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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220817T103000
DTEND;TZID=America/Los_Angeles:20220817T113000
DTSTAMP:20231011T032408Z
CREATED:20231011T032408Z
LAST-MODIFIED:20231011T032408Z
UID:14153-1660732200-1660735800@www.cecs.uci.edu
SUMMARY:An End-to-End Platform for Multi-Modal Machine Learning Affective Computing Services
DESCRIPTION:Name: Emad Kasaeyan Naeini \nChair: Nikil Dutt \nDate: August 17\, 2022 \nTime: 10:30 AM \nLocation: DBH 3011 \nCommittee: Amir Rahmani\, Fadi Kurdahi \nTitle:  An End-to-End Platform for Multi-Modal Machine Learning Affective Computing Services \nAbstract: \nSmart affective computing applications deliberately influence pain\, emotion and other affective phenomena\, and are fundamental to human experience\, health and well-being. Affective states such as pain\, stress\, and emotion are intrinsically subjective in nature\, posing challenges for objective assessment and quantification of such affective phenomena. Prior efforts in affective computing provide a foundation for the automated analysis of affective states\, but still face challenges in real-life\, everyday settings. We use pain as an exemplar for affective computing services. Pain assessment is critical for optimal treatment\, and is particularly important during periods of acute pain since inadequately treated acute pain increases the risk of chronic pain. Historically\, patients have served as the main assessment tool as they are able to self-report their pain presence and severity on standard\, but somewhat subjective pain scales. However\, it remains a challenge to assess pain from patients who cannot self-report. Automatic pain recognition systems could be crucial to facilitating accurate\, objective\, and real-time pain measurement\, that can in turn improve pain management and ultimately lead to improved patient outcomes. Using pain as an exemplar of smart affective computing services\, this thesis proposes the use of multimodal sensing of physiological and behavioral input data\, transmits the data to edge and/or cloud nodes\, and processes data with compute-intensive machine learning (ML) algorithms. The efficiency of ML-driven applications for affective computing (e.g.\, pain assessment) is greatly affected by run-time variations resulting from continuous stream of noisy input data\, unreliable network connections\, and the variations in computational requirements of ML algorithms. Towards that end\, this thesis evaluates and automates objective and real-time multimodal pain assessment algorithms\, and performs design space exploration of accuracy-performance-energy trade-offs and sense-compute co-optimization for multimodal machine learning (MMML) methods. The approach developed in this thesis could be used for the design and development of an end-to-end platform for multimodal machine learning affective computing services\, together with a thorough analysis of their roles in the prediction\, performance and energy in future studies. \nShort bio: \nEmad Kasaeyan Naeini is a Ph.D. candidate in Computer Engineering at the University of California\, Irvine. He received his M.Sc. degree in Electrical and Computer Engineering from the Department of Electrical Engineering and Computer Science at the University of California Irvine in 2020 and his B.Sc. in Electrical Engineering from Sharif University of Technology\, Tehran\, Iran\, in 2017. His research interests primarily include deep learning and machine learning\, signal processing\, healthcare Internet of Things (IoT)\, and healthcare data analysis.
URL:https://www.cecs.uci.edu/event/an-end-to-end-platform-for-multi-modal-machine-learning-affective-computing-services/
LOCATION:DBH 3011\, Donald Bren Hall University of California\, Irve\, Irvine\, CA\, 92617\, United States
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220810T110000
DTEND;TZID=America/Los_Angeles:20220810T120000
DTSTAMP:20231011T032348Z
CREATED:20231011T032346Z
LAST-MODIFIED:20231011T032348Z
UID:14139-1660129200-1660132800@www.cecs.uci.edu
SUMMARY:"DRAC: Designing RISC-V-based Accelerators for next generation Computers"
DESCRIPTION:Speaker: Miquel Moreto\nDate and Time: Wednesday\, August 10\, 11:00 am \nLocation: DBH 3011 OR Zoom Link \nAbstract: \nDesigning RISC-V-based Accelerators for next-generation Computers (DRAC) is a 3-year project (2019-2022) funded by the ERDF Operational Program of Catalonia 2014-2020. DRAC will design\, verify\, implement and fabricate a high-performance general purpose processor that will incorporate different accelerators based on the RISC-V technology\, with specific applications in the field of post-quantum security\, genomics\, and autonomous navigation. In this talk\, we will provide an overview of the main achievements in the DRAC project\, including the fabrication of Lagarto\, the first RISC-V processor developed in Spain. \nBiography: \nMiquel Moreto is a Ramon y Cajal Fellow at the Computer Architecture Departament (DAC) at the Universitat Politècnica de Catalunya (UPC)\, and he leads the High Performance Domain Specific Architectures team at the Barcelona Supercomputing Center (BSC). Miquel received his Ph.D. from UPC in 2010. After finishing the PhD\, he spent 15 months at the International Computer Science Institute (ICSI)\, affiliated with UC Berkeley\, as a Fulbright Postdoctoral Research Fellowship Holder during 2011 and 2012. In 2013\, he returned to Barcelona to work on multiple European projects (RoMoL\, Mont-Blanc\, EPI\, DeepHealth\, eProcessor) and industrial projects (Arm\, IBM\, Lenovo). In 2019\, he led the design and fabrication of Lagarto\, the first processor developed in Spain based on the open source RISC-V instruction set architecture. Currently\, he coordinates the DRAC project\, which is promoting the Lagarto initiative with new generations of the Lagarto processor and accelerators. \nHosted By: Prof. Veidenbaum \n 
URL:https://www.cecs.uci.edu/event/drac-designing-risc-v-based-accelerators-for-next-generation-computers/
LOCATION:DBH 3011\, Donald Bren Hall University of California\, Irve\, Irvine\, CA\, 92617\, United States
CATEGORIES:Seminars at CECS
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220802T140000
