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Wi-Fi Signal Analysis for Heartbeat and Metal Detection: A Comparative Study of Reliable Contactless Systems 6

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Wi-Fi Signal Analysis for Heartbeat and Metal Detection: A C...

6th International Conference on Dependable Systems and Their Applications, DSA 2019

作者： Khan, Faiza Alam Bhuiyan, Md Zakirul Islam, Md Monirul Wang, Tian Zaman, Aliuz Tao, Hai Fordham University Department of Computer and Information Sciences New York United States Huaqiao University College of Computer Science and Technology Key Laboratory of Computer Vision and Machine Learning Huaqiao361021 China School of Computer Science Baoji University of Arts and Sciences Baoji Shaanxi721007 China

ISBN: (纸本)9781728160573

In this survey paper, we present various proposals of Wi-Fi applications which can benefit society, namely through detection of vital signs and detection of suspicious objects. Based on the previous research conducted in this field, we analyze how heart rate detection using Wi-Fi dependent technologies compare to older, sensor-based methods and the necessity of modern implementations especially in the medical field. People can have heart problems such as tachycardia and bradycardia at any instance, thus systems which do not require contact are most practical in detection of vital sign irregularities. Sensorbased systems may depend on Wi-Fi technologies only for the communication between different components in the system, such as in the Message Queuing Telemetry Transport System. However, the presence of a sensor in these systems can be replaced by Wi-Fi technologies to detect the presence of people and monitor their corresponding heart rates in a contactless manner. Additionally, Wi-Fi dependent systems such as Vital Radio and Wi-Sleep can monitor the heart rates of multiple people at once. Therefore contactless, Wi-Fi based systems are most efficient and practical for daily use. Moreover, although contactless systems demonstrate the benefits of technological advancements, these systems can still be further improved since they tend to produce higher accuracy rates from shorter distances, as demonstrated by the UWB radar system and the Fresnel model. Contactless systems can be enhanced to detect multiple users at once at extended distances. After providing details of contact requiring and contactless systems for heart rate detection, we also provide other useful applications of Wi-Fi technology, such as in the field of metal detection, human fall detection, etc. © 2020 IEEE.

关键词： Heart

Wi-Fi Signal Analysis for Heartbeat and Metal Detection: A Comparative Study of Reliable Contactless Systems

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Wi-Fi Signal Analysis for Heartbeat and Metal Detection: A C...

International Conference on Dependable Systems and Their Applications (DSA)

作者： Faiza Khan Md Zakirul Alam Bhuiyan Md Monirul Islam Tian Wang Aliuz Zaman Hai Tao Department of Computer and Information Sciences Fordham University NY USA Fordham University New York NY USA College of Computer Science and Technology Huaqiao University China Key Laboratory of Computer Vision and Machine Learning Huaqiao University China School of Computer Science Baoji University of Arts and Sciences Baoji Shaanxi China

ISBN: (数字)9781728160573

ISBN: (纸本)9781728160580

In this survey paper, we present various proposals of Wi-Fi applications which can benefit society, namely through detection of vital signs and detection of suspicious objects. Based on the previous research conducted in this field, we analyze how heart rate detection using Wi-Fi dependent technologies compare to older, sensor-based methods and the necessity of modern implementations especially in the medical field. People can have heart problems such as tachycardia and bradycardia at any instance, thus systems which do not require contact are most practical in detection of vital sign irregularities. Sensor-based systems may depend on Wi-Fi technologies only for the communication between different components in the system, such as in the Message Queuing Telemetry Transport System. However, the presence of a sensor in these systems can be replaced by Wi-Fi technologies to detect the presence of people and monitor their corresponding heart rates in a contactless manner. Additionally, Wi-Fi dependent systems such as Vital Radio and Wi-Sleep can monitor the heart rates of multiple people at once. Therefore contactless, Wi-Fi based systems are most efficient and practical for daily use. Moreover, although contactless systems demonstrate the benefits of technological advancements, these systems can still be further improved since they tend to produce higher accuracy rates from shorter distances, as demonstrated by the UWB radar system and the Fresnel model. Contactless systems can be enhanced to detect multiple users at once at extended distances. After providing details of contact requiring and contactless systems for heart rate detection, we also provide other useful applications of Wi-Fi technology, such as in the field of metal detection, human fall detection, etc.

