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is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.16735">arXiv:2309.16735</a> <span> [<a href="https://arxiv.org/pdf/2309.16735">pdf</a>, <a href="https://arxiv.org/format/2309.16735">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Biological Physics">physics.bio-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> Learnable real-time inference of molecular composition from diffuse spectroscopy of brain tissue </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ezhov%2C+I">Ivan Ezhov</a>, <a href="/search/eess?searchtype=author&query=Scibilia%2C+K">Kevin Scibilia</a>, <a href="/search/eess?searchtype=author&query=Giannoni%2C+L">Luca Giannoni</a>, <a href="/search/eess?searchtype=author&query=Kofler%2C+F">Florian Kofler</a>, <a href="/search/eess?searchtype=author&query=Iliash%2C+I">Ivan Iliash</a>, <a href="/search/eess?searchtype=author&query=Hsieh%2C+F">Felix Hsieh</a>, <a href="/search/eess?searchtype=author&query=Shit%2C+S">Suprosanna Shit</a>, <a href="/search/eess?searchtype=author&query=Caredda%2C+C">Charly Caredda</a>, <a href="/search/eess?searchtype=author&query=Lange%2C+F">Fred Lange</a>, <a href="/search/eess?searchtype=author&query=Tachtsidis%2C+I">Ilias Tachtsidis</a>, <a href="/search/eess?searchtype=author&query=Rueckert%2C+D">Daniel Rueckert</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2309.16735v6-abstract-short" style="display: inline;"> Diffuse optical modalities such as broadband near-infrared spectroscopy (bNIRS) and hyperspectral imaging (HSI) represent a promising alternative for low-cost, non-invasive, and fast monitoring of functional and structural properties of living tissue. Particularly, the possibility of extracting the molecular composition of the tissue from the optical spectra in real-time deems the spectroscopy tec… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.16735v6-abstract-full').style.display = 'inline'; document.getElementById('2309.16735v6-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.16735v6-abstract-full" style="display: none;"> Diffuse optical modalities such as broadband near-infrared spectroscopy (bNIRS) and hyperspectral imaging (HSI) represent a promising alternative for low-cost, non-invasive, and fast monitoring of functional and structural properties of living tissue. Particularly, the possibility of extracting the molecular composition of the tissue from the optical spectra in real-time deems the spectroscopy techniques as a unique diagnostic tool. However, no established method exists to streamline the inference of the biochemical composition from the optical spectrum for real-time applications such as surgical monitoring. In this paper, we analyse a machine learning technique for fast and accurate inference of changes in the molecular composition of brain tissue. We reconsider and propose modifications to the existing learnable methodology based on the Beer-Lambert law, which analytically connects the spectra with concentrations. We evaluate the method's applicability to linear and non-linear formulations of the Beer-Lambert law. The approach is tested on real data obtained from the bNIRS- and HSI-based optical monitoring of brain tissue. The results demonstrate that the proposed method enables real-time molecular composition inference while maintaining the accuracy of traditional linear and non-linear optimization solvers. Preliminary findings show that Beer-Lambert law-based spectral unmixing allows to contrast brain anatomy semantics such as the vessel tree and tumor area. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.16735v6-abstract-full').style.display = 'none'; document.getElementById('2309.16735v6-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 27 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2007.14485">arXiv:2007.14485</a> <span> [<a href="https://arxiv.org/pdf/2007.14485">pdf</a>, <a href="https://arxiv.org/format/2007.14485">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1371/journal.pone.0251258">10.1371/journal.pone.0251258 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Color-complexity enabled exhaustive color-dots identification and spatial patterns testing in images </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Liao%2C+S">Shuting Liao</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+L">Li-Yu Liu</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+T">Ting-An Chen</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+K">Kuang-Yu Chen</a>, <a href="/search/eess?searchtype=author&query=Hsieh%2C+F">Fushing Hsieh</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2007.14485v1-abstract-short" style="display: inline;"> Targeted color-dots with varying shapes and sizes in images are first exhaustively identified, and then their multiscale 2D geometric patterns are extracted for testing spatial uniformness in a progressive fashion. Based on color theory in physics, we develop a new color-identification algorithm relying on highly associative relations among the three color-coordinates: RGB or HSV. Such high