DTEND;TZID=America/Los_Angeles:20220802T150000
DTSTAMP:20231011T032348Z
CREATED:20231011T032346Z
LAST-MODIFIED:20231011T032348Z
UID:14138-1659448800-1659452400@www.cecs.uci.edu
SUMMARY:“Securing Hardware for Designing Trustworthy Systems”
DESCRIPTION:Speaker: Prabhat Mishra\nDate and Time: Tuesday\, August 2\, 2:00 pm \nLocation: DBH 4011 OR Zoom Link \nAbstract: \nSystem-on-Chip (SoC) is the brain behind computing and communication in a wide variety of embedded systems. Reusable hardware Intellectual Property (IP) based SoC design has emerged as a pervasive design practice in the industry to dramatically reduce SoC design and verification cost while meeting aggressive time-to-market constraints. Growing reliance on these pre-verified hardware IPs\, often gathered from untrusted third-party vendors\, severely affects the security and trustworthiness of computing platforms. It is crucial to evaluate the integrity and trustworthiness of third-party IPs for designing trustworthy systems. In this talk\, I will introduce a wide variety of hardware security vulnerabilities\, design-for-security solutions\, and possible attacks and countermeasures. I will briefly describe how the complementary abilities of simulation-based validation\, formal verification as well as side channel analysis can be effectively utilized for comprehensive SoC security and trust validation. \nBiography: \nPrabhat Mishra is a Professor in the Department of Computer and Information Science and Engineering and a UF Research Foundation Professor at the University of Florida. He received his Ph.D. in Computer Science from the University of California at Irvine in 2004. His research interests include embedded and cyber-physical systems\, hardware security and trust\, energy-aware computing\, system-on-chip validation\, machine learning\, and quantum computing. He has published 8 books\, 35 book chapters\, and more than 200 research articles in premier international journals and conferences. His research has been recognized by several awards including the NSF CAREER Award\, IBM Faculty Award\, three best paper awards\, and EDAA Outstanding Dissertation Award. He currently serves as an Associate Editor of IEEE Transactions on VLSI Systems and ACM Transactions on Embedded Computing Systems. He is an IEEE Fellow and an ACM Distinguished Scientist. \nHosted By: Prof. Nikil Dutt \n 
URL:https://www.cecs.uci.edu/event/securing-hardware-for-designing-trustworthy-systems/
LOCATION:DBH 4011\, Donald Bren Hall\, University of California\, Irvine\, Irvine\, CA\, 92617\, United States
CATEGORIES:Seminars at CECS
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220715T100000
DTEND;TZID=America/Los_Angeles:20220715T110000
DTSTAMP:20231011T032348Z
CREATED:20231011T032346Z
LAST-MODIFIED:20231011T032348Z
UID:14137-1657879200-1657882800@www.cecs.uci.edu
SUMMARY:"Electric Power to the People: Secure & Resilient Cyber-Physical Systems in the Age of Renewable Energy"
DESCRIPTION:Speaker: Charalambos Konstantinou\nDate and Time: Friday\, July 15\, 10:00 am \nLocation: EH 2430 \nAbstract: \nRapid advancements in power electronics along with the increasing penetration of distributed energy resources (DERs) are transforming the electric power grids. Furthermore\, increasing types and number of loads and electric transportation are stressing the network. Overall\, the power system is facing unprecedented changes in operation and control as more and diverse sources and loads are being connected to this complex cyber-physical energy system. In light of this modernization\, and due to the growing number of Internet-of-things (IoT) connected controllers\, and the use of communication and control interfaces\, making cyber-physical energy systems resilient to high-impact\, low-probability cyber-physical adverse events\, such as cyber-attacks\, is a major priority for power grid operations. Such incidents\, if left unabated\, can intensify and elicit system dynamics instabilities\, eventually causing outages and system failures. In this talk\, we will give an overview of the research of the Secure Next Generation Resilient Systems (SENTRY) lab (sentry.kaust.edu.sa) at KAUST\, presenting different methodologies\, in the age of renewable energy\, contributing towards building secure and resilient cyber-physical grids. \nBiography: \nCharalambos Konstantinou is an Assistant Professor of Computer Science (CS) and Affiliate Professor of Electrical and Computer Engineering (ECE) at the Computer\, Electrical and Mathematical Science and Engineering Division (CEMSE) of King Abdullah University of Science and Technology (KAUST)\, Thuwal\, Saudi Arabia. He is the Principal Investigator of the SENTRY Lab (Secure Next Generation Resilient Systems – sentry.kaust.edu.sa) and a member of the Resilient Computing and Cybersecurity Center (RC3 – rc3.kaust.edu.sa) at KAUST. Before joining KAUST in 2021\, he was an Assistant Professor with the Center for Advanced Power Systems (CAPS) at Florida State University (FSU). His research interests are in secure\, trustworthy\, and resilient cyber-physical and embedded IoT systems. He is also interested in critical infrastructures security and resilience with special focus on smart grid technologies\, renewable energy integration\, and real-time simulation. He received a Ph.D. in Electrical Engineering from New York University (NYU)\, NY\, in 2018\, and an M.Eng. Degree in Electrical and Computer Engineering from National Technical University of Athens (NTUA)\, Greece\, in 2012. \nHosted By: Prof. Mohammad Al Faruque \n 
URL:https://www.cecs.uci.edu/event/electric-power-to-the-people-secure-resilient-cyber-physical-systems-in-the-age-of-renewable-energy/
LOCATION:EH 2430\, Engineering Hall\, University of California\, Irvine\, Irvine\, CA\, 92697\, United States
CATEGORIES:Seminars at CECS
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220707T140000
DTEND;TZID=America/Los_Angeles:20220707T150000
DTSTAMP:20231011T032340Z
CREATED:20231011T032340Z
LAST-MODIFIED:20231011T032340Z
UID:14134-1657202400-1657206000@www.cecs.uci.edu
SUMMARY:"Anti-virus hardware: Applications in Embedded\, Automotive and Power Systems Security"
