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X-WR-CALNAME:Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
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X-WR-CALDESC:Events for Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
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BEGIN:VTIMEZONE
TZID:Asia/Hong_Kong
BEGIN:STANDARD
TZOFFSETFROM:+0800
TZOFFSETTO:+0800
TZNAME:HKT
DTSTART:20230101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241121T150000
DTEND;TZID=Asia/Hong_Kong:20241121T160000
DTSTAMP:20260512T161531
CREATED:20241115T035452Z
LAST-MODIFIED:20250114T032348Z
UID:19447-1732201200-1732204800@ece.hku.hk
SUMMARY:Data-Centric Architecture and Algorithm Co-design for Data-Intensive Modern Applications
DESCRIPTION:Mode: Online via Zoom\nZoom Link: https://hku.zoom.us/j/97278710788?pwd=VOGfxnayhDjXu3PTxj37IfWDzCGQ1i.1\nMeeting ID: 972 7871 0788\nPassword: 533887 \nAbstract\nIn today’s digital landscape\, the exponential growth of data has become the driving force behind modern applications\, such as genome analysis and machine learning applications\, revolutionizing our approach to healthcare and overall living quality. However\, this unprecedented deluge of data poses a formidable challenge to traditional von Neumann computer architectures. The inefficiencies arising from the constant data movement between processors and memory consume a substantial portion of both execution time and energy when running modern applications on conventional von Neumann computers. To reduce this significant data movement\, data-centric architectures\, particularly processing-in-memory accelerators\, emerge as a promising solution by enabling the processing of data directly where it resides. Nonetheless\, most existing data-centric architectures primarily focus on accelerating specific arithmetic operations\, inadvertently leaving a substantial gap between the architectural enhancements and the holistic needs of modern applications. Concurrently\, conventional software optimizations often treat the architecture as a black box\, which inherently limits the potential acceleration of applications. \nThis talk seeks to bridge the gaps between modern applications and data-centric architectures and revolutionize the landscape of data-centric acceleration. First\, this talk delves into the distinctive features of modern applications\, illustrating these features with a focus on genome analysis within the realm of bioinformatics. Second\, challenges arise when data-intensive modern applications are executed on conventional computers. The talk then transitions to a compelling remedy: the adoption of a data-centric architecture. Third\, this talk outlines the intricacies involved in designing a data-centric architecture for modern applications. It explores the challenges inherent in this process and concurrently offers potential solutions. Following this analysis\, the talk advances to put forth an innovative architecture specifically designed for genome analysis via algorithm-architecture co-design. In conclusion\, the talk wraps up with a summary and a glimpse into future avenues of exploration. \nSpeaker\nDr. Haiyu Mao\nLecturer (Assistant Professor)\,\nDepartment of Engineering\,\nKing’s College London \nBiography of the Speaker\nDr. Haiyu Mao is a Lecturer (Assistant Professor) in the Department of Engineering at King’s College London. Before that\, she was a postdoctoral researcher in the SAFARI Research group led by Prof. Onur Mutlu at ETH Zurich\, Switzerland\, since September 2020. In July 2020\, she received her Ph.D. degree in computer science from Tsinghua University\, China. Her research interests intersect between computer architecture\, memory systems\, data-centric acceleration\, bioinformatics\, machine learning accelerators\, non-volatile memory\, and secure memory. Visit Haiyu’s website for more info: https://hybol1993.github.io \nOrganiser\nProf. Kaibin Huang\nHead\,\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20241121-1/
LOCATION:Online via Zoom
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/1280-6.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241121T160000
DTEND;TZID=Asia/Hong_Kong:20241121T170000
DTSTAMP:20260512T161531
CREATED:20241119T072932Z
LAST-MODIFIED:20250114T032246Z
UID:19454-1732204800-1732208400@ece.hku.hk
SUMMARY:Integrated Sensing and Communications: From Signal Processing to Prototype
DESCRIPTION:Abstract\nIntegrating sensing functionality into communication devices is emerging as a key feature of the 6G Radio access network. Dual-function radar communication (DFRC) systems implement both sensing and communication using the same hardware thus saving in power\, cost and spectral efficiency. In this talk\, we focus on some of the signal processing aspects of designing and implementing DFRC systems and discuss how the convergence of sensing and communication can be utilized to efficiently exploit congested resources and to communicatee intelligence via sensing.  In particular\, we begin by introducing several approaches to reduce hardware cost by exploiting sub-Nyquist principles and sparse arrays to sense and communicate jointly at low sampling and bit rates. We then introduce new hardware designs that allow continuous monitoring using event-based sampling and high dynamic range. We next consider several different approaches to waveform design and receive signal processing considering both radar detection mode and target localization including spectrum sharing\, joint precoder design\, and index modulation techniques. Our approaches allow design flexibility in trading off radar and communication performance\, while preserving the radar ambiguity function. We end by discussing future trends in DFRC systems including model-based AI for communication and radar under uncertain channels\, near-field communication and radar\, and hybrid RIS/DMA to create configurable radiation patterns for scalable and low power sensing and communication. Throughout the talk we will consider both the theory and hardware prototypes and show several demos of real-time DFRC systems\, low bit and low power ADCs\, and cognitive joint radio and radar systems. \nSpeaker\nProf. Yonina Eldar\nWeizmann Institute of Science\, Israel \nBiography of the Speaker\nYonina Eldar is a Professor in the Department of Mathematics and Computer Science\, Weizmann Institute of Science\, Rehovot\, Israel where she heads the Center for Biomedical Engineering and Signal Processing and holds the Dorothy and Patrick Gorman Professorial Chair. She is also a Visiting Professor at MIT\, a Visiting Scientist at the Broad Institute\, and an Adjunct Professor at Duke University and was a Visiting Professor at Stanford.  She is a member of the Israel Academy of Sciences and Humanities\, an IEEE Fellow and a EURASIP Fellow. She received the B.Sc. degree in physics and the B.Sc. degree in electrical engineering from Tel-Aviv University\, and the Ph.D. degree in electrical engineering and computer science from MIT\, in 2002. She has received many awards for excellence in research and teaching\, including the IEEE Signal Processing Society Technical Achievement Award (2013)\, the IEEE/AESS Fred Nathanson Memorial Radar Award (2014) and the IEEE Kiyo Tomiyasu Award (2016). She was a Horev Fellow of the Leaders in Science and Technology program at the Technion and an Alon Fellow. She received the Michael Bruno Memorial Award from the Rothschild Foundation\, the Weizmann Prize for Exact Sciences\, the Wolf Foundation Krill Prize for Excellence in Scientific Research\, the Henry Taub Prize for Excellence in Research (twice)\, the Hershel Rich Innovation Award (three times)\, and the Award for Women with Distinguished Contributions. She received several best paper awards and best demo awards together with her research students and colleagues\, was selected as one of the 50 most influential women in Israel\, and was a member of the Israel Committee for Higher Education. She is the Editor in Chief of Foundations and Trends in Signal Processing\, a member of several IEEE Technical Committees and Award Committees\, and heads the Committee for Promoting Gender Fairness in Higher Education Institutions in Israel. \nOrganiser\nProf. Kaibin Huang\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20241121-3/
LOCATION:Room CB-601J\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241125T140000
DTEND;TZID=Asia/Hong_Kong:20241125T150000
DTSTAMP:20260512T161531
CREATED:20241115T070011Z
LAST-MODIFIED:20250114T032150Z
UID:19451-1732543200-1732546800@ece.hku.hk
SUMMARY:RPG Seminar – High-performance E-mode GaN HEMTs and Inverters Using a CTL-based Monolithically Integrated Platform for Power ICs Applications
DESCRIPTION:Abstract\nGaN high-electron-mobility transistors (HEMTs) have been extensively studied and commercialized due to their superior material properties for high-frequency and high-power applications. To fully harness their potential\, GaN-based power ICs have been proposed to develop energy-efficient\, high-density integrated circuits and systems. \nTo ensure fail-safe operation\, minimize standby power consumption\, and facilitate circuit simplicity\, enhancement-mode (E-mode) GaN HEMTs with high VTH are required. Moreover\, E-mode GaN HEMTs in direct-coupled FET logic (DCFL) integration show considerable advantages and promising potential for GaN power IC applications. \nIn this seminar\, we will introduce a novel charge trapping layer (CTL)-based monolithically integrated platform to address existing technological challenges and demonstrate high-performance E-mode GaN HEMTs and inverters for power IC applications. This work offers a promising approach for implementing E-mode GaN HEMTs with high VTH and excellent breakdown characteristics without performance degradation. It unlocks their potential for the monolithic integration of GaN HEMTs. \nSpeaker\nMr. Jiang Yang\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nBiography of the speaker\nMr. Jiang Yang received his Bachelor of Science (B.S.) degree from the Southern University of Science and Technology (SUSTech)\, Shenzhen\, China\, in 2019. From 2019 to 2021\, he was with the wide-bandgap semiconductor electronic materials and devices group at SUSTech as a research assistant. He is currently pursuing his Ph.D. degree with the Department of Electrical and Electronic Engineering at The University of Hong Kong. His research interests include GaN-based semiconductor materials and devices\, integrated GaN power device technology\, and emerging in-memory/sensor computing. \nOrganizer\nProf. Han Wang \nAll are welcome!