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A sensor fusion-based GNSS Spoofing attack detection framework for autonomous vehicles

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arXiv 2021年

作者： Dasgupta, Sagar Rahman, Mizanur Islam, Mhafuzul Chowdhury, Mashrur Department of Civil Construction & Environmental Engineering The University of Alabama 3014 Cyber Hall Box 870205 248 Kirkbride Lane TuscaloosaAL35487 United States Department of Civil Construction & Environmental Engineering The University of Alabama 3015 Cyber Hall Box 870205 248 Kirkbride Lane TuscaloosaAL35487 United States 3D Computer Vision Machine Learning Engineer MicroVision Inc. WA United States Department of Civil Engineering Clemson University 216 Lowry Hall ClemsonSC29634 United States

This paper presents a sensor fusion based Global Navigation Satellite System (GNSS) spoofing attack detection framework for autonomous vehicles (AV) that consists of two concurrent strategies: (i) detection of vehicle state using predicted location shift-i.e., distance traveled between two consecutive timestamps-and monitoring of vehicle motion state-i.e., standstill/ in motion;and (ii) detection and classification of turns (i.e., left or right). Data from multiple low-cost in-vehicle sensors (i.e., accelerometer, steering angle sensor, speed sensor, and GNSS) are fused and fed into a recurrent neural network model, which is a long short-term memory (LSTM) network for predicting the location shift, i.e., the distance that an AV travels between two consecutive timestamps. This location shift is then compared with the GNSS-based location shift to detect an attack. We have then combined k-Nearest Neighbors (k-NN) and Dynamic Time Warping (DTW) algorithms to detect and classify left and right turns using data from the steering angle sensor. To prove the efficacy of the sensor fusion-based attack detection framework, attack datasets are created for four unique and sophisticated spoofing attacks-turn-by-turn, overshoot, wrong turn, and stop, using the publicly available real-world Honda Research Institute Driving Dataset (HDD). Our analysis reveals that the sensor fusion-based detection framework successfully detects all four types of spoofing attacks within the required computational latency threshold. © 2021, CC BY-NC-ND.

关键词： Global positioning system

Eye tracking: empirical foundations for a minimal reporting guideline

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Behavior research methods 2023年第1期55卷 364-416页