associ… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.14485v1-abstract-full').style.display = 'inline'; document.getElementById('2007.14485v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2007.14485v1-abstract-full" style="display: none;"> Targeted color-dots with varying shapes and sizes in images are first exhaustively identified, and then their multiscale 2D geometric patterns are extracted for testing spatial uniformness in a progressive fashion. Based on color theory in physics, we develop a new color-identification algorithm relying on highly associative relations among the three color-coordinates: RGB or HSV. Such high associations critically imply low color-complexity of a color image, and renders potentials of exhaustive identification of targeted color-dots of all shapes and sizes. Via heterogeneous shaded regions and lighting conditions, our algorithm is shown being robust, practical and efficient comparing with the popular Contour and OpenCV approaches. Upon all identified color-pixels, we form color-dots as individually connected networks with shapes and sizes. We construct minimum spanning trees (MST) as spatial geometries of dot-collectives of various size-scales. Given a size-scale, the distribution of distances between immediate neighbors in the observed MST is extracted, so do many simulated MSTs under the spatial uniformness assumption. We devise a new algorithm for testing 2D spatial uniformness based on a Hierarchical clustering tree upon all involving MSTs. Our developments are illustrated on images obtained by mimicking chemical spraying via drone in Precision Agriculture. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.14485v1-abstract-full').style.display = 'none'; document.getElementById('2007.14485v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 July, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">21 pages, 21 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2005.10405">arXiv:2005.10405</a> <span> [<a href="https://arxiv.org/pdf/2005.10405">pdf</a>, <a href="https://arxiv.org/format/2005.10405">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> From learning gait signatures of many individuals to reconstructing gait dynamics of one single individual </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Hsieh%2C+F">Fushing Hsieh</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+X">Xiaodong Wang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2005.10405v1-abstract-short" style="display: inline;"> Based on the same databases, we computationally address two seemingly highly related, in fact drastically distinct, questions via computational data-driven algorithms: 1) how to precisely achieve the big task of differentiating gait signatures of many individuals? 2) how to reconstruct an individual's complex gait dynamics in full? Our brains can "effortlessly" resolve the first question, but will… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.10405v1-abstract-full').style.display = 'inline'; document.getElementById('2005.10405v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2005.10405v1-abstract-full" style="display: none;"> Based on the same databases, we computationally address two seemingly highly related, in fact drastically distinct, questions via computational data-driven algorithms: 1) how to precisely achieve the big task of differentiating gait signatures of many individuals? 2) how to reconstruct an individual's complex gait dynamics in full? Our brains can "effortlessly" resolve the first question, but will definitely fail in the second one. Since many fine temporal scale gait patterns surely escape our eyes. Based on accelerometers' 3D gait time series databases, we link the answers toward both questions via multiscale structural dependency within gait dynamics of our musculoskeletal system. Two types of dependency manifestations are explored. We first develop simple algorithmic computing called Principle System-State Analysis (PSSA) for the coarse dependency in implicit forms. PSSA is shown to be able to efficiently classifying among many subjects. We then develop a multiscale Local-1st-Global-2nd (L1G2) Coding Algorithm and a landmark computing algorithm. With both algorithms, we can precisely dissect rhythmic gait cycles, and then decompose each cycle into a series of cyclic gait phases. With proper color-coding and stacking, we reconstruct and represent an individual's gait dynamics via a 3D cylinder to collectively reveal universal deterministic and stochastic structural patterns on centisecond (10 milliseconds) scale across all rhythmic cycles. This 3D cylinder can serve as "passtensor" for authentication purposes related to clinical diagnoses and cybersecurity. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.10405v1-abstract-full').style.display = 'none'; document.getElementById('2005.10405v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 May, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2020. </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a 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