card-contents-for-ri="28235" type="text/json">{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="39920" rel="nofollow" href="https://www.academia.edu/Documents/in/Parameter_estimation">Parameter estimation</a><script data-card-contents-for-ri="39920" type="text/json">{"id":39920,"name":"Parameter estimation","url":"https://www.academia.edu/Documents/in/Parameter_estimation?f_ri=49146","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=35751914]'), work: {"id":35751914,"title":"A Robust Null Space Method for Linear Equality Constrained State Estimation","created_at":"2018-01-24T15:04:31.569-08:00","url":"https://www.academia.edu/35751914/A_Robust_Null_Space_Method_for_Linear_Equality_Constrained_State_Estimation?f_ri=49146","dom_id":"work_35751914","summary":"We present a robust null space method for linear equality constrained state space estimation. Exploiting a degeneracy in the estimator statistics, an orthogonal factorization is used to decompose the problem into stochastic and deterministic components, which are then solved separately. The resulting dimension reduction algorithm has enhanced numerical stability, solves the constrained problem completely, and can reduce computational load by reducing the problem size. The new method addresses deficiencies in commonly used pseudo-observation or projection methods, which either do not solve the constrained problem completely or have unstable numerical implementations, due in part to the degeneracy in the estimator statistics. We present a numerical example demonstrating the effectiveness of the new method compared to other current methods.","downloadable_attachments":[{"id":55627284,"asset_id":35751914,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":41807546,"first_name":"Mark","last_name":"Butala","domain_name":"illinois","page_name":"MarkButala","display_name":"Mark Butala","profile_url":"https://illinois.academia.edu/MarkButala?f_ri=49146","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":2141,"name":"Signal Processing","url":"https://www.academia.edu/Documents/in/Signal_Processing?f_ri=49146","nofollow":true},{"id":26817,"name":"Algorithm","url":"https://www.academia.edu/Documents/in/Algorithm?f_ri=49146","nofollow":true},{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary?f_ri=49146","nofollow":true},{"id":39920,"name":"Parameter estimation","url":"https://www.academia.edu/Documents/in/Parameter_estimation?f_ri=49146","nofollow":true},{"id":42279,"name":"Dimension Reduction","url":"https://www.academia.edu/Documents/in/Dimension_Reduction?f_ri=49146"},{"id":43131,"name":"Stochastic processes","url":"https://www.academia.edu/Documents/in/Stochastic_processes?f_ri=49146"},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146"},{"id":60658,"name":"Numerical Simulation","url":"https://www.academia.edu/Documents/in/Numerical_Simulation?f_ri=49146"},{"id":67380,"name":"Kalman Filtering","url":"https://www.academia.edu/Documents/in/Kalman_Filtering?f_ri=49146"},{"id":132898,"name":"Estimation","url":"https://www.academia.edu/Documents/in/Estimation?f_ri=49146"},{"id":135913,"name":"State Space","url":"https://www.academia.edu/Documents/in/State_Space?f_ri=49146"},{"id":238655,"name":"Implementation","url":"https://www.academia.edu/Documents/in/Implementation?f_ri=49146"},{"id":292775,"name":"FACTORISATION","url":"https://www.academia.edu/Documents/in/FACTORISATION?f_ri=49146"},{"id":333167,"name":"Factorization","url":"https://www.academia.edu/Documents/in/Factorization?f_ri=49146"},{"id":606723,"name":"Numerical Stability","url":"https://www.academia.edu/Documents/in/Numerical_Stability?f_ri=49146"},{"id":891630,"name":"Projection Method","url":"https://www.academia.edu/Documents/in/Projection_Method?f_ri=49146"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_33876272" data-work_id="33876272" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/33876272/Fusion_motion_capture_a_prototype_system_using_inertial_measurement_units_and_GPS_for_the_biomechanical_analysis_of_ski_racing">Fusion motion capture: a prototype system using inertial measurement units and GPS for the biomechanical analysis of ski racing</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In this pilot study fusion motion capture (FMC) has been used to capture 3-D kinetics and kinematics of alpine ski racing. The new technology has overcome the technological difficulties associated with athlete performance monitoring in an... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_33876272" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this pilot study fusion motion capture (FMC) has been used to capture 3-D kinetics and kinematics of alpine ski racing. The new technology has overcome the technological difficulties associated with athlete performance monitoring in an alpine environment. FMC is a general term to describe motion capture when several different streams of data are fused to measure athlete motion. In this article inertial measurement units (IMU), global positioning system (GPS) pressure sensitive insoles, video and theodolite measurements have been combined. The core of the FMC is the fusion of IMU and GPS data. IMU may contain accelerometers, gyroscopes, magnetometers and a thermometer, and they track local orientation and acceleration of each limb segment of interest. GPS data are fused with local acceleration data to track the global trajectory of the athlete. Fusion integration algorithms designed by the authors [1] were used to improve the accuracy of the independent Kalman filter solutions provided by the vendors of both the GPS and IMU. The GPS accuracy was improved from a dilution of precision of 75 m (meaning 50% of the measurements will be within 5 m of the true value) to a maximum error of 71.5 m over the race course, while the IMU orientation error was reduced from over 201 to less than 51. The reader is invited to assess the validity of these results by comparing videos of the motion to the fusion motion capture output in the electronic version of this manuscript. Accuracy in laboratory situations has been validated, [2,3] but because such systems are becoming more popular, this system needs to be validated on the snow. As more accurate dual frequency GPS systems become less expensive this type of system will become more accurate and affordable. A biomechanical analysis was undertaken of a New Zealand Alpine Ski Racing Team member negotiating a 10-gate giant slalom course over 300 m in length. The abundant data in the results were used to create new tools for measuring alpine ski racing technique, such as colour-coded force vector analysis. The new parameters introduced in this article, such as effective inclination and ground reaction force power, are independent of the stylistic constraints often imposed by the coach or athlete. Two ski runs have been compared. Although the difference between the two run times was only 0.14 s or 1%, FMC and force vector analysis were able to</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/33876272" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="e369ebef4078960f87bc92038edd30cf" rel="nofollow" data-download="{"attachment_id":53852752,"asset_id":33876272,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/53852752/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="66420129" href="https://independent.academia.edu/MatthewBrodie1">Matthew Brodie</a><script data-card-contents-for-user="66420129" type="text/json">{"id":66420129,"first_name":"Matthew","last_name":"Brodie","domain_name":"independent","page_name":"MatthewBrodie1","display_name":"Matthew Brodie","profile_url":"https://independent.academia.edu/MatthewBrodie1?f_ri=49146","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_33876272 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="33876272"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 33876272, container: ".js-paper-rank-work_33876272", }); });</script></li><li class="js-percentile-work_33876272 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 33876272; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_33876272"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_33876272 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="33876272"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 33876272; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=33876272]").text(description); $(".js-view-count-work_33876272").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_33876272").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="33876272"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">19</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="60" rel="nofollow" href="https://www.academia.edu/Documents/in/Mechanical_Engineering">Mechanical Engineering</a>, <script data-card-contents-for-ri="60" type="text/json">{"id":60,"name":"Mechanical Engineering","url":"https://www.academia.edu/Documents/in/Mechanical_Engineering?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="4987" rel="nofollow" href="https://www.academia.edu/Documents/in/Kinetics">Kinetics</a>, <script data-card-contents-for-ri="4987" type="text/json">{"id":4987,"name":"Kinetics","url":"https://www.academia.edu/Documents/in/Kinetics?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="5673" rel="nofollow" href="https://www.academia.edu/Documents/in/Augmented_Reality">Augmented Reality</a>, <script data-card-contents-for-ri="5673" type="text/json">{"id":5673,"name":"Augmented Reality","url":"https://www.academia.edu/Documents/in/Augmented_Reality?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="48627" rel="nofollow" href="https://www.academia.edu/Documents/in/Anterior_Cruciate_Ligament">Anterior Cruciate Ligament</a><script data-card-contents-for-ri="48627" type="text/json">{"id":48627,"name":"Anterior Cruciate Ligament","url":"https://www.academia.edu/Documents/in/Anterior_Cruciate_Ligament?f_ri=49146","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=33876272]'), work: {"id":33876272,"title":"Fusion motion capture: a prototype system using inertial measurement units and GPS for the biomechanical analysis of ski racing","created_at":"2017-07-13T16:07:43.038-07:00","url":"https://www.academia.edu/33876272/Fusion_motion_capture_a_prototype_system_using_inertial_measurement_units_and_GPS_for_the_biomechanical_analysis_of_ski_racing?f_ri=49146","dom_id":"work_33876272","summary":"In this pilot study fusion motion capture (FMC) has been used to capture 3-D kinetics and kinematics of alpine ski racing. The new technology has overcome the technological difficulties associated with athlete performance monitoring in an alpine environment. FMC is a general term to describe motion capture when several different streams of data are fused to measure athlete motion. In this article inertial measurement units (IMU), global positioning system (GPS) pressure sensitive insoles, video and theodolite measurements have been combined. The core of the FMC is the fusion of IMU and GPS data. IMU may contain accelerometers, gyroscopes, magnetometers and a thermometer, and they track local orientation and acceleration of each limb segment of interest. GPS data are fused with local acceleration data to track the global trajectory of the athlete. Fusion integration algorithms designed by the authors [1] were used to improve the accuracy of the independent Kalman filter solutions provided by the vendors of both the GPS and IMU. The GPS accuracy was improved from a dilution of precision of 75 m (meaning 50% of the measurements will be within 5 m of the true value) to a maximum error of 71.5 m over the race course, while the IMU orientation error was reduced from over 201 to less than 51. The reader is invited to assess the validity of these results by comparing videos of the motion to the fusion motion capture output in the electronic version of this manuscript. Accuracy in laboratory situations has been validated, [2,3] but because such systems are becoming more popular, this system needs to be validated on the snow. As more accurate dual frequency GPS systems become less expensive this type of system will become more accurate and affordable. A biomechanical analysis was undertaken of a New Zealand Alpine Ski Racing Team member negotiating a 10-gate giant slalom course over 300 m in length. The abundant data in the results were used to create new tools for measuring alpine ski racing technique, such as colour-coded force vector analysis. The new parameters introduced in this article, such as effective inclination and ground reaction force power, are independent of the stylistic constraints often imposed by the coach or athlete. Two ski runs have been compared. Although the difference between the two run times was only 0.14 s or 1%, FMC and force vector analysis were able to","downloadable_attachments":[{"id":53852752,"asset_id":33876272,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":66420129,"first_name":"Matthew","last_name":"Brodie","domain_name":"independent","page_name":"MatthewBrodie1","display_name":"Matthew Brodie","profile_url":"https://independent.academia.edu/MatthewBrodie1?f_ri=49146","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":60,"name":"Mechanical Engineering","url":"https://www.academia.edu/Documents/in/Mechanical_Engineering?f_ri=49146","nofollow":true},{"id":4987,"name":"Kinetics","url":"https://www.academia.edu/Documents/in/Kinetics?f_ri=49146","nofollow":true},{"id":5673,"name":"Augmented Reality","url":"https://www.academia.edu/Documents/in/Augmented_Reality?f_ri=49146","nofollow":true},{"id":48627,"name":"Anterior Cruciate Ligament","url":"https://www.academia.edu/Documents/in/Anterior_Cruciate_Ligament?f_ri=49146","nofollow":true},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146"},{"id":53993,"name":"GPS","url":"https://www.academia.edu/Documents/in/GPS?f_ri=49146"},{"id":59546,"name":"New Technology","url":"https://www.academia.edu/Documents/in/New_Technology?f_ri=49146"},{"id":116108,"name":"New Zealand","url":"https://www.academia.edu/Documents/in/New_Zealand?f_ri=49146"},{"id":116787,"name":"Algorithm Design","url":"https://www.academia.edu/Documents/in/Algorithm_Design?f_ri=49146"},{"id":221756,"name":"Motion Capture","url":"https://www.academia.edu/Documents/in/Motion_Capture?f_ri=49146"},{"id":308908,"name":"Pilot study","url":"https://www.academia.edu/Documents/in/Pilot_study?f_ri=49146"},{"id":337498,"name":"Ground Reaction Force","url":"https://www.academia.edu/Documents/in/Ground_Reaction_Force?f_ri=49146"},{"id":521841,"name":"Sports Technology","url":"https://www.academia.edu/Documents/in/Sports_Technology?f_ri=49146"},{"id":525088,"name":"Key Performance Indicator","url":"https://www.academia.edu/Documents/in/Key_Performance_Indicator?f_ri=49146"},{"id":691413,"name":"Athletic performance","url":"https://www.academia.edu/Documents/in/Athletic_performance?f_ri=49146"},{"id":1009208,"name":"Centre of Mass","url":"https://www.academia.edu/Documents/in/Centre_of_Mass?f_ri=49146"},{"id":1237788,"name":"Electrical And Electronic Engineering","url":"https://www.academia.edu/Documents/in/Electrical_And_Electronic_Engineering?f_ri=49146"},{"id":1594510,"name":"Inertial Measurement Unit","url":"https://www.academia.edu/Documents/in/Inertial_Measurement_Unit?f_ri=49146"},{"id":1688205,"name":"Global position ing system","url":"https://www.academia.edu/Documents/in/Global_position_ing_system?f_ri=49146"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_32586908" data-work_id="32586908" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/32586908/Sensors_on_Instrumented_Socks_for_Detection_of_Lower_Leg_Edema_An_In_Vitro_Study">Sensors on Instrumented Socks for Detection of Lower Leg Edema – An In Vitro Study</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper presents the design, sensing principles and in vitro evaluation of a novel instrumented sock intended for prediction and prevention of acute decompensated heart failure. The sock contains a drift-free ankle size sensor and a... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_32586908" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper presents the design, sensing principles and in vitro evaluation of a novel instrumented sock intended for prediction and prevention of acute decompensated heart failure. The sock contains a drift-free ankle size sensor and a leg tissue elasticity sensor. Both sensors are inexpensive and developed using innovative new sensing ideas. Preliminary tests with the sensor prototypes show promising results: The ankle size sensor is capable of measuring 1 mm changes in ankle diameter and the tissue elasticity sensor can detect 0.15 MPa differences in elasticity. A low-profile instrumented sock prototype with these two sensors has been successfully fabricated and will be evaluated in the future in an IRB-approved human study.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/32586908" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="514fd3fc5e4c35abab6ca8caa2b6fb9d" rel="nofollow" data-download="{"attachment_id":52763291,"asset_id":32586908,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/52763291/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="63330568" href="https://spanalumni.academia.edu/SongZhang">Song Zhang</a><script data-card-contents-for-user="63330568" type="text/json">{"id":63330568,"first_name":"Song","last_name":"Zhang","domain_name":"spanalumni","page_name":"SongZhang","display_name":"Song Zhang","profile_url":"https://spanalumni.academia.edu/SongZhang?f_ri=49146","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_32586908 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="32586908"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 32586908, container: ".js-paper-rank-work_32586908", }); 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The sock contains a drift-free ankle size sensor and a leg tissue elasticity sensor. Both sensors are inexpensive and developed using innovative new sensing ideas. Preliminary tests with the sensor prototypes show promising results: The ankle size sensor is capable of measuring 1 mm changes in ankle diameter and the tissue elasticity sensor can detect 0.15 MPa differences in elasticity. A low-profile instrumented sock prototype with these two sensors has been successfully fabricated and will be evaluated in the future in an IRB-approved human study.","downloadable_attachments":[{"id":52763291,"asset_id":32586908,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":63330568,"first_name":"Song","last_name":"Zhang","domain_name":"spanalumni","page_name":"SongZhang","display_name":"Song Zhang","profile_url":"https://spanalumni.academia.edu/SongZhang?f_ri=49146","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":1526,"name":"Sensors and Sensing","url":"https://www.academia.edu/Documents/in/Sensors_and_Sensing?f_ri=49146","nofollow":true},{"id":4758,"name":"Electronics","url":"https://www.academia.edu/Documents/in/Electronics?f_ri=49146","nofollow":true},{"id":9038,"name":"Digital Signal Processing","url":"https://www.academia.edu/Documents/in/Digital_Signal_Processing?f_ri=49146","nofollow":true},{"id":9687,"name":"Wearable Technologies","url":"https://www.academia.edu/Documents/in/Wearable_Technologies?f_ri=49146","nofollow":true},{"id":39920,"name":"Parameter estimation","url":"https://www.academia.edu/Documents/in/Parameter_estimation?f_ri=49146"},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146"},{"id":133363,"name":"Swelling","url":"https://www.academia.edu/Documents/in/Swelling?f_ri=49146"},{"id":213658,"name":"Health monitoring","url":"https://www.academia.edu/Documents/in/Health_monitoring?f_ri=49146"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_3238614" data-work_id="3238614" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/3238614/Position_and_orientation_estimation_based_on_Kalman_filtering_of_stereo_images">Position and orientation estimation based on Kalman filtering of stereo images</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/3238614" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="0f278112e96933a9ddf81954d4e4a205" rel="nofollow" data-download="{"attachment_id":31280263,"asset_id":3238614,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button 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itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/4755974/A_fuzzy_logic_based_approach_to_the_SLAM_problem_using_pseudolinear_models_with_two_sensors_data_association">A fuzzy logic based approach to the SLAM problem using pseudolinear models with two sensors data association</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">A fuzzy logic based approach to the SLAM problem using pseudolinear models with multiframe data association unknown location in an unknown environment and build a map (consisting of environmental features) of its environment incrementally... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_4755974" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">A fuzzy logic based approach to the SLAM problem using pseudolinear models with multiframe data association unknown location in an unknown environment and build a map (consisting of environmental features) of its environment incrementally by using the uncertain information extracted from its sensors, whilst simultaneously using that map to localize itself with respect to a reference coordinate frame and navigate in real time. The fi rst solution to the SLAM problem was proposed by Smith et al. 2 They emphasized the importance of map and vehicle correlations in SLAM and introduced the extended Kalman fi lter (EKF)based stochastic mapping framework, which estimated the vehicle pose and the map feature (landmark) positions in an augmented state vector using second order statistics. Although the EKF-based SLAM within the stochastic mapping framework gained wide popularity among the SLAM research community, over time, it was shown to have several shortcomings. 3,4 Notable shortcomings are its susceptibility to data-association errors and inconsistent treatment of nonlinearities.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/4755974" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="d9615642d3d3ec1cad315d851fa6038a" rel="nofollow" data-download="{"attachment_id":49642043,"asset_id":4755974,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/49642043/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="6097873" href="https://hct.academia.edu/LankaUdawatta">Lanka Udawatta</a><script data-card-contents-for-user="6097873" type="text/json">{"id":6097873,"first_name":"Lanka","last_name":"Udawatta","domain_name":"hct","page_name":"LankaUdawatta","display_name":"Lanka Udawatta","profile_url":"https://hct.academia.edu/LankaUdawatta?f_ri=49146","photo":"https://0.academia-photos.com/6097873/2551155/2961899/s65_lanka.udawatta.png"}</script></span></span></li><li class="js-paper-rank-work_4755974 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="4755974"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 4755974, container: ".js-paper-rank-work_4755974", }); 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The fi rst solution to the SLAM problem was proposed by Smith et al. 2 They emphasized the importance of map and vehicle correlations in SLAM and introduced the extended Kalman fi lter (EKF)based stochastic mapping framework, which estimated the vehicle pose and the map feature (landmark) positions in an augmented state vector using second order statistics. Although the EKF-based SLAM within the stochastic mapping framework gained wide popularity among the SLAM research community, over time, it was shown to have several shortcomings. 3,4 Notable shortcomings are its susceptibility to data-association errors and inconsistent treatment of nonlinearities.","downloadable_attachments":[{"id":49642043,"asset_id":4755974,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":6097873,"first_name":"Lanka","last_name":"Udawatta","domain_name":"hct","page_name":"LankaUdawatta","display_name":"Lanka Udawatta","profile_url":"https://hct.academia.edu/LankaUdawatta?f_ri=49146","photo":"https://0.academia-photos.com/6097873/2551155/2961899/s65_lanka.udawatta.png"}],"research_interests":[{"id":4165,"name":"Fuzzy Logic","url":"https://www.academia.edu/Documents/in/Fuzzy_Logic?f_ri=49146","nofollow":true},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true},{"id":55265,"name":"Simultaneous Localization and Mapping","url":"https://www.academia.edu/Documents/in/Simultaneous_Localization_and_Mapping?f_ri=49146","nofollow":true},{"id":56503,"name":"Linear Model","url":"https://www.academia.edu/Documents/in/Linear_Model?f_ri=49146","nofollow":true},{"id":66823,"name":"Mobile Robots","url":"https://www.academia.edu/Documents/in/Mobile_Robots?f_ri=49146"},{"id":278182,"name":"Data Association","url":"https://www.academia.edu/Documents/in/Data_Association?f_ri=49146"},{"id":506858,"name":"Nonlinear system","url":"https://www.academia.edu/Documents/in/Nonlinear_system?f_ri=49146"},{"id":959544,"name":"Process Model","url":"https://www.academia.edu/Documents/in/Process_Model?f_ri=49146"},{"id":1237788,"name":"Electrical And Electronic Engineering","url":"https://www.academia.edu/Documents/in/Electrical_And_Electronic_Engineering?f_ri=49146"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_12728098" data-work_id="12728098" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/12728098/A_parallel_VLSI_architecture_of_Kalman_filter_based_algorithms_for_signal_reconstruction">A parallel VLSI architecture of Kalman-filter-based algorithms for signal reconstruction</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest">& bec a % Trois-Rivie % res, C.P. 500, Trois-Rivie % res,</div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/12728098" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="1dfb9fc97b0852b9ccc4a35d15dc829b" rel="nofollow" data-download="{"attachment_id":45976524,"asset_id":12728098,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/45976524/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="31759500" href="https://uqtr.academia.edu/DanielMassicotte">Daniel Massicotte</a><script data-card-contents-for-user="31759500" type="text/json">{"id":31759500,"first_name":"Daniel","last_name":"Massicotte","domain_name":"uqtr","page_name":"DanielMassicotte","display_name":"Daniel Massicotte","profile_url":"https://uqtr.academia.edu/DanielMassicotte?f_ri=49146","photo":"https://0.academia-photos.com/31759500/12355907/13754889/s65_daniel.massicotte.jpg"}</script></span></span></li><li class="js-paper-rank-work_12728098 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="12728098"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 12728098, container: ".js-paper-rank-work_12728098", }); 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$(".js-view-count[data-work-id=12728098]").text(description); $(".js-view-count-work_12728098").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_12728098").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="12728098"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">7</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="15759" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Hardware">Computer Hardware</a>, <script data-card-contents-for-ri="15759" type="text/json">{"id":15759,"name":"Computer Hardware","url":"https://www.academia.edu/Documents/in/Computer_Hardware?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="17167" rel="nofollow" href="https://www.academia.edu/Documents/in/Parallel_Processing">Parallel Processing</a>, <script data-card-contents-for-ri="17167" type="text/json">{"id":17167,"name":"Parallel Processing","url":"https://www.academia.edu/Documents/in/Parallel_Processing?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="49146" rel="nofollow" href="https://www.academia.edu/Documents/in/Kalman_Filter">Kalman Filter</a>, <script data-card-contents-for-ri="49146" type="text/json">{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="99915" rel="nofollow" href="https://www.academia.edu/Documents/in/Integration">Integration</a><script data-card-contents-for-ri="99915" type="text/json">{"id":99915,"name":"Integration","url":"https://www.academia.edu/Documents/in/Integration?f_ri=49146","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=12728098]'), work: {"id":12728098,"title":"A parallel VLSI architecture of Kalman-filter-based algorithms for signal reconstruction","created_at":"2015-06-01T10:03:31.918-07:00","url":"https://www.academia.edu/12728098/A_parallel_VLSI_architecture_of_Kalman_filter_based_algorithms_for_signal_reconstruction?f_ri=49146","dom_id":"work_12728098","summary":"\u0026 bec a % Trois-Rivie % res, C.P. 500, Trois-Rivie % res,","downloadable_attachments":[{"id":45976524,"asset_id":12728098,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":31759500,"first_name":"Daniel","last_name":"Massicotte","domain_name":"uqtr","page_name":"DanielMassicotte","display_name":"Daniel Massicotte","profile_url":"https://uqtr.academia.edu/DanielMassicotte?f_ri=49146","photo":"https://0.academia-photos.com/31759500/12355907/13754889/s65_daniel.massicotte.jpg"}],"research_interests":[{"id":15759,"name":"Computer Hardware","url":"https://www.academia.edu/Documents/in/Computer_Hardware?f_ri=49146","nofollow":true},{"id":17167,"name":"Parallel Processing","url":"https://www.academia.edu/Documents/in/Parallel_Processing?f_ri=49146","nofollow":true},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true},{"id":99915,"name":"Integration","url":"https://www.academia.edu/Documents/in/Integration?f_ri=49146","nofollow":true},{"id":229390,"name":"Real Time","url":"https://www.academia.edu/Documents/in/Real_Time?f_ri=49146"},{"id":957052,"name":"Signal Reconstruction","url":"https://www.academia.edu/Documents/in/Signal_Reconstruction?f_ri=49146"},{"id":1993758,"name":"Autoregressive model","url":"https://www.academia.edu/Documents/in/Autoregressive_model?f_ri=49146"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_82517233 coauthored" data-work_id="82517233" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/82517233/Estimation_of_Core_Inflation_in_Iran_and_Its_Provinces_Using_Space_State_Model">Estimation of Core Inflation in Iran and Its Provinces Using Space State Model</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The inflation rate, which measured using consumer price index, can be separated into a combination of two persistent and temporary components. This separating is particularly important in analyzing inflation rate and policies to control... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_82517233" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The inflation rate, which measured using consumer price index, can be separated into a combination of two persistent and temporary components. This separating is particularly important in analyzing inflation rate and policies to control it. In fact, without knowing the persistent component of inflation, called core inflation, quantitative targeting of inflation may not be accurate. Core inflation as a more persistent component can be measured stripping out the transitory movements in prices. The understanding of the behavior of the national core inflation rate series needs to understand provincial core inflation since the construction of the former is based on the provincial series. So, the purpose of this paper is the estimation of provincial and national core inflation in Iran. Core inflation is unobservable variable, so it estimated using Space State Model and Kalman Filter. Results show that average core inflation in all of the provinces, as well as Iran, is less than average underlying inflation. The standard deviation of core inflation in some provinces is more than underlying inflation. While core inflation in other provinces, as well as Iran, has more standard deviation as compared to underlying inflation.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/82517233" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="2f0947826c1031b599b3fce28ce0f3c2" rel="nofollow" data-download="{"attachment_id":88201396,"asset_id":82517233,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/88201396/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="228046506" href="https://thran.academia.edu/economicmodeling">Economic Modeling Research</a><script data-card-contents-for-user="228046506" type="text/json">{"id":228046506,"first_name":"Economic Modeling","last_name":"Research","domain_name":"thran","page_name":"economicmodeling","display_name":"Economic Modeling Research","profile_url":"https://thran.academia.edu/economicmodeling?f_ri=49146","photo":"https://0.academia-photos.com/228046506/84884443/73524275/s65_economic_modeling.research.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-82517233">+1</span><div class="hidden js-additional-users-82517233"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/mehdipedram2">mehdi pedram</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-82517233'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-82517233').html(); } } new HoverPopover(popoverSettings); })();</script></li><li class="js-paper-rank-work_82517233 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="82517233"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 82517233, container: ".js-paper-rank-work_82517233", }); });</script></li><li class="js-percentile-work_82517233 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 82517233; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_82517233"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_82517233 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="82517233"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 82517233; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=82517233]").text(description); $(".js-view-count-work_82517233").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_82517233").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="82517233"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">2</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="49146" rel="nofollow" href="https://www.academia.edu/Documents/in/Kalman_Filter">Kalman Filter</a>, <script data-card-contents-for-ri="49146" type="text/json">{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="752891" rel="nofollow" href="https://www.academia.edu/Documents/in/Core_Inflation">Core Inflation</a><script data-card-contents-for-ri="752891" type="text/json">{"id":752891,"name":"Core Inflation","url":"https://www.academia.edu/Documents/in/Core_Inflation?f_ri=49146","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=82517233]'), work: {"id":82517233,"title":"Estimation of Core Inflation in Iran and Its Provinces Using Space State Model","created_at":"2022-07-03T01:04:27.726-07:00","url":"https://www.academia.edu/82517233/Estimation_of_Core_Inflation_in_Iran_and_Its_Provinces_Using_Space_State_Model?f_ri=49146","dom_id":"work_82517233","summary":"The inflation rate, which measured using consumer price index, can be separated into a combination of two persistent and temporary components. This separating is particularly important in analyzing inflation rate and policies to control it. In fact, without knowing the persistent component of inflation, called core inflation, quantitative targeting of inflation may not be accurate. Core inflation as a more persistent component can be measured stripping out the transitory movements in prices. The understanding of the behavior of the national core inflation rate series needs to understand provincial core inflation since the construction of the former is based on the provincial series. So, the purpose of this paper is the estimation of provincial and national core inflation in Iran. Core inflation is unobservable variable, so it estimated using Space State Model and Kalman Filter. Results show that average core inflation in all of the provinces, as well as Iran, is less than average underlying inflation. The standard deviation of core inflation in some provinces is more than underlying inflation. While core inflation in other provinces, as well as Iran, has more standard deviation as compared to underlying inflation.","downloadable_attachments":[{"id":88201396,"asset_id":82517233,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":228046506,"first_name":"Economic Modeling","last_name":"Research","domain_name":"thran","page_name":"economicmodeling","display_name":"Economic Modeling Research","profile_url":"https://thran.academia.edu/economicmodeling?f_ri=49146","photo":"https://0.academia-photos.com/228046506/84884443/73524275/s65_economic_modeling.research.jpg"},{"id":228373549,"first_name":"mehdi","last_name":"pedram","domain_name":"independent","page_name":"mehdipedram2","display_name":"mehdi pedram","profile_url":"https://independent.academia.edu/mehdipedram2?f_ri=49146","photo":"https://0.academia-photos.com/228373549/85168405/73812276/s65_mehdi.pedram.png"}],"research_interests":[{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true},{"id":752891,"name":"Core Inflation","url":"https://www.academia.edu/Documents/in/Core_Inflation?f_ri=49146","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_76905184" data-work_id="76905184" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/76905184/Kalman_Filters_in_Geotechnical_Monitoring_of_Ground_Subsidence_Using_Data_from_MEMS_Sensors">Kalman Filters in Geotechnical Monitoring of Ground Subsidence Using Data from MEMS Sensors</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The fast development of wireless sensor networks and MEMS make it possible to set up today real-time wireless geotechnical monitoring. To handle interferences and noises from the output data, Kalman filter can be selected as a method to... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_76905184" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The fast development of wireless sensor networks and MEMS make it possible to set up today real-time wireless geotechnical monitoring. To handle interferences and noises from the output data, Kalman filter can be selected as a method to achieve a more realistic estimate of the observations. In this paper, a one-day wireless measurement using accelerometers and inclinometers was deployed on top of a tunnel section under construction in order to monitor ground subsidence. The normal vectors of the sensors were firstly obtained with the help of rotation matrices, and then be projected to the plane of longitudinal section, by which the dip angles over time would be obtained via a trigonometric function. Finally, a centralized Kalman filter was applied to estimate the tilt angles of the sensor nodes based on the data from the embedded accelerometer and the inclinometer. Comparing the results from two sensor nodes deployed away and on the track respectively, the passing of the tunnel boring machine can be identified from unusual performances. Using this method, the ground settlement due to excavation can be measured and a real-time monitoring of ground subsidence can be realized.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/76905184" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="b832f17a6767189705e343ebfb197cbb" rel="nofollow" data-download="{"attachment_id":84478589,"asset_id":76905184,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/84478589/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="32878025" href="https://independent.academia.edu/RafigAzzam">Rafig Azzam</a><script data-card-contents-for-user="32878025" type="text/json">{"id":32878025,"first_name":"Rafig","last_name":"Azzam","domain_name":"independent","page_name":"RafigAzzam","display_name":"Rafig Azzam","profile_url":"https://independent.academia.edu/RafigAzzam?f_ri=49146","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_76905184 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="76905184"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 76905184, container: ".js-paper-rank-work_76905184", }); });</script></li><li class="js-percentile-work_76905184 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 76905184; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_76905184"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_76905184 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="76905184"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 76905184; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=76905184]").text(description); $(".js-view-count-work_76905184").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_76905184").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="76905184"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">9</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="48" rel="nofollow" href="https://www.academia.edu/Documents/in/Engineering">Engineering</a>, <script data-card-contents-for-ri="48" type="text/json">{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="422" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="524" rel="nofollow" href="https://www.academia.edu/Documents/in/Analytical_Chemistry">Analytical Chemistry</a>, <script data-card-contents-for-ri="524" type="text/json">{"id":524,"name":"Analytical Chemistry","url":"https://www.academia.edu/Documents/in/Analytical_Chemistry?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="16064" rel="nofollow" href="https://www.academia.edu/Documents/in/Subsidence">Subsidence</a><script data-card-contents-for-ri="16064" type="text/json">{"id":16064,"name":"Subsidence","url":"https://www.academia.edu/Documents/in/Subsidence?f_ri=49146","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=76905184]'), work: {"id":76905184,"title":"Kalman Filters in Geotechnical Monitoring of Ground Subsidence Using Data from MEMS Sensors","created_at":"2022-04-18T23:27:22.676-07:00","url":"https://www.academia.edu/76905184/Kalman_Filters_in_Geotechnical_Monitoring_of_Ground_Subsidence_Using_Data_from_MEMS_Sensors?f_ri=49146","dom_id":"work_76905184","summary":"The fast development of wireless sensor networks and MEMS make it possible to set up today real-time wireless geotechnical monitoring. To handle interferences and noises from the output data, Kalman filter can be selected as a method to achieve a more realistic estimate of the observations. In this paper, a one-day wireless measurement using accelerometers and inclinometers was deployed on top of a tunnel section under construction in order to monitor ground subsidence. The normal vectors of the sensors were firstly obtained with the help of rotation matrices, and then be projected to the plane of longitudinal section, by which the dip angles over time would be obtained via a trigonometric function. Finally, a centralized Kalman filter was applied to estimate the tilt angles of the sensor nodes based on the data from the embedded accelerometer and the inclinometer. Comparing the results from two sensor nodes deployed away and on the track respectively, the passing of the tunnel boring machine can be identified from unusual performances. Using this method, the ground settlement due to excavation can be measured and a real-time monitoring of ground subsidence can be realized.","downloadable_attachments":[{"id":84478589,"asset_id":76905184,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":32878025,"first_name":"Rafig","last_name":"Azzam","domain_name":"independent","page_name":"RafigAzzam","display_name":"Rafig Azzam","profile_url":"https://independent.academia.edu/RafigAzzam?f_ri=49146","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering?f_ri=49146","nofollow":true},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=49146","nofollow":true},{"id":524,"name":"Analytical Chemistry","url":"https://www.academia.edu/Documents/in/Analytical_Chemistry?f_ri=49146","nofollow":true},{"id":16064,"name":"Subsidence","url":"https://www.academia.edu/Documents/in/Subsidence?f_ri=49146","nofollow":true},{"id":26327,"name":"Medicine","url":"https://www.academia.edu/Documents/in/Medicine?f_ri=49146"},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146"},{"id":55405,"name":"Sensors","url":"https://www.academia.edu/Documents/in/Sensors?f_ri=49146"},{"id":479186,"name":"Accelerometer","url":"https://www.academia.edu/Documents/in/Accelerometer?f_ri=49146"},{"id":1237788,"name":"Electrical And Electronic Engineering","url":"https://www.academia.edu/Documents/in/Electrical_And_Electronic_Engineering?f_ri=49146"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_66552963" data-work_id="66552963" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/66552963/Multicell_converters_active_control_and_observation_of_flying_capacitor_voltages">Multicell converters: active control and observation of flying-capacitor voltages</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The multicell converters introduced more than ten years ago make it possible to distribute the voltage constraints among series-connected switches and to improve the output waveforms (increased number of levels and apparent frequency).... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_66552963" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The multicell converters introduced more than ten years ago make it possible to distribute the voltage constraints among series-connected switches and to improve the output waveforms (increased number of levels and apparent frequency). The balance of the constraints requires an appropriate distribution of the flying voltages. This paper presents some solutions for the active control of the voltages across the flying capacitors in the presence of rapid variation of the input voltage. The latter part of this paper is dedicated to the observation of these voltages using an original modeling of the converter.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/66552963" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="a6ccec00b6dce35b27707ac147d4a642" rel="nofollow" data-download="{"attachment_id":77699816,"asset_id":66552963,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/77699816/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="52394873" href="https://univ-tlse.academia.edu/PascalMaussion">Pascal Maussion</a><script data-card-contents-for-user="52394873" type="text/json">{"id":52394873,"first_name":"Pascal","last_name":"Maussion","domain_name":"univ-tlse","page_name":"PascalMaussion","display_name":"Pascal Maussion","profile_url":"https://univ-tlse.academia.edu/PascalMaussion?f_ri=49146","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_66552963 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="66552963"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 66552963, container: ".js-paper-rank-work_66552963", }); });</script></li><li class="js-percentile-work_66552963 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 66552963; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_66552963"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_66552963 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="66552963"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 66552963; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=66552963]").text(description); $(".js-view-count-work_66552963").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_66552963").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="66552963"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">7</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="48" rel="nofollow" href="https://www.academia.edu/Documents/in/Engineering">Engineering</a>, <script data-card-contents-for-ri="48" type="text/json">{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="422" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2696" rel="nofollow" href="https://www.academia.edu/Documents/in/Power_Electronics">Power Electronics</a>, <script data-card-contents-for-ri="2696" type="text/json">{"id":2696,"name":"Power Electronics","url":"https://www.academia.edu/Documents/in/Power_Electronics?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="49146" rel="nofollow" href="https://www.academia.edu/Documents/in/Kalman_Filter">Kalman Filter</a><script data-card-contents-for-ri="49146" type="text/json">{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=66552963]'), work: {"id":66552963,"title":"Multicell converters: active control and observation of flying-capacitor voltages","created_at":"2021-12-30T05:22:44.883-08:00","url":"https://www.academia.edu/66552963/Multicell_converters_active_control_and_observation_of_flying_capacitor_voltages?f_ri=49146","dom_id":"work_66552963","summary":"The multicell converters introduced more than ten years ago make it possible to distribute the voltage constraints among series-connected switches and to improve the output waveforms (increased number of levels and apparent frequency). The balance of the constraints requires an appropriate distribution of the flying voltages. This paper presents some solutions for the active control of the voltages across the flying capacitors in the presence of rapid variation of the input voltage. The latter part of this paper is dedicated to the observation of these voltages using an original modeling of the converter.","downloadable_attachments":[{"id":77699816,"asset_id":66552963,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":52394873,"first_name":"Pascal","last_name":"Maussion","domain_name":"univ-tlse","page_name":"PascalMaussion","display_name":"Pascal Maussion","profile_url":"https://univ-tlse.academia.edu/PascalMaussion?f_ri=49146","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering?f_ri=49146","nofollow":true},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=49146","nofollow":true},{"id":2696,"name":"Power Electronics","url":"https://www.academia.edu/Documents/in/Power_Electronics?f_ri=49146","nofollow":true},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true},{"id":59530,"name":"Industrial","url":"https://www.academia.edu/Documents/in/Industrial?f_ri=49146"},{"id":137847,"name":"Active Control","url":"https://www.academia.edu/Documents/in/Active_Control?f_ri=49146"},{"id":506858,"name":"Nonlinear system","url":"https://www.academia.edu/Documents/in/Nonlinear_system?f_ri=49146"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_48282745" data-work_id="48282745" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/48282745/Heading_aided_odometry_and_range_data_integration_for_positioning_of_autonomous_mining_vehicles">Heading-aided odometry and range-data integration for positioning of autonomous mining vehicles</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">An enhanced odometry technique based on the heading sensor called "clino-gyro " that fuses the data from a fiber optic gyro and a simple inclinometer is proposed. I n the proposed scheme, inclinometer data are used t o compensate f o r... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_48282745" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">An enhanced odometry technique based on the heading sensor called "clino-gyro " that fuses the data from a fiber optic gyro and a simple inclinometer is proposed. I n the proposed scheme, inclinometer data are used t o compensate f o r the gyro drift due t o roll/pitch perturbation of the vehicle while moving o n the rough terrain. Providing independent information about the rotation (yaw) of the vehicle, clino-gyro is used t o correct differential odometry adversely affected by the wheel slippage. Position estimation using this technique can be improved significantly, however for the long t e r m applications it still suffers from the drifts of the gyro and translational components of wheel skidding. Fusing this enhanced odometry with the data from environmental sensors (sonars, laser range finder) through Kalmanfilter-type procedure a reliable positioning can be obtained. This technique has been implemented on-board of an experimental skid-steered vehicle. Obtained precision is suficient f o r navigation in underground mining drifts. For open-pit mining applications further improvements can be obtained by fusing proposed localization algorithm with GPS data.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/48282745" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="5b35dcb7fb3999ae04e684f77a5187ca" rel="nofollow" data-download="{"attachment_id":66977344,"asset_id":48282745,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/66977344/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="7309310" href="https://etsmtl.academia.edu/VladimirPolotski">Vladimir Polotski</a><script data-card-contents-for-user="7309310" type="text/json">{"id":7309310,"first_name":"Vladimir","last_name":"Polotski","domain_name":"etsmtl","page_name":"VladimirPolotski","display_name":"Vladimir Polotski","profile_url":"https://etsmtl.academia.edu/VladimirPolotski?f_ri=49146","photo":"https://0.academia-photos.com/7309310/14653137/15493361/s65_vladimir.polotski.jpg"}</script></span></span></li><li class="js-paper-rank-work_48282745 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="48282745"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 48282745, container: ".js-paper-rank-work_48282745", }); });</script></li><li class="js-percentile-work_48282745 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 48282745; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_48282745"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_48282745 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="48282745"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 48282745; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=48282745]").text(description); $(".js-view-count-work_48282745").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_48282745").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="48282745"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">14</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="18496" rel="nofollow" href="https://www.academia.edu/Documents/in/Mining">Mining</a>, <script data-card-contents-for-ri="18496" type="text/json">{"id":18496,"name":"Mining","url":"https://www.academia.edu/Documents/in/Mining?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="25384" rel="nofollow" href="https://www.academia.edu/Documents/in/Global_Positioning_System">Global Positioning System</a>, <script data-card-contents-for-ri="25384" type="text/json">{"id":25384,"name":"Global Positioning System","url":"https://www.academia.edu/Documents/in/Global_Positioning_System?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="30791" rel="nofollow" href="https://www.academia.edu/Documents/in/Path_planning">Path planning</a>, <script data-card-contents-for-ri="30791" type="text/json">{"id":30791,"name":"Path planning","url":"https://www.academia.edu/Documents/in/Path_planning?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="49146" rel="nofollow" href="https://www.academia.edu/Documents/in/Kalman_Filter">Kalman Filter</a><script data-card-contents-for-ri="49146" type="text/json">{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=48282745]'), work: {"id":48282745,"title":"Heading-aided odometry and range-data integration for positioning of autonomous mining vehicles","created_at":"2021-05-04T10:27:53.570-07:00","url":"https://www.academia.edu/48282745/Heading_aided_odometry_and_range_data_integration_for_positioning_of_autonomous_mining_vehicles?f_ri=49146","dom_id":"work_48282745","summary":"An enhanced odometry technique based on the heading sensor called \"clino-gyro \" that fuses the data from a fiber optic gyro and a simple inclinometer is proposed. I n the proposed scheme, inclinometer data are used t o compensate f o r the gyro drift due t o roll/pitch perturbation of the vehicle while moving o n the rough terrain. Providing independent information about the rotation (yaw) of the vehicle, clino-gyro is used t o correct differential odometry adversely affected by the wheel slippage. Position estimation using this technique can be improved significantly, however for the long t e r m applications it still suffers from the drifts of the gyro and translational components of wheel skidding. Fusing this enhanced odometry with the data from environmental sensors (sonars, laser range finder) through Kalmanfilter-type procedure a reliable positioning can be obtained. This technique has been implemented on-board of an experimental skid-steered vehicle. Obtained precision is suficient f o r navigation in underground mining drifts. For open-pit mining applications further improvements can be obtained by fusing proposed localization algorithm with GPS data.","downloadable_attachments":[{"id":66977344,"asset_id":48282745,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":7309310,"first_name":"Vladimir","last_name":"Polotski","domain_name":"etsmtl","page_name":"VladimirPolotski","display_name":"Vladimir Polotski","profile_url":"https://etsmtl.academia.edu/VladimirPolotski?f_ri=49146","photo":"https://0.academia-photos.com/7309310/14653137/15493361/s65_vladimir.polotski.jpg"}],"research_interests":[{"id":18496,"name":"Mining","url":"https://www.academia.edu/Documents/in/Mining?f_ri=49146","nofollow":true},{"id":25384,"name":"Global Positioning System","url":"https://www.academia.edu/Documents/in/Global_Positioning_System?f_ri=49146","nofollow":true},{"id":30791,"name":"Path planning","url":"https://www.academia.edu/Documents/in/Path_planning?f_ri=49146","nofollow":true},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true},{"id":59695,"name":"Navigation","url":"https://www.academia.edu/Documents/in/Navigation?f_ri=49146"},{"id":65971,"name":"Control Applications","url":"https://www.academia.edu/Documents/in/Control_Applications?f_ri=49146"},{"id":66823,"name":"Mobile Robots","url":"https://www.academia.edu/Documents/in/Mobile_Robots?f_ri=49146"},{"id":143115,"name":"Sensor Fusion","url":"https://www.academia.edu/Documents/in/Sensor_Fusion?f_ri=49146"},{"id":580733,"name":"Fiber Optic","url":"https://www.academia.edu/Documents/in/Fiber_Optic?f_ri=49146"},{"id":619635,"name":"Path Planning","url":"https://www.academia.edu/Documents/in/Path_Planning-1?f_ri=49146"},{"id":877397,"name":"Optical Fibers","url":"https://www.academia.edu/Documents/in/Optical_Fibers?f_ri=49146"},{"id":1222660,"name":"Inclinometer","url":"https://www.academia.edu/Documents/in/Inclinometer?f_ri=49146"},{"id":1660912,"name":"Yaw","url":"https://www.academia.edu/Documents/in/Yaw?f_ri=49146"},{"id":2003373,"name":"Odometry","url":"https://www.academia.edu/Documents/in/Odometry?f_ri=49146"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_28173264" data-work_id="28173264" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/28173264/Restricted_Kalman_filter_applied_to_dynamic_style_analysis_of_actuarial_funds">Restricted Kalman filter applied to dynamic style analysis of actuarial funds</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We use dynamic style analysis to unveil the strategies followed by Brazilian actuarial funds from January 2004 to August 2008 and investigate whether managers' decisions were compatible with the intention of protecting the investor... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_28173264" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We use dynamic style analysis to unveil the strategies followed by Brazilian actuarial funds from January 2004 to August 2008 and investigate whether managers' decisions were compatible with the intention of protecting the investor against the negative effects of inflation. The main goal of this paper is to show that this methodology is suitable for allowing insurance companies to increase their capacity to monitor the behavior of portfolios and to control the amount of risk they assume. The basic steps of the method are to build and/or choose market indexes capable of characterizing the returns of the main securities available and to apply restricted linear state space models estimated with a Kalman filter with exact initialization. The main conclusions of this paper are the following: (1) the use of exact initialization of the Kalman filter promotes numerical stability; (2) there is no need to consider the entire set of market indicators because a subset containing only three indexes spans the relevant space of investment opportunities; and (3) the actuarial funds' resources were primarily invested in inflation-indexed bonds, but fund managers also left room to adjust their exposure to other assets not directly related to the objective of providing protection against inflation. a Inflation-indexed assets or liabilities are those with returns protected against inflation, which means that their purchasing power does not decrease over time. b Loosely speaking, an exposure (or an allocation) is the proportion of total investment resources that is directed to a specific market or asset class. For instance, equity funds invest the bulk of their resources in buying shares; therefore, they have a higher exposure to some indexes that follow the returns provided by this kind of asset (such as the Dow Jones or the S&P 500 indexes in the US financial market). From a statistical point of view, they are nothing more than the coefficients of the time series regression models considered within. In our framework, a time-varying exposure is a random coefficient of a time series regression model. Appl. Stochastic Models Bus. Ind. 2011 d The acronym WN stands for 'White Noise', a term used for (possibly multivariate) second-order stochastic processes with uncorrelated components with zero mean and common variance. Because the covariance matrices H t and Q t can be time-varying, the use of the term here is perhaps inappropriate.