DESCRIPTION:Speaker: Kanad Basu\nDate and Time: Tuesday\, June 7\, 2:00 pm \nLocation: Zoom https://uci.zoom.us/j/97807443602 \nAbstract: \nAnti-virus software (AVS) tools are used to detect Malware in a system. However\, software-based AVS are vulnerable to attacks. A malicious entity can exploit these vulnerabilities to subvert the AVS. Recently\, hardware components such as Hardware Performance Counters (HPC) have been used for Malware detection\, in the form of Anti-virus Hardware (AVH). In this talk\, we will discuss HPC-based AVHs for improving embedded security and privacy. Furthermore\, we will discuss the application of HPCs in security cyber physical systems (CPS)\, namely automotive and microgrid systems. Subsequently\, we will discuss their pitfalls. Finally\, we will present PREEMPT\, a zero overhead\, high-accuracy and low-latency technique to detect Malware by re-purposing the embedded trace buffer (ETB)\, a debug hardware component available in most modern processors.  PREEMPT combines these hardware-level observations with machine learning-based classifiers to preempt Malware before it can cause damage. We will conclude the talk with future research directions and challenges. \nBiography: \nKanad Basu received his Ph.D. from the department of Computer and Information Science and Engineering\, University of Florida. His thesis was focused on improving signal observability for post-silicon validation. Post-PhD\, Kanad worked in various semiconductor companies like IBM and Synopsys. During his PhD days\, Kanad interned at Intel. Currently\, Kanad is an Assistant Professor at the Electrical and Computer Engineering Department of the University of Texas at Dallas\, where he leads the Trustworthy and Intelligent Embedded Systems (TIES) lab. Prior to this\, Kanad was an Assistant Research Professor at the Electrical and Computer Engineering Department of NYU. He has authored 1 book\, 2 US patents\, 2 book chapters and several peer reviewed journal and conference articles. His research is supported by SRC\, NSF\, DARPA and Ford Motors. Kanad was awarded the ”Best Paper Award” at the International Conference on VLSI Design 2011 and an honorable mention award at the same conference in 2021. Several News agencies have covered his research including NBC Austin and CBS Dallas-Fort Worth. Kanad’s current research interests are hardware and systems security as well as Deep learning hardware. \nHosted By: Prof. Nikil Dutt \n 
URL:https://www.cecs.uci.edu/event/anti-virus-hardware-applications-in-embedded-automotive-and-power-systems-security/
LOCATION:CA
CATEGORIES:Seminars at CECS
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20220707T100000
DTEND;TZID=America/Los_Angeles:20220707T230000
DTSTAMP:20231011T032340Z
CREATED:20231011T032340Z
LAST-MODIFIED:20231011T032340Z
UID:14136-1657188000-1657234800@www.cecs.uci.edu
SUMMARY:Transaction-Level Modeling of Deep Neural Networks for Efficient Parallelism and Memory Accuracy
DESCRIPTION:Name: Emad Arasteh\nChair: Prof. Rainer Doemer \nDate: July 7\, 2022 \nTime: 10:00 am \nLocation: EH 3206 \nCommittee: Prof. Fadi Kurdahi\, Prof. Ian Harris \nTitle: Transaction-Level Modeling of Deep Neural Networks for Efficient Parallelism and Memory Accuracy \nAbstract:  \nThe emergence of data-intensive applications\, such as Deep Neural Networks (DNNs)\, exacerbates the well-known memory bottleneck in computer systems and demands early attention in the design flow. Electronic System-Level (ESL) design using SystemC Transaction Level Modeling (TLM) enables effective performance estimation\, design space exploration\, and gradual refinement. In this dissertation\, we present our exploratory modeling framework for hardware-software codesign based on SystemC TLM with particular focus on exposing parallelism and memory contention. We demonstrate the effectiveness of our approach for representative complex DNNs such as GoogLeNet and Single Shot MultiBox Detector. \nFirst\, we study the impact of communication mechanisms on the available parallelism in TLM models. Specifically\, we demonstrate the impact of varying synchronization mechanisms and buffering schemes on the exposed parallelism using different modeling styles of a DNN. We measure the performance of aggressive out-of-order parallel discrete event simulation and analyze the available parallelism in the models. Our study suggests that increased parallel simulation performance indicates better models with higher amounts of parallelism exposed. \nSecond\, we explore the critical aspects of modeling and analysis of timing accuracy and memory contention. A major hurdle in tackling the memory bottleneck is the detection of memory contention late in the design cycle once detailed timed or cycle-accurate models are developed. A memory bottleneck detected at such a late stage can severely limit the available design choices or even require costly redesign. To explore new architectures prior to RTL implementation\, we propose a novel TLM-2.0 loosely-timed contention-aware (LT-CA) modeling style that offers high-speed simulation close to traditional loosely-timed (LT) models\, yet shows the same accuracy for memory contention as low level approximately-timed (AT) models. \nFinally\, we refine further the TLM-2.0 AT model by adding a cycle-accurate model of a memory subsystem. This model provides a higher timing accuracy for contention analysis\, hence it gives a more accurate estimation of the performance. We revise our LT-CA memory delay modeling to provide further accuracy comparable to the cycle-accurate TLM model of the shared memory subsystem. The high amount of contention on the shared memory suggests new processor architectures with local memories. \n\nShort Bio: \nEmad Arasteh is a Ph.D. candidate in Computer Engineering at University of California\, Irvine and a graduate researcher at the Center for Embedded and Cyber-physical Systems (CECS). He received the M.Sc. degree in Electronic Design from Lund University in Sweden. His current research interests include system-level modeling and design of embedded systems\, scalable memory system design\, and massively-parallel processor architecture. Previously\, he worked as a hardware and software engineer at semiconductor companies in Sweden and USA including Ericsson\, Canon (Axis Communications) and Samsung.
