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DTSTART;TZID=America/Los_Angeles:20220516T090000
DTEND;TZID=America/Los_Angeles:20220516T100000
DTSTAMP:20231011T032339Z
CREATED:20231011T032339Z
LAST-MODIFIED:20231011T032339Z
UID:14132-1652691600-1652695200@www.cecs.uci.edu
SUMMARY:Internet of Things-Based Collaborative Position-Sensing Systems for Cardiopulmonary Resuscitation and Indoor Localization
DESCRIPTION:Name: Chen\, Hsin-Chung “Andrew” \nChair: Pai Chou \nDate:  Wednesday\, March 16\, 2022 \nTime: 9:00 am \nLocation:  https://uci.zoom.us/j/93264053012 \nCommittee: Nader Bagherzadeh\, Rainer Doemer \nTitle: Internet of Things-Based Collaborative Position-Sensing Systems for Cardiopulmonary Resuscitation and Indoor Localization \nAbstract: \nThe ubiquitous IoT devices have promoted the growth of applications that require target positioning. While precise positioning remains a challenge for IoT devices\, existing methods mainly focus on optimizing the positioning algorithms or expanding the sensing modalities. One opportunity area of improvement is to leverage their wireless communication capability to exchange sensing data of different modalities with nearby devices. This work proposes collaborative sensing frameworks to enhance the accuracy of position inference. \nOur approach to collaborative sensing is divided into selection of reliable devices\, the data-exchange mechanism\, and the data-fusion algorithm. We demonstrate our approach on two different position-sensing systems: one for chest compression-depth estimation and one for indoor pedestrian localization. The key components used in the frameworks include quality assessment of sensor data using mutual information\, data-fusion algorithms based on the chest-compression model and the pedestrian-encounter model\, and the data-exchange mechanism using Bluetooth Low Energy (BLE). A real-time position-estimation method based on our chest-compression model is proposed to remove the noise and handle the cumulative error. A collaborative conditional random field algorithm is developed to reduce the convergence distance in the localization estimation. Experimental results show our collaborative-sensing approach outperforms the existing solutions in terms of real-time estimated position. \nThis dissertation proposes an alternative approach to optimizing the position-sensing systems through collaborative sensing. Our work represents the first step towards the universal positioning on IoT devices and provides research opportunities for further improvement and exploration. \n\nShort Bio: Hsinchung Chen is a Ph.D. candidate in computer engineering. His research interests include signal processing\, machine learning\, and embedded system design.
URL:https://www.cecs.uci.edu/event/internet-of-things-based-collaborative-position-sensing-systems-for-cardiopulmonary-resuscitation-and-indoor-localization/
LOCATION:CA
CATEGORIES:PhD Defenses
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220517T140000
DTEND;TZID=America/Los_Angeles:20220517T150000
DTSTAMP:20231011T032340Z
CREATED:20231011T032340Z
LAST-MODIFIED:20231011T032340Z
UID:14133-1652796000-1652799600@www.cecs.uci.edu
SUMMARY:"Beyond Approximate Computing: Quality-Scalability for Low-Power Embedded Systems and Machine Learning"
DESCRIPTION:Speaker: Younghyun Kim\nDate and Time: Tuesday\, May 17\, 2022 at 2:00 p.m. \nLocation: Zoom https://uci.zoom.us/j/98632011722 \nAbstract: \nApproximate computing is a new paradigm to accomplish energy-efficient computing in this twilight of Moore’s law by relaxing the exactness requirement of computation results for intrinsically error-resilient applications\, such as deep learning and signal processing\, and producing results that are “just good enough.” It exploits that the output quality of such error-resilient applications is not fundamentally degraded even if the underlying computations are greatly approximated. This favorable energy-quality tradeoff opens up new opportunities to improve the energy efficiency of computing\, and a large body of approximate computing methods for energy-efficient “data processing” have been proposed. In this talk\, I will introduce approximate computing methods to accomplish “full-system energy-quality scalability.” It extends the scope of approximation from the processor to other system components including sensors\, interconnects\, etc.