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/28173264" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="4eff4eaf52d4b5419ff7616bb0e6e8b5" rel="nofollow" data-download="{"attachment_id":48486592,"asset_id":28173264,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/48486592/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="52809341" href="https://iduff.academia.edu/LucianoVereda">Luciano Vereda</a><script data-card-contents-for-user="52809341" type="text/json">{"id":52809341,"first_name":"Luciano","last_name":"Vereda","domain_name":"iduff","page_name":"LucianoVereda","display_name":"Luciano Vereda","profile_url":"https://iduff.academia.edu/LucianoVereda?f_ri=49146","photo":"https://0.academia-photos.com/52809341/13949277/28355694/s65_luciano.vereda.jpg"}</script></span></span></li><li class="js-paper-rank-work_28173264 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="28173264"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 28173264, container: ".js-paper-rank-work_28173264", }); });</script></li><li class="js-percentile-work_28173264 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 28173264; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_28173264"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_28173264 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="28173264"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 28173264; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=28173264]").text(description); $(".js-view-count-work_28173264").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_28173264").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="28173264"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">3</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="305" rel="nofollow" href="https://www.academia.edu/Documents/in/Applied_Mathematics">Applied Mathematics</a>, <script data-card-contents-for-ri="305" type="text/json">{"id":305,"name":"Applied Mathematics","url":"https://www.academia.edu/Documents/in/Applied_Mathematics?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="892" rel="nofollow" href="https://www.academia.edu/Documents/in/Statistics">Statistics</a>, <script data-card-contents-for-ri="892" type="text/json">{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="49146" rel="nofollow" href="https://www.academia.edu/Documents/in/Kalman_Filter">Kalman Filter</a><script data-card-contents-for-ri="49146" type="text/json">{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=28173264]'), work: {"id":28173264,"title":"Restricted Kalman filter applied to dynamic style analysis of actuarial funds","created_at":"2016-09-01T03:55:15.291-07:00","url":"https://www.academia.edu/28173264/Restricted_Kalman_filter_applied_to_dynamic_style_analysis_of_actuarial_funds?f_ri=49146","dom_id":"work_28173264","summary":"We use dynamic style analysis to unveil the strategies followed by Brazilian actuarial funds from January 2004 to August 2008 and investigate whether managers' decisions were compatible with the intention of protecting the investor against the negative effects of inflation. The main goal of this paper is to show that this methodology is suitable for allowing insurance companies to increase their capacity to monitor the behavior of portfolios and to control the amount of risk they assume. The basic steps of the method are to build and/or choose market indexes capable of characterizing the returns of the main securities available and to apply restricted linear state space models estimated with a Kalman filter with exact initialization. The main conclusions of this paper are the following: (1) the use of exact initialization of the Kalman filter promotes numerical stability; (2) there is no need to consider the entire set of market indicators because a subset containing only three indexes spans the relevant space of investment opportunities; and (3) the actuarial funds' resources were primarily invested in inflation-indexed bonds, but fund managers also left room to adjust their exposure to other assets not directly related to the objective of providing protection against inflation. a Inflation-indexed assets or liabilities are those with returns protected against inflation, which means that their purchasing power does not decrease over time. b Loosely speaking, an exposure (or an allocation) is the proportion of total investment resources that is directed to a specific market or asset class. For instance, equity funds invest the bulk of their resources in buying shares; therefore, they have a higher exposure to some indexes that follow the returns provided by this kind of asset (such as the Dow Jones or the S\u0026P 500 indexes in the US financial market). From a statistical point of view, they are nothing more than the coefficients of the time series regression models considered within. In our framework, a time-varying exposure is a random coefficient of a time series regression model. Appl. Stochastic Models Bus. Ind. 2011 d The acronym WN stands for 'White Noise', a term used for (possibly multivariate) second-order stochastic processes with uncorrelated components with zero mean and common variance. Because the covariance matrices H t and Q t can be time-varying, the use of the term here is perhaps inappropriate.","downloadable_attachments":[{"id":48486592,"asset_id":28173264,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":52809341,"first_name":"Luciano","last_name":"Vereda","domain_name":"iduff","page_name":"LucianoVereda","display_name":"Luciano Vereda","profile_url":"https://iduff.academia.edu/LucianoVereda?f_ri=49146","photo":"https://0.academia-photos.com/52809341/13949277/28355694/s65_luciano.vereda.jpg"}],"research_interests":[{"id":305,"name":"Applied Mathematics","url":"https://www.academia.edu/Documents/in/Applied_Mathematics?f_ri=49146","nofollow":true},{"id":892,"name":"Statistics","url":"https://www.academia.edu/Documents/in/Statistics?f_ri=49146","nofollow":true},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_17253192" data-work_id="17253192" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/17253192/Scribbles_to_vectors">Scribbles to vectors</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper describes the work carried out on off-line paper based scribbles such that they can be incorporated into a sketch-based interface without forcing designers to change their natural drawing habits. In this work, the scribbled... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_17253192" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper describes the work carried out on off-line paper based scribbles such that they can be incorporated into a sketch-based interface without forcing designers to change their natural drawing habits. In this work, the scribbled drawings are converted into a vectorial format which can be recognized by a CAD system. This is achieved by using pattern analysis techniques, namely the Gabor filter to simplify the scribbled drawing. Vector line are then extracted from the resulting drawing by means of Kalman filtering.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/17253192" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="b1d0d2ebc7ef145dca7d49f3828dd40c" rel="nofollow" data-download="{"attachment_id":41903562,"asset_id":17253192,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/41903562/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="25066735" href="https://malta.academia.edu/SFabri">Simon G . Fabri</a><script data-card-contents-for-user="25066735" type="text/json">{"id":25066735,"first_name":"Simon","last_name":"Fabri","domain_name":"malta","page_name":"SFabri","display_name":"Simon G . Fabri","profile_url":"https://malta.academia.edu/SFabri?f_ri=49146","photo":"https://0.academia-photos.com/25066735/9485436/21083180/s65_simon.fabri.jpg"}</script></span></span></li><li class="js-paper-rank-work_17253192 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="17253192"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 17253192, container: ".js-paper-rank-work_17253192", }); });</script></li><li class="js-percentile-work_17253192 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 17253192; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_17253192"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_17253192 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="17253192"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 17253192; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=17253192]").text(description); $(".js-view-count-work_17253192").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_17253192").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="17253192"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">2</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="49146" rel="nofollow" href="https://www.academia.edu/Documents/in/Kalman_Filter">Kalman Filter</a>, <script data-card-contents-for-ri="49146" type="text/json">{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1569002" rel="nofollow" href="https://www.academia.edu/Documents/in/Gabor_Filter">Gabor Filter</a><script data-card-contents-for-ri="1569002" type="text/json">{"id":1569002,"name":"Gabor Filter","url":"https://www.academia.edu/Documents/in/Gabor_Filter?f_ri=49146","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=17253192]'), work: {"id":17253192,"title":"Scribbles to vectors","created_at":"2015-10-24T10:46:55.823-07:00","url":"https://www.academia.edu/17253192/Scribbles_to_vectors?f_ri=49146","dom_id":"work_17253192","summary":"This paper describes the work carried out on off-line paper based scribbles such that they can be incorporated into a sketch-based interface without forcing designers to change their natural drawing habits. In this work, the scribbled drawings are converted into a vectorial format which can be recognized by a CAD system. This is achieved by using pattern analysis techniques, namely the Gabor filter to simplify the scribbled drawing. Vector line are then extracted from the resulting drawing by means of Kalman filtering.","downloadable_attachments":[{"id":41903562,"asset_id":17253192,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":25066735,"first_name":"Simon","last_name":"Fabri","domain_name":"malta","page_name":"SFabri","display_name":"Simon G . Fabri","profile_url":"https://malta.academia.edu/SFabri?f_ri=49146","photo":"https://0.academia-photos.com/25066735/9485436/21083180/s65_simon.fabri.jpg"}],"research_interests":[{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true},{"id":1569002,"name":"Gabor Filter","url":"https://www.academia.edu/Documents/in/Gabor_Filter?f_ri=49146","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_52121280" data-work_id="52121280" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/52121280/Estimation_de_l_%C3%A9tat_pour_la_surveillance_des_syst%C3%A8mes_de_grandes_dimensions_Application_aux_r%C3%A9seaux_%C3%A9lectriques">Estimation de l’état pour la surveillance des systèmes de grandes dimensions. Application aux réseaux électriques</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This work deals with the state estimation and diagnosis of nonlinear systems with application to power systems. Dynamic modeling is performed using an index 1 property and decoupling techniques. New methods of state estimation, based on... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_52121280" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This work deals with the state estimation and diagnosis of nonlinear systems with application to power systems. Dynamic modeling is performed using an index 1 property and decoupling techniques. New methods of state estimation, based on Extended Kalman Filter including a sliding window of measurements, are proposed to improve the robustness and accuracy. A new convergence study based on Lyapunov function and conditioning of the observability matrix is proposed to ensure the convergence of the observers. A combination of extended Kalman ﬁlter with moving horizon and the version with unknown input is considered to ensure the monitoring task. Performances of the proposed approaches were evaluated by numerical simulations of IEEE power system test.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/52121280" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="35b0f4a384a4ad6a492343de7cc48cb8" rel="nofollow" data-download="{"attachment_id":69575376,"asset_id":52121280,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/69575376/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="141977405" href="https://univgb.academia.edu/AssemTHABET">Assem THABET</a><script data-card-contents-for-user="141977405" type="text/json">{"id":141977405,"first_name":"Assem","last_name":"THABET","domain_name":"univgb","page_name":"AssemTHABET","display_name":"Assem THABET","profile_url":"https://univgb.academia.edu/AssemTHABET?f_ri=49146","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_52121280 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="52121280"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 52121280, container: ".js-paper-rank-work_52121280", }); });</script></li><li class="js-percentile-work_52121280 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 52121280; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_52121280"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_52121280 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="52121280"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 52121280; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=52121280]").text(description); $(".js-view-count-work_52121280").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_52121280").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="52121280"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="5748" rel="nofollow" href="https://www.academia.edu/Documents/in/Power_System">Power System</a>, <script data-card-contents-for-ri="5748" type="text/json">{"id":5748,"name":"Power System","url":"https://www.academia.edu/Documents/in/Power_System?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="15124" rel="nofollow" href="https://www.academia.edu/Documents/in/Convergence">Convergence</a>, <script data-card-contents-for-ri="15124" type="text/json">{"id":15124,"name":"Convergence","url":"https://www.academia.edu/Documents/in/Convergence?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="49146" rel="nofollow" href="https://www.academia.edu/Documents/in/Kalman_Filter">Kalman Filter</a>, <script data-card-contents-for-ri="49146" type="text/json">{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="73017" rel="nofollow" href="https://www.academia.edu/Documents/in/Nonlinear_Systems">Nonlinear Systems</a><script data-card-contents-for-ri="73017" type="text/json">{"id":73017,"name":"Nonlinear Systems","url":"https://www.academia.edu/Documents/in/Nonlinear_Systems?f_ri=49146","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=52121280]'), work: {"id":52121280,"title":"Estimation de l’état pour la surveillance des systèmes de grandes dimensions. Application aux réseaux électriques","created_at":"2021-09-13T04:02:32.465-07:00","url":"https://www.academia.edu/52121280/Estimation_de_l_%C3%A9tat_pour_la_surveillance_des_syst%C3%A8mes_de_grandes_dimensions_Application_aux_r%C3%A9seaux_%C3%A9lectriques?f_ri=49146","dom_id":"work_52121280","summary":"This work deals with the state estimation and diagnosis of nonlinear systems with application to power systems. Dynamic modeling is performed using an index 1 property and decoupling techniques. New methods of state estimation, based on Extended Kalman Filter including a sliding window of measurements, are proposed to improve the robustness and accuracy. A new convergence study based on Lyapunov function and conditioning of the observability matrix is proposed to ensure the convergence of the observers. A combination of extended Kalman ﬁlter with moving horizon and the version with unknown input is considered to ensure the monitoring task. Performances of the proposed approaches were evaluated by numerical simulations of IEEE power system test.","downloadable_attachments":[{"id":69575376,"asset_id":52121280,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":141977405,"first_name":"Assem","last_name":"THABET","domain_name":"univgb","page_name":"AssemTHABET","display_name":"Assem THABET","profile_url":"https://univgb.academia.edu/AssemTHABET?f_ri=49146","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":5748,"name":"Power System","url":"https://www.academia.edu/Documents/in/Power_System?f_ri=49146","nofollow":true},{"id":15124,"name":"Convergence","url":"https://www.academia.edu/Documents/in/Convergence?f_ri=49146","nofollow":true},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true},{"id":73017,"name":"Nonlinear Systems","url":"https://www.academia.edu/Documents/in/Nonlinear_Systems?f_ri=49146","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_30647515" data-work_id="30647515" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/30647515/Moving_Vehicle_Detection_and_Tracking_Using_Modified_Mean_Shift_Method_and_Kalman_Filter_and_Research">Moving Vehicle Detection and Tracking Using Modified Mean Shift Method and Kalman Filter and Research</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">— The goal of object tracking is segmenting a region of interest from a video scene and keeping track of its motion, positioning and occlusion. The object detection and object classification are preceding steps for tracking an object in... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_30647515" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">— The goal of object tracking is segmenting a region of interest from a video scene and keeping track of its motion, positioning and occlusion. The object detection and object classification are preceding steps for tracking an object in sequence of images. Mean shift algorithm is recently widely used in tracking clustering, etc. First phase of the system is to detect the moving objects in the video. Second phase of the system will track the detected object. In this paper, detection of the moving object has been done using simple background subtraction and tracking of single moving object has been done using modified mean shift method and Kalman filter. Further result of both algorithm is compared on basis on time and accuracy. Index Terms— object tracking; kalman filter; mean shift method..</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/30647515" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="2565f7a61968f08ca44cbaa105bca0fd" rel="nofollow" data-download="{"attachment_id":51091696,"asset_id":30647515,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/51091696/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="33137887" href="https://ijntr.academia.edu/NextgenResearchPublication">Nextgen Research Publication</a><script data-card-contents-for-user="33137887" type="text/json">{"id":33137887,"first_name":"Nextgen Research","last_name":"Publication","domain_name":"ijntr","page_name":"NextgenResearchPublication","display_name":"Nextgen Research Publication","profile_url":"https://ijntr.academia.edu/NextgenResearchPublication?f_ri=49146","photo":"https://0.academia-photos.com/33137887/9830342/10955353/s65_nextgen_research.publication.png"}</script></span></span></li><li class="js-paper-rank-work_30647515 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="30647515"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 30647515, container: ".js-paper-rank-work_30647515", }); 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$(".js-view-count[data-work-id=30647515]").text(description); $(".js-view-count-work_30647515").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_30647515").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="30647515"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="49146" rel="nofollow" href="https://www.academia.edu/Documents/in/Kalman_Filter">Kalman Filter</a>, <script data-card-contents-for-ri="49146" type="text/json">{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="67380" rel="nofollow" href="https://www.academia.edu/Documents/in/Kalman_Filtering">Kalman Filtering</a>, <script data-card-contents-for-ri="67380" type="text/json">{"id":67380,"name":"Kalman Filtering","url":"https://www.academia.edu/Documents/in/Kalman_Filtering?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="509558" rel="nofollow" href="https://www.academia.edu/Documents/in/Moving_Vehicle_Detection">Moving Vehicle Detection</a>, <script data-card-contents-for-ri="509558" type="text/json">{"id":509558,"name":"Moving Vehicle Detection","url":"https://www.academia.edu/Documents/in/Moving_Vehicle_Detection?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="872410" rel="nofollow" href="https://www.academia.edu/Documents/in/Extended_Kalman_Filter">Extended Kalman Filter</a><script data-card-contents-for-ri="872410" type="text/json">{"id":872410,"name":"Extended Kalman Filter","url":"https://www.academia.edu/Documents/in/Extended_Kalman_Filter?f_ri=49146","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=30647515]'), work: {"id":30647515,"title":"Moving Vehicle Detection and Tracking Using Modified Mean Shift Method and Kalman Filter and Research","created_at":"2016-12-28T06:19:54.900-08:00","url":"https://www.academia.edu/30647515/Moving_Vehicle_Detection_and_Tracking_Using_Modified_Mean_Shift_Method_and_Kalman_Filter_and_Research?f_ri=49146","dom_id":"work_30647515","summary":"— The goal of object tracking is segmenting a region of interest from a video scene and keeping track of its motion, positioning and occlusion. The object detection and object classification are preceding steps for tracking an object in sequence of images. Mean shift algorithm is recently widely used in tracking clustering, etc. First phase of the system is to detect the moving objects in the video. Second phase of the system will track the detected object. In this paper, detection of the moving object has been done using simple background subtraction and tracking of single moving object has been done using modified mean shift method and Kalman filter. Further result of both algorithm is compared on basis on time and accuracy. Index Terms— object tracking; kalman filter; mean shift method..","downloadable_attachments":[{"id":51091696,"asset_id":30647515,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":33137887,"first_name":"Nextgen Research","last_name":"Publication","domain_name":"ijntr","page_name":"NextgenResearchPublication","display_name":"Nextgen Research Publication","profile_url":"https://ijntr.academia.edu/NextgenResearchPublication?f_ri=49146","photo":"https://0.academia-photos.com/33137887/9830342/10955353/s65_nextgen_research.publication.png"}],"research_interests":[{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true},{"id":67380,"name":"Kalman Filtering","url":"https://www.academia.edu/Documents/in/Kalman_Filtering?f_ri=49146","nofollow":true},{"id":509558,"name":"Moving Vehicle Detection","url":"https://www.academia.edu/Documents/in/Moving_Vehicle_Detection?f_ri=49146","nofollow":true},{"id":872410,"name":"Extended Kalman Filter","url":"https://www.academia.edu/Documents/in/Extended_Kalman_Filter?f_ri=49146","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_26611081" data-work_id="26611081" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/26611081/Using_short_term_measures_of_behaviour_to_estimate_long_term_fitness_of_southern_elephant_seals">Using short-term measures of behaviour to estimate long-term fitness of southern elephant seals</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Environmental changes (a type of disturbance) are altering the habitat of southern elephant seals Mirounga leonina, an apex marine predator in the Southern Ocean. As a result, individuals may shift their behaviour, spending more time in... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_26611081" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Environmental changes (a type of disturbance) are altering the habitat of southern elephant seals Mirounga leonina, an apex marine predator in the Southern Ocean. As a result, individuals may shift their behaviour, spending more time in transit and less time foraging. The effects of these sublethal changes in behaviour can accumulate, indirectly impacting lifetime fitness through changes in individual survival and reproduction. If a sufficient proportion of the population is affected, the probability of population persistence will be altered. We used data from long-term telemetry studies of female elephant seals at Macquarie Island, Australia, to model the effect of behaviour on the seals' health (i.e. all internal factors that affect homeostasis). Through simulation, we investigated the effect of increasing periods of behavioural shifts, quantifying how the exclusion of maternal southern elephant seals from foraging habitat may affect their health, offspring survival, individual fitness and population growth rate. A long period of altered behaviour (> 50% of an average foraging trip at sea) in 1 yr resulted in a small (0.4%) decline in population size the following year. However, a persistent disruption (e.g. 30 yr), caused for example by the long-term effects of climate change, could result in a 0.3% decline in individual fitness and a 10% decline in population size. Our approach to estimating the long-term population effects of short-term changes in individual behaviour can be generalised to include physiological effects and other causes of behavioural and physiological disruption, such as anthropogenic disturbance, for any species.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/26611081" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="a2e5541a6c1697d1e14ae3bef9395122" rel="nofollow" data-download="{"attachment_id":46897168,"asset_id":26611081,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/46897168/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="27243283" href="https://imos.academia.edu/CliveMcMahon">Clive McMahon</a><script data-card-contents-for-user="27243283" type="text/json">{"id":27243283,"first_name":"Clive","last_name":"McMahon","domain_name":"imos","page_name":"CliveMcMahon","display_name":"Clive McMahon","profile_url":"https://imos.academia.edu/CliveMcMahon?f_ri=49146","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_26611081 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="26611081"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 26611081, container: ".js-paper-rank-work_26611081", }); 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$(".js-view-count[data-work-id=26611081]").text(description); $(".js-view-count-work_26611081").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_26611081").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="26611081"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="173" rel="nofollow" href="https://www.academia.edu/Documents/in/Zoology">Zoology</a>, <script data-card-contents-for-ri="173" type="text/json">{"id":173,"name":"Zoology","url":"https://www.academia.edu/Documents/in/Zoology?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="9846" rel="nofollow" href="https://www.academia.edu/Documents/in/Ecology">Ecology</a>, <script data-card-contents-for-ri="9846" type="text/json">{"id":9846,"name":"Ecology","url":"https://www.academia.edu/Documents/in/Ecology?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="49146" rel="nofollow" href="https://www.academia.edu/Documents/in/Kalman_Filter">Kalman Filter</a>, <script data-card-contents-for-ri="49146" type="text/json">{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="407864" rel="nofollow" href="https://www.academia.edu/Documents/in/Predators">Predators</a><script data-card-contents-for-ri="407864" type="text/json">{"id":407864,"name":"Predators","url":"https://www.academia.edu/Documents/in/Predators?f_ri=49146","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=26611081]'), work: {"id":26611081,"title":"Using short-term measures of behaviour to estimate long-term fitness of southern elephant seals","created_at":"2016-06-29T14:44:23.223-07:00","url":"https://www.academia.edu/26611081/Using_short_term_measures_of_behaviour_to_estimate_long_term_fitness_of_southern_elephant_seals?f_ri=49146","dom_id":"work_26611081","summary":"Environmental changes (a type of disturbance) are altering the habitat of southern elephant seals Mirounga leonina, an apex marine predator in the Southern Ocean. As a result, individuals may shift their behaviour, spending more time in transit and less time foraging. The effects of these sublethal changes in behaviour can accumulate, indirectly impacting lifetime fitness through changes in individual survival and reproduction. If a sufficient proportion of the population is affected, the probability of population persistence will be altered. We used data from long-term telemetry studies of female elephant seals at Macquarie Island, Australia, to model the effect of behaviour on the seals' health (i.e. all internal factors that affect homeostasis). Through simulation, we investigated the effect of increasing periods of behavioural shifts, quantifying how the exclusion of maternal southern elephant seals from foraging habitat may affect their health, offspring survival, individual fitness and population growth rate. A long period of altered behaviour (\u003e 50% of an average foraging trip at sea) in 1 yr resulted in a small (0.4%) decline in population size the following year. However, a persistent disruption (e.g. 30 yr), caused for example by the long-term effects of climate change, could result in a 0.3% decline in individual fitness and a 10% decline in population size. Our approach to estimating the long-term population effects of short-term changes in individual behaviour can be generalised to include physiological effects and other causes of behavioural and physiological disruption, such as anthropogenic disturbance, for any species.","downloadable_attachments":[{"id":46897168,"asset_id":26611081,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":27243283,"first_name":"Clive","last_name":"McMahon","domain_name":"imos","page_name":"CliveMcMahon","display_name":"Clive McMahon","profile_url":"https://imos.academia.edu/CliveMcMahon?f_ri=49146","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":173,"name":"Zoology","url":"https://www.academia.edu/Documents/in/Zoology?f_ri=49146","nofollow":true},{"id":9846,"name":"Ecology","url":"https://www.academia.edu/Documents/in/Ecology?f_ri=49146","nofollow":true},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true},{"id":407864,"name":"Predators","url":"https://www.academia.edu/Documents/in/Predators?f_ri=49146","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_12635510" data-work_id="12635510" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/12635510/A_kalman_filter_gravity_equation_approach_to_assess_the_trade_impact_of_economic_integration_The_case_of_Spain_1986_1992">A kalman filter-gravity equation approach to assess the trade impact of economic integration: The case of Spain, 1986–1992</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/12635510" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="bb1d945419cea6fd0747210d7a661c69" rel="nofollow" data-download="{"attachment_id":46035803,"asset_id":12635510,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/46035803/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="31606621" href="https://unizar.academia.edu/FernandoSanz">Fernando Sanz</a><script data-card-contents-for-user="31606621" type="text/json">{"id":31606621,"first_name":"Fernando","last_name":"Sanz","domain_name":"unizar","page_name":"FernandoSanz","display_name":"Fernando Sanz","profile_url":"https://unizar.academia.edu/FernandoSanz?f_ri=49146","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_12635510 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="12635510"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 12635510, container: ".js-paper-rank-work_12635510", }); });</script></li><li class="js-percentile-work_12635510 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 12635510; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_12635510"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_12635510 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="12635510"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 12635510; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); 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The main goal of the proposal is to integrate the voluntary activity of a person in the control... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_17582824" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The paper presents a novel control approach for the robot-assisted motion augmentation of disabled subjects during the standing-up manoeuvre. The main goal of the proposal is to integrate the voluntary activity of a person in the control scheme of the rehabilitation robot. The algorithm determines the supportive force to be tracked by a robot force controller. The basic idea behind</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/17582824" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="986608a3a938d30744f80325b91fb11f" rel="nofollow" data-download="{"attachment_id":39594662,"asset_id":17582824,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/39594662/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="37403937" href="https://independent.academia.edu/RomanKamnik">Roman Kamnik</a><script data-card-contents-for-user="37403937" type="text/json">{"id":37403937,"first_name":"Roman","last_name":"Kamnik","domain_name":"independent","page_name":"RomanKamnik","display_name":"Roman Kamnik","profile_url":"https://independent.academia.edu/RomanKamnik?f_ri=49146","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_17582824 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="17582824"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 17582824, container: ".js-paper-rank-work_17582824", }); 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The main goal of the proposal is to integrate the voluntary activity of a person in the control scheme of the rehabilitation robot. The algorithm determines the supportive force to be tracked by a robot force controller. The basic idea behind","downloadable_attachments":[{"id":39594662,"asset_id":17582824,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":37403937,"first_name":"Roman","last_name":"Kamnik","domain_name":"independent","page_name":"RomanKamnik","display_name":"Roman Kamnik","profile_url":"https://independent.academia.edu/RomanKamnik?f_ri=49146","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering?f_ri=49146","nofollow":true},{"id":77,"name":"Robotics","url":"https://www.academia.edu/Documents/in/Robotics?f_ri=49146","nofollow":true},{"id":428,"name":"Algorithms","url":"https://www.academia.edu/Documents/in/Algorithms?f_ri=49146","nofollow":true},{"id":6160,"name":"Computer Aided Design","url":"https://www.academia.edu/Documents/in/Computer_Aided_Design?f_ri=49146","nofollow":true},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146"},{"id":69542,"name":"Computer Simulation","url":"https://www.academia.edu/Documents/in/Computer_Simulation?f_ri=49146"},{"id":118582,"name":"Physical sciences","url":"https://www.academia.edu/Documents/in/Physical_sciences?f_ri=49146"},{"id":128454,"name":"Human Robot Interaction","url":"https://www.academia.edu/Documents/in/Human_Robot_Interaction?f_ri=49146"},{"id":137633,"name":"Feedback","url":"https://www.academia.edu/Documents/in/Feedback?f_ri=49146"},{"id":198527,"name":"Motion","url":"https://www.academia.edu/Documents/in/Motion?f_ri=49146"},{"id":255094,"name":"Computer User Interface Design","url":"https://www.academia.edu/Documents/in/Computer_User_Interface_Design?f_ri=49146"},{"id":509785,"name":"Simulation Study","url":"https://www.academia.edu/Documents/in/Simulation_Study?f_ri=49146"},{"id":704276,"name":"Postural Balance","url":"https://www.academia.edu/Documents/in/Postural_Balance?f_ri=49146"},{"id":1144215,"name":"Lower Extremity","url":"https://www.academia.edu/Documents/in/Lower_Extremity?f_ri=49146"},{"id":1157424,"name":"Equipment Failure Analysis","url":"https://www.academia.edu/Documents/in/Equipment_Failure_Analysis?f_ri=49146"},{"id":1365957,"name":"Behavior Control","url":"https://www.academia.edu/Documents/in/Behavior_Control?f_ri=49146"},{"id":1551329,"name":"Dynamic equilibrium","url":"https://www.academia.edu/Documents/in/Dynamic_equilibrium?f_ri=49146"},{"id":1902384,"name":"Self-help devices","url":"https://www.academia.edu/Documents/in/Self-help_devices?f_ri=49146"},{"id":1971458,"name":"Disabled Persons","url":"https://www.academia.edu/Documents/in/Disabled_Persons?f_ri=49146"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_16584336" data-work_id="16584336" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/16584336/Enhanced_mobile_robot_outdoor_localization_using_INS_GPS_integration">Enhanced mobile robot outdoor localization using INS/GPS integration</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">An unprecedented surge of developments in mobile robot outdoor navigation was witnessed after the US government removed selective availability of the global positioning system (GPS). However, in certain situations GPS becomes unreliable... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_16584336" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">An unprecedented surge of developments in mobile robot outdoor navigation was witnessed after the US government removed selective availability of the global positioning system (GPS). However, in certain situations GPS becomes unreliable or unavailable due to obstructions such as buildings and trees. During GPS outages, a positioning solution with a minimum cost is preferred for small wheeled robots. A low-cost inertial measurement unit (IMU) is a good choice to provide such a solution; however, low-cost MEMS-based inertial sensors suffer from several errors that are stochastic in nature. These errors accumulate and cause a rapid deterioration in the quality of position estimate. The purpose of this paper is to describe an enhanced low-cost 3-D navigation system using a Kalman filter (KF) that integrates odometry from wheel encoders, low cost MEMS-based inertial sensors, and GPS. The proposed technique uses reduced inertial sensor system (RISS). The RISS used here includes three accelerometers and one gyroscope aligned with the vertical axis of the body frame of the robot. The benefits of eliminating the two other gyroscopes normally used are decreasing the cost further, and improving the performance by having less inertial sensors and thus less contribution of these sensors errors towards positional errors. These two eliminated gyroscopes were used to calculate pitch and roll which are now calculated using the two horizontal accelerometers. The experimental results show that, during GPS outages, this KF with velocity update derived from the forward speed from wheel encoders is a good technique for greatly reducing localization errors. Real localization data from one trajectory is presented. This data is post-processed and some simulated GPS outages are introduced to assess the effectiveness of the proposed technique.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/16584336" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="5a68eac9d0cc4220e580ac14781569e1" rel="nofollow" data-download="{"attachment_id":40036578,"asset_id":16584336,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/40036578/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="22414092" href="https://independent.academia.edu/AboelmagdNoureldin">Aboelmagd Noureldin</a><script data-card-contents-for-user="22414092" type="text/json">{"id":22414092,"first_name":"Aboelmagd","last_name":"Noureldin","domain_name":"independent","page_name":"AboelmagdNoureldin","display_name":"Aboelmagd Noureldin","profile_url":"https://independent.academia.edu/AboelmagdNoureldin?f_ri=49146","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_16584336 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="16584336"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 16584336, container: ".js-paper-rank-work_16584336", }); 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However, in certain situations GPS becomes unreliable or unavailable due to obstructions such as buildings and trees. During GPS outages, a positioning solution with a minimum cost is preferred for small wheeled robots. A low-cost inertial measurement unit (IMU) is a good choice to provide such a solution; however, low-cost MEMS-based inertial sensors suffer from several errors that are stochastic in nature. These errors accumulate and cause a rapid deterioration in the quality of position estimate. The purpose of this paper is to describe an enhanced low-cost 3-D navigation system using a Kalman filter (KF) that integrates odometry from wheel encoders, low cost MEMS-based inertial sensors, and GPS. The proposed technique uses reduced inertial sensor system (RISS). The RISS used here includes three accelerometers and one gyroscope aligned with the vertical axis of the body frame of the robot. The benefits of eliminating the two other gyroscopes normally used are decreasing the cost further, and improving the performance by having less inertial sensors and thus less contribution of these sensors errors towards positional errors. These two eliminated gyroscopes were used to calculate pitch and roll which are now calculated using the two horizontal accelerometers. The experimental results show that, during GPS outages, this KF with velocity update derived from the forward speed from wheel encoders is a good technique for greatly reducing localization errors. Real localization data from one trajectory is presented. This data is post-processed and some simulated GPS outages are introduced to assess the effectiveness of the proposed technique.","downloadable_attachments":[{"id":40036578,"asset_id":16584336,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":22414092,"first_name":"Aboelmagd","last_name":"Noureldin","domain_name":"independent","page_name":"AboelmagdNoureldin","display_name":"Aboelmagd Noureldin","profile_url":"https://independent.academia.edu/AboelmagdNoureldin?f_ri=49146","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true},{"id":61597,"name":"Engineering Systems","url":"https://www.academia.edu/Documents/in/Engineering_Systems?f_ri=49146","nofollow":true},{"id":160102,"name":"Systems","url":"https://www.academia.edu/Documents/in/Systems?f_ri=49146","nofollow":true},{"id":179654,"name":"Mobile Robot","url":"https://www.academia.edu/Documents/in/Mobile_Robot?f_ri=49146","nofollow":true},{"id":566372,"name":"Position Estimation","url":"https://www.academia.edu/Documents/in/Position_Estimation?f_ri=49146"},{"id":965809,"name":"Inertial Sensor","url":"https://www.academia.edu/Documents/in/Inertial_Sensor?f_ri=49146"},{"id":1330015,"name":"Navigation System","url":"https://www.academia.edu/Documents/in/Navigation_System?f_ri=49146"},{"id":1594510,"name":"Inertial Measurement Unit","url":"https://www.academia.edu/Documents/in/Inertial_Measurement_Unit?f_ri=49146"},{"id":1688205,"name":"Global position ing system","url":"https://www.academia.edu/Documents/in/Global_position_ing_system?f_ri=49146"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_73000571" data-work_id="73000571" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/73000571/Sequential_calibration_of_options">Sequential calibration of options</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Robust calibration of option valuation models to quoted option prices is non-trivial but crucial for good performance. A framework based on the state-space formulation of the option valuation model is introduced. Non-linear (Kalman)... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_73000571" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Robust calibration of option valuation models to quoted option prices is non-trivial but crucial for good performance. A framework based on the state-space formulation of the option valuation model is introduced. Non-linear (Kalman) filters are needed to do inference since the models have latent variables (e.g. volatility). The statistical framework is made adaptive by introducing stochastic dynamics for the parameters. This allows the parameters to change over time, while treating the measurement noise in a statistically consistent way and using all data efficiently. The performance and computational efficiency of standard and iterated extended Kalman filters (EKF and IEKF) are investigated. These methods are compared to common calibration such as weighted least squares (WLS) and penalized weighted least squares (PWLS). A simulation study, using the Bates model, shows that the adaptive framework is capable of tracking time varying parameters and latent processes such as stochastic ...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/73000571" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="1cd411c13c8684406bf89f6e89f633c1" rel="nofollow" data-download="{"attachment_id":81697351,"asset_id":73000571,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/81697351/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="37154078" href="https://independent.academia.edu/MagnusWiktorsson">Magnus Wiktorsson</a><script data-card-contents-for-user="37154078" type="text/json">{"id":37154078,"first_name":"Magnus","last_name":"Wiktorsson","domain_name":"independent","page_name":"MagnusWiktorsson","display_name":"Magnus Wiktorsson","profile_url":"https://independent.academia.edu/MagnusWiktorsson?f_ri=49146","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_73000571 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="73000571"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 73000571, container: ".js-paper-rank-work_73000571", }); 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A framework based on the state-space formulation of the option valuation model is introduced. Non-linear (Kalman) filters are needed to do inference since the models have latent variables (e.g. volatility). The statistical framework is made adaptive by introducing stochastic dynamics for the parameters. This allows the parameters to change over time, while treating the measurement noise in a statistically consistent way and using all data efficiently. The performance and computational efficiency of standard and iterated extended Kalman filters (EKF and IEKF) are investigated. These methods are compared to common calibration such as weighted least squares (WLS) and penalized weighted least squares (PWLS). A simulation study, using the Bates model, shows that the adaptive framework is capable of tracking time varying parameters and latent processes such as stochastic ...","downloadable_attachments":[{"id":81697351,"asset_id":73000571,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":37154078,"first_name":"Magnus","last_name":"Wiktorsson","domain_name":"independent","page_name":"MagnusWiktorsson","display_name":"Magnus Wiktorsson","profile_url":"https://independent.academia.edu/MagnusWiktorsson?f_ri=49146","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=49146","nofollow":true},{"id":311,"name":"Approximation Theory","url":"https://www.academia.edu/Documents/in/Approximation_Theory?f_ri=49146","nofollow":true},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=49146","nofollow":true},{"id":747,"name":"Econometrics","url":"https://www.academia.edu/Documents/in/Econometrics?f_ri=49146","nofollow":true},{"id":4205,"name":"Data Analysis","url":"https://www.academia.edu/Documents/in/Data_Analysis?f_ri=49146"},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146"},{"id":54697,"name":"Option Valuation","url":"https://www.academia.edu/Documents/in/Option_Valuation?f_ri=49146"},{"id":69730,"name":"Latent variable","url":"https://www.academia.edu/Documents/in/Latent_variable?f_ri=49146"},{"id":96893,"name":"Calibration","url":"https://www.academia.edu/Documents/in/Calibration?f_ri=49146"},{"id":135913,"name":"State Space","url":"https://www.academia.edu/Documents/in/State_Space?f_ri=49146"},{"id":168071,"name":"Computational Efficiency","url":"https://www.academia.edu/Documents/in/Computational_Efficiency?f_ri=49146"},{"id":509785,"name":"Simulation Study","url":"https://www.academia.edu/Documents/in/Simulation_Study?f_ri=49146"},{"id":601424,"name":"Option pricing","url":"https://www.academia.edu/Documents/in/Option_pricing?f_ri=49146"},{"id":749302,"name":"Indexation","url":"https://www.academia.edu/Documents/in/Indexation?f_ri=49146"},{"id":872410,"name":"Extended Kalman Filter","url":"https://www.academia.edu/Documents/in/Extended_Kalman_Filter?f_ri=49146"},{"id":1208732,"name":"Simulation Model","url":"https://www.academia.edu/Documents/in/Simulation_Model?f_ri=49146"},{"id":1327249,"name":"Least Square Method","url":"https://www.academia.edu/Documents/in/Least_Square_Method?f_ri=49146"},{"id":1496485,"name":"Computational Statistics and Data Analysis","url":"https://www.academia.edu/Documents/in/Computational_Statistics_and_Data_Analysis?f_ri=49146"},{"id":2595821,"name":"Least squares method","url":"https://www.academia.edu/Documents/in/Least_squares_method?f_ri=49146"},{"id":4037853,"name":"measurement noise","url":"https://www.academia.edu/Documents/in/measurement_noise?f_ri=49146"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_20037860" data-work_id="20037860" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/20037860/Experimental_validation_of_the_Kalman_type_filters_for_online_and_real_time_state_and_input_estimation">Experimental validation of the Kalman-type filters for online and real-time state and input estimation</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In this study, a novel dual implementation of the Kalman filter proposed by Eftekhar Azam et al. (2014, 2015) is experimentally validated for simultaneous estimation of the states and input of structural systems. By means of numerical... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_20037860" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this study, a novel dual implementation of the Kalman filter proposed by Eftekhar Azam et al. (2014, 2015) is<br />experimentally validated for simultaneous estimation of the states and input of structural systems. By means of numerical<br />simulations, it has been shown that the proposed method outperforms existing techniques in terms of robustness and<br />accuracy for the estimated displacement and velocity time histories. Herein, dynamic response measurements, in the<br />form of displacement and acceleration time histories from a small-scale laboratory building structure excited at the base<br />by a shake table, are considered for evaluating the performance of the proposed Dual Kalman filter and in order to<br />compare this with available alternatives, such as the augmented Kalman filter (Lourens et al., 2012b) and the Gillijn De<br />Moore filter (GDF) (2007b). The suggested Bayesian approach requires the availability of a physical model of the system<br />in addition to output-only measurements from limited degrees of freedom. Two categories of such physical models are<br />herein studied to evaluate the effect of model error on the filter performances; the first, is a model that comprises<br />identified modal parameters, i.e., natural frequencies, mode shapes, modal damping ratios and modal participation<br />factors; the second, is a model that is extracted from a recently developed subspace identification procedure, namely<br />the transformed stochastic subspace identification method. The results are encouraging for the further use of the dual<br />Kalman filter and its available alternatives for addressing the important problems of full response reconstruction and<br />fatigue estimation in the entire body of linear structures, using a limited number of output-only vibration measurements.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/20037860" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="53799af52e6e5a37bed733bbdcee587b" rel="nofollow" data-download="{"attachment_id":40970174,"asset_id":20037860,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/40970174/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="29366254" href="https://unh.academia.edu/SaeedEftekharAzam">Saeed Eftekhar Azam</a><script data-card-contents-for-user="29366254" type="text/json">{"id":29366254,"first_name":"Saeed","last_name":"Eftekhar Azam","domain_name":"unh","page_name":"SaeedEftekharAzam","display_name":"Saeed Eftekhar Azam","profile_url":"https://unh.academia.edu/SaeedEftekharAzam?f_ri=49146","photo":"https://0.academia-photos.com/29366254/8406899/10123084/s65_saeed.eftekhar_azam.jpg"}</script></span></span></li><li class="js-paper-rank-work_20037860 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="20037860"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 20037860, container: ".js-paper-rank-work_20037860", }); 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(2014, 2015) is\nexperimentally validated for simultaneous estimation of the states and input of structural systems. By means of numerical\nsimulations, it has been shown that the proposed method outperforms existing techniques in terms of robustness and\naccuracy for the estimated displacement and velocity time histories. Herein, dynamic response measurements, in the\nform of displacement and acceleration time histories from a small-scale laboratory building structure excited at the base\nby a shake table, are considered for evaluating the performance of the proposed Dual Kalman filter and in order to\ncompare this with available alternatives, such as the augmented Kalman filter (Lourens et al., 2012b) and the Gillijn De\nMoore filter (GDF) (2007b). The suggested Bayesian approach requires the availability of a physical model of the system\nin addition to output-only measurements from limited degrees of freedom. Two categories of such physical models are\nherein studied to evaluate the effect of model error on the filter performances; the first, is a model that comprises\nidentified modal parameters, i.e., natural frequencies, mode shapes, modal damping ratios and modal participation\nfactors; the second, is a model that is extracted from a recently developed subspace identification procedure, namely\nthe transformed stochastic subspace identification method. The results are encouraging for the further use of the dual\nKalman filter and its available alternatives for addressing the important problems of full response reconstruction and\nfatigue estimation in the entire body of linear structures, using a limited number of output-only vibration measurements.","downloadable_attachments":[{"id":40970174,"asset_id":20037860,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":29366254,"first_name":"Saeed","last_name":"Eftekhar Azam","domain_name":"unh","page_name":"SaeedEftekharAzam","display_name":"Saeed Eftekhar Azam","profile_url":"https://unh.academia.edu/SaeedEftekharAzam?f_ri=49146","photo":"https://0.academia-photos.com/29366254/8406899/10123084/s65_saeed.eftekhar_azam.jpg"}],"research_interests":[{"id":60,"name":"Mechanical Engineering","url":"https://www.academia.edu/Documents/in/Mechanical_Engineering?f_ri=49146","nofollow":true},{"id":73,"name":"Civil Engineering","url":"https://www.academia.edu/Documents/in/Civil_Engineering?f_ri=49146","nofollow":true},{"id":1336,"name":"Structural Engineering","url":"https://www.academia.edu/Documents/in/Structural_Engineering?f_ri=49146","nofollow":true},{"id":14081,"name":"Structural Health Monitoring","url":"https://www.academia.edu/Documents/in/Structural_Health_Monitoring?f_ri=49146","nofollow":true},{"id":33252,"name":"State Estimation","url":"https://www.academia.edu/Documents/in/State_Estimation?f_ri=49146"},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146"},{"id":218728,"name":"Condition Assessment of Existing Structures","url":"https://www.academia.edu/Documents/in/Condition_Assessment_of_Existing_Structures?f_ri=49146"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_50708624" data-work_id="50708624" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/50708624/Understanding_the_Impact_of_Synergy_in_Multimedia_Communications">Understanding the Impact of Synergy in Multimedia Communications</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Many advertisers adopt the integrated marketing communications perspective that emphasizes the importance of synergy in planning multimedia activities. However, the role of synergy in multimedia communications is not well understood.... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_50708624" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Many advertisers adopt the integrated marketing communications perspective that emphasizes the importance of synergy in planning multimedia activities. However, the role of synergy in multimedia communications is not well understood. Thus, the authors investigate the theoretical and empirical effects of synergy by extending a commonly used dynamic advertising model to multimedia environments. They illustrate how advertisers can estimate and infer the effectiveness of and synergy among multimedia communications by applying Kalman filtering methodology. Using market data on Dockers brand advertising, the authors first calibrate the extended model to establish the presence of synergy between television and print advertisements in consumer markets. Second, they derive theoretical propositions to understand the impact of synergy on media budget, media mix, and advertising carryover. One of the propositions reveals that as synergy increases, advertisers should not only increase the media ...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/50708624" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="4d2a1ad776120bba01142d40631f7d82" rel="nofollow" data-download="{"attachment_id":68587600,"asset_id":50708624,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/68587600/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="186506167" href="https://independent.academia.edu/PrasadNaik49">Prasad Naik</a><script data-card-contents-for-user="186506167" type="text/json">{"id":186506167,"first_name":"Prasad","last_name":"Naik","domain_name":"independent","page_name":"PrasadNaik49","display_name":"Prasad Naik","profile_url":"https://independent.academia.edu/PrasadNaik49?f_ri=49146","photo":"https://0.academia-photos.com/186506167/82959989/71572887/s65_prasad.naik.jpeg"}</script></span></span></li><li class="js-paper-rank-work_50708624 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="50708624"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 50708624, container: ".js-paper-rank-work_50708624", }); });</script></li><li class="js-percentile-work_50708624 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 50708624; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_50708624"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_50708624 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="50708624"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 50708624; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=50708624]").text(description); $(".js-view-count-work_50708624").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_50708624").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="50708624"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="39" rel="nofollow" href="https://www.academia.edu/Documents/in/Marketing">Marketing</a>, <script data-card-contents-for-ri="39" type="text/json">{"id":39,"name":"Marketing","url":"https://www.academia.edu/Documents/in/Marketing?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="40903" rel="nofollow" href="https://www.academia.edu/Documents/in/Multimedia_Communication">Multimedia Communication</a>, <script data-card-contents-for-ri="40903" type="text/json">{"id":40903,"name":"Multimedia Communication","url":"https://www.academia.edu/Documents/in/Multimedia_Communication?