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name="searchtype"><option value="all">All fields</option><option value="title">Title</option><option selected value="author">Author(s)</option><option value="abstract">Abstract</option><option value="comments">Comments</option><option value="journal_ref">Journal reference</option><option value="acm_class">ACM classification</option><option value="msc_class">MSC classification</option><option value="report_num">Report number</option><option value="paper_id">arXiv identifier</option><option value="doi">DOI</option><option value="orcid">ORCID</option><option value="license">License (URI)</option><option value="author_id">arXiv author ID</option><option value="help">Help pages</option><option value="full_text">Full text</option></select> <input id="query" name="query" type="text" value="S, V"> <ul id="abstracts"><li><input checked id="abstracts-0" name="abstracts" type="radio" value="show"> <label for="abstracts-0">Show abstracts</label></li><li><input id="abstracts-1" name="abstracts" type="radio" value="hide"> <label for="abstracts-1">Hide abstracts</label></li></ul> </div> <div class="box field is-grouped is-grouped-multiline level-item"> <div class="control"> <span class="select is-small"> <select id="size" name="size"><option value="25">25</option><option selected value="50">50</option><option value="100">100</option><option value="200">200</option></select> </span> <label for="size">results per page</label>. </div> <div class="control"> <label for="order">Sort results by</label> <span class="select is-small"> <select id="order" name="order"><option selected value="-announced_date_first">Announcement date (newest first)</option><option value="announced_date_first">Announcement date (oldest first)</option><option value="-submitted_date">Submission date (newest first)</option><option value="submitted_date">Submission date (oldest first)</option><option value="">Relevance</option></select> </span> </div> <div class="control"> <button class="button is-small 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/2501.16334">arXiv:2501.16334</a> <span> [<a href="https://arxiv.org/pdf/2501.16334">pdf</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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> RNN-Based Models for Predicting Seizure Onset in Epileptic Patients </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Mounagurusamy%2C+M+K">Mathan Kumar Mounagurusamy</a>, <a href="/search/eess?searchtype=author&query=S%2C+T+V">Thiyagarajan V S</a>, <a href="/search/eess?searchtype=author&query=Rahman%2C+A">Abdur Rahman</a>, <a href="/search/eess?searchtype=author&query=Chandak%2C+S">Shravan Chandak</a>, <a href="/search/eess?searchtype=author&query=Balaji%2C+D">D. Balaji</a>, <a href="/search/eess?searchtype=author&query=Jallepalli%2C+V+R">Venkateswara Rao Jallepalli</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="2501.16334v1-abstract-short" style="display: inline;"> Early management and better clinical outcomes for epileptic patients depend on seizure prediction. The accuracy and false alarm rates of existing systems are often compromised by their dependence on static thresholds and basic Electroencephalogram (EEG) properties. A novel Recurrent Neural Network (RNN)-based method for seizure start prediction is proposed in the article to overcome these limitati… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.16334v1-abstract-full').style.display = 'inline'; document.getElementById('2501.16334v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.16334v1-abstract-full" style="display: none;"> Early management and better clinical outcomes for epileptic patients depend on seizure prediction. The accuracy and false alarm rates of existing systems are often compromised by their dependence on static thresholds and basic Electroencephalogram (EEG) properties. A novel Recurrent Neural Network (RNN)-based method for seizure start prediction is proposed in the article to overcome these limitations. As opposed to conventional techniques, the proposed system makes use of Long Short-Term Memory (LSTM) networks to extract temporal correlations from unprocessed EEG data. It enables the system to adapt dynamically to the unique EEG patterns of each patient, improving prediction accuracy. The methodology of the system comprises thorough data collecting, preprocessing, and LSTM-based feature extraction. Annotated EEG datasets are then used for model training and validation. Results show a considerable reduction in false alarm rates (average of 6.8%) and an improvement in prediction accuracy (90.2% sensitivity, 88.9% specificity, and AUC-ROC of 93). Additionally, computational efficiency is significantly higher than that of existing systems (12 ms processing time, 45 MB memory consumption). About improving seizure prediction reliability, these results demonstrate the effectiveness of the proposed RNN-based strategy, opening up possibilities for its practical application to improve epilepsy treatment. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.16334v1-abstract-full').style.display = 'none'; document.getElementById('2501.16334v1-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> 24 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.03539">arXiv:2501.03539</a> <span> [<a href="https://arxiv.org/pdf/2501.03539">pdf</a>, <a href="https://arxiv.org/format/2501.03539">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link 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="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Enhanced Tuberculosis Bacilli Detection using Attention-Residual U-Net and Ensemble Classification </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=K%2C+G">Greeshma K</a>, <a href="/search/eess?searchtype=author&query=S%2C+V">Vishnukumar S</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="2501.03539v1-abstract-short" style="display: inline;"> Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a critical global health issue, necessitating timely diagnosis and treatment. Current methods for detecting tuberculosis bacilli from bright field microscopic sputum smear images suffer from low automation, inadequate