URL:https://ece.hku.hk/events/20241125-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241125T150000
DTEND;TZID=Asia/Hong_Kong:20241125T160000
DTSTAMP:20260512T161531
CREATED:20241115T070331Z
LAST-MODIFIED:20250114T032117Z
UID:19452-1732546800-1732550400@ece.hku.hk
SUMMARY:RPG Seminar – Electrically Reconfigurable Surface Acoustic Wave Phase Shifters Based on ZnO TFTs on LiNbO3 Substrate
DESCRIPTION:Abstract\nReconfigurable surface acoustic wave (SAW) phase shifters are emerging as important components for advancing secure wireless communication\, adaptable signal processing\, and intelligent sensing technologies.   A promising approach to SAW modulation involves gate voltage-controlled tuning through acoustoelectric interactions\, which offers efficient modulation at low bias voltages.   However\, current acoustoelectric devices face limitations\, such as restricted tunability\, complex heterostructures\, and demanding fabrication processes\, hindering their practical deployment. \nIn this seminar\, we will introduce a new voltage-tunable SAW phase shifter material system\, based on an atomic layer deposition (ALD) ZnO thin-film transistor (TFT) integrated with a LiNbO3 substrate. This novel structure leverages the high electromechanical coupling coefficient (K²) of LiNbO3 and the superior conductivity tuning capability of ZnO\, while also featuring a simplified architecture that facilitates fabrication.  This ZnO-on-LiNbO3 platform holds promise for a broad range of applications requiring adaptable acoustic components. \nSpeaker\nMr. Zhang Yi\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong\n \nBiography of the speaker\nZhang Yi is a Ph.D. candidate at The University of Hong Kong specializing in the design and fabrication of MEMS-based piezoelectric acoustic wave devices. Zhang holds a Master’s and bachelor’s degrees in engineering from Wuhan University\, where he developed foundational expertise in acoustic wave technology. His current work aims to drive innovations in “sensing-memory-computing” integrated chip systems\, advancing next-generation piezoelectric devices with broad potential in smart and wireless technologies. \nOrganizer\nProf. Han Wang \nAll are welcome.
URL:https://ece.hku.hk/events/20241125-2/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241128T090000
DTEND;TZID=Asia/Hong_Kong:20241128T100000
DTSTAMP:20260512T161531
CREATED:20241125T073223Z
LAST-MODIFIED:20250114T032042Z
UID:19483-1732784400-1732788000@ece.hku.hk
SUMMARY:RPG Seminar – Design of High-Performance Microwave Active Circuits Using Synthesis Techniques
DESCRIPTION:Abstract\nMicrowave active circuits\, such as low-noise amplifiers\, power amplifiers\, and oscillators\, are essential components of the transceivers in the front-end of RF communication systems. As modern communication systems evolve\, the demand for higher performance increases. This necessitates compact circuit designs for integration applications\, along with reduced jitter and bit error rates for enhanced communication accuracy. Additionally\, multi-functionality\, such as multi-band operation\, is currently the research hotspot. However\, achieving these high-performance goals with existing circuit design methods\, which are generally based on experience and trial-and-error\, is often time-consuming and lacks a systematic design approach. \nIn this seminar\, we will introduce a series synthesis techniques for microwave active circuits. Using these techniques\, we have developed oscillators with ultra-low phase noise\, an ultra-wideband voltage-controlled oscillator (VCO)\, and dual-band power amplifier circuits. Simulation and measurement results demonstrate that these novel circuits achieve state-of-the-art performance compared to existing designs. \nSpeaker\nMiss Sun Menghan\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the speaker\nMenghan Sun received the B.S. degree from the University of Electronic Science and Technology of China\, Chengdu\, China\, in 2020. She is pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering at the University of Hong Kong\, Hong Kong. Her current research interests include RF/mm-wave circuit designs. \nOrganizer\nProf. Lawrence Yeung \nAll are welcome.
URL:https://ece.hku.hk/events/20241128-6/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241128T093000
DTEND;TZID=Asia/Hong_Kong:20241128T103000
DTSTAMP:20260512T161531
CREATED:20241108T035312Z
LAST-MODIFIED:20250114T032000Z
UID:19429-1732786200-1732789800@ece.hku.hk
SUMMARY:RPG Seminar – Decision-Oriented Modeling in Building Energy Systems
DESCRIPTION:Abstract\nBuilding energy systems (BESs) serve as crucial flexible resources within power systems. Among various building load types\, thermostatically controlled loads (TCLs) for indoor temperature regulation are the primary contributors to flexible grid services. To harness this flexibility\, it is essential to acquire a thermal dynamics model that represents temperature variations influenced by TCLs. Data-driven methods are commonly employed to capture thermal dynamics accurately\, and model performance is assessed using statistical metrics\, such as mean square error. However\, this approach may lead to biases in model applications because of the unaligned modeling errors and application objectives. In other words\, maximal model accuracy does not necessarily translate to maximal application objectives in terms of actual thermal dynamics within buildings. \nTo tackle this problem\, our research focuses on a new perspective of decision-oriented modeling of thermal dynamics\, ensuring that the modeling objectives are closely aligned with application purposes. We demonstrate the potential of the perspective through two topics: individual building modeling for energy-efficient temperature control\, and multiple building modeling for flexibility aggregation. \nSpeaker\nMr. Cui Xueyuan\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the speaker\nXueyuan Cui received the B.E. degree and the M.S. degree from Zhejiang University\, Hangzhou\, China\, in 2019 and 2022. He is currently a Ph.D. Candidate at the Department of Electrical and Electronic Engineering\, The University of Hong Kong\, Hong Kong\, China. His research interests include grid-interactive buildings\, virtual power plants\, and learning to optimize. \nOrganizer\nProf. Yi Wang \nAll are welcome.