作者： Kenneth Holmqvist Saga Lee Örbom Ignace T C Hooge Diederick C Niehorster Robert G Alexander Richard Andersson Jeroen S Benjamins Pieter Blignaut Anne-Marie Brouwer Lewis L Chuang Kirsten A Dalrymple Denis Drieghe Matt J Dunn Ulrich Ettinger Susann Fiedler Tom Foulsham Jos N van der Geest Dan Witzner Hansen Samuel B Hutton Enkelejda Kasneci Alan Kingstone Paul C Knox Ellen M Kok Helena Lee Joy Yeonjoo Lee Jukka M Leppänen Stephen Macknik Päivi Majaranta Susana Martinez-Conde Antje Nuthmann Marcus Nyström Jacob L Orquin Jorge Otero-Millan Soon Young Park Stanislav Popelka Frank Proudlock Frank Renkewitz Austin Roorda Michael Schulte-Mecklenbeck Bonita Sharif Frederick Shic Mark Shovman Mervyn G Thomas Ward Venrooij Raimondas Zemblys Roy S Hessels Department of Psychology Nicolaus Copernicus University Torun Poland. kenneth.holmqvist@ur.de. Department of Computer Science and Informatics University of the Free State Bloemfontein South Africa. kenneth.holmqvist@ur.de. Department of Psychology Regensburg University Regensburg Germany. kenneth.holmqvist@ur.de. Department of Psychology Regensburg University Regensburg Germany. Experimental Psychology Helmholtz Institute Utrecht University Utrecht The Netherlands. Lund University Humanities Lab and Department of Psychology Lund University Lund Sweden. Department of Ophthalmology SUNY Downstate Health Sciences University Brooklyn NY USA. Tobii Pro AB Danderyd Sweden. Social Health and Organizational Psychology Utrecht University Utrecht The Netherlands. Department of Computer Science and Informatics University of the Free State Bloemfontein South Africa. TNO Soesterberg The Netherlands. Department of Ergonomics Leibniz Institute for Working Environments and Human Factors Dortmund Germany. Institute of Informatics LMU Munich Munich Germany. Institute of Child Development University of Minnesota Minneapolis USA. School of Psychology University of Southampton Southampton UK. School of Optometry and Vision Sciences Cardiff University Cardiff UK. Department of Psychology University of Bonn Bonn Germany. Vienna University of Economics and Business Vienna Austria. Department of Psychology University of Essex Essex UK. Department of Neuroscience Erasmus MC Rotterdam The Netherlands. Machine Learning Group Department of Computer Science IT University of Copenhagen Copenhagen Denmark. SR Research Ltd Ottawa Canada. Human-Computer Interaction University of Tübingen Tübingen Germany. University of British Columbia Columbia Canada. Department of Eye and Vision Science Institute of Life Course and Medical Sciences University of Liverpool Liverpool UK. Department of Education and Pedagogy Division Education Faculty of Social and Behavioral Sciences Ut

In this paper, we present a review of how the various aspects of any study using an eye tracker (such as the instrument, methodology, environment, participant, etc.) affect the quality of the recorded eye-tracking data and the obtained eye-movement and gaze measures. We take this review to represent the empirical foundation for reporting guidelines of any study involving an eye tracker. We compare this empirical foundation to five existing reporting guidelines and to a database of 207 published eye-tracking studies. We find that reporting guidelines vary substantially and do not match with actual reporting practices. We end by deriving a minimal, flexible reporting guideline based on empirical research (Section "An empirically based minimal reporting guideline").

关键词： Data quality Eye movements Eye tracking Replicability Reporting guidelines Reporting practices Reporting standards Reproducibility

A Review of Generalized Zero-Shot learning Methods

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arXiv 2020年

作者： Pourpanah, Farhad Abdar, Moloud Luo, Yuxuan Zhou, Xinlei Wang, Ran Lim, Chee Peng Wang, Xi-Zhao Jonathan Wu, Q.M. The Centre for Computer Vision and Deep Learning Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada Deakin University Australia The Department of Computer Science City University of Hong Kong Hong Kong The College of Mathematics and Statistics Shenzhen Key Lab. of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China The College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Shenzhen518060 China

Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples under the condition that some output classes are unknown during supervised learning. To address this challenging task, GZSL leverages semantic information of the seen (source) and unseen (target) classes to bridge the gap between both seen and unseen classes. Since its introduction, many GZSL models have been formulated. In this review paper, we present a comprehensive review on GZSL. Firstly, we provide an overview of GZSL including the problems and challenges. Then, we introduce a hierarchical categorization for the GZSL methods and discuss the representative methods in each category. In addition, we discuss the available benchmark data sets and applications of GZSL, along with a discussion on the research gaps and directions for future investigations. Copyright © 2020, The Authors. All rights reserved.