URL:https://www.cecs.uci.edu/event/transaction-level-modeling-of-deep-neural-networks-for-efficient-parallelism-and-memory-accuracy/
LOCATION:EH 3206\, Engineering Hall University of California\, Irve\, Irvine\, CA\, 92697\, United States
CATEGORIES:PhD Defenses
END:VEVENT
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
END:VEVENT
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
END:VEVENT
BEGIN:VEVENT
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20211031T080000
DTEND;TZID=America/Los_Angeles:20211031T170000
DTSTAMP:20231031T193007Z
CREATED:20231031T193007Z
LAST-MODIFIED:20231031T193007Z
UID:15800-1635667200-1635699600@www.cecs.uci.edu
SUMMARY:Luke Chen Received John and Joanna Duffy Graduate Fellowship Award
DESCRIPTION:Luke Chen received John and Joanna Duffy Graduate Fellowship Award\, supports M.S. student who can enhance the diversity\, equity\, and inclusion of EECS graduate program. Congrats Chen!
URL:https://www.cecs.uci.edu/event/luke-chen-received-john-and-joanna-duffy-graduate-fellowship-award/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20211031T080000
DTEND;TZID=America/Los_Angeles:20211031T170000
DTSTAMP:20231031T192907Z
CREATED:20231031T192907Z
LAST-MODIFIED:20231031T192907Z
UID:15796-1635667200-1635699600@www.cecs.uci.edu
SUMMARY:Yasser Shoukry Awarded Early-Career Award
DESCRIPTION:Assistant Professor Yasser Shoukry was awarded the Early-Career Award by the IEEE Technical Committee on Cyber-Physical Systems for the contributions to the theory and design of safe learning-enabled cyber-physical systems. Congrats Prof. Shoukry!
URL:https://www.cecs.uci.edu/event/yasser-shoukry-awarded-early-career-award/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20211031T080000
DTEND;TZID=America/Los_Angeles:20211031T170000
DTSTAMP:20231031T192557Z
CREATED:20231031T192557Z
LAST-MODIFIED:20231031T192557Z
UID:15792-1635667200-1635699600@www.cecs.uci.edu
SUMMARY:Payam Heydari Named UCI Chancellor’s Professor
DESCRIPTION:Professor Payam Heydari named UCI Chancellor’s Professor to recognize his research contribution in microelectronics\, specifically in the design and analysis of radio frequency\, millimeter-wave and terahertz integrated circuits. Congrats Prof. Heydari!
URL:https://www.cecs.uci.edu/event/payam-heydari-named-uci-chancellors-professor/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20211031T080000
DTEND;TZID=America/Los_Angeles:20211031T170000
DTSTAMP:20231031T192455Z
CREATED:20231031T192455Z
LAST-MODIFIED:20231031T192455Z
UID:15788-1635667200-1635699600@www.cecs.uci.edu
SUMMARY:Yasser Shoukry Won IEEE Early Career Award
DESCRIPTION:Assistant Professor Yasser Shoukry won IEEE Early Career Award for demonstrated outstanding contributions to the field of cyber-physical systems in the early stage of his career. Congrats Prof. Shoukry!
URL:https://www.cecs.uci.edu/event/yasser-shoukry-won-ieee-early-career-award/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20211031T080000
DTEND;TZID=America/Los_Angeles:20211031T170000
DTSTAMP:20231031T192402Z
CREATED:20231031T192402Z
LAST-MODIFIED:20231031T192402Z
UID:15784-1635667200-1635699600@www.cecs.uci.edu
SUMMARY:Ahmed Eltawil Awarded Innovator of the Year Award
DESCRIPTION:Professor Ahmed Eltawil was awarded Innovator of the Year Award by the Henry Samueli School of Engineering for his journey of innovation to commercialization of full duplex technology is a successful example of entrepreneurship based on “Made at UCI’ technology. Congrats Prof. Eltawil!
URL:https://www.cecs.uci.edu/event/ahmed-eltawil-awarded-innovator-of-the-year-award/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20211031T080000
DTEND;TZID=America/Los_Angeles:20211031T170000
DTSTAMP:20231031T192244Z
CREATED:20231031T192244Z
LAST-MODIFIED:20231031T192244Z
UID:15780-1635667200-1635699600@www.cecs.uci.edu
SUMMARY:Ardalan Amiri Sani Won ICS Dean’s Mid-Career Award for Research
DESCRIPTION:Assistant Professor Ardalan Amiri Sani won the ICS Dean’s Mid-Career Award for Research to recognize his innovative research\, exemplar service\, student mentorship and exceptional teaching skills. Congrats Prof. Sani!
URL:https://www.cecs.uci.edu/event/ardalan-amiri-sani-won-ics-deans-mid-career-award-for-research/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20211031T080000
DTEND;TZID=America/Los_Angeles:20211031T170000
DTSTAMP:20231031T192201Z
CREATED:20231031T192201Z
LAST-MODIFIED:20231031T192201Z
UID:15776-1635667200-1635699600@www.cecs.uci.edu
SUMMARY:Mohammad Al Faruque Received Distinguished Lecturer Nomination by IEEE CEDA
DESCRIPTION:Professor Mohammad Al Faruque Received Distinguished Lecturer Nomination by the IEEE Central Dredging Association for promoting the field of electronic design automation to the scientific community and the public at large. Congrats Prof. Al Faruque!
URL:https://www.cecs.uci.edu/event/mohammad-al-faruque-received-distinguished-lecturer-nomination-by-ieee-ceda/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20211031T080000
DTEND;TZID=America/Los_Angeles:20211031T170000
DTSTAMP:20231031T191904Z
CREATED:20231031T191904Z
LAST-MODIFIED:20231031T191904Z
UID:15772-1635667200-1635699600@www.cecs.uci.edu
SUMMARY:Rozhin Yasaei Became President of Women in CyberSecurity UCI chapter
DESCRIPTION:Assistant Professor Rozhin Yasaei became the President of Women in CyberSecurity (WiCyS) UCI chapter for her promotion of women’s participation in the field of Cyber Security. Congrats prof. Yasaei!