URL:https://ece.hku.hk/events/20241128-2/
LOCATION:Online via Zoom
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241128T103000
DTEND;TZID=Asia/Hong_Kong:20241128T113000
DTSTAMP:20260512T161531
CREATED:20241108T035452Z
LAST-MODIFIED:20250114T031919Z
UID:19430-1732789800-1732793400@ece.hku.hk
SUMMARY:RPG Seminar – Learning-Assisted Optimal Power Flow for Power Systems: Addressing Feasibility\, Adaptability\, and Scalability Issues
DESCRIPTION:Abstract\nOptimal Power Flow (OPF) problem has been studied for many years. Traditionally\, this problem is solved using an iterative algorithm like the Interior Point Method (IPM). However\, due to the non-convex and nonlinearity of the OPF problem\, it is challenging for iterative algorithm to meet the real-time requirement of power system operation and security analysis. Thanks to the powerful fitting capabilities of the machine learning models and the fact that they can provide a solution in real time\, leveraging such models to solve the OPF problems has become a hot topic. Even though a large number of papers are published to expedite the solving process\, three primary issues are hindering their application. In this seminar\, we would like to address feasibility\, adaptability\, and scalability issues of the existing data-driven OPF methods by employing learning and optimization. Additionally\, we will also discuss open issues in machine learning for solving OPF problem. \nSpeaker\nMr. Jia Yixiong\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the speaker\nYixiong Jia received the B.S. degree in science from Northwest Agriculture and Forestry University and the M.S. degree in electronic science and technology from ShanghaiTech University\, Shanghai\, China\, in 2019 and 2022\, respectively. He is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering\, The University of Hong Kong. His research interests include data-driven optimal power flow and power flow linearization in power systems. \nOrganizer\nProf. Yi Wang \nAll are welcome!
URL:https://ece.hku.hk/events/20241128-3/
LOCATION:Online via Zoom
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241128T113000
DTEND;TZID=Asia/Hong_Kong:20241128T123000
DTSTAMP:20260512T161531
CREATED:20241108T035057Z
LAST-MODIFIED:20250114T031829Z
UID:19428-1732793400-1732797000@ece.hku.hk
SUMMARY:RPG Seminar – Multi-time-scale management of virtual power plant for frequency regulation service provisio
DESCRIPTION:Abstract\nRenewable integration in power systems causes a surge of frequency fluctuations\, primarily due to the intermittent nature of renewable energy. To address this challenge\, distributed energy resources (DERs) pave one promising way for frequency regulation by their aggregation as a virtual power plant (VPP). However\, the technical heterogeneity of DERs results in varying frequency regulation performance across different DER portfolios. To promote the performance-based profit for frequency regulation\, we propose a data-driven approach to model the frequency regulation performance with the DER portfolio\, which is integrated into a VPP’s operation schemes. On this basis\, the optimal operations of the VPP in different time scales are explored for frequency regulation service provision\, including both hour-ahead frequency reserve bidding and real-time reserve activation. A comprehensive technical and economic analysis is conducted to validate the effectiveness of the proposed approach in enhancing VPP’s profit for frequency regulation service provision. \nSpeaker\nMiss Huang Mingyu\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the speaker\nMingyu Huang received the B.S. degree in automation and the M.S. degree in control science and engineering from Wuhan University\, Wuhan\, China\, in 2019 and 2022\, respectively. She is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering\, The University of Hong Kong. Her research interests include dynamic virtual power plants and ancillary services. \nOrganizer\nProf. Yi Wang \nAll are welcome.
URL:https://ece.hku.hk/events/20241128-1/
LOCATION:Online via Zoom
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241128T140000
DTEND;TZID=Asia/Hong_Kong:20241128T150000
DTSTAMP:20260512T161531
CREATED:20241120T015524Z
LAST-MODIFIED:20250114T031758Z
UID:19459-1732802400-1732806000@ece.hku.hk
SUMMARY:The Future of AI in Vision and Healthcare: Multimodal Large Language Models for Image Perception\, Restoration\, and Medical Applications
DESCRIPTION:Abstract\nThe rapid advancement of artificial intelligence\, driven by the emergence of multimodal large language models (MLLMs)\, is transforming the fields of computer vision and healthcare. By integrating diverse modalities\, these models enable groundbreaking progress in image perception\, restoration\, and medical applications. This talk explores how MLLMs leverage foundational model paradigms—incorporating billions of parameters and vast\, heterogeneous datasets—to unify vision-centric tasks within a natural language framework. Key innovations include open-world image understanding\, unified vision-language models\, MLLM-augmented image restoration\, and generalized medical diagnosis. By aligning vision tasks with language instructions\, these models overcome traditional constraints\, enabling user-defined operations\, advanced visual reasoning\, and complex diagnostic capabilities. This talk also explores how MLLMs are transforming the landscape of vision and healthcare\, establishing a robust foundation for the next generation of AI systems. \nSpeaker\nDr. Xiaowei Hu\nResearch Scientist\,\nShanghai Artificial Intelligence Laboratory \nBiography of the Speaker\nDr. Xiaowei Hu is a Research Scientist at the Shanghai Artificial Intelligence Laboratory\, with expertise spanning computer vision\, low-level vision\, vision perception\, medical AI\, and deep learning. He earned his Ph.D. in Computer Science and Engineering from the Chinese University of Hong Kong and has authored over 50 academic papers in top journals and conferences in computer vision\, including IEEE TPAMI\, CVPR\, and ICCV. Dr. Hu has earned recognition as one of the World’s Top 2% Scientists by Stanford University for 2022-2024 and is also a recipient of China’s Outstanding Young Talents Program (Overseas). \nOrganiser\nProf. Cheng Chen\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20241128-5/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241128T153000
DTEND;TZID=Asia/Hong_Kong:20241128T170000
DTSTAMP:20260512T161531
CREATED:20241112T041820Z
LAST-MODIFIED:20250220T080029Z
UID:19431-1732807800-1732813200@ece.hku.hk
SUMMARY:Navigating Your Dream Career in Finance and Technology
DESCRIPTION:The Department has extended an invitation to an entrepreneur and researcher\, Dr. David Ng\, together with three experienced professionals\, Mr. David Sun Tak-kei\, Ms. Yan Yu\, and Mr. Terry Hao to share their valuable experience with you. By attending\, you will have the chance to broaden your perspective on career options. \n \nDate: 28 November\, 2024\nTime: 15:30 – 17:00\nVenue: Room 603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong\nSpeakers: Dr. David Ng\, Mr. David Sun Tak-kei\, and Ms. Yan Yu \nBiography of the speakers: \n\nDr. David Ng\nPhD\, EMBA\, MSc\, BEng\, MHKIE\, MIEEE\nAdjunct Assistant Professor\, HKU-EEE \nCEO & CTO\, High Tech Technology (https://www.hightt.com/)\nCEO & CTO\, FinSiliconX (https://www.fsx100.com/) \nDr. Ng received his PhD degree in Electrical Engineering from HKU\, where his engineering talents were duly honed. Dr. Ng is a current member of HKIE and IEEE with more than 20 patents to his name from US and China over the years. Dr. Ng graduated with an electronic engineering degree from the HKUST. He subsequently completed an MSc degree in Electronic & Information Engineering\, and Executive MBA (EMBA) degree from the CityU\, and had worked in Motorola Semiconductor\, Arizona\, USA. \nAs an entrepreneur and architectural IC design engineer with over 20 years’ substantial work experiences in high-tech industry\, Dr. Ng has founded several local technology companies focused on IC design\, loT-smart home\, Al algorithm for big data mining\, and digital control under funding by multinational companies and venture capitals after his respective service with ASTRI (Hong Kong Government Agency)\, ON Semiconductor (a NASDAQ-Iisted semiconductors company) and Motorola (an NYSE-listed Telecom Company). Dr. Ng possesses a track record of raising funds amounting to millions of US dollars from the HKSAR Government and industries to sponsor next-generation IC design technologies. \nDuring his years in ASTRI\, Dr. Ng successfully helped a corporate client in getting listed in the GEM Board of the HKEX by licensing the IP of Dr. Ng’s team for production. \nWith special interests in both fields of green power management and sensors\, Dr. Ng’s research areas range from efficient & safe LED driver ICs\, digital-protocol fast-battery-charging ICs\, energy-harvesting enabling circuits\, to low- impedance bandgap reference.