关键词： Zero-shot learning

Braix Risk Score: An Automated Mammogram-Based Biomarker for Breast Cancer Created by Applying Artificial Intelligence

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SSRN

SSRN 2024年

作者： Hopper, John L. Nguyen, Tuong Linh Elliott, Michael S. Al-Qershi, Osamah Schmidt, Daniel F. Makalic, Enes Li, Shuai Fox, Samantha K. Dowty, James G. Peña-Solorzano, Carlos Andres Kwok, Chun Fung Chen, Yuanhong Wang, Chong Lippey, Jocelyn F. Brotchie, Peter Carneiro, Gustavo McCarthy, Davis J. Frazer, Helen M.L. Centre for Epidemiology & Biostatistics Melbourne School of Population and Global Health University of Melbourne Victoria Australia Bioinformatics and Cellular Genomics Unit St Vincent’s Institute of Medical Research Victoria Australia Melbourne Integrative Genomics School of Mathematics and Statistics School of BioSciences Faculty of Science University of Melbourne Victoria Australia Faculty of Information Technology Monash University ClaytonVIC Australia Centre for Cancer Genetic Epidemiology Department of Public Health and Primary Care University of Cambridge Cambridge United Kingdom Precision Medicine School of Clinical Sciences at Monash Health Monash University ClaytonVIC Australia Murdoch Children’s Research Institute Royal Children’s Hospital ParkvilleVIC Australia School of Computer Science Australian Institute for Machine Learning University of Adelaide SA Australia Department of Surgery St Vincent’s Hospital Melbourne VIC Australia Faculty of Medicine Dentistry and Health Sciences University of Melbourne Victoria Australia St Vincent’s BreastScreen St Vincent’s Hospital Melbourne VIC Australia Department of Radiology St Vincent’s Hospital Melbourne VIC Australia Centre for Vision Speech and Signal Processing University of Surrey United Kingdom BreastScreen Victoria VIC Australia

Background: Artificial intelligence (AI) algorithms to detect breast cancers from mammographic images are being developed. We evaluated the performance of an algorithm across all stages of the screening process and beyond. Methods: We used an image-specific AI-based breast cancer detection measure learnt from images of women attending BreastScreen Victoria (BRAIx cancer detection measure) to create a woman-specific standard normal mammography-based risk score (BRAIx risk score). Applying logistic regression to a random independent prospective testing cohort of 96,348 women, we estimated risk of invasive breast cancers: (i) detected at cohort entry (525);and (ii) for women given an all-clear, diagnosed during the next 4 years either at future screens (790) or during intervals between screens (308), as a function of the BRAIx risk score and other measures. We estimated the odds ratio per standard deviation (OR) and a greater risk in an upper ***: The BRAIx risk score predicted reader outcomes, recall, cancer detection and future risk. For 4-years risk, OR=2.70 (95% confidence interval (CI) 2.52 to 2.88) overall with evidence for a greater OR in the upper tail (P=10-18). The predicted absolute risk was >10% for 1% of all-clear women. BRAIx risk score was greater for women with a family history and explained 23% (95% CI 21-25%) of familial aspects of risk. Mammographic density was weakly associated with risk before (OR=1.27;95% CI 1.20 to 1.35), but not after, fitting ***: The BRAIx risk score is a strong biomarker for breast cancer that could be used to inform risk-based recall for further assessment, detection and future screening. Its use would automatically identify all-clear women at similar risk to BRCA1 and BRCA2 mutation carriers. BRAIx could also reveal insights into both familial and non-familial causes. Funding: Medical Research Future Fund, the Ramaciotti Foundation, National Breast Cancer Foundation, Cancer *** o

关键词： Mammography

222P Artificial intelligence (AI) based prognostication from baseline computed tomography (CT) scans in a phase III advanced non-small cell lung cancer (aNSCLC) trial

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Annals of Oncology 2024年 35卷 S304-S304页