URL:https://www.cecs.uci.edu/event/rozhin-yasaei-became-president-of-women-in-cybersecurity-uci-chapter/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20211031T080000
DTEND;TZID=America/Los_Angeles:20211031T170000
DTSTAMP:20231031T191655Z
CREATED:20231031T191655Z
LAST-MODIFIED:20231031T191655Z
UID:15768-1635667200-1635699600@www.cecs.uci.edu
SUMMARY:Zhou Li Received Early CAREER Award
DESCRIPTION:Assistant Professor Zhou Li received Early CAREER Award for his outstanding research on internet system security\, specifically data-driven security analytics\, side-channel analysis and Internet of Things (IoT) security. Congrats Prof. Li!
URL:https://www.cecs.uci.edu/event/zhou-li-received-early-career-award/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20201031T080000
DTEND;TZID=America/Los_Angeles:20201031T170000
DTSTAMP:20231031T191603Z
CREATED:20231031T191603Z
LAST-MODIFIED:20231031T191603Z
UID:15764-1604131200-1604163600@www.cecs.uci.edu
SUMMARY:Hamid Nejatollahi and Mohsen Imani Reached Best Paper Award Finalist at DAC 2020
DESCRIPTION:Hamid Nejatollahi and Assistant Professor Mohsen Imani reached Best Paper Award Finalist at Design Automation Conference (DAC) 2020 for research paper titled\, “CryptPIM:  In-memory Acceleration for Lattice-based Crytographic Hardware”. Congrats Nejatollahi and Imani!
URL:https://www.cecs.uci.edu/event/hamid-nejatollahi-and-mohsen-imani-reached-best-paper-award-finalist-at-dac-2020/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20201031T080000
DTEND;TZID=America/Los_Angeles:20201031T170000
DTSTAMP:20231031T191347Z
CREATED:20231031T191347Z
LAST-MODIFIED:20231031T191347Z
UID:15760-1604131200-1604163600@www.cecs.uci.edu
SUMMARY:Fadi Kurdahi and Judit Giro Benet Won International Design Award and The James Dyson Award
DESCRIPTION:Professor Fadi Kurdahi and Judit Giro Benet won the International Design Award and The James Dyson Award for biomedical device innovation of the Blue Box\, a breast cancer detection system. Congrats Prof. Kurdahi and Benet!
URL:https://www.cecs.uci.edu/event/fadi-kurdahi-and-judit-giro-benet-won-international-design-award-and-the-james-dyson-award/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20201031T080000
DTEND;TZID=America/Los_Angeles:20201031T170000
DTSTAMP:20231031T191206Z
CREATED:20231031T191206Z
LAST-MODIFIED:20231031T191206Z
UID:15756-1604131200-1604163600@www.cecs.uci.edu
SUMMARY:Hamid Nejatollahi Reached Best Paper Award Finalist ICASSP 2020
DESCRIPTION:Hamid Nejatollahi reached Best Paper Award Finalist at the International Conference on Acoustics\, Speech and Signal Processing 2020 for research paper titled\, “Exploring Energy Efficient Quantum-resistant Signal Processing Using Array Processors”. Congrats Nejatollahi!
URL:https://www.cecs.uci.edu/event/hamid-nejatollahi-reached-best-paper-award-finalist-icassp-2020/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20201031T080000
DTEND;TZID=America/Los_Angeles:20201031T170000
DTSTAMP:20231031T190812Z
CREATED:20231031T190812Z
LAST-MODIFIED:20231031T190812Z
UID:15752-1604131200-1604163600@www.cecs.uci.edu
SUMMARY:Chuangtao Chen and Mohsen Imani Reached Best Paper Finalist at IEEE/ACM ICCAD 2020
DESCRIPTION:Assistant Professor Mohsen Imani and Chuangtao Chen reached Best Paper Finalist at IEEE/ACM International Conference on Computer-Aided Design 2020 for research paper titled\, “Optimally Approximated and Unbaised Floating-Point Multiplier with Runtime Configurability.” Congrats Chen and Imani!
URL:https://www.cecs.uci.edu/event/chuangtao-chen-and-mohsen-imani-reached-best-paper-finalist-at-ieee-acm-iccad-2020/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20201031T080000
DTEND;TZID=America/Los_Angeles:20201031T170000
DTSTAMP:20231031T190530Z
CREATED:20231031T190530Z
LAST-MODIFIED:20231031T190530Z
UID:15748-1604131200-1604163600@www.cecs.uci.edu
SUMMARY:Zhou Li and Chaoyi Lu Won IRTF ANRP Award
DESCRIPTION:Assistant Professor Zhou Li and Chaoyi Lu won the Internet Research Task Force (IRTF) Applied Networking Research Prize (ANRP) Award for research paper titled\, “An End-to-End\, Large-Scale Measurement of DNS-over-Encryption: How Far Have We Come?” Congrats Prof. Li and Lu!
URL:https://www.cecs.uci.edu/event/zhou-li-and-chaoyi-lu-won-irtf-anrp-award/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20201031T080000
DTEND;TZID=America/Los_Angeles:20201031T170000
DTSTAMP:20231031T190314Z
CREATED:20231031T190314Z
LAST-MODIFIED:20231031T190314Z
UID:15744-1604131200-1604163600@www.cecs.uci.edu
SUMMARY:Yeseong Kim and Mohsen Imani Reached Best Paper Finalist at IEEE/ACM DATE 2020
DESCRIPTION:Researcher Yeseong Kim and Assistant Professor Mohsen Imani Reached Best Paper Finalist at IEEE/ACM Design\, Automation\, and Test in Europe Conference 2020 for research paper “GenieHD: Efficient DNA Pattern Matching Accelerator Using Hyperdimensional Computing.” Congrats Kim and Prof. Imani!