\nAs far as sensors are concerned\, Dr. Ng specializes in low-noise amplifiers\, analog-to-digital\, digital-to-analog data converters\, µV (10 -6 V) bio-signal detection of brain or heart signals (EEG/ECG) and digital signal processing (DSP) like Fast Fourier Transform (FFT). \nOver the years\, Dr. Ng has gained solid experiences in building from scratch R&D teams of 10 to 50 engineers with fast revenue returns while commercializing product ICs for mass production (over 16 so far\, with some attaining Automotive Grade). \nDr. Ng has also completed CityU’s EMBA degree\, with special focuses on VC\, PE and merger & acquisitions (M&A) in tech companies. Being listed in “Beta Gamma Sigma”\, he keeps on sharpening his professional business development skills. \nDr. Ng frequently speaks in seminars on IC designs\, brain sensors\, power electronics\, fast battery-charging and LED driver ICs. He has published over ten academic papers. An adjunct assistant professor and honorary lecturer in HKU and other tertiary institutions\, Dr Ng has taught a variety of engineering courses including master-level ones and supervised postgraduate students for their dissertations for 16 years. \n\nMr. David Sun GBS JP \nDavid Sun Tak-kei\, GBS\, JP was the Director of Audit of Hong Kong\, a “principal official” position\, between 2012 and 2018\, and was the President of the Hong Kong Institute of Certified Public Accountants between 2003 and 2007 (Hong Kong Society of Accountants before 8 September 2004). \nSun began his career at Ernst & Young in 1977 and was the Far East Co-Area Managing Partner\, until his retirement in 2010. He was the partner in charge of the Akai Holdings account from 1991 to 1999. \nSun was a member of Securities and Futures Commission between 2001 and 2007. In 2003\, he became the president of the Hong Kong Institute of Certified Public Accountants (HKICPA) and served until 2007.Sun was later appointed as Director of Audit of Hong Kong in July 2012. He retired in December 2018. \n\nMiss Yan Yu \nYan Yu was born in China and moved to the UK in her teens. After obtaining an MSc from Warwick University (with Distinction) she joined Morgan Stanley as an analyst and spent 14 years on Wall Street as a derivatives trader\, working for banks like Goldmans Sachs and Merrill Lynch in London\, New York and Moscow before relocating to Hong Kong in 2010. \nIn 2018 Yan left her career on Wall Street to pursue seminary training at the Lutheran Theological Seminary\, obtaining an MA (in Theology) and an MTh (in Old Testament) by 2020\, before being invited to teach Biblical Hebrew and Old Testament as a visiting lecturer there. Yan is currently serving as a Church Minister. \nYan is also a serial entrepreneur and co-founder of a Medical AI company based in Cyberport. Yan recently started her PhD studies with HKU Med\, her research topic is Christian cancer care.
URL:https://ece.hku.hk/events/20241128-4/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Career Talks,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241129T100000
DTEND;TZID=Asia/Hong_Kong:20241129T110000
DTSTAMP:20260512T161531
CREATED:20241125T025653Z
LAST-MODIFIED:20250114T031324Z
UID:19464-1732874400-1732878000@ece.hku.hk
SUMMARY:RPG Seminar – Wireless Sensing for Speech Recovery and Recognition
DESCRIPTION:Abstract\nConsidering the microphone is easily affected by noise and soundproof materials\, the radio frequency (RF) signal is a promising candidate to recover audio as it is immune to noise and can traverse many soundproof objects. We introduce Radio2Speech\, a system that uses RF signals to recover high quality speech from the loudspeaker. Radio2Speech can recover speech comparable to the quality of the microphone\, advancing from recovering only single tone music or incomprehensible speech in existing approaches. Quantitative and qualitative evaluations show that in quiet\, noisy and soundproof scenarios\, Radio2Speech achieves state-of-the-art performance and is on par with the microphone that works in quiet scenarios. Moreover\, millimeter wave (mmWave) based speech recognition provides more possibility for audio-related applications\, such as conference speech transcription and eavesdropping. However\, considering the practicality in real scenarios\, latency and recognizable vocabulary size are two critical factors that cannot be overlooked. We also propose Radio2Text\, the first mmWave-based system for streaming automatic speech recognition (ASR) with a vocabulary size exceeding 13\,000 words. The experimental results show that our Radio2Text can achieve a character error rate of 5.7% and a word error rate of 9.4% for the recognition of a vocabulary consisting of over 13\,000 words. \nSpeaker\nMr. Zhao Running\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the speaker\nRunning Zhao is pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering at the University of Hong Kong\, and he is advised by Edith C.H. Ngai. He received the B.S. and M.S. degree from Wuhan University of Technology\, Wuhan\, China\, in 2018 and 2021. His current research interests include human-computer interaction\, ubiquitous computing\, and multimodal learning\, with a particular emphasis on using different modalities or sensors to investigate applications for HCI and healthcare. \nOrganizer\nProf. Edith C.H. Ngai \nAll are welcome.
URL:https://ece.hku.hk/events/20241129-1/
LOCATION:Online via Zoom
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241129T150000
DTEND;TZID=Asia/Hong_Kong:20241129T160000
DTSTAMP:20260512T161531
CREATED:20241125T025933Z
LAST-MODIFIED:20250114T031215Z
UID:19465-1732892400-1732896000@ece.hku.hk
SUMMARY:RPG Seminar – Discrimination of Developmental Trajectories in Brain Structural Features of ASD
DESCRIPTION:Zoom ID: 998 6183 8733\nPassword: 275656 \nAbstract\nAutism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by challenges in social interaction\, communication\, and repetitive behaviors. Structural brain abnormalities associated with ASD are known to evolve across the developmental trajectory\, making it difficult to identify consistent biomarkers for accurate individual diagnosis. In this seminar\, we explore age-related variations in cortical thickness (CT) and gray matter volume (GMV) using a novel Partial Least Squares (PLS)-based method combined with feature selection techniques. Key findings reveal significant developmental abnormalities in several brain regions\, which were strongly correlated with behavioral measures such as Autism Diagnostic Observation Schedule (ADOS) scores. The incorporation of age-related features not only enhanced classification accuracy but also provided new insights into the developmental and behavioral mechanisms underlying ASD. These results highlight the critical role of age in understanding ASD and improving diagnostic and therapeutic strategies. \nSpeaker\nMs. Guo Zifan\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the speaker\nZifan Guo received the B.S. degree from the University of Electronic Science and Technology of China\, Chengdu\, China\, in 2021. She is pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering at the University of Hong Kong\, Hong Kong. \nOrganizer\nProf. S. C. Chan \nAll are welcome.