作者： O.F. Khan J. Montalt-Tordera J. Riskas S.A. Haider V. Sivan O. Samorodova J. Hennessy A. Mitchell S.S. Mohammadi E. Di Tomaso T. Banerji F. Baldauf-Lenschen C. Glaus Oncology Department Cumming School of Medicine Tom Baker Cancer Centre Calgary AB Canada Computer Vision & Sound Analysis Bayer Hispania S.L. Sant Joan Despí Spain Machine Learning Altis Labs Inc. Toronto ON Canada Radiology Altis Labs Inc. Toronto ON Canada Data Altis Labs Inc. Toronto ON Canada Biostatistics Altis Labs Inc. Toronto ON Canada Computer Vision & Sound Analysis Bayer AG Leverkusen Germany Translational Sciences Oncology Bayer U.S. LLC Cambridge MA USA Digital Lead Global Brands Bayer U.S. LLC Whippany NJ USA Executive Altis Labs Inc. Toronto ON Canada

CVPR19 Tracking and Detection Challenge: How crowded can it get?

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arXiv 2019年

作者： Dendorfer, Patrick Rezatofighi, Hamid Milan, Anton Shi, Javen Cremers, Daniel Reid, Ian Roth, Stefan Schindler, Konrad Leal-Taixa, Laura Dynamic Vision and Learning Group at Tum Munich Germany Australian Institute for Machine Learning and School of Computer Science at University of Adelaide. Amazon Berlin Germany Photogrammetry and Remote Sensing Group at Eth Zurich Switzerland Computer Vision Group at Tum Munich Germany Department of Computer Science Technische Universität Darmstadt Germany

Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for research. The benchmark for Multiple Object Tracking, MOTChallenge, was launched with the goal to establish a standardized evaluation of multiple object tracking methods. The challenge focuses on multiple people tracking, since pedestrians are well studied in the tracking community, and precise tracking and detection has high practical relevance. Since the first release, MOT15 [5], MOT16 [8], and MOT17 [8] have tremendously contributed to the community by introducing a clean dataset and precise framework to benchmark multi-object trackers. In this paper, we present our CVPR19 benchmark, consisting of 8 new sequences depicting very crowded challenging scenes. The benchmark will be presented at the 4th BMTT MOT Challenge Workshop at the computer vision and Pattern Recognition Conference (CVPR) 2019, and will evaluate the state-of-the-art in multiple object tracking whend handling extremely crowded scenarios. Copyright © 2019, The Authors. All rights reserved.

关键词： computer vision

Continuous-time deep glioma growth models

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arXiv 2021年

作者： Petersen, Jens Isensee, Fabian Köhler, Gregor Jäger, Paul F. Zimmerer, David Neuberger, Ulf Wick, Wolfgang Debus, Jürgen Heiland, Sabine Bendszus, Martin Vollmuth, Philipp Maier-Hein, Klaus H. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany HIP Applied Computer Vision Lab Division of Medical Image Computing German Cancer Research Center Germany Interactive Machine Learning Group German Cancer Research Center Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Neurology Clinic Heidelberg University Hospital Germany DKTK CCU Neurooncology German Cancer Research Center Germany Heidelberg Germany Heidelberg University Hospital Germany Clinical Cooperation Unit Radiation Oncology German Cancer Research Center Germany

The ability to estimate how a tumor might evolve in the future could have tremendous clinical benefits, from improved treatment decisions to better dose distribution in radiation therapy. Recent work has approached the glioma growth modeling problem via deep learning and variational inference, thus learning growth dynamics entirely from a real patient data distribution. So far, this approach was constrained to predefined image acquisition intervals and sequences of fixed length, which limits its applicability in more realistic scenarios. We overcome these limitations by extending Neural Processes, a class of conditional generative models for stochastic time series, with a hierarchical multi-scale representation encoding including a spatio-temporal attention mechanism. The result is a learned growth model that can be conditioned on an arbitrary number of observations, and that can produce a distribution of temporally consistent growth trajectories on a continuous time axis. On a dataset of 379 patients, the approach successfully captures both global and finer-grained variations in the images, exhibiting superior performance compared to other learned growth models. © 2021, CC BY-SA.

关键词： Tumors