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DTSTART;TZID=America/Los_Angeles:20240605T110000
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SUMMARY:Advanced Machine Learning and AI Techniques for Enhancing Wearable Health Monitoring Systems
DESCRIPTION:Name: Amir Hosein Afandizadeh Zargari \nChair: Prof. Fadi Kurdahi \nDate: June 5\, 2024 \nTime:  11 AM \nLocation: EH 3206 \nCommittee: Prof. Peter Tseng\, Prof. Amir Rahmani \nTitle: Advanced Machine Learning and AI Techniques for Enhancing Wearable Health Monitoring Systems \nAbstract: This research advances wearable technology for real-time health monitoring using advanced machine learning (ML) and innovative hardware solutions. Key contributions include a novel ML-based model for accurate tracking of in-mouth nutrient sensors\, employing a wide range of frequencies and advanced algorithms to enhance measurement reliability. An innovative use of Cycle Generative Adversarial Networks (CycleGAN) addresses motion artifacts in photoplethysmography (PPG) signals\, transforming noisy data into clean\, artifact-free signals without relying on accelerometers. The development of smart clothing systems with passive sensors and copper coils\, using Near Field Communication (NFC) and energy harvesting technologies\, ensures wireless connectivity and power in various environments\, including underwater. AI-Coach\, a personalized wearable system\, integrates multiple sensors and a BERT model for real-time workout monitoring\, adapting to individual needs for accurate activity tracking. The research also addresses security vulnerabilities threatening health monitoring systems with robust anomaly detection systems and adversarial training methods. These advancements aim to improve the accuracy\, power efficiency\, and security of wearable health devices\, with significant implications for individual and public health monitoring. \nShort Bio: Amir Hosein Afandizadeh Zargari is a Ph.D. candidate in Computer Engineering at the University of California\, Irvine. He earned his B.Sc. in Computer Engineering from Sharif University of Technology in Iran in 2018 and his M.S. in Computer Engineering from UC Irvine in 2020. Throughout his master’s and Ph.D. studies\, Amir has published multiple papers in prestigious journals\, including Natures\, ACMs\, IEEEs\, and conferences like ESWEEK. His research focuses on advancing wearable technology for real-time health monitoring using machine learning and artificial intelligence.
URL:https://www.cecs.uci.edu/event/advanced-machine-learning-and-ai-techniques-for-enhancing-wearable-health-monitoring-systems/
LOCATION:EH 3206\, Engineering Hall University of California\, Irve\, Irvine\, CA\, 92697\, United States
CATEGORIES:PhD Defenses
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LAST-MODIFIED:20240611T214758Z
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SUMMARY:Object-Oriented Visual Localization in Urban Mobile-Edge Environments
DESCRIPTION:Name: Bo Tsai \nChair: Prof. Kwei-Jay Lin \nDate: June 6\, 2024 \nTime:  8:30 AM \nLocation: Virtual/Zoom \nCommittee: Prof. Rainer Doemer\, Prof. Yasser Shoukry \nTitle: Object-Oriented Visual Localization in Urban Mobile-Edge Environments \nAbstract: The advent of the Internet of Things (IoT) and the progress in robotic technology have brought a new era where intelligent services significantly benefit society. As robots evolve from executing simple tasks to providing complex\, interactive services\, the need of precise and reliable localization becomes paramount. In this context\, the ubiquity of low-cost cameras and the maturity of object detection technologies have paved the way for advanced semantic visual-based localization solutions. “OOPose\,” introduced in this thesis\, exemplifies this advancement by providing an object-oriented pose estimation framework tailored for robots with small embedded devices. It leverages dense features from conventional object detection neural networks\, striking a balance between pixel-matching accuracy and processing speed\, thus enhancing the effectiveness and efficiency of robot localization. Amidst escalating demand for robots capable of facilitating intelligent interactive activities\, integrating high-quality cameras on mobile IoT devices\, combined with wireless connectivity\, edge computing\, and advanced computer vision algorithms\, enables a novel approach to processing offload with mobile video. This thesis details the deployment of OOPose in a mobile-edge computing paradigm\, emphasizing real-time performance and the feasibility of interactive applications. The research encapsulates the development and application of the OOPose framework\, demonstrating its significant role in the ecosystem of mobile-edge computing. This approach not only satisfies the requirements for real-time performance but also opens avenues for innovative robot localization applications\, ensuring interactive\, intelligent services across various environments. \nShort Bio: Bo Tsai is a PhD candidate in EECS advised by Professor Kwei-Jay Lin. His research focuses on intelligent distributed systems\, specifically addressing applications in visual-based localization\, autonomous robots\, and real-time internet of things.
URL:https://www.cecs.uci.edu/event/object-oriented-visual-localization-in-urban-mobile-edge-environments/
LOCATION:Zoom
CATEGORIES:PhD Defenses
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