URL:https://www.cecs.uci.edu/event/yeseong-kim-and-mohsen-imani-reached-best-paper-finalist-at-ieee-acm-date-2020/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200529T140000
DTEND;TZID=America/Los_Angeles:20200529T150000
DTSTAMP:20231011T190957Z
CREATED:20231011T190957Z
LAST-MODIFIED:20231011T190957Z
UID:14579-1590760800-1590764400@www.cecs.uci.edu
SUMMARY:Advancing Compiler and Simulator Techniques for Highly Parallel Simulation of Embedded Systems
DESCRIPTION:Name: Zhongqi Cheng\nChair: Prof. Rainer Doemer \nDate: May 29th\, 2020 \nTime: 02:00 PM \nLocation:  Zoom \nCommittee: Rainer Doemer (Chair)\, Prof. Mohammad Al Faruque and Prof. Aparna Chandramowlishwaran \nTitle: “Advancing Compiler and Simulator Techniques for Highly Parallel Simulation of Embedded Systems” \nAbstract: \nAs an Electronic System Level (ESL) design language\, the IEEE SystemC standard is widely used for testing\, validation and verification of embedded system models. Discrete Event Simulation (DES) has been used for decades as the default SystemC simulation semantic. However\, due to the sequential nature of DES\, Parallel DES has recently gained an increasing amount of attention for performing high speed simulations on parallel computing platforms. To further exploit the parallel computation power of modern multi- and many-core platforms\, Out-of-order Parallel Discrete Event Simulation (OoO PDES) has been proposed. In OoO PDES\, threads comply with a partial order such that different simulation threads may run in different time cycles to increase the parallelism of execution. The Recoding Infrastructure for SystemC (RISC) has been introduced as a tool flow to fully support OoO PDES. \nTo preserve the SystemC semantics under OoO PDES\, a compiler based approach statically analyzes the race conditions in the input model. However\, there are severe restrictions: the source code for the input design must be available in one file\, which does not scale. This disables the use of Intellectual Property (IP) and hierarchical file structures. In this dissertation\, we propose a partial-graph based approach to scale the static analysis to support separate files and IP reuse. Specifically\, the Partial Segment Graph (PSG) data structure is proposed and is used to abstract the behaviours and communication of modules within a single translation unit. These partial graphs are combined at top level to reconstruct the complete behaviors and communication of the entire model. \nWe also propose new algorithms to support the static analysis for modern SystemC TLM-2.0 standard. SystemC TLM-2.0 is widely used in industrial ESL designs for better interoperability and higher simulation speed. However\, it is identified as an obstacle for parallel SystemC simulation due to the disappearance of channels. To solve the problem\, we propose a compile time approach to statically analyze potential conflicts among threads in SystemC TLM-2.0 loosely- and approximately-timed models. A new Socket Call Path (SCP) technique is introduced which provides the compiler with socket binding information for precise static analysis. Based on SCP\, an algorithm is proposed to analyze entangled variable pairs for automatic and accurate conflict analysis. \nBesides the works on the compiler side\, we focus as well on increasing the simulation speed of OoO PDES. We observe that the granularity of the Segment Graph (SG) data structure used in static analysis has a high impact on OoO PDES. This motivates us to propose a set of coding guidelines for the RISC users to properly refine their SystemC model for a higher simulation speed. \nFurthermore\, in this dissertation\, an algorithm is proposed to optimize directly the event delivery strategy in OoO PDES. Event delivery in OoO PDES was very conservative\, which often postponed the execution of waiting threads due to unknown future behaviors of the SystemC model\, and in turn became a bottleneck of simulation speed. The algorithm we propose takes advantage of the prediction of future thread behaviors\, and therefore allows waiting threads to resume execution earlier\, resulting in significantly increased simulation speed. \nTo summarize\, the contributions of this dissertation include: 1) a scalable RISC tool flow for statically analyzing and protecting 3rd party IPs in models with multiple files\, 2) an advanced static analysis approach for modern SystemC TLM-2.0 models\, 3) a set of coding guidelines for RISC users to achieve higher simulation speed\, and 4) a more efficient event delivery algorithm in OoO PDES scheduler using prediction information. \nTogether\, these compiler and simulator advances enable OoO PDES for larger and modern model simulation and thus improve the design of embedded systems significantly\, leading to better devices at lower cost in the end. \n 
URL:https://www.cecs.uci.edu/event/advancing-compiler-and-simulator-techniques-for-highly-parallel-simulation-of-embedded-systems/
LOCATION:CA
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200310T093000
DTEND;TZID=America/Los_Angeles:20200310T103000
DTSTAMP:20231011T190957Z
CREATED:20231011T190957Z
LAST-MODIFIED:20231011T190957Z
UID:14578-1583832600-1583836200@www.cecs.uci.edu
SUMMARY:Design and Implementation of Robust Full-Duplex Wireless Network
DESCRIPTION:Name: Sergey Shaboyan\nChair: Prof. Ahmed Eltawil \nDate: March 10th\, 2020 \nTime: 09:30 AM \nLocation:  EH 3206 \nCommittee: Prof. Ahmed Eltawil (Chair)\, Prof. Ender Ayanoglu\, Prof. Zhiying Wang \nTitle: “Design and Implementation of Robust Full-Duplex Wireless Network” \nAbstract: \nRecently Full-Duplex (FD) communication has gained significant interest due to demonstrable increase in throughput and spectral efficiency. Conventional Half-duplex (HD) communication systems use either time-duplexing or frequency-duplexing to avoid self-interference. In contrast\, full-duplex systems transmit and receive simultaneously on the same frequency band\, thus optimally utilizing available resources. The main challenge in FD systems is managing the self-interference (SI) signal at each node\, which is typically orders of magnitude larger than the intended signal of interest (SOI). To achieve sufficient SI suppression\, FD systems rely on cancellation across multiple domains such as spatial\, analog\, and digital. However\, number of practical\, FD specific challenges arise impacting quality of service\, when at least one node in a network operates in full-duplex mode. \nIn this thesis\, we consider practical issues of wireless networks containing a full-duplex node. The ultimate goal of this work is to design and implement real-time\, end-to-end networks consisting of at least one FD node that is capable of improving network performance under limited available bandwidth constraint. First\, we identify synchronization issues in a network consisting of a