URL:https://ece.hku.hk/events/20241129-2/
LOCATION:Online via Zoom
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241202T090000
DTEND;TZID=Asia/Hong_Kong:20241202T100000
DTSTAMP:20260512T161531
CREATED:20241127T040706Z
LAST-MODIFIED:20250114T030152Z
UID:19508-1733130000-1733133600@ece.hku.hk
SUMMARY:Multi-Objective Management for Many-Core Computing Servers
DESCRIPTION:Abstract\nIn recent decades\, the rapid growth of cloud computing has posed significant challenges in managing the reliability\, performance\, and energy efficiency of complex multi-core computing servers. This talk introduces strategies to address these challenges using multi-objective optimization methods that span MultiProcessor System On Chip (MPSoC)\, server\, and application levels. Topics include the development of an accelerated thermal simulation framework at the MPSoC level\, advanced reliability management schemes in the context of high-performance computing servers\, and energy-efficient\, workload-aware frequency scaling techniques at the application level. Together\, we will explore ways to create more sustainable and efficient cloud computing systems. \nSpeaker\nDr. Darong Huang\nÉcole Polytechnique Fédérale de Lausanne (EPFL) \nBiography of the Speaker\nDarong Huang received his B.Sc. and M.Sc. degrees in Electrical Engineering from the University of Electronic Science and Technology of China (UESTC) in 2016 and 2019\, respectively. He obtained his Ph.D. degree from École Polytechnique Fédérale de Lausanne (EPFL) in 2024 and will join as a postdoctoral researcher in 2025. His research focuses on thermal and reliability management for MultiProcessor System On Chip (MPSoC); multi-objective management and optimization of cloud servers. Moving forward\, he aims to explore hardware and software co-design and optimization for both cloud and edge devices\, striving for holistic global optimization. \nAll are welcome!
URL:https://ece.hku.hk/events/20241202-2/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241202T103000
DTEND;TZID=Asia/Hong_Kong:20241202T113000
DTSTAMP:20260512T161531
CREATED:20241125T064139Z
LAST-MODIFIED:20250114T030110Z
UID:19470-1733135400-1733139000@ece.hku.hk
SUMMARY:An Overview of Fluid Antenna Systems
DESCRIPTION:Abstract\n“Be formless … shapeless\, like water!”\, which were the words used by Bruce Lee\, as he was revealing the philosophy of Jeet Kune Do\, the martial arts system Lee founded in 1967. Many parallels can be drawn in wireless communications technologies where engineers have been seeking greater flexibility in using the spectral and energy resources for improving network performance. In this talk\, I will speak on the emerging concept of fluid antenna system (FAS) which represents position-flexible shape-flexible antenna technologies for wireless communications. I will cover some basics of FAS and briefly review the latest results. \nSpeaker\nProf. Kai-Kit WONG\nChair Professor of Wireless Communications\,\nDepartment of Electronic and Electrical Engineering\,\nUniversity College London \nBiography of the Speaker\nProf. Kai-Kit Wong received the BEng\, the MPhil\, and the PhD degrees\, all in Electrical and Electronic Engineering\, from the Hong Kong University of Science and Technology\, Hong Kong\, in 1996\, 1998\, and 2001\, respectively. He is Chair Professor of Wireless Communications at the Department of Electronic and Electrical Engineering\, University College London. His current research centers around 6G mobile communications. He is one of the early researchers who proposed multiuser MIMO. His first paper on multiuser MIMO was published in WCNC 2000 which appeared to be the first ever research paper on this topic. He is Fellow of IEEE and IET. He served as the Editor-in-Chief for IEEE Wireless Communications Letters between 2020 and 2023. \nOrganizer\nProf. Yuanwei Liu \nAll are welcome!
URL:https://ece.hku.hk/events/20241202-3/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241202T140000
DTEND;TZID=Asia/Hong_Kong:20241202T150000
DTSTAMP:20260512T161531
CREATED:20241119T074449Z
LAST-MODIFIED:20250114T025342Z
UID:19456-1733148000-1733151600@ece.hku.hk
SUMMARY:EEE MasterClass (EEE 大師講堂) - Controls in Power Systems with High Renewable Penetration
DESCRIPTION:Abstract\nThis presentation will discuss converter control when power systems are operating with high (over 50%) inverter-based renewable generation.  Traditional synchronous generator controls are set to handle transient\, voltage\, damping\, and frequency stability issues arising from contingencies.  With the anticipated retirement of many steam units and increased replacement by renewable resources and storage systems\, the total system inertia and synchronizing torque will be decreased\, affecting power system stability properties and increasing the likelihood of cascading blackouts.  The focus of this talk is to discuss various issues and approaches to enable a smooth transition from fully synchronous generation to a high percentage of renewable resources with inverter interfaces. The topics covered include frequency and voltage regulation\, grid-forming and grid-following converters\, inertia estimation\, transient stability enhancement\, and oscillation detection. The presentation is aimed at addressing an audience with an interest in energy systems and feedback controls. \nSpeaker\nProf. Joe H. Chow\nElectrical\, Computer\, and Systems Engineering\,\nRensselaer Polytechnic Institute \nBiography of the Speaker\nJoe Chow is Institute Professor Emeritus and Senior Research Scientist\, Electrical\, Computer\, and Systems Engineering at Rensselaer Polytechnic Institute\, Troy\, New York. He received his MS and PhD degrees from the University of Illinois\, Urbana-Champaign. He is a fellow of IEEE and a member of the US National Academy of Engineering. His research interests include power system dynamics and control\, synchronized phasor measurements\, and integration of renewable resources.  He has received several awards\, including the Donald Eckman Award from the American Automatic Control Council\, the Control Technology Award from the IEEE Control Systems Society\, the Charles Concordia Power Engineering Award from the IEEE Power and Energy Society\, and the IEEE Herman Halperin Electric Transmission and Distribution Award. He is the author and co-author of several books\, including a recent Wiley textbook\, entitled “Power System Modeling\, Computation\, and Control” and the upcoming book “Power System Oscillations – second edition.” \nOrganiser\nProf. Y. Hou\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20241202-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/1280-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241204T100000
DTEND;TZID=Asia/Hong_Kong:20241204T113000
DTSTAMP:20260512T161531
CREATED:20241127T084902Z
LAST-MODIFIED:20250114T025206Z
UID:19511-1733306400-1733311800@ece.hku.hk
SUMMARY:EPFL’s Centers Blueprint to Revolutionize Sustainable AI Co-Design and Optimization