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="49146" rel="nofollow" href="https://www.academia.edu/Documents/in/Kalman_Filter">Kalman Filter</a>, <script data-card-contents-for-ri="49146" type="text/json">{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="170198" rel="nofollow" href="https://www.academia.edu/Documents/in/Synergy">Synergy</a><script data-card-contents-for-ri="170198" type="text/json">{"id":170198,"name":"Synergy","url":"https://www.academia.edu/Documents/in/Synergy?f_ri=49146","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=50708624]'), work: {"id":50708624,"title":"Understanding the Impact of Synergy in Multimedia Communications","created_at":"2021-08-04T02:14:22.726-07:00","url":"https://www.academia.edu/50708624/Understanding_the_Impact_of_Synergy_in_Multimedia_Communications?f_ri=49146","dom_id":"work_50708624","summary":"Many advertisers adopt the integrated marketing communications perspective that emphasizes the importance of synergy in planning multimedia activities. However, the role of synergy in multimedia communications is not well understood. Thus, the authors investigate the theoretical and empirical effects of synergy by extending a commonly used dynamic advertising model to multimedia environments. They illustrate how advertisers can estimate and infer the effectiveness of and synergy among multimedia communications by applying Kalman filtering methodology. Using market data on Dockers brand advertising, the authors first calibrate the extended model to establish the presence of synergy between television and print advertisements in consumer markets. Second, they derive theoretical propositions to understand the impact of synergy on media budget, media mix, and advertising carryover. One of the propositions reveals that as synergy increases, advertisers should not only increase the media ...","downloadable_attachments":[{"id":68587600,"asset_id":50708624,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":186506167,"first_name":"Prasad","last_name":"Naik","domain_name":"independent","page_name":"PrasadNaik49","display_name":"Prasad Naik","profile_url":"https://independent.academia.edu/PrasadNaik49?f_ri=49146","photo":"https://0.academia-photos.com/186506167/82959989/71572887/s65_prasad.naik.jpeg"}],"research_interests":[{"id":39,"name":"Marketing","url":"https://www.academia.edu/Documents/in/Marketing?f_ri=49146","nofollow":true},{"id":40903,"name":"Multimedia Communication","url":"https://www.academia.edu/Documents/in/Multimedia_Communication?f_ri=49146","nofollow":true},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true},{"id":170198,"name":"Synergy","url":"https://www.academia.edu/Documents/in/Synergy?f_ri=49146","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_61377985" data-work_id="61377985" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/61377985/Adaptive_weather_forecasting_using_local_meteorological_information">Adaptive weather forecasting using local meteorological information</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In general, meteorological parameters such as temperature, rain and global radiation are important for agricultural systems. Anticipating on future conditions is most often needed in these systems. Weather forecasts then become of... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_61377985" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In general, meteorological parameters such as temperature, rain and global radiation are important for agricultural systems. Anticipating on future conditions is most often needed in these systems. Weather forecasts then become of substantial importance. As weather forecasts are subject to uncertainties, there is a need in minimising the uncertainties. In this paper, a framework is presented in which local weather forecasts are updated using local measurements. Kalman filtering is used for this purpose as assimilation technique. This method is compared and combined with diurnal bias correction. It is shown that the standard deviation of the forecast error can be reduced up to 6 h ahead for temperature, up to 31 h ahead for wind speed, and up to 3 h for global radiation using local measurements. Combining the method with diurnal bias correction leads to a further increase in performance in terms of both bias and standard deviation.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/61377985" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="984763afc829ab5779b17fd96c604f5d" rel="nofollow" data-download="{"attachment_id":74426692,"asset_id":61377985,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/74426692/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="34256567" href="https://wur.academia.edu/KarelKeesman">Karel J Keesman</a><script data-card-contents-for-user="34256567" type="text/json">{"id":34256567,"first_name":"Karel","last_name":"Keesman","domain_name":"wur","page_name":"KarelKeesman","display_name":"Karel J Keesman","profile_url":"https://wur.academia.edu/KarelKeesman?f_ri=49146","photo":"https://0.academia-photos.com/34256567/18308467/64087089/s65_karel.keesman.jpg"}</script></span></span></li><li class="js-paper-rank-work_61377985 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="61377985"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 61377985, container: ".js-paper-rank-work_61377985", }); });</script></li><li class="js-percentile-work_61377985 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 61377985; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_61377985"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_61377985 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="61377985"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 61377985; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=61377985]").text(description); $(".js-view-count-work_61377985").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_61377985").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="61377985"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">7</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="1131" rel="nofollow" href="https://www.academia.edu/Documents/in/Biomedical_Engineering">Biomedical Engineering</a>, <script data-card-contents-for-ri="1131" type="text/json">{"id":1131,"name":"Biomedical Engineering","url":"https://www.academia.edu/Documents/in/Biomedical_Engineering?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="49146" rel="nofollow" href="https://www.academia.edu/Documents/in/Kalman_Filter">Kalman Filter</a>, <script data-card-contents-for-ri="49146" type="text/json">{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="60762" rel="nofollow" href="https://www.academia.edu/Documents/in/Biosystems_engineering">Biosystems engineering</a>, <script data-card-contents-for-ri="60762" type="text/json">{"id":60762,"name":"Biosystems engineering","url":"https://www.academia.edu/Documents/in/Biosystems_engineering?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="69841" rel="nofollow" href="https://www.academia.edu/Documents/in/Standard_Deviation">Standard Deviation</a><script data-card-contents-for-ri="69841" type="text/json">{"id":69841,"name":"Standard Deviation","url":"https://www.academia.edu/Documents/in/Standard_Deviation?f_ri=49146","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=61377985]'), work: {"id":61377985,"title":"Adaptive weather forecasting using local meteorological information","created_at":"2021-11-09T00:39:03.020-08:00","url":"https://www.academia.edu/61377985/Adaptive_weather_forecasting_using_local_meteorological_information?f_ri=49146","dom_id":"work_61377985","summary":"In general, meteorological parameters such as temperature, rain and global radiation are important for agricultural systems. Anticipating on future conditions is most often needed in these systems. Weather forecasts then become of substantial importance. As weather forecasts are subject to uncertainties, there is a need in minimising the uncertainties. In this paper, a framework is presented in which local weather forecasts are updated using local measurements. Kalman filtering is used for this purpose as assimilation technique. This method is compared and combined with diurnal bias correction. It is shown that the standard deviation of the forecast error can be reduced up to 6 h ahead for temperature, up to 31 h ahead for wind speed, and up to 3 h for global radiation using local measurements. Combining the method with diurnal bias correction leads to a further increase in performance in terms of both bias and standard deviation.","downloadable_attachments":[{"id":74426692,"asset_id":61377985,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":34256567,"first_name":"Karel","last_name":"Keesman","domain_name":"wur","page_name":"KarelKeesman","display_name":"Karel J Keesman","profile_url":"https://wur.academia.edu/KarelKeesman?f_ri=49146","photo":"https://0.academia-photos.com/34256567/18308467/64087089/s65_karel.keesman.jpg"}],"research_interests":[{"id":1131,"name":"Biomedical Engineering","url":"https://www.academia.edu/Documents/in/Biomedical_Engineering?f_ri=49146","nofollow":true},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true},{"id":60762,"name":"Biosystems engineering","url":"https://www.academia.edu/Documents/in/Biosystems_engineering?f_ri=49146","nofollow":true},{"id":69841,"name":"Standard Deviation","url":"https://www.academia.edu/Documents/in/Standard_Deviation?f_ri=49146","nofollow":true},{"id":195260,"name":"Weather Forecasting","url":"https://www.academia.edu/Documents/in/Weather_Forecasting?f_ri=49146"},{"id":755542,"name":"Wind Speed","url":"https://www.academia.edu/Documents/in/Wind_Speed?f_ri=49146"},{"id":3052845,"name":"Other Engineering","url":"https://www.academia.edu/Documents/in/Other_Engineering?f_ri=49146"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_58907354" data-work_id="58907354" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/58907354/The_purchasing_power_parity_of_Southeast_Asian_currencies_A_time_varying_coefficient_approach">The purchasing power parity of Southeast Asian currencies: A time-varying coefficient approach</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The economies of Southeast Asia have undergone several structural changes, including the Asian currency crisis, during the post-Bretton Woods era. We use a time-varying coefficient cointegration model to test for purchasing power parity... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_58907354" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The economies of Southeast Asia have undergone several structural changes, including the Asian currency crisis, during the post-Bretton Woods era. We use a time-varying coefficient cointegration model to test for purchasing power parity (PPP) of Southeast Asian currencies and to track changes in purchasing power relationships over time. The main empirical findings are as follows. First, the stability of the relationship between exchange rates and price differentials is strongly rejected. Second, a major structural change occurs at the outbreak of the Asian currency crisis in 1997. Third, when the cointegration vector is allowed to vary with time, we find evidence of a cointegration relationship for four countries in terms of the US dollar and for four countries in terms of the Japanese yen. Therefore, it seems unlikely that Southeast Asian currencies form a "yen bloc."</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/58907354" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="c702eae82387dc69faf60d8b003def53" rel="nofollow" data-download="{"attachment_id":73090376,"asset_id":58907354,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/73090376/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="36439035" href="https://independent.academia.edu/HongkeeKim">Hongkee Kim</a><script data-card-contents-for-user="36439035" type="text/json">{"id":36439035,"first_name":"Hongkee","last_name":"Kim","domain_name":"independent","page_name":"HongkeeKim","display_name":"Hongkee Kim","profile_url":"https://independent.academia.edu/HongkeeKim?f_ri=49146","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_58907354 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="58907354"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 58907354, container: ".js-paper-rank-work_58907354", }); });</script></li><li class="js-percentile-work_58907354 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 58907354; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_58907354"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_58907354 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="58907354"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 58907354; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=58907354]").text(description); $(".js-view-count-work_58907354").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_58907354").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="58907354"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">10</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="747" rel="nofollow" href="https://www.academia.edu/Documents/in/Econometrics">Econometrics</a>, <script data-card-contents-for-ri="747" type="text/json">{"id":747,"name":"Econometrics","url":"https://www.academia.edu/Documents/in/Econometrics?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="6746" rel="nofollow" href="https://www.academia.edu/Documents/in/Southeast_Asia">Southeast Asia</a>, <script data-card-contents-for-ri="6746" type="text/json">{"id":6746,"name":"Southeast Asia","url":"https://www.academia.edu/Documents/in/Southeast_Asia?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="27659" rel="nofollow" href="https://www.academia.edu/Documents/in/Applied_Economics">Applied Economics</a>, <script data-card-contents-for-ri="27659" type="text/json">{"id":27659,"name":"Applied Economics","url":"https://www.academia.edu/Documents/in/Applied_Economics?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="49146" rel="nofollow" href="https://www.academia.edu/Documents/in/Kalman_Filter">Kalman Filter</a><script data-card-contents-for-ri="49146" type="text/json">{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=58907354]'), work: {"id":58907354,"title":"The purchasing power parity of Southeast Asian currencies: A time-varying coefficient approach","created_at":"2021-10-18T14:33:29.914-07:00","url":"https://www.academia.edu/58907354/The_purchasing_power_parity_of_Southeast_Asian_currencies_A_time_varying_coefficient_approach?f_ri=49146","dom_id":"work_58907354","summary":"The economies of Southeast Asia have undergone several structural changes, including the Asian currency crisis, during the post-Bretton Woods era. We use a time-varying coefficient cointegration model to test for purchasing power parity (PPP) of Southeast Asian currencies and to track changes in purchasing power relationships over time. The main empirical findings are as follows. First, the stability of the relationship between exchange rates and price differentials is strongly rejected. Second, a major structural change occurs at the outbreak of the Asian currency crisis in 1997. Third, when the cointegration vector is allowed to vary with time, we find evidence of a cointegration relationship for four countries in terms of the US dollar and for four countries in terms of the Japanese yen. Therefore, it seems unlikely that Southeast Asian currencies form a \"yen bloc.\"","downloadable_attachments":[{"id":73090376,"asset_id":58907354,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":36439035,"first_name":"Hongkee","last_name":"Kim","domain_name":"independent","page_name":"HongkeeKim","display_name":"Hongkee Kim","profile_url":"https://independent.academia.edu/HongkeeKim?f_ri=49146","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":747,"name":"Econometrics","url":"https://www.academia.edu/Documents/in/Econometrics?f_ri=49146","nofollow":true},{"id":6746,"name":"Southeast Asia","url":"https://www.academia.edu/Documents/in/Southeast_Asia?f_ri=49146","nofollow":true},{"id":27659,"name":"Applied Economics","url":"https://www.academia.edu/Documents/in/Applied_Economics?f_ri=49146","nofollow":true},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true},{"id":213801,"name":"Structural Change","url":"https://www.academia.edu/Documents/in/Structural_Change?f_ri=49146"},{"id":228986,"name":"Exchange rate","url":"https://www.academia.edu/Documents/in/Exchange_rate?f_ri=49146"},{"id":471353,"name":"Purchasing Power Parity","url":"https://www.academia.edu/Documents/in/Purchasing_Power_Parity?f_ri=49146"},{"id":737174,"name":"Economic Modelling","url":"https://www.academia.edu/Documents/in/Economic_Modelling?f_ri=49146"},{"id":826617,"name":"Currency Crisis","url":"https://www.academia.edu/Documents/in/Currency_Crisis?f_ri=49146"},{"id":3079415,"name":"Finance and Investment Banking","url":"https://www.academia.edu/Documents/in/Finance_and_Investment_Banking?f_ri=49146"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_49919272" data-work_id="49919272" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/49919272/Autonomous_pool_cleaning_Self_localization_and_autonomous_navigation_for_cleaning">Autonomous pool cleaning: Self localization and autonomous navigation for cleaning</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Cleaning is a major problem associated with pools. Since the manual cleaning is tedious and boring there is an interest in automating the task. This paper presents methods for autonomous localization and navigation for a pool cleaner to... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_49919272" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Cleaning is a major problem associated with pools. Since the manual cleaning is tedious and boring there is an interest in automating the task. This paper presents methods for autonomous localization and navigation for a pool cleaner to enable full coverage of pools. Path following cannot be ensured through use of internal position estimation methods alone; therefore sensing is needed. Sensor based estimation enable automatic correction of slippage. For this application we use ultrasonic sonars. Based on an analysis of the overall task and performance of the system a strategy for cleaning/navigation is developed. For the automatic localization a Kalman filtering technique is proposed: the Kalman filter uses sonar measurements and a dynamic model of the robot to provide estimates of the pose of the pool cleaner. Using this localization method we derive an optimal control strategy for traversal of a pool. The system has been implemented and successfully tested on the "WEDA B400" pool cleaner.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/49919272" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="faa73002fe996d711ff5c9e75f0c265b" rel="nofollow" data-download="{"attachment_id":68096850,"asset_id":49919272,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/68096850/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="192861225" href="https://independent.academia.edu/HenrikIskovChristensen">Henrik Iskov Christensen</a><script data-card-contents-for-user="192861225" type="text/json">{"id":192861225,"first_name":"Henrik Iskov","last_name":"Christensen","domain_name":"independent","page_name":"HenrikIskovChristensen","display_name":"Henrik Iskov Christensen","profile_url":"https://independent.academia.edu/HenrikIskovChristensen?f_ri=49146","photo":"https://gravatar.com/avatar/f4109478634624a02f2f0412739e30ae?s=65"}</script></span></span></li><li class="js-paper-rank-work_49919272 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="49919272"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 49919272, container: ".js-paper-rank-work_49919272", }); });</script></li><li class="js-percentile-work_49919272 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 49919272; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_49919272"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_49919272 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="49919272"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 49919272; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=49919272]").text(description); $(".js-view-count-work_49919272").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_49919272").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="49919272"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">10</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="60" rel="nofollow" href="https://www.academia.edu/Documents/in/Mechanical_Engineering">Mechanical Engineering</a>, <script data-card-contents-for-ri="60" type="text/json">{"id":60,"name":"Mechanical Engineering","url":"https://www.academia.edu/Documents/in/Mechanical_Engineering?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="237" rel="nofollow" href="https://www.academia.edu/Documents/in/Cognitive_Science">Cognitive Science</a>, <script data-card-contents-for-ri="237" type="text/json">{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="2200" rel="nofollow" href="https://www.academia.edu/Documents/in/Optimal_Control">Optimal Control</a>, <script data-card-contents-for-ri="2200" type="text/json">{"id":2200,"name":"Optimal Control","url":"https://www.academia.edu/Documents/in/Optimal_Control?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="49146" rel="nofollow" href="https://www.academia.edu/Documents/in/Kalman_Filter">Kalman Filter</a><script data-card-contents-for-ri="49146" type="text/json">{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=49919272]'), work: {"id":49919272,"title":"Autonomous pool cleaning: Self localization and autonomous navigation for cleaning","created_at":"2021-07-14T13:49:18.465-07:00","url":"https://www.academia.edu/49919272/Autonomous_pool_cleaning_Self_localization_and_autonomous_navigation_for_cleaning?f_ri=49146","dom_id":"work_49919272","summary":"Cleaning is a major problem associated with pools. Since the manual cleaning is tedious and boring there is an interest in automating the task. This paper presents methods for autonomous localization and navigation for a pool cleaner to enable full coverage of pools. Path following cannot be ensured through use of internal position estimation methods alone; therefore sensing is needed. Sensor based estimation enable automatic correction of slippage. For this application we use ultrasonic sonars. Based on an analysis of the overall task and performance of the system a strategy for cleaning/navigation is developed. For the automatic localization a Kalman filtering technique is proposed: the Kalman filter uses sonar measurements and a dynamic model of the robot to provide estimates of the pose of the pool cleaner. Using this localization method we derive an optimal control strategy for traversal of a pool. The system has been implemented and successfully tested on the \"WEDA B400\" pool cleaner.","downloadable_attachments":[{"id":68096850,"asset_id":49919272,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":192861225,"first_name":"Henrik Iskov","last_name":"Christensen","domain_name":"independent","page_name":"HenrikIskovChristensen","display_name":"Henrik Iskov Christensen","profile_url":"https://independent.academia.edu/HenrikIskovChristensen?f_ri=49146","photo":"https://gravatar.com/avatar/f4109478634624a02f2f0412739e30ae?s=65"}],"research_interests":[{"id":60,"name":"Mechanical Engineering","url":"https://www.academia.edu/Documents/in/Mechanical_Engineering?f_ri=49146","nofollow":true},{"id":237,"name":"Cognitive Science","url":"https://www.academia.edu/Documents/in/Cognitive_Science?f_ri=49146","nofollow":true},{"id":2200,"name":"Optimal Control","url":"https://www.academia.edu/Documents/in/Optimal_Control?f_ri=49146","nofollow":true},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true},{"id":61603,"name":"Uncertainty","url":"https://www.academia.edu/Documents/in/Uncertainty?f_ri=49146"},{"id":66694,"name":"Autonomous Robots","url":"https://www.academia.edu/Documents/in/Autonomous_Robots?f_ri=49146"},{"id":179654,"name":"Mobile Robot","url":"https://www.academia.edu/Documents/in/Mobile_Robot?f_ri=49146"},{"id":446452,"name":"Path Following Methods","url":"https://www.academia.edu/Documents/in/Path_Following_Methods?f_ri=49146"},{"id":566372,"name":"Position Estimation","url":"https://www.academia.edu/Documents/in/Position_Estimation?f_ri=49146"},{"id":2626792,"name":"dynamic model","url":"https://www.academia.edu/Documents/in/dynamic_model?f_ri=49146"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_45059083" data-work_id="45059083" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/45059083/Mobile_Robot_Localization_via_Unscented_Kalman_Filter">Mobile Robot Localization via Unscented Kalman Filter</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Mobile robot localization concerns estimating the position and heading of the robot relative to its environment. Basically , the mobile robot moves around without initial knowledge of the environment. Therefore, a scheme to handle it is... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_45059083" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Mobile robot localization concerns estimating the position and heading of the robot relative to its environment. Basically , the mobile robot moves around without initial knowledge of the environment. Therefore, a scheme to handle it is necessary, such as the Kalman Filters. Rather than the Extended Kalman Filter, we choose to employ the sigma points approach. In this paper, we take into consideration the method proposed by Van Der Merwe to determine the sigma points in Unscented Kalman Filter. The simulation and results verify that the Unscented Kalman Filter works pretty well for locating the mobile robot.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/45059083" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="38c2bc5f1e1824179efe27e3774b9294" rel="nofollow" data-download="{"attachment_id":65613814,"asset_id":45059083,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/65613814/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="68105146" href="https://ekasangaji.academia.edu/FreddyKurniawan">Freddy Kurniawan</a><script data-card-contents-for-user="68105146" type="text/json">{"id":68105146,"first_name":"Freddy","last_name":"Kurniawan","domain_name":"ekasangaji","page_name":"FreddyKurniawan","display_name":"Freddy Kurniawan","profile_url":"https://ekasangaji.academia.edu/FreddyKurniawan?f_ri=49146","photo":"https://0.academia-photos.com/68105146/17693327/17732791/s65_freddy.kurniawan.jpg"}</script></span></span></li><li class="js-paper-rank-work_45059083 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="45059083"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 45059083, container: ".js-paper-rank-work_45059083", }); });</script></li><li class="js-percentile-work_45059083 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 45059083; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_45059083"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_45059083 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="45059083"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 45059083; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=45059083]").text(description); $(".js-view-count-work_45059083").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_45059083").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="45059083"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">8</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="11988" rel="nofollow" href="https://www.academia.edu/Documents/in/Robotics_Navigation">Robotics Navigation</a>, <script data-card-contents-for-ri="11988" type="text/json">{"id":11988,"name":"Robotics Navigation","url":"https://www.academia.edu/Documents/in/Robotics_Navigation?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="49146" rel="nofollow" href="https://www.academia.edu/Documents/in/Kalman_Filter">Kalman Filter</a>, <script data-card-contents-for-ri="49146" type="text/json">{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="59695" rel="nofollow" href="https://www.academia.edu/Documents/in/Navigation">Navigation</a>, <script data-card-contents-for-ri="59695" type="text/json">{"id":59695,"name":"Navigation","url":"https://www.academia.edu/Documents/in/Navigation?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="236258" rel="nofollow" href="https://www.academia.edu/Documents/in/Mobile_Robot_Navigation">Mobile Robot Navigation</a><script data-card-contents-for-ri="236258" type="text/json">{"id":236258,"name":"Mobile Robot Navigation","url":"https://www.academia.edu/Documents/in/Mobile_Robot_Navigation?f_ri=49146","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=45059083]'), work: {"id":45059083,"title":"Mobile Robot Localization via Unscented Kalman Filter","created_at":"2021-02-05T05:52:57.013-08:00","url":"https://www.academia.edu/45059083/Mobile_Robot_Localization_via_Unscented_Kalman_Filter?f_ri=49146","dom_id":"work_45059083","summary":"Mobile robot localization concerns estimating the position and heading of the robot relative to its environment. Basically , the mobile robot moves around without initial knowledge of the environment. Therefore, a scheme to handle it is necessary, such as the Kalman Filters. Rather than the Extended Kalman Filter, we choose to employ the sigma points approach. In this paper, we take into consideration the method proposed by Van Der Merwe to determine the sigma points in Unscented Kalman Filter. The simulation and results verify that the Unscented Kalman Filter works pretty well for locating the mobile robot.","downloadable_attachments":[{"id":65613814,"asset_id":45059083,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":68105146,"first_name":"Freddy","last_name":"Kurniawan","domain_name":"ekasangaji","page_name":"FreddyKurniawan","display_name":"Freddy Kurniawan","profile_url":"https://ekasangaji.academia.edu/FreddyKurniawan?f_ri=49146","photo":"https://0.academia-photos.com/68105146/17693327/17732791/s65_freddy.kurniawan.jpg"}],"research_interests":[{"id":11988,"name":"Robotics Navigation","url":"https://www.academia.edu/Documents/in/Robotics_Navigation?f_ri=49146","nofollow":true},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true},{"id":59695,"name":"Navigation","url":"https://www.academia.edu/Documents/in/Navigation?f_ri=49146","nofollow":true},{"id":236258,"name":"Mobile Robot Navigation","url":"https://www.academia.edu/Documents/in/Mobile_Robot_Navigation?f_ri=49146","nofollow":true},{"id":325034,"name":"Unscented Kalman Filter","url":"https://www.academia.edu/Documents/in/Unscented_Kalman_Filter?f_ri=49146"},{"id":847570,"name":"Kalman Filtering Algorithm","url":"https://www.academia.edu/Documents/in/Kalman_Filtering_Algorithm?f_ri=49146"},{"id":872410,"name":"Extended Kalman Filter","url":"https://www.academia.edu/Documents/in/Extended_Kalman_Filter?f_ri=49146"},{"id":1445733,"name":"Matlab Kalman","url":"https://www.academia.edu/Documents/in/Matlab_Kalman?f_ri=49146"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_37005928" data-work_id="37005928" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/37005928/High_Order_Filtering_of_LIDAR_Data_to_Assist_Coal_Shiploading">High Order Filtering of LIDAR Data to Assist Coal Shiploading</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">A high order signal model is proposed in which the states are Kronecker tensor products of probability distributions. This model enables an optimal linear filter to be specified. A minimum residual error variance criterion may be used to... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_37005928" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">A high order signal model is proposed in which the states are Kronecker tensor products of probability distributions. This model enables an optimal linear filter to be specified. A minimum residual error variance criterion may be used to select the number of discretizations and Kronecker products. The filtering of LIDAR data from a coal shiploader environment is investigated. It is demonstrated that the proposed method can outperform conventional Kalman and hidden Markov model filters.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/37005928" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="8e5594c722030001034e98c0922810a6" rel="nofollow" data-download="{"attachment_id":56955392,"asset_id":37005928,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/56955392/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="37984560" href="https://independent.academia.edu/GEinicke">G. Einicke</a><script data-card-contents-for-user="37984560" type="text/json">{"id":37984560,"first_name":"G.","last_name":"Einicke","domain_name":"independent","page_name":"GEinicke","display_name":"G. Einicke","profile_url":"https://independent.academia.edu/GEinicke?f_ri=49146","photo":"https://0.academia-photos.com/37984560/20043966/19817119/s65_g..einicke.jpg"}</script></span></span></li><li class="js-paper-rank-work_37005928 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="37005928"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 37005928, container: ".js-paper-rank-work_37005928", }); });</script></li><li class="js-percentile-work_37005928 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 37005928; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_37005928"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_37005928 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="37005928"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 37005928; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=37005928]").text(description); $(".js-view-count-work_37005928").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_37005928").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="37005928"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="2446" rel="nofollow" href="https://www.academia.edu/Documents/in/Estimation_and_Filtering_Theory">Estimation and Filtering Theory</a>, <script data-card-contents-for-ri="2446" type="text/json">{"id":2446,"name":"Estimation and Filtering Theory","url":"https://www.academia.edu/Documents/in/Estimation_and_Filtering_Theory?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="27567" rel="nofollow" href="https://www.academia.edu/Documents/in/LiDAR_for_topographic_mapping">LiDAR for topographic mapping</a>, <script data-card-contents-for-ri="27567" type="text/json">{"id":27567,"name":"LiDAR for topographic mapping","url":"https://www.academia.edu/Documents/in/LiDAR_for_topographic_mapping?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="33251" rel="nofollow" href="https://www.academia.edu/Documents/in/Adaptive_Filtering">Adaptive Filtering</a>, <script data-card-contents-for-ri="33251" type="text/json">{"id":33251,"name":"Adaptive Filtering","url":"https://www.academia.edu/Documents/in/Adaptive_Filtering?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="49146" rel="nofollow" href="https://www.academia.edu/Documents/in/Kalman_Filter">Kalman Filter</a><script data-card-contents-for-ri="49146" type="text/json">{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=37005928]'), work: {"id":37005928,"title":"High Order Filtering of LIDAR Data to Assist Coal Shiploading","created_at":"2018-07-08T18:09:50.998-07:00","url":"https://www.academia.edu/37005928/High_Order_Filtering_of_LIDAR_Data_to_Assist_Coal_Shiploading?f_ri=49146","dom_id":"work_37005928","summary":"A high order signal model is proposed in which the states are Kronecker tensor products of probability distributions. This model enables an optimal linear filter to be specified. A minimum residual error variance criterion may be used to select the number of discretizations and Kronecker products. The filtering of LIDAR data from a coal shiploader environment is investigated. It is demonstrated that the proposed method can outperform conventional Kalman and hidden Markov model filters.","downloadable_attachments":[{"id":56955392,"asset_id":37005928,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":37984560,"first_name":"G.","last_name":"Einicke","domain_name":"independent","page_name":"GEinicke","display_name":"G. Einicke","profile_url":"https://independent.academia.edu/GEinicke?f_ri=49146","photo":"https://0.academia-photos.com/37984560/20043966/19817119/s65_g..einicke.jpg"}],"research_interests":[{"id":2446,"name":"Estimation and Filtering Theory","url":"https://www.academia.edu/Documents/in/Estimation_and_Filtering_Theory?f_ri=49146","nofollow":true},{"id":27567,"name":"LiDAR for topographic mapping","url":"https://www.academia.edu/Documents/in/LiDAR_for_topographic_mapping?f_ri=49146","nofollow":true},{"id":33251,"name":"Adaptive Filtering","url":"https://www.academia.edu/Documents/in/Adaptive_Filtering?f_ri=49146","nofollow":true},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_33622388" data-work_id="33622388" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/33622388/Kalman_filtering_with_inequality_constraints_for_turbofan_engine_health_estimation">Kalman filtering with inequality constraints for turbofan engine health estimation</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance,... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_33622388" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state-variable constraints (which may be based on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. Thus, two analytical methods to incorporate state-variable inequality con straints into the Kalman filter are now derived. The first method is a general technique that uses hard constraints to enforce inequalities on the state-variable estimates. The resultant filter is a com bination of a standard Kalman filter and a quadratic programming problem. The second method uses soft constraints to estimate those state variables that are known to vary slowly with time.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/33622388" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="41508dbeccb3d770e605d415613e50d2" rel="nofollow" data-download="{"attachment_id":53637989,"asset_id":33622388,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/53637989/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="45665102" href="https://independent.academia.edu/DanielSimon25">Daniel Simon</a><script data-card-contents-for-user="45665102" type="text/json">{"id":45665102,"first_name":"Daniel","last_name":"Simon","domain_name":"independent","page_name":"DanielSimon25","display_name":"Daniel Simon","profile_url":"https://independent.academia.edu/DanielSimon25?f_ri=49146","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_33622388 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="33622388"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 33622388, container: ".js-paper-rank-work_33622388", }); });</script></li><li class="js-percentile-work_33622388 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 33622388; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_33622388"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_33622388 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="33622388"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 33622388; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=33622388]").text(description); $(".js-view-count-work_33622388").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_33622388").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="33622388"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">23</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="60" rel="nofollow" href="https://www.academia.edu/Documents/in/Mechanical_Engineering">Mechanical Engineering</a>, <script data-card-contents-for-ri="60" type="text/json">{"id":60,"name":"Mechanical Engineering","url":"https://www.academia.edu/Documents/in/Mechanical_Engineering?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="305" rel="nofollow" href="https://www.academia.edu/Documents/in/Applied_Mathematics">Applied Mathematics</a>, <script data-card-contents-for-ri="305" type="text/json">{"id":305,"name":"Applied Mathematics","url":"https://www.academia.edu/Documents/in/Applied_Mathematics?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="307" rel="nofollow" href="https://www.academia.edu/Documents/in/Mathematical_Statistics">Mathematical Statistics</a>, <script data-card-contents-for-ri="307" type="text/json">{"id":307,"name":"Mathematical Statistics","url":"https://www.academia.edu/Documents/in/Mathematical_Statistics?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="428" rel="nofollow" href="https://www.academia.edu/Documents/in/Algorithms">Algorithms</a><script data-card-contents-for-ri="428" type="text/json">{"id":428,"name":"Algorithms","url":"https://www.academia.edu/Documents/in/Algorithms?f_ri=49146","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=33622388]'), work: {"id":33622388,"title":"Kalman filtering with inequality constraints for turbofan engine health estimation","created_at":"2017-06-23T07:41:48.492-07:00","url":"https://www.academia.edu/33622388/Kalman_filtering_with_inequality_constraints_for_turbofan_engine_health_estimation?f_ri=49146","dom_id":"work_33622388","summary":"Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state-variable constraints (which may be based on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. Thus, two analytical methods to incorporate state-variable inequality con straints into the Kalman filter are now derived. The first method is a general technique that uses hard constraints to enforce inequalities on the state-variable estimates. The resultant filter is a com bination of a standard Kalman filter and a quadratic programming problem. The second method uses soft constraints to estimate those state variables that are known to vary slowly with time.","downloadable_attachments":[{"id":53637989,"asset_id":33622388,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":45665102,"first_name":"Daniel","last_name":"Simon","domain_name":"independent","page_name":"DanielSimon25","display_name":"Daniel Simon","profile_url":"https://independent.academia.edu/DanielSimon25?f_ri=49146","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":60,"name":"Mechanical Engineering","url":"https://www.academia.edu/Documents/in/Mechanical_Engineering?f_ri=49146","nofollow":true},{"id":305,"name":"Applied Mathematics","url":"https://www.academia.edu/Documents/in/Applied_Mathematics?f_ri=49146","nofollow":true},{"id":307,"name":"Mathematical Statistics","url":"https://www.academia.edu/Documents/in/Mathematical_Statistics?f_ri=49146","nofollow":true},{"id":428,"name":"Algorithms","url":"https://www.academia.edu/Documents/in/Algorithms?f_ri=49146","nofollow":true},{"id":6177,"name":"Modeling","url":"https://www.academia.edu/Documents/in/Modeling?f_ri=49146"},{"id":29414,"name":"Estimation Theory","url":"https://www.academia.edu/Documents/in/Estimation_Theory?f_ri=49146"},{"id":36533,"name":"Fault Detection and Isolation","url":"https://www.academia.edu/Documents/in/Fault_Detection_and_Isolation?f_ri=49146"},{"id":39920,"name":"Parameter estimation","url":"https://www.academia.edu/Documents/in/Parameter_estimation?f_ri=49146"},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146"},{"id":67380,"name":"Kalman Filtering","url":"https://www.academia.edu/Documents/in/Kalman_Filtering?f_ri=49146"},{"id":117101,"name":"Fault diagnosis","url":"https://www.academia.edu/Documents/in/Fault_diagnosis?f_ri=49146"},{"id":228734,"name":"Dynamical System","url":"https://www.academia.edu/Documents/in/Dynamical_System?f_ri=49146"},{"id":250422,"name":"Random Noise","url":"https://www.academia.edu/Documents/in/Random_Noise?f_ri=49146"},{"id":251170,"name":"Soft Constraints","url":"https://www.academia.edu/Documents/in/Soft_Constraints?f_ri=49146"},{"id":330839,"name":"Analytical Method","url":"https://www.academia.edu/Documents/in/Analytical_Method?f_ri=49146"},{"id":419504,"name":"Heuristic","url":"https://www.academia.edu/Documents/in/Heuristic?f_ri=49146"},{"id":679783,"name":"Boolean Satisfiability","url":"https://www.academia.edu/Documents/in/Boolean_Satisfiability?f_ri=49146"},{"id":794925,"name":"Linear Filtering","url":"https://www.academia.edu/Documents/in/Linear_Filtering?f_ri=49146"},{"id":868912,"name":"Dynamic System","url":"https://www.academia.edu/Documents/in/Dynamic_System?f_ri=49146"},{"id":1237788,"name":"Electrical And Electronic Engineering","url":"https://www.academia.edu/Documents/in/Electrical_And_Electronic_Engineering?f_ri=49146"},{"id":1302340,"name":"Electrical and Computer Engineering","url":"https://www.academia.edu/Documents/in/Electrical_and_Computer_Engineering?f_ri=49146"},{"id":1460800,"name":"Quadratic Programming","url":"https://www.academia.edu/Documents/in/Quadratic_Programming?f_ri=49146"},{"id":1950746,"name":"Heuristic algorithm","url":"https://www.academia.edu/Documents/in/Heuristic_algorithm?f_ri=49146"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_1525989" data-work_id="1525989" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/1525989/Online_Calibration_of_Inertial_Sensors_Using_Kalman_Filters_and_Artificial_Neural_Networks">Online Calibration of Inertial Sensors Using Kalman Filters and Artificial Neural Networks</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Navigation is defined as finding the position of a moving vehicle and inertial navigation is among these methods. Unfortunately, inertial navigation has errors due to different reasons such as inertial sensors. These errors must be... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_1525989" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Navigation is defined as finding the position of a moving vehicle and inertial navigation is among these methods. Unfortunately, inertial navigation has errors due to different reasons such as inertial sensors. These errors must be corrected by some means. In this paper, a method based on Kalman filters and artificial neural networks is introduced to calibrate inertial sensors during the navigation. Moreover, the proposed method provides better accuracy of the sensor models, when the navigation aid is not present for some times. Simulation results show the effectiveness of the proposed method as compared to the Kalman filter.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/1525989" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="6f021163ef973edbc30b2d3ff05e7cc2" rel="nofollow" data-download="{"attachment_id":12472126,"asset_id":1525989,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/12472126/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="1620834" href="https://independent.academia.edu/GaneshKumar5">Ganesh Kumar</a><script data-card-contents-for-user="1620834" type="text/json">{"id":1620834,"first_name":"Ganesh","last_name":"Kumar","domain_name":"independent","page_name":"GaneshKumar5","display_name":"Ganesh Kumar","profile_url":"https://independent.academia.edu/GaneshKumar5?f_ri=49146","photo":"https://0.academia-photos.com/1620834/565217/704023/s65_ganesh.kumar.jpg"}</script></span></span></li><li class="js-paper-rank-work_1525989 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="1525989"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 1525989, container: ".js-paper-rank-work_1525989", }); 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$(".js-view-count[data-work-id=1525989]").text(description); $(".js-view-count-work_1525989").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_1525989").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="1525989"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="10003" rel="nofollow" href="https://www.academia.edu/Documents/in/Inertial_navigation">Inertial navigation</a>, <script data-card-contents-for-ri="10003" type="text/json">{"id":10003,"name":"Inertial navigation","url":"https://www.academia.edu/Documents/in/Inertial_navigation?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="44389" rel="nofollow" href="https://www.academia.edu/Documents/in/Artificial_Neural_Networks_for_modeling_purposes">Artificial Neural Networks for modeling purposes</a>, <script data-card-contents-for-ri="44389" type="text/json">{"id":44389,"name":"Artificial Neural Networks for modeling purposes","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Networks_for_modeling_purposes?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="49146" rel="nofollow" href="https://www.academia.edu/Documents/in/Kalman_Filter">Kalman Filter</a>, <script data-card-contents-for-ri="49146" type="text/json">{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1211304" rel="nofollow" href="https://www.academia.edu/Documents/in/Artificial_Neural_Network">Artificial Neural Network</a><script data-card-contents-for-ri="1211304" type="text/json">{"id":1211304,"name":"Artificial Neural Network","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Network?f_ri=49146","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=1525989]'), work: {"id":1525989,"title":"Online Calibration of Inertial Sensors Using Kalman Filters and Artificial Neural Networks","created_at":"2012-04-27T04:14:30.588-07:00","url":"https://www.academia.edu/1525989/Online_Calibration_of_Inertial_Sensors_Using_Kalman_Filters_and_Artificial_Neural_Networks?f_ri=49146","dom_id":"work_1525989","summary":"Navigation is defined as finding the position of a moving vehicle and inertial navigation is among these methods. Unfortunately, inertial navigation has errors due to different reasons such as inertial sensors. These errors must be corrected by some means. In this paper, a method based on Kalman filters and artificial neural networks is introduced to calibrate inertial sensors during the navigation. Moreover, the proposed method provides better accuracy of the sensor models, when the navigation aid is not present for some times. Simulation results show the effectiveness of the proposed method as compared to the Kalman filter.","downloadable_attachments":[{"id":12472126,"asset_id":1525989,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":1620834,"first_name":"Ganesh","last_name":"Kumar","domain_name":"independent","page_name":"GaneshKumar5","display_name":"Ganesh Kumar","profile_url":"https://independent.academia.edu/GaneshKumar5?f_ri=49146","photo":"https://0.academia-photos.com/1620834/565217/704023/s65_ganesh.kumar.jpg"}],"research_interests":[{"id":10003,"name":"Inertial navigation","url":"https://www.academia.edu/Documents/in/Inertial_navigation?f_ri=49146","nofollow":true},{"id":44389,"name":"Artificial Neural Networks for modeling purposes","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Networks_for_modeling_purposes?f_ri=49146","nofollow":true},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true},{"id":1211304,"name":"Artificial Neural Network","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Network?f_ri=49146","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_2403292" data-work_id="2403292" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/2403292/Restoration_of_color_images_by_multichannel_Kalman_filtering">Restoration of color images by multichannel Kalman filtering</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/2403292" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="4367be040dab5cb31b230aa4d4a1afdc" rel="nofollow" data-download="{"attachment_id":50644684,"asset_id":2403292,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/50644684/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="3122313" href="https://upatras.academia.edu/NikolasPGalatsanos">Nikolas P. Galatsanos</a><script data-card-contents-for-user="3122313" type="text/json">{"id":3122313,"first_name":"Nikolas P.","last_name":"Galatsanos","domain_name":"upatras","page_name":"NikolasPGalatsanos","display_name":"Nikolas P. 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In the present paper we move forward in the application of graph theoretical tools in functional connectivity by investigating high-level cognitive processing in healthy adults, in a manner similar to that used in psychological research in the framework of event-related potentials (ERPs). More specifically we aim at investigating how graph theoretical approaches can help to discover systematic and task-dependent differences in high-level cognitive processes such as language perception. 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Collection","url":"https://www.academia.edu/Documents/in/Data_Collection?f_ri=49146"},{"id":153240,"name":"Reference Data","url":"https://www.academia.edu/Documents/in/Reference_Data?f_ri=49146"},{"id":181597,"name":"Root-Mean Square Error","url":"https://www.academia.edu/Documents/in/Root-Mean_Square_Error?f_ri=49146"},{"id":244814,"name":"Clinical Sciences","url":"https://www.academia.edu/Documents/in/Clinical_Sciences?f_ri=49146"},{"id":307156,"name":"Gait and Posture","url":"https://www.academia.edu/Documents/in/Gait_and_Posture?f_ri=49146"},{"id":329007,"name":"Filter Design","url":"https://www.academia.edu/Documents/in/Filter_Design?f_ri=49146"},{"id":377624,"name":"Medical Linear Accelerator","url":"https://www.academia.edu/Documents/in/Medical_Linear_Accelerator?f_ri=49146"},{"id":611814,"name":"Correlation coefficient","url":"https://www.academia.edu/Documents/in/Correlation_coefficient?f_ri=49146"},{"id":737958,"name":"Optimal Bayesian Estimation","url":"https://www.academia.edu/Documents/in/Optimal_Bayesian_Estimation?f_ri=49146"},{"id":965809,"name":"Inertial Sensor","url":"https://www.academia.edu/Documents/in/Inertial_Sensor?f_ri=49146"},{"id":1142759,"name":"Healthy Subjects","url":"https://www.academia.edu/Documents/in/Healthy_Subjects?f_ri=49146"},{"id":1594510,"name":"Inertial Measurement Unit","url":"https://www.academia.edu/Documents/in/Inertial_Measurement_Unit?f_ri=49146"},{"id":1902100,"name":"Biomechanical Phenomena","url":"https://www.academia.edu/Documents/in/Biomechanical_Phenomena?f_ri=49146"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_21887715" data-work_id="21887715" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/21887715/Infinite_dimensional_sampled_data_Kalman_filter">Infinite-dimensional sampled-data Kalman filter</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper contains a brief overview of the infinitedimensional sampled-data Kalman filter (ISKF) derivation [1]. The ISKF is essentially a mathematical extension of the (finitedimensional) sampled-data Kalman filter that applies to a... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_21887715" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper contains a brief overview of the infinitedimensional sampled-data Kalman filter (ISKF) derivation [1]. The ISKF is essentially a mathematical extension of the (finitedimensional) sampled-data Kalman filter that applies to a larger class of problems that satisfy the strongly continuous semigroup property which includes certain partial and delay differential equations.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/21887715" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="fe19fb2ea9d82de8478cb12651ce0a7f" rel="nofollow" data-download="{"attachment_id":42624517,"asset_id":21887715,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/42624517/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="32290804" href="https://afit.academia.edu/MarkEOxley">Mark E Oxley</a><script data-card-contents-for-user="32290804" type="text/json">{"id":32290804,"first_name":"Mark","last_name":"Oxley","domain_name":"afit","page_name":"MarkEOxley","display_name":"Mark E Oxley","profile_url":"https://afit.academia.edu/MarkEOxley?f_ri=49146","photo":"https://0.academia-photos.com/32290804/9939132/11082453/s65_mark.oxley.jpg"}</script></span></span></li><li class="js-paper-rank-work_21887715 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="21887715"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 21887715, container: ".js-paper-rank-work_21887715", }); });</script></li><li class="js-percentile-work_21887715 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 21887715; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_21887715"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_21887715 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="21887715"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 21887715; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=21887715]").text(description); $(".js-view-count-work_21887715").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_21887715").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="21887715"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">12</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="377" rel="nofollow" href="https://www.academia.edu/Documents/in/Partial_Differential_Equations">Partial Differential Equations</a>, <script data-card-contents-for-ri="377" type="text/json">{"id":377,"name":"Partial Differential Equations","url":"https://www.academia.edu/Documents/in/Partial_Differential_Equations?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="9355" rel="nofollow" href="https://www.academia.edu/Documents/in/Equations_of_State">Equations of State</a>, <script data-card-contents-for-ri="9355" type="text/json">{"id":9355,"name":"Equations of State","url":"https://www.academia.edu/Documents/in/Equations_of_State?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="43131" rel="nofollow" href="https://www.academia.edu/Documents/in/Stochastic_processes">Stochastic processes</a>, <script data-card-contents-for-ri="43131" type="text/json">{"id":43131,"name":"Stochastic processes","url":"https://www.academia.edu/Documents/in/Stochastic_processes?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="49146" rel="nofollow" href="https://www.academia.edu/Documents/in/Kalman_Filter">Kalman Filter</a><script data-card-contents-for-ri="49146" type="text/json">{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=21887715]'), work: {"id":21887715,"title":"Infinite-dimensional sampled-data Kalman filter","created_at":"2016-02-12T12:22:44.029-08:00","url":"https://www.academia.edu/21887715/Infinite_dimensional_sampled_data_Kalman_filter?f_ri=49146","dom_id":"work_21887715","summary":"This paper contains a brief overview of the infinitedimensional sampled-data Kalman filter (ISKF) derivation [1]. The ISKF is essentially a mathematical extension of the (finitedimensional) sampled-data Kalman filter that applies to a larger class of problems that satisfy the strongly continuous semigroup property which includes certain partial and delay differential equations.","downloadable_attachments":[{"id":42624517,"asset_id":21887715,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":32290804,"first_name":"Mark","last_name":"Oxley","domain_name":"afit","page_name":"MarkEOxley","display_name":"Mark E Oxley","profile_url":"https://afit.academia.edu/MarkEOxley?f_ri=49146","photo":"https://0.academia-photos.com/32290804/9939132/11082453/s65_mark.oxley.jpg"}],"research_interests":[{"id":377,"name":"Partial Differential Equations","url":"https://www.academia.edu/Documents/in/Partial_Differential_Equations?f_ri=49146","nofollow":true},{"id":9355,"name":"Equations of State","url":"https://www.academia.edu/Documents/in/Equations_of_State?f_ri=49146","nofollow":true},{"id":43131,"name":"Stochastic processes","url":"https://www.academia.edu/Documents/in/Stochastic_processes?f_ri=49146","nofollow":true},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true},{"id":84487,"name":"Delay Differential Equation","url":"https://www.academia.edu/Documents/in/Delay_Differential_Equation?f_ri=49146"},{"id":134653,"name":"THERMAL DIFFUSIVITY","url":"https://www.academia.edu/Documents/in/THERMAL_DIFFUSIVITY?f_ri=49146"},{"id":235663,"name":"Temperature Distribution","url":"https://www.academia.edu/Documents/in/Temperature_Distribution?f_ri=49146"},{"id":679783,"name":"Boolean Satisfiability","url":"https://www.academia.edu/Documents/in/Boolean_Satisfiability?f_ri=49146"},{"id":694897,"name":"Dynamic Model of WSN","url":"https://www.academia.edu/Documents/in/Dynamic_Model_of_WSN?f_ri=49146"},{"id":991097,"name":"Continuous Time Systems","url":"https://www.academia.edu/Documents/in/Continuous_Time_Systems?f_ri=49146"},{"id":991101,"name":"Discrete Time Systems","url":"https://www.academia.edu/Documents/in/Discrete_Time_Systems?f_ri=49146"},{"id":1132242,"name":"Multidimensional Systems","url":"https://www.academia.edu/Documents/in/Multidimensional_Systems?f_ri=49146"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_4598638" data-work_id="4598638" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/4598638/Mobility_estimation_for_wireless_networks_using_round_trip_time_RTT">Mobility estimation for wireless networks using round trip time (RTT</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We propose an asynchronous, low cost, and accurate mobility estimation scheme for wireless mobile networks. This scheme considers the round-trip time (RTT) of the signal from the mobile station to the base station as observation and... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_4598638" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We propose an asynchronous, low cost, and accurate mobility estimation scheme for wireless mobile networks. This scheme considers the round-trip time (RTT) of the signal from the mobile station to the base station as observation and estimates position and speed of the mobile user in two dimensions. Our scheme uses an earlier proposed autoregressive mobility model,</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/4598638" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="d7bc6c251460e5bb6333bc463ea444f2" rel="nofollow" data-download="{"attachment_id":49770232,"asset_id":4598638,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/49770232/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="5765623" href="https://karachi.academia.edu/AsadFaraz">Asad Faraz</a><script data-card-contents-for-user="5765623" type="text/json">{"id":5765623,"first_name":"Asad","last_name":"Faraz","domain_name":"karachi","page_name":"AsadFaraz","display_name":"Asad Faraz","profile_url":"https://karachi.academia.edu/AsadFaraz?f_ri=49146","photo":"https://0.academia-photos.com/5765623/2488315/2892800/s65_asad.faraz.jpg"}</script></span></span></li><li class="js-paper-rank-work_4598638 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="4598638"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 4598638, container: ".js-paper-rank-work_4598638", }); });</script></li><li class="js-percentile-work_4598638 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 4598638; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_4598638"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_4598638 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="4598638"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 4598638; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=4598638]").text(description); $(".js-view-count-work_4598638").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_4598638").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="4598638"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">10</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="49146" rel="nofollow" href="https://www.academia.edu/Documents/in/Kalman_Filter">Kalman Filter</a>, <script data-card-contents-for-ri="49146" type="text/json">{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="65429" rel="nofollow" href="https://www.academia.edu/Documents/in/Wireless_Network">Wireless Network</a>, <script data-card-contents-for-ri="65429" type="text/json">{"id":65429,"name":"Wireless Network","url":"https://www.academia.edu/Documents/in/Wireless_Network?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="607446" rel="nofollow" href="https://www.academia.edu/Documents/in/Mobile_Network">Mobile Network</a>, <script data-card-contents-for-ri="607446" type="text/json">{"id":607446,"name":"Mobile Network","url":"https://www.academia.edu/Documents/in/Mobile_Network?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="970277" rel="nofollow" href="https://www.academia.edu/Documents/in/Two_Dimensions">Two Dimensions</a><script data-card-contents-for-ri="970277" type="text/json">{"id":970277,"name":"Two Dimensions","url":"https://www.academia.edu/Documents/in/Two_Dimensions?f_ri=49146","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=4598638]'), work: {"id":4598638,"title":"Mobility estimation for wireless networks using round trip time (RTT","created_at":"2013-09-26T16:24:55.985-07:00","url":"https://www.academia.edu/4598638/Mobility_estimation_for_wireless_networks_using_round_trip_time_RTT?f_ri=49146","dom_id":"work_4598638","summary":"We propose an asynchronous, low cost, and accurate mobility estimation scheme for wireless mobile networks. This scheme considers the round-trip time (RTT) of the signal from the mobile station to the base station as observation and estimates position and speed of the mobile user in two dimensions. Our scheme uses an earlier proposed autoregressive mobility model,","downloadable_attachments":[{"id":49770232,"asset_id":4598638,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":5765623,"first_name":"Asad","last_name":"Faraz","domain_name":"karachi","page_name":"AsadFaraz","display_name":"Asad Faraz","profile_url":"https://karachi.academia.edu/AsadFaraz?f_ri=49146","photo":"https://0.academia-photos.com/5765623/2488315/2892800/s65_asad.faraz.jpg"}],"research_interests":[{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true},{"id":65429,"name":"Wireless Network","url":"https://www.academia.edu/Documents/in/Wireless_Network?f_ri=49146","nofollow":true},{"id":607446,"name":"Mobile Network","url":"https://www.academia.edu/Documents/in/Mobile_Network?f_ri=49146","nofollow":true},{"id":970277,"name":"Two Dimensions","url":"https://www.academia.edu/Documents/in/Two_Dimensions?f_ri=49146","nofollow":true},{"id":1011798,"name":"Mobility Model","url":"https://www.academia.edu/Documents/in/Mobility_Model?f_ri=49146"},{"id":1158774,"name":"Round Trip Time","url":"https://www.academia.edu/Documents/in/Round_Trip_Time?f_ri=49146"},{"id":1555652,"name":"Base station","url":"https://www.academia.edu/Documents/in/Base_station?f_ri=49146"},{"id":1839586,"name":"Time of Arrival","url":"https://www.academia.edu/Documents/in/Time_of_Arrival?f_ri=49146"},{"id":1993758,"name":"Autoregressive model","url":"https://www.academia.edu/Documents/in/Autoregressive_model?f_ri=49146"},{"id":2069911,"name":"Mobile Station","url":"https://www.academia.edu/Documents/in/Mobile_Station?f_ri=49146"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_55051634" data-work_id="55051634" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/55051634/Integrating_generic_sensor_fusion_algorithms_with_sound_state_representations_through_encapsulation_of_manifolds">Integrating generic sensor fusion algorithms with sound state representations through encapsulation of manifolds</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Common estimation algorithms, such as least squares estimation or the Kalman filter, operate on a state in a state space S that is represented as a real-valued vector. However, for many quantities, most notably orientations in 3D, S is... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_55051634" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Common estimation algorithms, such as least squares estimation or the Kalman filter, operate on a state in a state space S that is represented as a real-valued vector. However, for many quantities, most notably orientations in 3D, S is not a vector space, but a so-called manifold, i.e. it behaves like a vector space locally but has a more complex global topological structure. For integrating these quantities, several ad-hoc approaches have been proposed. Here, we present a principled solution to this problem where the structure of the manifold S is encapsulated by two operators, state displacement : S × R n → S and its inverse : S × S → R n. These operators provide a local vector-space view δ → x δ around a given state x. Generic estimation algorithms can then work on the manifold S mainly by replacing +/− with / where appropriate. We analyze these operators axiomatically, and demonstrate their use in least-squares estimation and the Unscented Kalman Filter. Moreover, we exploit the idea of encapsulation from a software engineering perspective in the Manifold Toolkit, where the / operators mediate between a "flat-vector" view for the generic algorithm and a "named-members" view for the problem specific functions.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/55051634" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="56fd4e48a2a7059552489d7674d0492e" rel="nofollow" data-download="{"attachment_id":71111337,"asset_id":55051634,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/71111337/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="35384910" href="https://independent.academia.edu/UdoFrese">Udo Frese</a><script data-card-contents-for-user="35384910" type="text/json">{"id":35384910,"first_name":"Udo","last_name":"Frese","domain_name":"independent","page_name":"UdoFrese","display_name":"Udo Frese","profile_url":"https://independent.academia.edu/UdoFrese?f_ri=49146","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_55051634 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="55051634"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 55051634, container: ".js-paper-rank-work_55051634", }); });</script></li><li class="js-percentile-work_55051634 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 55051634; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_55051634"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_55051634 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="55051634"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 55051634; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=55051634]").text(description); $(".js-view-count-work_55051634").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_55051634").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="55051634"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">7</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="449" rel="nofollow" href="https://www.academia.edu/Documents/in/Software_Engineering">Software Engineering</a>, <script data-card-contents-for-ri="449" type="text/json">{"id":449,"name":"Software Engineering","url":"https://www.academia.edu/Documents/in/Software_Engineering?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="49146" rel="nofollow" href="https://www.academia.edu/Documents/in/Kalman_Filter">Kalman Filter</a>, <script data-card-contents-for-ri="49146" type="text/json">{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="92088" rel="nofollow" href="https://www.academia.edu/Documents/in/Information_Fusion">Information Fusion</a>, <script data-card-contents-for-ri="92088" type="text/json">{"id":92088,"name":"Information Fusion","url":"https://www.academia.edu/Documents/in/Information_Fusion?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="135913" rel="nofollow" href="https://www.academia.edu/Documents/in/State_Space">State Space</a><script data-card-contents-for-ri="135913" type="text/json">{"id":135913,"name":"State Space","url":"https://www.academia.edu/Documents/in/State_Space?f_ri=49146","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=55051634]'), work: {"id":55051634,"title":"Integrating generic sensor fusion algorithms with sound state representations through encapsulation of manifolds","created_at":"2021-10-03T03:06:31.563-07:00","url":"https://www.academia.edu/55051634/Integrating_generic_sensor_fusion_algorithms_with_sound_state_representations_through_encapsulation_of_manifolds?f_ri=49146","dom_id":"work_55051634","summary":"Common estimation algorithms, such as least squares estimation or the Kalman filter, operate on a state in a state space S that is represented as a real-valued vector. However, for many quantities, most notably orientations in 3D, S is not a vector space, but a so-called manifold, i.e. it behaves like a vector space locally but has a more complex global topological structure. For integrating these quantities, several ad-hoc approaches have been proposed. Here, we present a principled solution to this problem where the structure of the manifold S is encapsulated by two operators, state displacement : S × R n → S and its inverse : S × S → R n. These operators provide a local vector-space view δ → x δ around a given state x. Generic estimation algorithms can then work on the manifold S mainly by replacing +/− with / where appropriate. We analyze these operators axiomatically, and demonstrate their use in least-squares estimation and the Unscented Kalman Filter. Moreover, we exploit the idea of encapsulation from a software engineering perspective in the Manifold Toolkit, where the / operators mediate between a \"flat-vector\" view for the generic algorithm and a \"named-members\" view for the problem specific functions.","downloadable_attachments":[{"id":71111337,"asset_id":55051634,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":35384910,"first_name":"Udo","last_name":"Frese","domain_name":"independent","page_name":"UdoFrese","display_name":"Udo Frese","profile_url":"https://independent.academia.edu/UdoFrese?f_ri=49146","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":449,"name":"Software Engineering","url":"https://www.academia.edu/Documents/in/Software_Engineering?f_ri=49146","nofollow":true},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true},{"id":92088,"name":"Information Fusion","url":"https://www.academia.edu/Documents/in/Information_Fusion?f_ri=49146","nofollow":true},{"id":135913,"name":"State Space","url":"https://www.academia.edu/Documents/in/State_Space?f_ri=49146","nofollow":true},{"id":143115,"name":"Sensor Fusion","url":"https://www.academia.edu/Documents/in/Sensor_Fusion?f_ri=49146"},{"id":325034,"name":"Unscented Kalman Filter","url":"https://www.academia.edu/Documents/in/Unscented_Kalman_Filter?f_ri=49146"},{"id":732001,"name":"Vector Space","url":"https://www.academia.edu/Documents/in/Vector_Space?f_ri=49146"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_45181088" data-work_id="45181088" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/45181088/State_Estimation_of_Autonomous_Ground_Vehicle_using_Kalman_Filter">State Estimation of Autonomous Ground Vehicle using Kalman Filter</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">An autonomous ground vehicle has various devices from where it collects data and performs an action. State estimation with noise present in it is one of the key requirements for many real-time problems and engineering. State estimation is... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_45181088" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">An autonomous ground vehicle has various devices from where it collects data and performs an action. State estimation with noise present in it is one of the key requirements for many real-time problems and engineering. State estimation is an essential requirement for many real-life systems from local to multi-resource information integration. The Kalman filter and its variability have been used successfully in solving state equity problems. The Kalman filter can be used to predict the next set of actions our car will take based on the information received. In this paper we have implemented kalman filter for state estimation and results are obtained. I. INTRODUCTION An autonomous ground vehicle is a portable robot that combines multiple sensor navigation with positioning, intelligent decision making and control technology. The aim of the study was to identify autonomous driving in a car instead of human drivers and to improve vehicle safety and transport efficiency. The main purpose of self-driving cars is to transport people from one place to another without the help of a driver. The self-driving system should control many parameters, including speed, acceleration, orientation, and maneuvering, so that the car can be driven without human help. All of these control parameters are controlled by a decision-making module, which handles all visual data from the vehicle and sensors. The visual module determines the relationship between the ego car and the surrounding environment. Vehicle stability control is mainly based on various parameters (e.g. lateral acceleration, yaw rate) of movement to determine the appropriate control strategy and achieve the safety of vehicles moving in effective control. Often, the condition of a car can be measured by the variety of sensors in the car. Restricted by the current level of technology, some important variables require the use of more expensive measuring devices (such as speed, yaw scale), or were unable to take precise measurements (such as slip angle), parameter estimation is the best solution to meet the requirements of a robust vehicle control system. State estimation having noise in the system is one of the key requirements for many real-time and engineering problems. Accuracy is the primary constraint in applications such as target tracking, automotive land vehicle and flight control systems, non-linear process control and optimization, real time surveillance and life safety applications. The classical Kalman filtering technique (KF) which is a state estimation technique which can solve this problem and is widely used in many fields. The main objective of KF is tracking the dynamic states in presence of incomplete and noisy measurements. The KF dynamics results from cycles of filtering and prediction. The Gaussian probability density functions framework is used to derive and interpreters the cycle dynamics. On the other hand, mostly the problems in real are non-linear in nature, and the kalman filter performance degrades when there is a violation of assumptions of the system Gaussian distribution and system linearity To improve performance and overcome KF limitations compared to system incomprehensibility, the Extension Kalman Filter (EKF) was introduced. EKF manages system irregularities by converting non linear system to linear by inserting limitations of the first Taylor series around the current error and covariance error.It uses the output of the component to represent the rate of change of non-linear functions, which aims to maintain Gaussian sound. If the state is a vector, then the partial derivative parameters can be grouped into new matrix, called the Jacobian matrix. 1) Prediction: In this step the Kalman filter predicts new values from initial values and predicts the uncertainty / error / variation of our prediction according to the various processes present in the system. Our model will assume that the AGV will move at a constant speed due to zero acceleration but will actually have a dynamic speed i.e. the speed will change from time to time. This change in speed of this car is uncertain / error / variance and we bring it into our system by processing noise.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/45181088" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="e105a390f63b0a8a1521d54232337f5d" rel="nofollow" data-download="{"attachment_id":65766889,"asset_id":45181088,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/65766889/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="6079060" href="https://independent.academia.edu/IJRASETPublication">IJRASET Publication</a><script data-card-contents-for-user="6079060" type="text/json">{"id":6079060,"first_name":"IJRASET","last_name":"Publication","domain_name":"independent","page_name":"IJRASETPublication","display_name":"IJRASET Publication","profile_url":"https://independent.academia.edu/IJRASETPublication?f_ri=49146","photo":"https://0.academia-photos.com/6079060/2549300/33111525/s65_ijraset.publication.jpg"}</script></span></span></li><li class="js-paper-rank-work_45181088 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="45181088"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 45181088, container: ".js-paper-rank-work_45181088", }); });</script></li><li class="js-percentile-work_45181088 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 45181088; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_45181088"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_45181088 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="45181088"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 45181088; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=45181088]").text(description); $(".js-view-count-work_45181088").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_45181088").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="45181088"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">4</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="33252" rel="nofollow" href="https://www.academia.edu/Documents/in/State_Estimation">State Estimation</a>, <script data-card-contents-for-ri="33252" type="text/json">{"id":33252,"name":"State Estimation","url":"https://www.academia.edu/Documents/in/State_Estimation?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="49146" rel="nofollow" href="https://www.academia.edu/Documents/in/Kalman_Filter">Kalman Filter</a>, <script data-card-contents-for-ri="49146" type="text/json">{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="84688" rel="nofollow" href="https://www.academia.edu/Documents/in/Autonomous_Vehicles">Autonomous Vehicles</a>, <script data-card-contents-for-ri="84688" type="text/json">{"id":84688,"name":"Autonomous Vehicles","url":"https://www.academia.edu/Documents/in/Autonomous_Vehicles?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1918033" rel="nofollow" href="https://www.academia.edu/Documents/in/Velocity_Control">Velocity Control</a><script data-card-contents-for-ri="1918033" type="text/json">{"id":1918033,"name":"Velocity Control","url":"https://www.academia.edu/Documents/in/Velocity_Control?f_ri=49146","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=45181088]'), work: {"id":45181088,"title":"State Estimation of Autonomous Ground Vehicle using Kalman Filter","created_at":"2021-02-24T02:11:53.672-08:00","url":"https://www.academia.edu/45181088/State_Estimation_of_Autonomous_Ground_Vehicle_using_Kalman_Filter?f_ri=49146","dom_id":"work_45181088","summary":"An autonomous ground vehicle has various devices from where it collects data and performs an action. State estimation with noise present in it is one of the key requirements for many real-time problems and engineering. State estimation is an essential requirement for many real-life systems from local to multi-resource information integration. The Kalman filter and its variability have been used successfully in solving state equity problems. The Kalman filter can be used to predict the next set of actions our car will take based on the information received. In this paper we have implemented kalman filter for state estimation and results are obtained. I. INTRODUCTION An autonomous ground vehicle is a portable robot that combines multiple sensor navigation with positioning, intelligent decision making and control technology. The aim of the study was to identify autonomous driving in a car instead of human drivers and to improve vehicle safety and transport efficiency. The main purpose of self-driving cars is to transport people from one place to another without the help of a driver. The self-driving system should control many parameters, including speed, acceleration, orientation, and maneuvering, so that the car can be driven without human help. All of these control parameters are controlled by a decision-making module, which handles all visual data from the vehicle and sensors. The visual module determines the relationship between the ego car and the surrounding environment. Vehicle stability control is mainly based on various parameters (e.g. lateral acceleration, yaw rate) of movement to determine the appropriate control strategy and achieve the safety of vehicles moving in effective control. Often, the condition of a car can be measured by the variety of sensors in the car. Restricted by the current level of technology, some important variables require the use of more expensive measuring devices (such as speed, yaw scale), or were unable to take precise measurements (such as slip angle), parameter estimation is the best solution to meet the requirements of a robust vehicle control system. State estimation having noise in the system is one of the key requirements for many real-time and engineering problems. Accuracy is the primary constraint in applications such as target tracking, automotive land vehicle and flight control systems, non-linear process control and optimization, real time surveillance and life safety applications. The classical Kalman filtering technique (KF) which is a state estimation technique which can solve this problem and is widely used in many fields. The main objective of KF is tracking the dynamic states in presence of incomplete and noisy measurements. The KF dynamics results from cycles of filtering and prediction. The Gaussian probability density functions framework is used to derive and interpreters the cycle dynamics. On the other hand, mostly the problems in real are non-linear in nature, and the kalman filter performance degrades when there is a violation of assumptions of the system Gaussian distribution and system linearity To improve performance and overcome KF limitations compared to system incomprehensibility, the Extension Kalman Filter (EKF) was introduced. EKF manages system irregularities by converting non linear system to linear by inserting limitations of the first Taylor series around the current error and covariance error.It uses the output of the component to represent the rate of change of non-linear functions, which aims to maintain Gaussian sound. If the state is a vector, then the partial derivative parameters can be grouped into new matrix, called the Jacobian matrix. 1) Prediction: In this step the Kalman filter predicts new values from initial values and predicts the uncertainty / error / variation of our prediction according to the various processes present in the system. Our model will assume that the AGV will move at a constant speed due to zero acceleration but will actually have a dynamic speed i.e. the speed will change from time to time. This change in speed of this car is uncertain / error / variance and we bring it into our system by processing noise.","downloadable_attachments":[{"id":65766889,"asset_id":45181088,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":6079060,"first_name":"IJRASET","last_name":"Publication","domain_name":"independent","page_name":"IJRASETPublication","display_name":"IJRASET Publication","profile_url":"https://independent.academia.edu/IJRASETPublication?f_ri=49146","photo":"https://0.academia-photos.com/6079060/2549300/33111525/s65_ijraset.publication.jpg"}],"research_interests":[{"id":33252,"name":"State Estimation","url":"https://www.academia.edu/Documents/in/State_Estimation?f_ri=49146","nofollow":true},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true},{"id":84688,"name":"Autonomous Vehicles","url":"https://www.academia.edu/Documents/in/Autonomous_Vehicles?f_ri=49146","nofollow":true},{"id":1918033,"name":"Velocity Control","url":"https://www.academia.edu/Documents/in/Velocity_Control?f_ri=49146","nofollow":true}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_33545205" data-work_id="33545205" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/33545205/Fault_detection_in_sensor_information_fusion_Kalman_filter">Fault detection in sensor information fusion Kalman filter</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">An approach to the test of the sensor information fusion Kalman filter is proposed. It is based on the introduced statistics of mathematical expectation of the spectral norm of a normalized innovation matrix. The approach allows for... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_33545205" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">An approach to the test of the sensor information fusion Kalman filter is proposed. It is based on the introduced statistics of mathematical expectation of the spectral norm of a normalized innovation matrix. The approach allows for simultaneous test of the mathematical expectation and the variance of innovation sequence in real time and does not require a priori information on values of the change in its statistical characteristics under faults. Using this approach, fault detection algorithm for the sensor information fusion Kalman filter is developed. ᭧</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/33545205" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="00a06925c1bfef7d4366fa88b5d56858" rel="nofollow" data-download="{"attachment_id":53575018,"asset_id":33545205,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/53575018/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="64253856" href="https://esenyurt.academia.edu/AL%C4%B0OKATAN">ALİ OKATAN</a><script data-card-contents-for-user="64253856" type="text/json">{"id":64253856,"first_name":"ALİ","last_name":"OKATAN","domain_name":"esenyurt","page_name":"ALİOKATAN","display_name":"ALİ OKATAN","profile_url":"https://esenyurt.academia.edu/AL%C4%B0OKATAN?f_ri=49146","photo":"https://0.academia-photos.com/64253856/19994586/19780120/s65_ali_.okatan.jpg"}</script></span></span></li><li class="js-paper-rank-work_33545205 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="33545205"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 33545205, container: ".js-paper-rank-work_33545205", }); });</script></li><li class="js-percentile-work_33545205 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 33545205; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_33545205"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_33545205 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="33545205"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 33545205; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=33545205]").text(description); $(".js-view-count-work_33545205").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_33545205").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="33545205"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">9</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="431" rel="nofollow" href="https://www.academia.edu/Documents/in/Parallel_Algorithms">Parallel Algorithms</a>, <script data-card-contents-for-ri="431" type="text/json">{"id":431,"name":"Parallel Algorithms","url":"https://www.academia.edu/Documents/in/Parallel_Algorithms?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="11898" rel="nofollow" href="https://www.academia.edu/Documents/in/Fault_Detection">Fault Detection</a>, <script data-card-contents-for-ri="11898" type="text/json">{"id":11898,"name":"Fault Detection","url":"https://www.academia.edu/Documents/in/Fault_Detection?