full-duplex base station communicating with half-duplex nodes. Novel synchronization techniques specific for full-duplex networks are proposed that allow compensation of synchronization errors in time and frequency. The proposed techniques are implemented and tested experimentally on a real-time full-duplex wireless network. Second\, we characterize the dynamic environment impact on the received self-interference in the FD system\, which is equipped with a reconfigurable antenna as a passive SI suppression mechanism. The self-interference channel delay profile is measured using the FD system operating on 5MHz\, 10MHz\, and 20MHz bandwidths. The measured channel profiles collected under suppressing and non-suppressing antenna patterns are compared\, and channel changes in static\, as well as dynamic environments\, are highlighted. We then statistically model the SI channel by performing probability distribution fitting to SI channel data. Third\, the thesis proposes a Wi-Fi compliant self-interference active cancellation technique for amplify and forward\, as well as decode and forward full-duplex relays. Finally\, we design and implement an end-to-end wireless network extended with the aid of a custom-designed amplify and forward full-duplex relay. We then analyze the relay coverage limitation under the stability and transmit power constraints. The network performance is analyzed as a function of relay location for constant gain and constant transmit power modes\, consequently suggesting optimal relay location that will maximize signal to noise plus interference ratio (SINR) at the destination node. We evaluate the overall network performance by simulation\, as well as experimentally in outdoor/indoor environments.
URL:https://www.cecs.uci.edu/event/design-and-implementation-of-robust-full-duplex-wireless-network/
LOCATION:CA
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200227T100000
DTEND;TZID=America/Los_Angeles:20200227T110000
DTSTAMP:20231011T190957Z
CREATED:20231011T190957Z
LAST-MODIFIED:20231011T190957Z
UID:14576-1582797600-1582801200@www.cecs.uci.edu
SUMMARY:"Answering Multi-Dimensional Analytical Queries under Local Differential Privacy"
DESCRIPTION:Name: Tianhao Wang\nDate and Time: Thursday\, February 27\, 2020 \nLocation: Engineering Hall 2430 \nAbstract: \nWhen collecting information\, local differential privacy (LDP) relieves users’ privacy concerns\, as it adds noise to users’ private information.  The LDP technique has been deployed by Google\, Apple\, and Microsoft for data collection and monitoring.  In this talk\, I will share the key algorithms we developed in a Chinese e-commercial company Alibaba.  We study the problem of answering multi-dimensional queries under LDP.  Several algorithms are proposed to handle queries with different types of predicates and aggregation functions.  We built a prototype that enables different departments to collect\, share\, and analyze data within the company. \nBiography: \nTianhao Wang is a Ph.D. candidate in the department of computer science\, Purdue University\, advised by Prof. Ninghui Li. He received his B.Eng. degree from software school\, Fudan University in 2015. His research interests include differential privacy and local differential privacy. He is a recipient of the Bilsland Dissertation Fellowship and the Emil Stefanov Memorial Fellowship. He was a member of DPSyn\, which won the second-place award in the NIST PSCR Differential Privacy Synthetic Data Challenge.
URL:https://www.cecs.uci.edu/event/answering-multi-dimensional-analytical-queries-under-local-differential-privacy/
LOCATION:CA
CATEGORIES:Seminars at CECS
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200220T100000
DTEND;TZID=America/Los_Angeles:20200220T110000
DTSTAMP:20231011T190957Z
CREATED:20231011T190957Z
LAST-MODIFIED:20231011T190957Z
UID:14577-1582192800-1582196400@www.cecs.uci.edu
SUMMARY:Novel Monitoring\, Detection\, and Optimization Techniques for Neurological Disorders
DESCRIPTION:Name: Seyede Mahya Safavi\nChair: Prof. Pai Chou \nDate: February 20th\, 2020 \nTime: 10:00 AM \nLocation:  EH 3404 \nCommittee: Prof. Pai Chou (Chair)\, Prof. Beth Lopour\, Prof. Phillip Sheu \nTitle: “Novel Monitoring\, Detection\, and Optimization Techniques for Neurological Disorders” \nAbstract: \nThe advancement in chronically implanted neural recording devices has led to advent of assisting devices for rehabilitation and restoring lost sensorimotor functions in patients suffering from paralysis. Electrocorticogram (ECoG) signal can record high-Gamma sub-band activity known to be related to hand movements. In the first part of this work\, we Propose a finger movement detection technique based on ECoG source localization. In fact\, the finger flexion and extension are originating in slightly different areas of motor cortex. The origin of the brain activity is used as the distinctive feature for decoding the finger movement. \nThe real-time implementation of brain source localization is challenging due to extensive iterations in the existing solutions. In the second part of this work\, we have proposed two techniques to reduce the computational complexity of the Multiple Signal Classification (MUSIC) algorithm. First the cortex surface is parsed into several regions. Next\, a novel nominating procedure will pick a number of regions to be searched for brain activity. In the second step\, an electrode selection technique based on the Cramer-Rao bound of the errors is proposed to select the best set of an arbitrary number of electrodes. The proposed techniques lead to 90% reduction in computational complexity while maintaining a good concordance in terms of localization error compared to regular MUSIC algorithm. \nEpilepsy is a neurological disorder with multiple comorbid conditions including cardiovascular and respiratory disorders. The cardiovascular imbalance is of great importance since the mechanisms of Sudden Unexpected Death in Epilepsy (SUDEP) is still unknown. The ictal tachycardia is the most well-known cardiac imbalance during the seizure. In the third part of this dissertation\, we used an optical sensor called photoplethysmogram (PPG) to investigate the variations in ictal blood flow in limbs. Six different features related to hemodynamics were derived from PPG pulse morphology. A consistent pattern of ictal change was observed across all the subjects/seizures. These variations suggest an increase in vascular resistance due to an increase in sympathetic tone. The timing analysis of the PPG features revealed some PPG feature variations can precede the ictal tachycardia by 50 seconds. These features were used to train a neural network based on Long Short Term Memory LSTM architecture for automatic seizure detection. We were able to reduce the False Alarm rate by 50% compared to other heart rate variability based detectors.