DESCRIPTION:Abstract\nArtificial Intelligence (AI) is transforming our world by including self-learning capabilities or extracting intriguing hidden patterns from input data. While the cloud-based computing paradigm has been a baseline approach for AI inferences in recent years\, technological advances and AI optimization methods advocate a shift toward an edge-computing alternative. Nevertheless\, combining cloud computing with the new edge AI paradigm poses storage\, computational\, and efficient communication challenges that must be addressed to support the deployment of compute-intense algorithms in embedded devices. In particular\, with the continuous increase in the quality of their outputs\, large AI models trained in the cloud have high memory and computing requirements that strain our natural resources worldwide and limit their porting in edge nodes. \nAware of this challenge\, the Swiss Federal Institute of Technology Lausanne (EPFL) is studying the problem from different perspectives by combining the works of its different Research Centers. Accordingly\, this presentation will first cover how a multi-center focused approach (in close collaboration with key industrial players) was used to conceive a new energy-efficient AI supercomputer and sustainable data center to explore the cooperation of cloud and edge AI systems. Second\, this presentation will cover a new co-design strategy for new edge AI systems that interact with the latest sustainable data center designs. This new co-design strategy advocates hardware-aware algorithmic transformations of large AI systems to enable accuracy-driven embedded ensembles of convolutional Neural Networks (ECNNs) to improve the accuracy and robustness of final edge devices. Third\, this presentation will discuss the possible use of codebook-based representations\, approximate computing\, and in-memory computing accelerators to reduce further the energy consumption of the next-generation edge AI systems. \nSpeaker\nProf. David Atienza\nEmbedded Systems Laboratory (ESL)\,\nEcole Polytechnique Federale de Lausanne (EPFL)\,\nLausanne\, Switzerland \nBiography of the Speaker\nDavid Atienza is a Professor of Electrical and Computer Engineering\, Heads the Embedded Systems Laboratory (ESL)\, and is the Associate Vice President of Research Centers and Plaforms for the period 2024-2028 at Ecole Polytechnique Federale de Lausanne (EPFL)\, Switzerland. His research interests include system-level design methodologies for multi-processor system-on-chip (MPSoC) targeting low-power Cyber-Physical Systems (CPS) and energy-efficient computing servers. His latest works include new 2.5D/3D power/thermal-aware design and architectures for MPSoCs targeting edge AI systems\, as well as HW/SW co-design and AI-based multi-level optimization for sustainable computing in the Internet of Things (IoT) context. \nProf. David Atienza has co-authored over 450 papers\, one book\, and 14 patents in these previous areas. He has also received multiple recognitions and awards\, among them the IEEE/ACM HW/SW Co-Design Conference (CODES-ISSS) 2024 Test-of-Time Award for the most influential paper in the last 15 years\, the ICCAD 10-Year Retrospective Most Influential Paper Award in 2020\, the Design Automation Conference (DAC) Under-40 Innovators Award in 2018\, and IEEE CEDA and ACM SIGDA Early Career Awards on EDA tools and systems research. He is a Fellow of IEEE\, Fellow of ACM\, and has been the Chair of the European Design Automation Association (EDAA) since 2022 until 2024. He is currently the Editor-in-Chief of IEEE Trans. on CAD (TCAD) and ACM Computing Surveys. \nOrganiser\nProf. Kaibin Huang\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nCo-organiser\nProf. Xianhao Chen\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20241204-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241205T110000
DTEND;TZID=Asia/Hong_Kong:20241205T120000
DTSTAMP:20260512T161531
CREATED:20241126T020836Z
LAST-MODIFIED:20250211T042315Z
UID:19487-1733396400-1733400000@ece.hku.hk
SUMMARY:Empowering High-Performance Computing Through Heterogeneous Integration of Power Electronics
DESCRIPTION:Abstract\nMost power electronic equipment\, including power supplies for Artificial Intelligence (AI) and high-performance computing systems\, has traditionally been designed using discrete active and passive components. However\, the power electronics industry has reached a stage where improving one performance attribute often comes at the expense of others. As AI and high-performance computing systems demand increasingly higher power levels\, existing power delivery architectures are proving inadequate. Delivering the required power to server racks\, circuit boards\, accelerator cards\, and AI processors has become an escalating challenge. The emergence of wide-bandgap (WBG) power semiconductor devices\, such as silicon carbide (SiC) and gallium nitride (GaN)\, presents a breakthrough opportunity. Compared to silicon (Si) devices\, WBG technologies offer significantly lower losses\, providing a promising solution to the energy challenges of high-performance computing. However\, current design practices often adopt a ‘plug-and-play’ approach\, merely replacing Si with WBG components without altering the underlying design principles. This results in only incremental improvements in efficiency and power density\, leaving the transformative potential of WBG devices untapped. This presentation will explore a paradigm shift in powering AI and high-performance computing systems through the use of high-frequency heterogeneous integration in WBG power electronics design. This innovative approach delivers simultaneous advancements in all critical performance metrics\, including efficiency\, power density\, and electromagnetic compatibility (EMC). Furthermore\, this approach can streamline traditionally labor-intensive manufacturing processes\, paving the way for significant advancements in overall production efficiency. \nSpeaker\nProf. Qiang Li\nFull Professor\,\nCenter for Power Electronics Systems (CPES)\,\nVirginia Tech \nBiography of the Speaker\nQiang Li received the B.S. and M.S. degrees from Zhejiang University\, China\, in 2003 and 2006\, respectively\, and the Ph.D. degree from Virginia Tech\, Blacksburg\, VA\, in 2011. He is currently a Full Professor in the Center for Power Electronics Systems (CPES) at Virginia Tech. His research interests include high-frequency power conversion and control\, high-density electronics packaging and magnetic integration\, as well as power solutions for high-performance computing\, data centers\, electric vehicles\, and energy storage systems. With over 300 peer-reviewed technical publications\, including 100 journal articles\, he has received eight prize paper awards and holds 26 U.S. patents. He currently serves as the Chair of Academic Affairs for the IEEE Power Electronics Society and is an associate editor for both the IEEE Transactions on Power Electronics and the IEEE Journal of Emerging and Selected Topics in Power Electronics. Dr. Li is also a recipient of the U.S. National Science Foundation (NSF) Career Award. \nOrganiser\nProf. Han Wang \nAll are welcome!
URL:https://ece.hku.hk/events/20241205-2/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241205T150000
DTEND;TZID=Asia/Hong_Kong:20241205T160000
DTSTAMP:20260512T161531
CREATED:20241120T071853Z
LAST-MODIFIED:20250211T042315Z
UID:19462-1733410800-1733414400@ece.hku.hk
SUMMARY:Multimode Fiber Communication and Multicore Fiber Endoscopy Exploiting Physics-informed Deep Neural Networks
DESCRIPTION:Abstract\nAdvances in fiber-optic systems are crucial for the internet. To cope with this continued exponential growth as Keck´s law\, multimode fibers (MMF) with spatial division multiplexing (SDM) are proposed. However\, there are challenging mode scattering effects in MMF. Physics-informed deep learning enables to correct the scattering\, resulting in advancing security and data rate. We highlight also 3D imaging with lensless multicore fiber endoscopes exploiting learnable multiple Wiener net. Multimode fiber communication and multicore fiber endoscopy are promising for advancements of the internet of things and of biomedical diagnostics and therapy. \nSpeaker\nProf. Jürgen Czarske\nDirector and Full Chair Professor，\nTU Dresden\, Germany \nBiography of the Speaker\nJuergen W Czarske (Fellow EOS\, OPTICA\, SPIE\, IET\, IOP) is director and full chair professor of the TU Dresden\, Germany. His awards include the 2019 OPTICA Joseph-Fraunhofer-Award/Robert-M.-Burley-Prize in Optical Engineering and the 2024 SPIE Dennis Gabor Award in Diffractive Optics. Juergen fosters talented students early. The students and members of his lab have won over 100 prizes\, including Bertha-Benz award of Daimler Benz Foundation (10\,000 Euro). Juergen is Vice President of International Commission for Optics\, ICO\, and was the general chair of the world congress ICO-25\, which was co-sponsored by OPTICA\, SPIE\, IEEE\, Zeiss\, DGaO-The German Branch of EOS\, IUPAP. 3 Nobel laureates have delivered plenary lectures and the participants came from 5A (America\, Asia\, Australia\, Africa and Amazing Europe). \nOrganiser\nProf. Kevin K.M. Tsia\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nSupported by\nInnovation Wing Two \nAll are welcome!