\, for energy-efficient “data generation” and “data transfer” to fully exploit the energy-quality tradeoffs across the entire system. I will also discuss how approximate computing can benefit the implementation of machine learning on ultra low-power embedded systems. \nBiography: \nProf. Younghyun Kim is an Assistant Professor in the Department of Electrical and Computer Engineering and an ECE Grainger Faculty Scholar at the University of Wisconsin-Madison\, where leads the Wisconsin Embedded Systems and Computing (WISEST) Laboratory (https://wisest.ece.wisc.edu/). Prof. Kim received his B.S. degree in computer science and engineering and his Ph.D. degree in electrical engineering and computer science from Seoul National University in 2007 and 2013\, respectively. He was a Postdoctoral Research Assistant at Purdue University and a visiting scholar at the University of Southern California. His current research interests include energy-efficient computing and security and privacy of the Internet-of-Things. Prof. Kim was a recipient of several awards\, including the NSF Faculty Early Career Development Program (CAREER) Award\, Facebook Research Award\, IEEE Micro Top Pick\, the EDAA Outstanding Dissertation Award\, and the Design Contest Awards at the ACM/IEEE International Symposium on Low Power Electronics and Design (ISLPED). He served on the Technical Program Committees of various conferences on design automation and embedded systems\, including the Design Automation Conference (DAC)\, ISLPED\, Asia and South Pacific Design Automation Conference (ASP-DAC)\, International Conference on VLSI Design (VLSID)\, and Symposium on Applied Computing (SAC). He served as a Guest Editor for a Special Issue of VLSI Integration Journal (Elsevier). \nHosted By: Prof. Nikil Dutt \n 
URL:https://www.cecs.uci.edu/event/beyond-approximate-computing-quality-scalability-for-low-power-embedded-systems-and-machine-learning/
LOCATION:CA
CATEGORIES:Seminars at CECS
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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220523T140000
DTEND;TZID=America/Los_Angeles:20220523T150000
DTSTAMP:20231011T032340Z
CREATED:20231011T032340Z
LAST-MODIFIED:20231011T032340Z
UID:14135-1653314400-1653318000@www.cecs.uci.edu
SUMMARY:"Runtime Monitoring of Distributed Cyber-physical Systems"
DESCRIPTION:Speaker: Borzoo Bonakdarpour\nDate and Time: Monday\, May 23\, 2022 at 2:00 pm \nLocation: DBH 3011 \nAbstract:  \nWe consider the problem of detecting violations of specification In the signal temporal logic over distributed continuous-time and continuous-valued signals in cyber-physical systems (CPS). We assume a partially synchronous setting\, where a clock synchronization algorithm guarantees a bound on clock drifts among all signals. We introduce a novel retiming method that allows reasoning about the correctness of predicates among continuous-time signals that do not share a global view of time. The resulting problem is encoded as an SMT problem and we introduce techniques to solve the SMT encoding efficiently. Leveraging simple knowledge of physical dynamics allows further runtime reductions. We will discuss case studies on monitoring a network of autonomous ground vehicles\, a network of aerial vehicles\, and a water distribution system. \nBiography: Borzoo Bonakdarpour is currently an Associate Professor of Computer Science at Michigan State University. His research interests include formal methods and its application in distributed systems\, computer security\, and cyber-physical systems. He has published more than 100 articles and papers in top journals and conferences. His work in these areas have received multiple best paper awards from highly prestigious conferences\, including\, RV’21\, SRDS’17\, SSS’14\, and SIES’10. He chaired the Technical Program Committee of the SRDS’20\, SSS’16\, and RV’14 conferences. \nHosted By: Prof. Eli Bozorgzadeh \n 
URL:https://www.cecs.uci.edu/event/runtime-monitoring-of-distributed-cyber-physical-systems/
LOCATION:DBH 3011\, Donald Bren Hall University of California\, Irve\, Irvine\, CA\, 92617\, United States
CATEGORIES:Seminars at CECS
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