URL:https://www.cecs.uci.edu/event/novel-monitoring-detection-and-optimization-techniques-for-neurological-disorders/
LOCATION:CA
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20200206T093000
DTEND;TZID=America/Los_Angeles:20200206T103000
DTSTAMP:20231011T190957Z
CREATED:20231011T190957Z
LAST-MODIFIED:20231011T190957Z
UID:14575-1580981400-1580985000@www.cecs.uci.edu
SUMMARY:Efficient Offline and Online Training of Memristive Neuromorphic Hardware
DESCRIPTION:Name: Mohammed Fouda\nChair: Prof. Ahmed Eltawil \nDate: February 6th\, 2020 \nTime: 9:30 AM \nLocation:  EH 3206 \nCommittee: Ahmed Eltawil (Chair)\, Prof. Fadi Kurdahi\, Prof. Nikil Dutt\, Prof. Emre Neftci \nTitle: “Efficient Offline and Online Training of Memristive Neuromorphic Hardware” \nAbstract: \nBrain-inspired neuromorphic systems have witnessed rapid development over the last decade from both algorithmic and hardware perspectives. Neuromorphic hardware promises to be more energy- and speed- efficient as compared to traditional Von-Neumann architectures. Thanks to the recent progress in solid-state devices\, different nanoscale-nonvolatile memory devices\, such as RRAMs (memristors)\, STT-RAM and PCM\, support computations based on mimicking biological synaptic response. The most important advantage of these devices is their ability to be sandwiched between interconnect wires creating crossbar array structures that are inherently able to perform matrix-vector multiplication (MVM) in one step. Despite the great potential of RRAMs\, they suffer from numerous nonidealities limiting the performance\, including\, high variability\, asymmetric and nonlinear weight update\, endurance\, retention and stuck at fault (SAF) defects in addition to the interconnect wire resistance that creates sneak paths. This thesis will focus on the application of RRAMs for neuromorphic computation while accounting for the impact of device nonidealities on neuromorphic hardware. \n\n\n\n\nIn this thesis\, we propose software-level solutions to mitigate the impact of nonidealities\, that highly affect the offline (ex-situ) training\, without incorporating expensive SPICE or numerical simulations. We propose two techniques to incorporate the effect of sneak path problem during training\, in addition to the device’s variability\, with negligible overhead. The first technique is inspired by the impact of the sneak path problem on the stored weights (devices’ conductances) which we referred to as the mask technique. This mask is element-wise multiplied by the weights during the training. This mask can be obtained from measured weights of fabricated hardware. The other solution is a neural network estimator which is trained by our SPICE-like simulator. The test validation results\, done through our SPICE-like framework\, show significant improvement in performance\, close to the baseline BNNs and QNNs\, which demonstrates the efficiency of the proposed methods. Both techniques show the high ability to capture the problem for multilayer perceptron networks for MNIST dataset with negligible runtime overhead. In addition\, the proposed neural estimator outperforms the mask technique for challenging datasets such as CIFAR10. Furthermore\, other nonidealities such as SAF defects and retention are evaluated. \nWe also develop a model to incorporate the stochastic asymmetric nonlinear weight update in online (in-situ) training. We propose two solutions for this problem; 1) a compensation technique which is tested on a small scale problem to separate two Laplacian mixed sources using online independent component analysis. 2) stochastic rounding and is tested on a spiking neural network with deep local learning dynamics showing only a 1~2% drop in the baseline accuracy for three different RRAM devices. We also propose Error-triggered learning to overcome the limited endurance problem with only 0.3% and 3% drop in the accuracy for N-MNIST and DVSGesture datasets with around 33X and 100X reduction in the number of writes\, respectively. \nFinally\, the prospects of this neuromorphic hardware are discussed to develop new algorithms with the existing resistive crossbar hardware including its nonidealities.
URL:https://www.cecs.uci.edu/event/efficient-offline-and-online-training-of-memristive-neuromorphic-hardware/
LOCATION:CA
CATEGORIES:PhD Defenses
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20191031T080000
DTEND;TZID=America/Los_Angeles:20191031T170000
DTSTAMP:20231031T190008Z
CREATED:20231031T190008Z
LAST-MODIFIED:20231031T190008Z
UID:15740-1572508800-1572541200@www.cecs.uci.edu
SUMMARY:Daniel Gajski and Lukai Cai Won Test-of-Time Award
DESCRIPTION:Professor Daniel Gajski and Lukai Cai Won Test-of-Time Award at Embedded Systems Week (ESWEEK) 2019 for the paper titled\, “Transaction Level Modeling: An Overview” which defined a new era in embedded computer system design.” Congrats Prof. Gajski and Cai!
URL:https://www.cecs.uci.edu/event/daniel-gajski-and-lukai-cai-won-test-of-time-award/
LOCATION:CA
CATEGORIES:Awards
END:VEVENT
END:VCALENDAR