URL:https://ece.hku.hk/events/20241205-1/
LOCATION:Tam Wing Fan Innovation Wing Two\, G/F\, Run Run Shaw Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241209T140000
DTEND;TZID=Asia/Hong_Kong:20241209T150000
DTSTAMP:20260512T161531
CREATED:20241205T041102Z
LAST-MODIFIED:20250211T042315Z
UID:19528-1733752800-1733756400@ece.hku.hk
SUMMARY:Advancing Accessible Ultra-low-field MRI with Deep Learning
DESCRIPTION:Abstract\nMagnetic Resonance Imaging (MRI) is a versatile medical imaging modality. Despite its crucial role in modern healthcare\, MRI remains largely inaccessible to the general population. The development of ultra-low-field (ULF) MRI offers opportunities for enabling low-cost\, low-power\, and potentially portable clinical applications. However\, the imaging performance of these emerging ULF MRI scanners remains poor due to the significantly lower signal-to-noise ratio resulting from the weaker magnetic field. Recent advancements in deep learning have opened new frontiers to tackle this unique challenge. This talk will introduce a paradigm shift in improving ULF MR image quality and reconstructing MR image through supervised deep learning techniques. Leveraging existing high-quality high-field MRI data for training\, DL models can recover structural details buried in noise\, artefacts and poor resolution of raw ULF MR images. Such approaches have the potential to overcome the limitations of poor image quality and pave the way for clinical adoption of ULF MRI. \nSpeaker\nDr. Vick Lau\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nBiography of the Speaker\nDr. Vick Lau obtained his MEng degree in Biomedical Engineering from Imperial College London in 2019. He recently received his PhD degree in Electrical and Electronic Engineering from the University of Hong Kong in 2024. His research focuses on the application of deep learning techniques to MRI image processing\, restoration\, reconstruction and analysis\, particularly for ULF and accessible MRI. \nOrganiser\nProf. Ed X. Wu\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20241209-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241218T103000
DTEND;TZID=Asia/Hong_Kong:20241218T113000
DTSTAMP:20260512T161531
CREATED:20241210T012223Z
LAST-MODIFIED:20250211T042315Z
UID:107413-1734517800-1734521400@ece.hku.hk
SUMMARY:Unlocking the Value of Single Modality through Multi-Modal Knowledge Transfer for Healthcare
DESCRIPTION:Abstract\nRecent years have witnessed the remarkable success of deep neural networks in healthcare\, particularly in the analysis of medical images and signals. However\, their performance is often constrained by the scarcity of labelled data\, driven by high labelling costs and challenges related to data privacy and sharing. In this talk\, I will explore how we can overcome these limitations by leveraging multi-modal data through advanced learning frameworks to enhance the capabilities of single modality analysis. Specifically\, I will present our recent and ongoing work\, including those accepted at MICCAI 2023 and MICCAI 2024. This talk will delve into the details of innovative techniques such as large language model-informed pretraining and multi-modal learning for X-ray images and ECG signals\, as well as demonstrating how these approaches can significantly contribute to more accurate\, reliable\, and cost-effective healthcare solutions. \nSpeaker\nProf. Chen (Cherise) Chen\nLecturer (Assistant Professor) in Computer Vision\,\nSchool of Computer Science\,\nUniversity of Sheffield\, UK \nBiography of the Speaker\nChen (Cherise) Chen is currently a Lecturer (assistant professor) in Computer Vision at the School of Computer Science\, University of Sheffield\, UK. Previously\, she was a postdoc at Imperial College London (ICL) and then the University of Oxford. She obtained her MSc and Ph.D. from the Department of Computing at Imperial College London in 2016 and 2022\, respectively\, where she worked closely with Prof. Daniel Rueckert and Dr. Wenjia Bai. Chen also has accumulated valuable industrial experience. She worked as a research scientist at Infervision Inc. in Beijing in 2017\, prior to her PhD\, and later as a part-time research scientist at HeartFlow\, UK\, in 2022 following her PhD. Her research focuses on the intersection of AI and healthcare\, particularly in developing data-efficient\, robust\, and explainable AI for clinical applications. So far\, she has published more than 40 papers in leading conferences and high-impact journals on deep learning for medical data analysis such as MICCAI\, ECCV\, IEEE TMI\, and Medical Image Analysis\, accumulating over 2\,000 Google Scholar citations and an h-index of 20. She is a program chair for MIDL 2025; session and area chair for MICCAI 2024 and serves as lead organisers in several MICCAI workshops and challenges including\, MICCAI ADSMI 2024\, DALI 2023\, and the CMRxMotion Challenge. Very recently\, she has also been appointed as an ELLIS Scholar at the European Laboratory for Learning and Intelligent Systems in 2024. https://cherise215.github.io. \nOrganiser\nProf. Cheng Chen\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20241218-2/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241218T150000
DTEND;TZID=Asia/Hong_Kong:20241218T160000
DTSTAMP:20260512T161531
CREATED:20241128T022442Z
LAST-MODIFIED:20250211T042315Z
UID:19513-1734534000-1734537600@ece.hku.hk
SUMMARY:Toward Scalable Generative AI via Mixture of Experts in Mobile Edge Networks
DESCRIPTION:Abstract\nThe evolution of generative artificial intelligence (GAI) has driven revolutionary applications like ChatGPT. The proliferation of these applications is underpinned by the mixture of experts (MoE)\, which contains multiple experts and selectively engages them for each task to lower operation costs while maintaining performance. Despite MoE’s efficiencies\, GAI still faces challenges in resource utilization when deployed on local user devices. Therefore\, we first propose mobile edge networks supported MoE-based GAI. Rigorously\, we review the MoE from traditional AI and GAI perspectives\, scrutinizing its structure\, principles\, and applications. Next\, we present a new framework for using MoE for GAI services in Metaverse. Moreover\, we propose a framework that transfers subtasks to devices in mobile edge networks\, aiding GAI model operation on user devices. Moreover\, we introduce a novel approach utilizing MoE\, augmented with Large Language Models (LLMs)\, to analyze user objectives and constraints of optimization problems based on deep reinforcement learning (DRL) effectively. This approach selects specialized DRL experts\, and weights each decision from the participating experts. In this process\, the LLM acts as the gate network to oversee the expert models\, facilitating a collective of experts to tackle a wide range of new tasks. Furthermore\, it can also leverage LLM’s advanced reasoning capabilities to manage the output of experts for joint decisions. Lastly\, we insightfully identify research opportunities of MoE and mobile edge networks. \nSpeaker\nProf. Dusit Niyato\nPresident’s Chair Professor\,\nCollege of Computing & Data Science (CCDS)\,\nNanyang Technological University\, Singapore \nBiography of the Speaker\nDusit Niyato is a President’s Chair Professor in the College of Computing & Data Science (CCDS)\, Nanyang Technological University\, Singapore. Dusit’s research interests are in the areas of mobile generative AI\, edge intelligence\, quantum computing and networking\, and incentive mechanism design. Dusit won the IEEE Vehicular Technology Society Stuart Meyer Memorial Award. Dusit won the IEEE Vehicular Technology Society Stuart Meyer Memorial Award. Currently\, Dusit is serving as Editor-in-Chief of IEEE Communications Surveys and Tutorials (impact factor of 34.4 for 2023) and will serve as the Editor-in-Chief of IEEE Transactions on Network Science and Engineering (TNSE) from 2025. He is also an area editor of IEEE Transactions on Vehicular Technology (TVT)\, topical editor of IEEE Internet of Things Journal (IoTJ)\, lead series editor of IEEE Communications Magazine\, and associate editor of IEEE Transactions on Wireless Communications (TWC)\, IEEE Transactions on Mobile Computing (TMC)\, IEEE Wireless Communications\, IEEE Network\, IEEE Transactions on Information Forensics and Security (TIFS)\, IEEE Transactions on Cognitive Communications and Networking (TCCN)\, IEEE Transactions on Services Computing (TSC)\, and ACM Computing Surveys. Dusit is the Members-at-Large to the Board of Governors of IEEE Communications Society for 2024-2026. He was named the 2017-2023 highly cited researcher in computer science. He is a Fellow of IEEE and a Fellow of IET. \nOrganiser\nProf. Hongyang Du\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20241218-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/1280.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250101
DTEND;VALUE=DATE:20250301
DTSTAMP:20260512T161531
CREATED:20250213T015036Z
LAST-MODIFIED:20250227T090050Z
UID:109013-1735689600-1740787199@ece.hku.hk
SUMMARY:Call for applications: 2025 TSMC DNA Summer Internship
DESCRIPTION:TSMC\, a world-leading semiconductor foundry\, is recruiting summer interns. Eligible and interested students can find details in the flyer.