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="49146" rel="nofollow" href="https://www.academia.edu/Documents/in/Kalman_Filter">Kalman Filter</a>, <script data-card-contents-for-ri="49146" type="text/json">{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="92088" rel="nofollow" href="https://www.academia.edu/Documents/in/Information_Fusion">Information Fusion</a><script data-card-contents-for-ri="92088" type="text/json">{"id":92088,"name":"Information Fusion","url":"https://www.academia.edu/Documents/in/Information_Fusion?f_ri=49146","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=33545205]'), work: {"id":33545205,"title":"Fault detection in sensor information fusion Kalman filter","created_at":"2017-06-18T22:52:55.259-07:00","url":"https://www.academia.edu/33545205/Fault_detection_in_sensor_information_fusion_Kalman_filter?f_ri=49146","dom_id":"work_33545205","summary":"An approach to the test of the sensor information fusion Kalman filter is proposed. It is based on the introduced statistics of mathematical expectation of the spectral norm of a normalized innovation matrix. The approach allows for simultaneous test of the mathematical expectation and the variance of innovation sequence in real time and does not require a priori information on values of the change in its statistical characteristics under faults. Using this approach, fault detection algorithm for the sensor information fusion Kalman filter is developed. ᭧","downloadable_attachments":[{"id":53575018,"asset_id":33545205,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":64253856,"first_name":"ALİ","last_name":"OKATAN","domain_name":"esenyurt","page_name":"ALİOKATAN","display_name":"ALİ OKATAN","profile_url":"https://esenyurt.academia.edu/AL%C4%B0OKATAN?f_ri=49146","photo":"https://0.academia-photos.com/64253856/19994586/19780120/s65_ali_.okatan.jpg"}],"research_interests":[{"id":431,"name":"Parallel Algorithms","url":"https://www.academia.edu/Documents/in/Parallel_Algorithms?f_ri=49146","nofollow":true},{"id":11898,"name":"Fault Detection","url":"https://www.academia.edu/Documents/in/Fault_Detection?f_ri=49146","nofollow":true},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true},{"id":92088,"name":"Information Fusion","url":"https://www.academia.edu/Documents/in/Information_Fusion?f_ri=49146","nofollow":true},{"id":143115,"name":"Sensor Fusion","url":"https://www.academia.edu/Documents/in/Sensor_Fusion?f_ri=49146"},{"id":229390,"name":"Real Time","url":"https://www.academia.edu/Documents/in/Real_Time?f_ri=49146"},{"id":348986,"name":"Parallel Algorithm","url":"https://www.academia.edu/Documents/in/Parallel_Algorithm?f_ri=49146"},{"id":1237788,"name":"Electrical And Electronic Engineering","url":"https://www.academia.edu/Documents/in/Electrical_And_Electronic_Engineering?f_ri=49146"},{"id":2720299,"name":"Matrix norm","url":"https://www.academia.edu/Documents/in/Matrix_norm?f_ri=49146"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_32762025" data-work_id="32762025" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/32762025/Implementation_of_Bayesian_Recursive_State_Space_Kalman_Filter_for_Noise_Reduction_of_Speech_Signal">Implementation of Bayesian Recursive State-Space Kalman Filter for Noise Reduction of Speech Signal</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">—Noise reduction of speech signals plays an important role in telecommunication systems. Various types of speech additive noise can be introduced such as babble, crowd, large city, and highway which are the main factor of degradation in... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_32762025" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">—Noise reduction of speech signals plays an important role in telecommunication systems. Various types of speech additive noise can be introduced such as babble, crowd, large city, and highway which are the main factor of degradation in perceived speech quality. There are some cases on the receiver side of telecommunication systems, where the direct value of interfering noise is not available and there is just access to noisy speech. In these cases the noise cannot be cancelled totally but it may be possible to reduce the noise in a sensible way by utilizing the statistics of the noise and speech signal. In this paper the proposed method for noise reduction is Bayesian recursive state-space Kalman filter, which is a method for estimation of a speech signal from its noisy version. It utilizes the prior probability distributions of the signal and noise processes, which are assumed to be zero-mean Gaussian processes. The function of Kalman filter is assessed for different types of noise such as babble, crowd, large city, and highway. The noise cancellation is implemented for each of aforementioned noises which their powers vary in a range of values. This method of noise reduction yields better speech perceived quality and efficient results compared to Wien-er filter.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/32762025" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="54c7f33dda71bfed18dc69ac67dba94c" rel="nofollow" data-download="{"attachment_id":52918513,"asset_id":32762025,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/52918513/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="2163127" href="https://concordia.academia.edu/AliSarafnia">Ali Sarafnia</a><script data-card-contents-for-user="2163127" type="text/json">{"id":2163127,"first_name":"Ali","last_name":"Sarafnia","domain_name":"concordia","page_name":"AliSarafnia","display_name":"Ali Sarafnia","profile_url":"https://concordia.academia.edu/AliSarafnia?f_ri=49146","photo":"https://0.academia-photos.com/2163127/721706/7835007/s65_ali.sarafnia.jpg"}</script></span></span></li><li class="js-paper-rank-work_32762025 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="32762025"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 32762025, container: ".js-paper-rank-work_32762025", }); });</script></li><li class="js-percentile-work_32762025 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 32762025; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_32762025"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_32762025 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="32762025"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 32762025; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=32762025]").text(description); $(".js-view-count-work_32762025").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_32762025").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="32762025"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">7</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="33251" rel="nofollow" href="https://www.academia.edu/Documents/in/Adaptive_Filtering">Adaptive Filtering</a>, <script data-card-contents-for-ri="33251" type="text/json">{"id":33251,"name":"Adaptive Filtering","url":"https://www.academia.edu/Documents/in/Adaptive_Filtering?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="36006" rel="nofollow" href="https://www.academia.edu/Documents/in/Adaptive_Signal_Processing">Adaptive Signal Processing</a>, <script data-card-contents-for-ri="36006" type="text/json">{"id":36006,"name":"Adaptive Signal Processing","url":"https://www.academia.edu/Documents/in/Adaptive_Signal_Processing?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="36835" rel="nofollow" href="https://www.academia.edu/Documents/in/Speech_Processing">Speech Processing</a>, <script data-card-contents-for-ri="36835" type="text/json">{"id":36835,"name":"Speech Processing","url":"https://www.academia.edu/Documents/in/Speech_Processing?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="49146" rel="nofollow" href="https://www.academia.edu/Documents/in/Kalman_Filter">Kalman Filter</a><script data-card-contents-for-ri="49146" type="text/json">{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=32762025]'), work: {"id":32762025,"title":"Implementation of Bayesian Recursive State-Space Kalman Filter for Noise Reduction of Speech Signal","created_at":"2017-05-01T22:23:42.978-07:00","url":"https://www.academia.edu/32762025/Implementation_of_Bayesian_Recursive_State_Space_Kalman_Filter_for_Noise_Reduction_of_Speech_Signal?f_ri=49146","dom_id":"work_32762025","summary":"—Noise reduction of speech signals plays an important role in telecommunication systems. Various types of speech additive noise can be introduced such as babble, crowd, large city, and highway which are the main factor of degradation in perceived speech quality. There are some cases on the receiver side of telecommunication systems, where the direct value of interfering noise is not available and there is just access to noisy speech. In these cases the noise cannot be cancelled totally but it may be possible to reduce the noise in a sensible way by utilizing the statistics of the noise and speech signal. In this paper the proposed method for noise reduction is Bayesian recursive state-space Kalman filter, which is a method for estimation of a speech signal from its noisy version. It utilizes the prior probability distributions of the signal and noise processes, which are assumed to be zero-mean Gaussian processes. The function of Kalman filter is assessed for different types of noise such as babble, crowd, large city, and highway. The noise cancellation is implemented for each of aforementioned noises which their powers vary in a range of values. This method of noise reduction yields better speech perceived quality and efficient results compared to Wien-er filter.","downloadable_attachments":[{"id":52918513,"asset_id":32762025,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":2163127,"first_name":"Ali","last_name":"Sarafnia","domain_name":"concordia","page_name":"AliSarafnia","display_name":"Ali Sarafnia","profile_url":"https://concordia.academia.edu/AliSarafnia?f_ri=49146","photo":"https://0.academia-photos.com/2163127/721706/7835007/s65_ali.sarafnia.jpg"}],"research_interests":[{"id":33251,"name":"Adaptive Filtering","url":"https://www.academia.edu/Documents/in/Adaptive_Filtering?f_ri=49146","nofollow":true},{"id":36006,"name":"Adaptive Signal Processing","url":"https://www.academia.edu/Documents/in/Adaptive_Signal_Processing?f_ri=49146","nofollow":true},{"id":36835,"name":"Speech Processing","url":"https://www.academia.edu/Documents/in/Speech_Processing?f_ri=49146","nofollow":true},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true},{"id":96797,"name":"Speech enhancement","url":"https://www.academia.edu/Documents/in/Speech_enhancement?f_ri=49146"},{"id":100300,"name":"Single-Channel Speech Enhancement","url":"https://www.academia.edu/Documents/in/Single-Channel_Speech_Enhancement?f_ri=49146"},{"id":263135,"name":"Speech Signal Processing","url":"https://www.academia.edu/Documents/in/Speech_Signal_Processing?f_ri=49146"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_8092913" data-work_id="8092913" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/8092913/Adaptive_Multicue_Background_Subtraction_for_Robust_Vehicle_Counting_and_Classification">Adaptive Multicue Background Subtraction for Robust Vehicle Counting and Classification</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In this paper we present a robust vision-based system for vehicle tracking and classification devised for traffic flow surveillance. The system performs in real time achieving good results even in challenging situations, such as with... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_8092913" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this paper we present a robust vision-based system for vehicle tracking and classification devised for traffic flow surveillance. The system performs in real time achieving good results even in challenging situations, such as with moving casted shadows on sunny days, headlight reflections on the road, rainy days and traffic jams, using only a single standard camera. We propose a robust adaptive multi-cue segmentation strategy that detects foreground pixels corresponding to moving and stopped vehicles, even with noisy images due to compression. First, the approach adaptively thresholds a combination of luminance and chromaticity disparity maps between the learned background and the current frame. It then adds extra features derived from gradient differences, in order to improve the segmentation of dark vehicles with casted shadows, and removes headlights reflections on the road. The segmentation is further used by a two-step tracking approach, which combines the simplicity of a linear 2D Kalman filter, and the complexity of a 3D volume estimation using Markov Chain Monte Carlo (MCMC) methods. Experimental results show that our method can count and classify vehicles in real time with a high level of performance under different environmental situations, comparable to those of inductive loop detectors (ILD).</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/8092913" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="9300cfc67bab430f0a9f1552f82e7e18" rel="nofollow" data-download="{"attachment_id":48216551,"asset_id":8092913,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/48216551/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="15666058" href="https://vicomtech.academia.edu/OihanaOtaegui">Oihana Otaegui</a><script data-card-contents-for-user="15666058" type="text/json">{"id":15666058,"first_name":"Oihana","last_name":"Otaegui","domain_name":"vicomtech","page_name":"OihanaOtaegui","display_name":"Oihana Otaegui","profile_url":"https://vicomtech.academia.edu/OihanaOtaegui?f_ri=49146","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_8092913 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="8092913"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 8092913, container: ".js-paper-rank-work_8092913", }); });</script></li><li class="js-percentile-work_8092913 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 8092913; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_8092913"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_8092913 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="8092913"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 8092913; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=8092913]").text(description); $(".js-view-count-work_8092913").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_8092913").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="8092913"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">15</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="73" rel="nofollow" href="https://www.academia.edu/Documents/in/Civil_Engineering">Civil Engineering</a>, <script data-card-contents-for-ri="73" type="text/json">{"id":73,"name":"Civil Engineering","url":"https://www.academia.edu/Documents/in/Civil_Engineering?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="854" rel="nofollow" href="https://www.academia.edu/Documents/in/Computer_Vision">Computer Vision</a>, <script data-card-contents-for-ri="854" type="text/json">{"id":854,"name":"Computer Vision","url":"https://www.academia.edu/Documents/in/Computer_Vision?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="9351" rel="nofollow" href="https://www.academia.edu/Documents/in/Image_Analysis">Image Analysis</a>, <script data-card-contents-for-ri="9351" type="text/json">{"id":9351,"name":"Image Analysis","url":"https://www.academia.edu/Documents/in/Image_Analysis?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="26870" rel="nofollow" href="https://www.academia.edu/Documents/in/Image_segmentation">Image segmentation</a><script data-card-contents-for-ri="26870" type="text/json">{"id":26870,"name":"Image segmentation","url":"https://www.academia.edu/Documents/in/Image_segmentation?f_ri=49146","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=8092913]'), work: {"id":8092913,"title":"Adaptive Multicue Background Subtraction for Robust Vehicle Counting and Classification","created_at":"2014-08-26T08:34:56.233-07:00","url":"https://www.academia.edu/8092913/Adaptive_Multicue_Background_Subtraction_for_Robust_Vehicle_Counting_and_Classification?f_ri=49146","dom_id":"work_8092913","summary":"In this paper we present a robust vision-based system for vehicle tracking and classification devised for traffic flow surveillance. The system performs in real time achieving good results even in challenging situations, such as with moving casted shadows on sunny days, headlight reflections on the road, rainy days and traffic jams, using only a single standard camera. We propose a robust adaptive multi-cue segmentation strategy that detects foreground pixels corresponding to moving and stopped vehicles, even with noisy images due to compression. First, the approach adaptively thresholds a combination of luminance and chromaticity disparity maps between the learned background and the current frame. It then adds extra features derived from gradient differences, in order to improve the segmentation of dark vehicles with casted shadows, and removes headlights reflections on the road. The segmentation is further used by a two-step tracking approach, which combines the simplicity of a linear 2D Kalman filter, and the complexity of a 3D volume estimation using Markov Chain Monte Carlo (MCMC) methods. Experimental results show that our method can count and classify vehicles in real time with a high level of performance under different environmental situations, comparable to those of inductive loop detectors (ILD).","downloadable_attachments":[{"id":48216551,"asset_id":8092913,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":15666058,"first_name":"Oihana","last_name":"Otaegui","domain_name":"vicomtech","page_name":"OihanaOtaegui","display_name":"Oihana Otaegui","profile_url":"https://vicomtech.academia.edu/OihanaOtaegui?f_ri=49146","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":73,"name":"Civil Engineering","url":"https://www.academia.edu/Documents/in/Civil_Engineering?f_ri=49146","nofollow":true},{"id":854,"name":"Computer Vision","url":"https://www.academia.edu/Documents/in/Computer_Vision?f_ri=49146","nofollow":true},{"id":9351,"name":"Image Analysis","url":"https://www.academia.edu/Documents/in/Image_Analysis?f_ri=49146","nofollow":true},{"id":26870,"name":"Image segmentation","url":"https://www.academia.edu/Documents/in/Image_segmentation?f_ri=49146","nofollow":true},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146"},{"id":85262,"name":"Markov Chain Monte Carlo","url":"https://www.academia.edu/Documents/in/Markov_Chain_Monte_Carlo?f_ri=49146"},{"id":154213,"name":"Background Subtraction","url":"https://www.academia.edu/Documents/in/Background_Subtraction?f_ri=49146"},{"id":220371,"name":"Traffic Flow","url":"https://www.academia.edu/Documents/in/Traffic_Flow?f_ri=49146"},{"id":229390,"name":"Real Time","url":"https://www.academia.edu/Documents/in/Real_Time?f_ri=49146"},{"id":491363,"name":"Vehicle Tracking","url":"https://www.academia.edu/Documents/in/Vehicle_Tracking?f_ri=49146"},{"id":838973,"name":"System performance","url":"https://www.academia.edu/Documents/in/System_performance?f_ri=49146"},{"id":1003616,"name":"Bayesian Methods (MCMC)","url":"https://www.academia.edu/Documents/in/Bayesian_Methods_MCMC_?f_ri=49146"},{"id":1241310,"name":"Adaptive Thresholding","url":"https://www.academia.edu/Documents/in/Adaptive_Thresholding?f_ri=49146"},{"id":1800837,"name":"Image Color Analysis","url":"https://www.academia.edu/Documents/in/Image_Color_Analysis?f_ri=49146"},{"id":2213585,"name":"Information System","url":"https://www.academia.edu/Documents/in/Information_System?f_ri=49146"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_23902835" data-work_id="23902835" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/23902835/A_6_DoF_Navigation_Algorithm_for_Autonomous_Underwater_Vehicles">A 6 DoF Navigation Algorithm for Autonomous Underwater Vehicles</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The objective of this paper is to compare the performance of a new proposed Measurement Assisted Partial Re-sampling (MAPR) filter against the performance of the Extended Kalman filter and the Mixture Monte Carlo Localizer within the... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_23902835" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The objective of this paper is to compare the performance of a new proposed Measurement Assisted Partial Re-sampling (MAPR) filter against the performance of the Extended Kalman filter and the Mixture Monte Carlo Localizer within the context of a navigation algorithm for a dynamic 6 DoF system. In this paper, an autonomous underwater vehicle (AUV) is used as the dynamic system. The performances of the above three filters in resolving a navigation solution are assessed by giving the AUV a sequence of trajectories that highlight the sensitivities of the navigation algorithm to noise. This paper demonstrates that the MAPR filter is capable of computing an estimate that, like the EKF, takes into account the dynamics of the system, but like all particle filters is also capable of estimating non-Gaussian distributions.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/23902835" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="686189741d73f8e3afaeea1185548c82" rel="nofollow" data-download="{"attachment_id":44291751,"asset_id":23902835,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/44291751/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="46187662" href="https://flinders.academia.edu/KarlSammut">Karl Sammut</a><script data-card-contents-for-user="46187662" type="text/json">{"id":46187662,"first_name":"Karl","last_name":"Sammut","domain_name":"flinders","page_name":"KarlSammut","display_name":"Karl Sammut","profile_url":"https://flinders.academia.edu/KarlSammut?f_ri=49146","photo":"https://0.academia-photos.com/46187662/174854153/164887302/s65_karl.sammut.jpg"}</script></span></span></li><li class="js-paper-rank-work_23902835 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="23902835"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 23902835, container: ".js-paper-rank-work_23902835", }); });</script></li><li class="js-percentile-work_23902835 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 23902835; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_23902835"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_23902835 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="23902835"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 23902835; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=23902835]").text(description); $(".js-view-count-work_23902835").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_23902835").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="23902835"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">15</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="8050" rel="nofollow" href="https://www.academia.edu/Documents/in/Vehicle_Dynamics">Vehicle Dynamics</a>, <script data-card-contents-for-ri="8050" type="text/json">{"id":8050,"name":"Vehicle Dynamics","url":"https://www.academia.edu/Documents/in/Vehicle_Dynamics?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="34019" rel="nofollow" href="https://www.academia.edu/Documents/in/Monte_Carlo_Methods">Monte Carlo Methods</a>, <script data-card-contents-for-ri="34019" type="text/json">{"id":34019,"name":"Monte Carlo Methods","url":"https://www.academia.edu/Documents/in/Monte_Carlo_Methods?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="49146" rel="nofollow" href="https://www.academia.edu/Documents/in/Kalman_Filter">Kalman Filter</a>, <script data-card-contents-for-ri="49146" type="text/json">{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="59695" rel="nofollow" href="https://www.academia.edu/Documents/in/Navigation">Navigation</a><script data-card-contents-for-ri="59695" type="text/json">{"id":59695,"name":"Navigation","url":"https://www.academia.edu/Documents/in/Navigation?f_ri=49146","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=23902835]'), work: {"id":23902835,"title":"A 6 DoF Navigation Algorithm for Autonomous Underwater Vehicles","created_at":"2016-04-01T04:48:04.634-07:00","url":"https://www.academia.edu/23902835/A_6_DoF_Navigation_Algorithm_for_Autonomous_Underwater_Vehicles?f_ri=49146","dom_id":"work_23902835","summary":"The objective of this paper is to compare the performance of a new proposed Measurement Assisted Partial Re-sampling (MAPR) filter against the performance of the Extended Kalman filter and the Mixture Monte Carlo Localizer within the context of a navigation algorithm for a dynamic 6 DoF system. In this paper, an autonomous underwater vehicle (AUV) is used as the dynamic system. The performances of the above three filters in resolving a navigation solution are assessed by giving the AUV a sequence of trajectories that highlight the sensitivities of the navigation algorithm to noise. This paper demonstrates that the MAPR filter is capable of computing an estimate that, like the EKF, takes into account the dynamics of the system, but like all particle filters is also capable of estimating non-Gaussian distributions.","downloadable_attachments":[{"id":44291751,"asset_id":23902835,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":46187662,"first_name":"Karl","last_name":"Sammut","domain_name":"flinders","page_name":"KarlSammut","display_name":"Karl Sammut","profile_url":"https://flinders.academia.edu/KarlSammut?f_ri=49146","photo":"https://0.academia-photos.com/46187662/174854153/164887302/s65_karl.sammut.jpg"}],"research_interests":[{"id":8050,"name":"Vehicle Dynamics","url":"https://www.academia.edu/Documents/in/Vehicle_Dynamics?f_ri=49146","nofollow":true},{"id":34019,"name":"Monte Carlo Methods","url":"https://www.academia.edu/Documents/in/Monte_Carlo_Methods?f_ri=49146","nofollow":true},{"id":49146,"name":"Kalman Filter","url":"https://www.academia.edu/Documents/in/Kalman_Filter?f_ri=49146","nofollow":true},{"id":59695,"name":"Navigation","url":"https://www.academia.edu/Documents/in/Navigation?f_ri=49146","nofollow":true},{"id":67380,"name":"Kalman Filtering","url":"https://www.academia.edu/Documents/in/Kalman_Filtering?f_ri=49146"},{"id":92331,"name":"Particle filters","url":"https://www.academia.edu/Documents/in/Particle_filters?f_ri=49146"},{"id":130124,"name":"Accelerometers","url":"https://www.academia.edu/Documents/in/Accelerometers?f_ri=49146"},{"id":148114,"name":"Remotely Operated Vehicles","url":"https://www.academia.edu/Documents/in/Remotely_Operated_Vehicles?f_ri=49146"},{"id":179654,"name":"Mobile Robot","url":"https://www.academia.edu/Documents/in/Mobile_Robot?f_ri=49146"},{"id":261121,"name":"Particle Filter","url":"https://www.academia.edu/Documents/in/Particle_Filter?f_ri=49146"},{"id":389829,"name":"Gaussian distribution","url":"https://www.academia.edu/Documents/in/Gaussian_distribution?f_ri=49146"},{"id":691180,"name":"Phasor Measurement units","url":"https://www.academia.edu/Documents/in/Phasor_Measurement_units?f_ri=49146"},{"id":728952,"name":"Filtering","url":"https://www.academia.edu/Documents/in/Filtering?f_ri=49146"},{"id":868912,"name":"Dynamic System","url":"https://www.academia.edu/Documents/in/Dynamic_System?f_ri=49146"},{"id":872410,"name":"Extended Kalman Filter","url":"https://www.academia.edu/Documents/in/Extended_Kalman_Filter?f_ri=49146"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_15268189" data-work_id="15268189" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/15268189/Cardiopulmonary_resuscitation_artefact_suppression_using_a_Kalman_filter_and_the_frequency_of_chest_compressions_as_the_reference_signal">Cardiopulmonary resuscitation artefact suppression using a Kalman filter and the frequency of chest compressions as the reference signal</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Aim: To develop a new method to suppress the artefact generated by chest compressions during cardiopulmonary resuscitation (CPR) using only the frequency of the compressions as additional information. Materials and methods: The CPR... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_15268189" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Aim: To develop a new method to suppress the artefact generated by chest compressions during cardiopulmonary resuscitation (CPR) using only the frequency of the compressions as additional information. Materials and methods: The CPR artefact suppression method was developed and tested using a database of 381 ECG records (89 shockable and 292 non-shockable) from 299 patients. All records were extracted from real out-of-hospital cardiac arrest episodes. The suppression method consists of a Kalman filter that uses the frequency of the measured compressions to estimate the artefact and to remove it from the ECG. The performance of the filter was evaluated by comparing the sensitivity and specificity of an automated external defibrillator before and after the artefact suppression. Results: For the test database, the sensitivity improved from 57.8% (95% confidence interval, 43.3-71.0%) to 93.3% (81.5-98.4%) and the specificity decreased from 92.5% (87.0-95.9%) to 89.1% (83.0-93.3%). Conclusion: For a similar sensitivity, we obtained better specificity than that reported for other methods, although still short of the values recommended by the American Heart Association. The results suggest that the CPR artefact can be accurately modelled using only the frequency of the compressions. This information could be easily acquired through the defibrillator's CPR help pads, with minimal hardware modifications.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/15268189" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="340a71949080a939a9d737f2157f5ca1" rel="nofollow" data-download="{"attachment_id":43382715,"asset_id":15268189,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/43382715/download_file?st=MTc0MDE2MTg2Nyw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="34345356" href="https://independent.academia.edu/TrygveEftest%C3%B8l">Trygve Eftestøl</a><script data-card-contents-for-user="34345356" type="text/json">{"id":34345356,"first_name":"Trygve","last_name":"Eftestøl","domain_name":"independent","page_name":"TrygveEftestøl","display_name":"Trygve Eftestøl","profile_url":"https://independent.academia.edu/TrygveEftest%C3%B8l?f_ri=49146","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_15268189 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="15268189"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 15268189, container: ".js-paper-rank-work_15268189", }); 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Materials and methods: The CPR artefact suppression method was developed and tested using a database of 381 ECG records (89 shockable and 292 non-shockable) from 299 patients. All records were extracted from real out-of-hospital cardiac arrest episodes. The suppression method consists of a Kalman filter that uses the frequency of the measured compressions to estimate the artefact and to remove it from the ECG. The performance of the filter was evaluated by comparing the sensitivity and specificity of an automated external defibrillator before and after the artefact suppression. Results: For the test database, the sensitivity improved from 57.8% (95% confidence interval, 43.3-71.0%) to 93.3% (81.5-98.4%) and the specificity decreased from 92.5% (87.0-95.9%) to 89.1% (83.0-93.3%). Conclusion: For a similar sensitivity, we obtained better specificity than that reported for other methods, although still short of the values recommended by the American Heart Association. The results suggest that the CPR artefact can be accurately modelled using only the frequency of the compressions. 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