segmentation performance, and limited classification accuracy. This paper proposes an efficient hybrid approach that… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.03539v1-abstract-full').style.display = 'inline'; document.getElementById('2501.03539v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.03539v1-abstract-full" style="display: none;"> Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a critical global health issue, necessitating timely diagnosis and treatment. Current methods for detecting tuberculosis bacilli from bright field microscopic sputum smear images suffer from low automation, inadequate segmentation performance, and limited classification accuracy. This paper proposes an efficient hybrid approach that combines deep learning for segmentation and an ensemble model for classification. An enhanced U-Net model incorporating attention blocks and residual connections is introduced to precisely segment microscopic sputum smear images, facilitating the extraction of Regions of Interest (ROIs). These ROIs are subsequently classified using an ensemble classifier comprising Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boost (XGBoost), resulting in an accurate identification of bacilli within the images. Experiments conducted on a newly created dataset, along with public datasets, demonstrate that the proposed model achieves superior segmentation performance, higher classification accuracy, and enhanced automation compared to existing methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.03539v1-abstract-full').style.display = 'none'; document.getElementById('2501.03539v1-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> 7 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.03538">arXiv:2501.03538</a> <span> [<a href="https://arxiv.org/pdf/2501.03538">pdf</a>, <a href="https://arxiv.org/format/2501.03538">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link 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="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Efficient and Accurate Tuberculosis Diagnosis: Attention Residual U-Net and Vision Transformer Based Detection Framework </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=K%2C+G">Greeshma K</a>, <a href="/search/eess?searchtype=author&query=S%2C+V">Vishnukumar S</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="2501.03538v1-abstract-short" style="display: inline;"> Tuberculosis (TB), an infectious disease caused by Mycobacterium tuberculosis, continues to be a major global health threat despite being preventable and curable. This burden is particularly high in low and middle income countries. Microscopy remains essential for diagnosing TB by enabling direct visualization of Mycobacterium tuberculosis in sputum smear samples, offering a cost effective approac… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.03538v1-abstract-full').style.display = 'inline'; document.getElementById('2501.03538v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.03538v1-abstract-full" style="display: none;"> Tuberculosis (TB), an infectious disease caused by Mycobacterium tuberculosis, continues to be a major global health threat despite being preventable and curable. This burden is particularly high in low and middle income countries. Microscopy remains essential for diagnosing TB by enabling direct visualization of Mycobacterium tuberculosis in sputum smear samples, offering a cost effective approach for early detection and effective treatment. Given the labour-intensive nature of microscopy, automating the detection of bacilli in microscopic images is crucial to improve both the expediency and reliability of TB diagnosis. The current methodologies for detecting tuberculosis bacilli in bright field microscopic sputum smear images are hindered by limited automation capabilities, inconsistent segmentation quality, and constrained classification precision. This paper proposes a twostage deep learning methodology for tuberculosis bacilli detection, comprising bacilli segmentation followed by classification. In the initial phase, an advanced U-Net model employing attention blocks and residual connections is proposed to segment microscopic sputum smear images, enabling the extraction of Regions of Interest (ROIs). The extracted ROIs are then classified using a Vision Transformer, which we specifically customized as TBViT to enhance the precise detection of bacilli within the images. For the experiments, a newly developed dataset of microscopic sputum smear images derived from Ziehl-Neelsen-stained slides is used in conjunction with existing public datasets. The qualitative and quantitative evaluation of the experiments using various metrics demonstrates that the proposed model achieves significantly improved segmentation performance, higher classification accuracy, and a greater level of automation, surpassing existing methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.03538v1-abstract-full').style.display = 'none'; document.getElementById('2501.03538v1-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> 7 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.06395">arXiv:2409.06395</a> <span> [<a href="https://arxiv.org/pdf/2409.06395">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> Soft Acoustic Curvature Sensor: Design and Development </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Sofla%2C+M+S">Mohammad Sheikh Sofla</a>, <a href="/search/eess?searchtype=author&query=Golshanian%2C+H">Hanita Golshanian</a>, <a href="/search/eess?searchtype=author&query=S%2C+V+R">Vishnu Rajendran S</a>, <a href="/search/eess?searchtype=author&query=E%2C+A+G">Amir Ghalamzan E</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="2409.06395v3-abstract-short" style="display: inline;"> This paper introduces a novel Soft Acoustic Curvature (SAC) sensor. SAC incorporates integrated audio components and features an acoustic channel within a flexible structure. A reference acoustic wave, generated by a speaker at one end of the channel, propagates and is received by a microphone at the other channel's end. Our previous study revealed that acoustic wave energy dissipation varies with… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.06395v3-abstract-full').style.display = 'inline'; document.getElementById('2409.06395v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.06395v3-abstract-full" style="display: none;"> This paper introduces a novel Soft Acoustic Curvature (SAC) sensor. SAC incorporates integrated audio components and features an acoustic channel within a flexible structure. A reference acoustic wave, generated by a speaker at one end of the channel, propagates and is received by a microphone at the other channel's end. Our previous study revealed that acoustic wave energy dissipation varies with acoustic channel deformation, leading us to design a novel channel capable of large deformation due to bending. We then use Machine Learning (ML) models to establish a complex mapping between channel deformations and sound modulation. Various sound frequencies and ML models were evaluated to enhance curvature detection accuracy. The sensor, constructed using soft material and 3D printing, was validated experimentally, with curvature measurement errors remaining within 3.5 m-1 for a range of 0 to 60 m-1 curvatures. These results demonstrate the effectiveness of the proposed method for estimating curvatures. With its flexible structure, the SAC sensor holds potential for applications in soft robotics, including shape measurement for continuum manipulators, soft grippers, and wearable devices. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.06395v3-abstract-full').style.display = 'none'; document.getElementById('2409.06395v3-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> 27 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </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">To appear in Robotics and Automation Letter</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.04953">arXiv:2407.04953</a> <span> [<a href="https://arxiv.org/pdf/2407.04953">pdf</a>, <a href="https://arxiv.org/format/2407.04953">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link 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="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Effective-LDAM: An Effective Loss Function To Mitigate Data Imbalance for Robust Chest X-Ray Disease Classification </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=S%2C+S+R+V">Sree Rama Vamsidhar S</a>, <a href="/search/eess?searchtype=author&query=Satya%2C+B">Bhargava Satya</a>, <a href="/search/eess?searchtype=author&query=Gorthi%2C+R+K">Rama Krishna Gorthi</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="2407.04953v1-abstract-short" style="display: inline;"> Deep Learning (DL) approaches have gained prominence in medical imaging for disease diagnosis. Chest X-ray (CXR) classification has emerged as an effective method for detecting various diseases. Among these methodologies, Chest X-ray (CXR) classification has proven to be an effective approach for detecting and analyzing various diseases. However, the reliable performance of DL classification algor… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.04953v1-abstract-full').style.display = 'inline'; document.getElementById('2407.04953v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.04953v1-abstract-full" style="display: none;"> Deep Learning (DL) approaches have gained prominence in medical imaging for disease diagnosis. Chest X-ray (CXR) classification has emerged as an effective method for detecting various diseases. Among these methodologies, Chest X-ray (CXR) classification has proven to be an effective approach for detecting and analyzing various diseases. However, the reliable performance of DL classification algorithms is dependent upon access to large and balanced datasets, which pose challenges in medical imaging due to the impracticality of acquiring sufficient data for every disease category. To tackle this problem, we propose an algorithmic-centric approach called Effective-Label Distribution Aware Margin (E-LDAM), which modifies the margin of the widely adopted Label Distribution Aware Margin (LDAM) loss function using an effective number of samples in each class. Experimental evaluations on the COVIDx CXR dataset focus on Normal, Pneumonia, and COVID-19 classification. The experimental results demonstrate the effectiveness of the proposed E-LDAM approach, achieving a remarkable recall score of 97.81% for the minority class (COVID-19) in CXR image prediction. Furthermore, the overall accuracy of the three-class classification task attains an impressive level of 95.26%. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.04953v1-abstract-full').style.display = 'none'; document.getElementById('2407.04953v1-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> 6 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.17730">arXiv:2403.17730</a> <span> [<a href="https://arxiv.org/pdf/2403.17730">pdf</a>, <a href="https://arxiv.org/ps/2403.17730">ps</a>, <a href="https://arxiv.org/format/2403.17730">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> On Structural Non-commutativity in Affine Feedback of SISO Nonlinear Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=S.%2C+V+G">Venkatesh G. S.</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="2403.17730v1-abstract-short" style="display: inline;"> The affine feedback connection of SISO nonlinear systems modeled by Chen--Fliess series is shown to be a group action on the plant which is isomorphic to the semi-direct product of shuffle and additive group of non-commutative formal power series. The additive and multiplicative feedback loops in an affine feedback connection are thus proven to be structurally non-commutative. A flip in the order… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.17730v1-abstract-full').style.display = 'inline'; document.getElementById('2403.17730v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.17730v1-abstract-full" style="display: none;"> The affine feedback connection of SISO nonlinear systems modeled by Chen--Fliess series is shown to be a group action on the plant which is isomorphic to the semi-direct product of shuffle and additive group of non-commutative formal power series. The additive and multiplicative feedback loops in an affine feedback connection are thus proven to be structurally non-commutative. A flip in the order of these loops results in a net additive feedback loop. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.17730v1-abstract-full').style.display = 'none'; document.getElementById('2403.17730v1-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> 26 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </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">submitted to $26^{th}$ International Symposium on Mathematical Theory of Networks and Systems, 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.07561">arXiv:2312.07561</a> <span> [<a href="https://arxiv.org/pdf/2312.07561">pdf</a>, <a href="https://arxiv.org/format/2312.07561">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> <span class="tag is-small is-grey 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="Computers and Society">cs.CY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Annotating sleep states in children from wrist-worn accelerometer data using Machine Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ram%2C+A">Ashwin Ram</a>, <a href="/search/eess?searchtype=author&query=S.%2C+S+S+V">Sundar Sripada V. S.</a>, <a href="/search/eess?searchtype=author&query=Keshari%2C+S">Shuvam Keshari</a>, <a href="/search/eess?searchtype=author&query=Jiang%2C+Z">Zizhe Jiang</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="2312.07561v1-abstract-short" style="display: inline;"> Sleep detection and annotation are crucial for researchers to understand sleep patterns, especially in children. With modern wrist-worn watches comprising built-in accelerometers, sleep logs can be collected. However, the annotation of these logs into distinct sleep events: onset and wakeup, proves to be challenging. These annotations must be automated, precise, and scalable. We propose to model t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.07561v1-abstract-full').style.display = 'inline'; document.getElementById('2312.07561v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.07561v1-abstract-full" style="display: none;"> Sleep detection and annotation are crucial for researchers to understand sleep patterns, especially in children. With modern wrist-worn watches comprising built-in accelerometers, sleep logs can be collected. However, the annotation of these logs into distinct sleep events: onset and wakeup, proves to be challenging. These annotations must be automated, precise, and scalable. We propose to model the accelerometer data using different machine learning (ML) techniques such as support vectors, boosting, ensemble methods, and more complex approaches involving LSTMs and Region-based CNNs. Later, we aim to evaluate these approaches using the Event Detection Average Precision (EDAP) score (similar to the IOU metric) to eventually compare the predictive power and model performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.07561v1-abstract-full').style.display = 'none'; document.getElementById('2312.07561v1-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> 9 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2210.04184">arXiv:2210.04184</a> <span> [<a href="https://arxiv.org/pdf/2210.04184">pdf</a>, <a href="https://arxiv.org/format/2210.04184">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Guided Nonlocal Patch Regularization and Efficient Filtering-Based Inversion for Multiband Fusion </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=S.%2C+U+V">Unni V. S.</a>, <a href="/search/eess?searchtype=author&query=Nair%2C+P">Pravin Nair</a>, <a href="/search/eess?searchtype=author&query=Chaudhury%2C+K+N">Kunal N. Chaudhury</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="2210.04184v1-abstract-short" style="display: inline;"> In multiband fusion, an image with a high spatial and low spectral resolution is combined with an image with a low spatial but high spectral resolution to produce a single multiband image having high spatial and spectral resolutions. This comes up in remote sensing applications such as pansharpening~(MS+PAN), hyperspectral sharpening~(HS+PAN), and HS-MS fusion~(HS+MS). Remote sensing images are te… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.04184v1-abstract-full').style.display = 'inline'; document.getElementById('2210.04184v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2210.04184v1-abstract-full" style="display: none;"> In multiband fusion, an image with a high spatial and low spectral resolution is combined with an image with a low spatial but high spectral resolution to produce a single multiband image having high spatial and spectral resolutions. This comes up in remote sensing applications such as pansharpening~(MS+PAN), hyperspectral sharpening~(HS+PAN), and HS-MS fusion~(HS+MS). Remote sensing images are textured and have repetitive structures. Motivated by nonlocal patch-based methods for image restoration, we propose a convex regularizer that (i) takes into account long-distance correlations, (ii) penalizes patch variation, which is more effective than pixel variation for capturing texture information, and (iii) uses the higher spatial resolution image as a guide image for weight computation. We come up with an efficient ADMM algorithm for optimizing the regularizer along with a standard least-squares loss function derived from the imaging model. The novelty of our algorithm is that by expressing patch variation as filtering operations and by judiciously splitting the original variables and introducing latent variables, we are able to solve the ADMM subproblems efficiently using FFT-based convolution and soft-thresholding. As far as the reconstruction quality is concerned, our method is shown to outperform state-of-the-art variational and deep learning techniques. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2210.04184v1-abstract-full').style.display = 'none'; document.getElementById('2210.04184v1-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> 9 October, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2022. </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">Accepted in IEEE Transactions on Computational Imaging</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2111.14558">arXiv:2111.14558</a> <span> [<a href="https://arxiv.org/pdf/2111.14558">pdf</a>, <a href="https://arxiv.org/format/2111.14558">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> BP-Net: Efficient Deep Learning for Continuous Arterial Blood Pressure Estimation using Photoplethysmogram </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=K%2C+R+V">Rishi Vardhan K</a>, <a href="/search/eess?searchtype=author&query=S%2C+V">Vedanth S</a>, <a href="/search/eess?searchtype=author&query=G%2C+P">Poojah G</a>, <a href="/search/eess?searchtype=author&query=K%2C+A">Abhishek K</a>, <a href="/search/eess?searchtype=author&query=M%2C+N+K">Nitish Kumar M</a>, <a href="/search/eess?searchtype=author&query=Vijayaraghavan%2C+V">Vineeth Vijayaraghavan</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="2111.14558v1-abstract-short" style="display: inline;"> Blood pressure (BP) is one of the most influential bio-markers for cardiovascular diseases and stroke; therefore, it needs to be regularly monitored to diagnose and prevent any advent of medical complications. Current cuffless approaches to continuous BP monitoring, though non-invasive and unobtrusive, involve explicit feature engineering surrounding fingertip Photoplethysmogram (PPG) signals. To… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2111.14558v1-abstract-full').style.display = 'inline'; document.getElementById('2111.14558v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2111.14558v1-abstract-full" style="display: none;"> Blood pressure (BP) is one of the most influential bio-markers for cardiovascular diseases and stroke; therefore, it needs to be regularly monitored to diagnose and prevent any advent of medical complications. Current cuffless approaches to continuous BP monitoring, though non-invasive and unobtrusive, involve explicit feature engineering surrounding fingertip Photoplethysmogram (PPG) signals. To circumvent this, we present an end-to-end deep learning solution, BP-Net, that uses PPG waveform to estimate Systolic BP (SBP), Mean Average Pressure (MAP), and Diastolic BP (DBP) through intermediate continuous Arterial BP (ABP) waveform. Under the terms of the British Hypertension Society (BHS) standard, BP-Net achieves Grade A for DBP and MAP estimation and Grade B for SBP estimation. BP-Net also satisfies Advancement of Medical Instrumentation (AAMI) criteria for DBP and MAP estimation and achieves Mean Absolute Error (MAE) of 5.16 mmHg and 2.89 mmHg for SBP and DBP, respectively. Further, we establish the ubiquitous potential of our approach by deploying BP-Net on a Raspberry Pi 4 device and achieve 4.25 ms inference time for our model to translate the PPG waveform to ABP waveform. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2111.14558v1-abstract-full').style.display = 'none'; document.getElementById('2111.14558v1-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> 29 November, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2021. </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">6 pages, 2 figures, Accepted at 20th IEEE International Conference on Machine Learning and Applications (ICMLA) 2021</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2111.03816">arXiv:2111.03816</a> <span> [<a href="https://arxiv.org/pdf/2111.03816">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</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/10.14445/22315381/IJETT-V69I10P211">10.14445/22315381/IJETT-V69I10P211 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Design, Modelling, and Simulation analysis of a Single Axis MEMS-based Capacitive Accelerometer </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=S%2C+V">Veena. S</a>, <a href="/search/eess?searchtype=author&query=Rai%2C+N">Newton Rai</a>, <a href="/search/eess?searchtype=author&query=Suresh%2C+H+L">H. L. Suresh</a>, <a href="/search/eess?searchtype=author&query=Nagaraja%2C+V+S">Veda Sandeep Nagaraja</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="2111.03816v1-abstract-short" style="display: inline;"> This paper presents the design, simulation, and analytical modeling of the single proposed axis MEMSbased capacitive accelerometer. Analytical modeling has been done for frequency and displacement sensitivity. The performance of the accelerometer was tested for both static and dynamic conditions, and the corresponding static capacitance value was calculated and was found to be C0=0.730455pF, a res… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2111.03816v1-abstract-full').style.display = 'inline'; document.getElementById('2111.03816v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2111.03816v1-abstract-full" style="display: none;"> This paper presents the design, simulation, and analytical modeling of the single proposed axis MEMSbased capacitive accelerometer. Analytical modeling has been done for frequency and displacement sensitivity. The performance of the accelerometer was tested for both static and dynamic conditions, and the corresponding static capacitance value was calculated and was found to be C0=0.730455pF, a response time of 95.17渭s, and settling time of 7.261ms and the displacement sensitivity Sd= 3.5362* m/g. It was observed that the sensitivity of the accelerometer depends on its design parameters like beam length, overlap area of comb, sensing mass, and the number of interdigital fingers. A novel capacitive accelerometer has been designed for an operating frequency of 2.1kHz The accelerometer was designed using COMSOL Multiphysics and analyzed using the MATLAB simulator tool. The single proposed axis MEMS-based capacitive accelerometer is suitable for automobile applications such as airbag deployment and navigation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2111.03816v1-abstract-full').style.display = 'none'; document.getElementById('2111.03816v1-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> 6 November, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2021. </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">7 pages, 14 figures, Published with International Journal of Engineering Trends and Technology (IJETT)</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> International Journal of Engineering Trends and Technology 69.10(2021):82-88 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2109.00481">arXiv:2109.00481</a> <span> [<a href="https://arxiv.org/pdf/2109.00481">pdf</a>, <a href="https://arxiv.org/format/2109.00481">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Autonomous Cooperative Multi-Vehicle System for Interception of Aerial and Stationary Targets in Unknown Environments </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Tony%2C+L+A">Lima Agnel Tony</a>, <a href="/search/eess?searchtype=author&query=Jana%2C+S">Shuvrangshu Jana</a>, <a href="/search/eess?searchtype=author&query=P.%2C+V+V">Varun V. P.</a>, <a href="/search/eess?searchtype=author&query=Bhise%2C+A+A">Aashay Anil Bhise</a>, <a href="/search/eess?searchtype=author&query=S.%2C+A+M+V">Aruul Mozhi Varman S.</a>, <a href="/search/eess?searchtype=author&query=V.%2C+V+B">Vidyadhara B. V.</a>, <a href="/search/eess?searchtype=author&query=Gadde%2C+M+S">Mohitvishnu S. Gadde</a>, <a href="/search/eess?searchtype=author&query=Krishnapuram%2C+R">Raghu Krishnapuram</a>, <a href="/search/eess?searchtype=author&query=Ghose%2C+D">Debasish Ghose</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="2109.00481v1-abstract-short" style="display: inline;"> This paper presents the design, development, and testing of hardware-software systems by the IISc-TCS team for Challenge 1 of the Mohammed Bin Zayed International Robotics Challenge 2020. The goal of Challenge 1 was to grab a ball suspended from a moving and maneuvering UAV and pop balloons anchored to the ground, using suitable manipulators. The important tasks carried out to address this challen… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.00481v1-abstract-full').style.display = 'inline'; document.getElementById('2109.00481v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2109.00481v1-abstract-full" style="display: none;"> This paper presents the design, development, and testing of hardware-software systems by the IISc-TCS team for Challenge 1 of the Mohammed Bin Zayed International Robotics Challenge 2020. The goal of Challenge 1 was to grab a ball suspended from a moving and maneuvering UAV and pop balloons anchored to the ground, using suitable manipulators. The important tasks carried out to address this challenge include the design and development of a hardware system with efficient grabbing and popping mechanisms, considering the restrictions in volume and payload, design of accurate target interception algorithms using visual information suitable for outdoor environments, and development of a software architecture for dynamic multi-agent aerial systems performing complex dynamic missions. In this paper, a single degree of freedom manipulator attached with an end-effector is designed for grabbing and popping, and robust algorithms are developed for the interception of targets in an uncertain environment. Vision-based guidance and tracking laws are proposed based on the concept of pursuit engagement and artificial potential function. The software architecture presented in this work proposes an Operation Management System (OMS) architecture that allocates static and dynamic tasks collaboratively among multiple UAVs to perform any given mission. An important aspect of this work is that all the systems developed were designed to operate in completely autonomous mode. A detailed description of the architecture along with simulations of complete challenge in the Gazebo environment and field experiment results are also included in this work. The proposed hardware-software system is particularly useful for counter-UAV systems and can also be modified in order to cater to several other applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.00481v1-abstract-full').style.display = 'none'; document.getElementById('2109.00481v1-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> 1 September, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2021. </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">Accepted for publication at Springer Field Robotics journal</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2008.06330">arXiv:2008.06330</a> <span> [<a href="https://arxiv.org/pdf/2008.06330">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link 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="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Automated detection and quantification of COVID-19 airspace disease on chest radiographs: A novel approach achieving radiologist-level performance using a CNN trained on digital reconstructed radiographs (DRRs) from CT-based ground-truth </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Barbosa%2C+E+M">Eduardo Mortani Barbosa Jr.</a>, <a href="/search/eess?searchtype=author&query=Gefter%2C+W+B">Warren B. Gefter</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+R">Rochelle Yang</a>, <a href="/search/eess?searchtype=author&query=Ghesu%2C+F+C">Florin C. Ghesu</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+S">Siqi Liu</a>, <a href="/search/eess?searchtype=author&query=Mailhe%2C+B">Boris Mailhe</a>, <a href="/search/eess?searchtype=author&query=Mansoor%2C+A">Awais Mansoor</a>, <a href="/search/eess?searchtype=author&query=Grbic%2C+S">Sasa Grbic</a>, <a href="/search/eess?searchtype=author&query=Piat%2C+S">Sebastian Piat</a>, <a href="/search/eess?searchtype=author&query=Chabin%2C+G">Guillaume Chabin</a>, <a href="/search/eess?searchtype=author&query=S.%2C+V+R">Vishwanath R S.</a>, <a href="/search/eess?searchtype=author&query=Balachandran%2C+A">Abishek Balachandran</a>, <a href="/search/eess?searchtype=author&query=Vogt%2C+S">Sebastian Vogt</a>, <a href="/search/eess?searchtype=author&query=Ziebandt%2C+V">Valentin Ziebandt</a>, <a href="/search/eess?searchtype=author&query=Kappler%2C+S">Steffen Kappler</a>, <a href="/search/eess?searchtype=author&query=Comaniciu%2C+D">Dorin Comaniciu</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="2008.06330v1-abstract-short" style="display: inline;"> Purpose: To leverage volumetric quantification of airspace disease (AD) derived from a superior modality (CT) serving as ground truth, projected onto digitally reconstructed radiographs (DRRs) to: 1) train a convolutional neural network to quantify airspace disease on paired CXRs; and 2) compare the DRR-trained CNN to expert human readers in the CXR evaluation of patients with confirmed COVID-19.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.06330v1-abstract-full').style.display = 'inline'; document.getElementById('2008.06330v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2008.06330v1-abstract-full" style="display: none;"> Purpose: To leverage volumetric quantification of airspace disease (AD) derived from a superior modality (CT) serving as ground truth, projected onto digitally reconstructed radiographs (DRRs) to: 1) train a convolutional neural network to quantify airspace disease on paired CXRs; and 2) compare the DRR-trained CNN to expert human readers in the CXR evaluation of patients with confirmed COVID-19. Materials and Methods: We retrospectively selected a cohort of 86 COVID-19 patients (with positive RT-PCR), from March-May 2020 at a tertiary hospital in the northeastern USA, who underwent chest CT and CXR within 48 hrs. The ground truth volumetric percentage of COVID-19 related AD (POv) was established by manual AD segmentation on CT. The resulting 3D masks were projected into 2D anterior-posterior digitally reconstructed radiographs (DRR) to compute area-based AD percentage (POa). A convolutional neural network (CNN) was trained with DRR images generated from a larger-scale CT dataset of COVID-19 and non-COVID-19 patients, automatically segmenting lungs, AD and quantifying POa on CXR. CNN POa results were compared to POa quantified on CXR by two expert readers and to the POv ground-truth, by computing correlations and mean absolute errors. Results: Bootstrap mean absolute error (MAE) and correlations between POa and POv were 11.98% [11.05%-12.47%] and 0.77 [0.70-0.82] for average of expert readers, and 9.56%-9.78% [8.83%-10.22%] and 0.78-0.81 [0.73-0.85] for the CNN, respectively. Conclusion: Our CNN trained with DRR using CT-derived airspace quantification achieved expert radiologist level of accuracy in the quantification of airspace disease on CXR, in patients with positive RT-PCR for COVID-19. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.06330v1-abstract-full').style.display = 'none'; document.getElementById('2008.06330v1-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> 13 August, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2001.02387">arXiv:2001.02387</a> <span> [<a href="https://arxiv.org/pdf/2001.02387">pdf</a>, <a href="https://arxiv.org/format/2001.02387">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link 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="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> A context based deep learning approach for unbalanced medical image segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Murugesan%2C+B">Balamurali Murugesan</a>, <a href="/search/eess?searchtype=author&query=Sarveswaran%2C+K">Kaushik Sarveswaran</a>, <a href="/search/eess?searchtype=author&query=S%2C+V+R">Vijaya Raghavan S</a>, <a href="/search/eess?searchtype=author&query=Shankaranarayana%2C+S+M">Sharath M Shankaranarayana</a>, <a href="/search/eess?searchtype=author&query=Ram%2C+K">Keerthi Ram</a>, <a href="/search/eess?searchtype=author&query=Sivaprakasam%2C+M">Mohanasankar Sivaprakasam</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="2001.02387v1-abstract-short" style="display: inline;"> Automated medical image segmentation is an important step in many medical procedures. Recently, deep learning networks have been widely used for various medical image segmentation tasks, with U-Net and generative adversarial nets (GANs) being some of the commonly used ones. Foreground-background class imbalance is a common occurrence in medical images, and U-Net has difficulty in handling class im… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2001.02387v1-abstract-full').style.display = 'inline'; document.getElementById('2001.02387v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2001.02387v1-abstract-full" style="display: none;"> Automated medical image segmentation is an important step in many medical procedures. Recently, deep learning networks have been widely used for various medical image segmentation tasks, with U-Net and generative adversarial nets (GANs) being some of the commonly used ones. Foreground-background class imbalance is a common occurrence in medical images, and U-Net has difficulty in handling class imbalance because of its cross entropy (CE) objective function. Similarly, GAN also suffers from class imbalance because the discriminator looks at the entire image to classify it as real or fake. Since the discriminator is essentially a deep learning classifier, it is incapable of correctly identifying minor changes in small structures. To address these issues, we propose a novel context based CE loss function for U-Net, and a novel architecture Seg-GLGAN. The context based CE is a linear combination of CE obtained over the entire image and its region of interest (ROI). In Seg-GLGAN, we introduce a novel context discriminator to which the entire image and its ROI are fed as input, thus enforcing local context. We conduct extensive experiments using two challenging unbalanced datasets: PROMISE12 and ACDC. We observe that segmentation results obtained from our methods give better segmentation metrics as compared to various baseline methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2001.02387v1-abstract-full').style.display = 'none'; document.getElementById('2001.02387v1-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> 8 January, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 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">Accepted in ISBI 2020</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1908.09262">arXiv:1908.09262</a> <span> [<a href="https://arxiv.org/pdf/1908.09262">pdf</a>, <a href="https://arxiv.org/format/1908.09262">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link 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="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Recon-GLGAN: A Global-Local context based Generative Adversarial Network for MRI Reconstruction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Murugesan%2C+B">Balamurali Murugesan</a>, <a href="/search/eess?searchtype=author&query=S%2C+V+R">Vijaya Raghavan S</a>, <a href="/search/eess?searchtype=author&query=Sarveswaran%2C+K">Kaushik Sarveswaran</a>, <a href="/search/eess?searchtype=author&query=Ram%2C+K">Keerthi Ram</a>, <a href="/search/eess?searchtype=author&query=Sivaprakasam%2C+M">Mohanasankar Sivaprakasam</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="1908.09262v1-abstract-short" style="display: inline;"> Magnetic resonance imaging (MRI) is one of the best medical imaging modalities as it offers excellent spatial resolution and soft-tissue contrast. But, the usage of MRI is limited by its slow acquisition time, which makes it expensive and causes patient discomfort. In order to accelerate the acquisition, multiple deep learning networks have been proposed. Recently, Generative Adversarial Networks… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1908.09262v1-abstract-full').style.display = 'inline'; document.getElementById('1908.09262v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1908.09262v1-abstract-full" style="display: none;"> Magnetic resonance imaging (MRI) is one of the best medical imaging modalities as it offers excellent spatial resolution and soft-tissue contrast. But, the usage of MRI is limited by its slow acquisition time, which makes it expensive and causes patient discomfort. In order to accelerate the acquisition, multiple deep learning networks have been proposed. Recently, Generative Adversarial Networks (GANs) have shown promising results in MRI reconstruction. The drawback with the proposed GAN based methods is it does not incorporate the prior information about the end goal which could help in better reconstruction. For instance, in the case of cardiac MRI, the physician would be interested in the heart region which is of diagnostic relevance while excluding the peripheral regions. In this work, we show that incorporating prior information about a region of interest in the model would offer better performance. Thereby, we propose a novel GAN based architecture, Reconstruction Global-Local GAN (Recon-GLGAN) for MRI reconstruction. The proposed model contains a generator and a context discriminator which incorporates global and local contextual information from images. Our model offers significant performance improvement over the baseline models. Our experiments show that the concept of a context discriminator can be extended to existing GAN based reconstruction models to offer better performance. We also demonstrate that the reconstructions from the proposed method give segmentation results similar to fully sampled images. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1908.09262v1-abstract-full').style.display = 'none'; document.getElementById('1908.09262v1-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> 25 August, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2019. </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">Accepted at MLMIR-MICCAIW 2019</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1908.05553">arXiv:1908.05553</a> <span> [<a href="https://arxiv.org/pdf/1908.05553">pdf</a>, <a href="https://arxiv.org/format/1908.05553">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> Speaker Verification Using Simple Temporal Features and Pitch Synchronous Cepstral Coefficients </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=S%2C+B+V">Bhavana V. S</a>, <a href="/search/eess?searchtype=author&query=Das%2C+P+K">Pradip K. Das</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="1908.05553v1-abstract-short" style="display: inline;"> Speaker verification is the process by which a speakers claim of identity is tested against a claimed speaker by his or her voice. Speaker verification is done by the use of some parameters (features) from the speakers voice which can be used to differentiate among many speakers. The efficiency of speaker verification system mainly depends on the feature set providing high inter-speaker variabilit… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1908.05553v1-abstract-full').style.display = 'inline'; document.getElementById('1908.05553v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1908.05553v1-abstract-full" style="display: none;"> Speaker verification is the process by which a speakers claim of identity is tested against a claimed speaker by his or her voice. Speaker verification is done by the use of some parameters (features) from the speakers voice which can be used to differentiate among many speakers. The efficiency of speaker verification system mainly depends on the feature set providing high inter-speaker variability and low intra-speaker variability. There are many methods used for speaker verification. Some systems use Mel Frequency Cepstral Coefficients as features (MFCCs), while others use Hidden Markov Models (HMM) based speaker recognition, Support Vector Machines (SVM), GMMs . In this paper simple intra-pitch temporal information in conjunction with pitch synchronous cepstral coefficients forms the feature set. The distinct feature of a speaker is determined from the steady state part of five cardinal spoken English vowels. The performance was found to be average when these features were used independently. But very encouraging results were observed when both features were combined to form a decision for speaker verification. For a database of twenty speakers of 100 utterances per speaker, an accuracy of 91.04% has been observed. The analysis of speakers whose recognition was incorrect is conducted and discussed . <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1908.05553v1-abstract-full').style.display = 'none'; document.getElementById('1908.05553v1-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, 2019; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2019. </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">13 pages, 3 figures</span> </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" 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