URL:https://ece.hku.hk/events/20250117-1/
LOCATION:N/A
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250120T110000
DTEND;TZID=Asia/Hong_Kong:20250120T120000
DTSTAMP:20260512T161531
CREATED:20250116T025553Z
LAST-MODIFIED:20250211T042315Z
UID:108129-1737370800-1737374400@ece.hku.hk
SUMMARY:Edges Empowering AI\, Embracing LLMs: Issues\, Technologies\, and Applications
DESCRIPTION:Abstract\nAccelerated by the rapid advancements in AI and IoT technologies\, there is an urgent need to extend AI capabilities to the network edge to fully harness the potential of big data. To address this demand\, edge Intelligence has emerged as a promising paradigm for enabling distributed\, computation-intensive AI applications on edge devices. This talk explores the key dimensions of edge intelligence: data\, models\, and systems. It involves data evaluation by analyzing its contribution to model performance and investigates strategies for optimizing edge models in dynamic edge environments. Special attention is given to technologies such as distributed training (federated edge learning as an example)\, inference acceleration\, and model compression tailored for edge deployments. Furthermore\, with the advent of large language models (LLMs) and their overwhelming computational requirements\, the talk examines the evolving role of edge intelligence. Some open questions remain: e.g.\, how edge systems can integrate with and complement these foundational models\, addressing challenges such as resource constraints and latency while exploring potential synergies in hybrid edge-cloud architectures. \nSpeaker\nProf. Yinglei Teng\nProfessor\,\nBeijing University of Posts and Telecommunications \nBiography of the Speaker\nYinglei Teng\, a professor at Beijing University of Posts and Telecommunications\, specializes in wireless communications\, stochastic optimization and edge intelligence. She received funding from renowned programs\, including the NSFC\, National Key R&D Young Scientist Project\, Huawei\, China Mobile\, etc. She authored over 30 high-quality SCI papers\, holds more than 80 invention patents\, and contributed to 8 industry standards. She was recognized with honors such as the China Association for Science and Technology Special Award and the Beijing Science and Technology Award. Her recent research focuses on edge intelligence\, ML/AI for PHY\, and millimeter-wave technologies\, etc. \nOrganisers\nProf. Kaibin Huang & Prof. Xianhao Chen\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome! 
URL:https://ece.hku.hk/events/20250120-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250210T110000
DTEND;TZID=Asia/Hong_Kong:20250210T120000
DTSTAMP:20260512T161531
CREATED:20250210T094548Z
LAST-MODIFIED:20250211T042315Z
UID:108637-1739185200-1739188800@ece.hku.hk
SUMMARY:Advanced Photonic Thin Films and Nanostructures for Next Generation Optoelectronic Systems
DESCRIPTION:Abstract\nPhotonic materials are the backbones of optical communication\, sensing and imaging systems. The advent of artificial intelligence\, internet of things and human-machine interfaces require optical information perception\, data communication and storage with a much higher bandwidth\, smaller footprint yet extremely low power consumption. Bulk materials can no longer support these tasks. Development of advanced photonic thin films and nanostructures becomes the key challenge. In this report\, I will introduce our recent progress on advanced photonic thin films and nanostructures for silicon photonic and free-space optoelectronic systems. I will cover two topics. First\, magneto-optical nonreciprocal photonics for silicon photonics\, including the development of wafer-scale high quality MO thin films\, nanophotonic structures\, nonreciprocal photonic devices and their application in laser module\, silicon photonic FMCW LiDAR systems. Second\, active optical metasurfaces\, including the development of phase change materials\, ferroelectric thin films and optical metasurfaces for optical switching and imaging applications.\n \nSpeaker\nProf. Lei Bi\nProfessor\, Department of Electronic Science and Engineering\,\nUniversity of Electronic Science and Technology of China (UESTC) \nBiography of the Speaker\nLei Bi is a professor in the department of Electronic Science and Engineering of University of Electronic Science and Technology of China (UESTC). He received his B.S. and M.S. degrees in Tsinghua University in 2004 and 2006 respectively\, both majored in materials science. He received his Ph.D. degree in MIT in 2011\, majored in materials science and engineering. He joined UESTC as a professor in 2013. His research interest includes nonreciprocal photonics\, magneto-photonics and optical metasurface. He has authored or co-authored more than 150 papers in peered-viewed journals. He is a senior member of IEEE\, and a member of Optica and SPIE. \nOrganisers\nProf. Han Wang\, Department of Electrical and Electronic Engineering\, HKU\nCenter for Advanced Semiconductors and Integrated Circuits \nAll are welcome!
URL:https://ece.hku.hk/events/20250210-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/02/34343425.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250213T143000
DTEND;TZID=Asia/Hong_Kong:20250213T153000
DTSTAMP:20260512T161531
CREATED:20250210T094926Z
LAST-MODIFIED:20250211T042315Z
UID:108642-1739457000-1739460600@ece.hku.hk
SUMMARY:Bridging Minds\, Not Just Devices: Semantic and Goal-Oriented Communication for the Internet of Intelligent Things
DESCRIPTION:Abstract\nThe next frontier of the Internet of Things (IoT) lies in transforming today’s smart devices into collaborative cognitive agents – an ecosystem termed the Internet of Intelligent Things (IoIT). While current IoT systems center on raw data exchange\, they fall short of enabling true collaboration: devices cannot share meaningful insights or align their objectives across dynamic\, real-world tasks. This talk presents a paradigm shift – semantic and goal-oriented communication – as the critical enabler for IoIT. I will introduce a theoretical framework that conceptualizes semantic communication through two key challenges: language exploitation and language design. The language exploitation problem focuses on optimizing the encoding and decoding of semantics to minimize distortion without modifying the underlying semantic language. In contrast\, the language design problem seeks to co-optimize both the encoder and decoder through joint source-channel coding\, particularly leveraging deep learning-based approaches. The talk will also explore the role of large language models in learning adaptive semantic representations\, making communication systems more resilient and context-aware. Finally\, I will discuss how the goal-oriented principle broadens classical Shannon theory by integrating decision-making objectives into communication system design. By framing communication as a meaning-driven\, goal-aware process\, we usher in a new era of collective intelligence – one where smart devices evolve into collaborative cognitive agents capable of shared understanding and coordinated action. \nSpeaker\nDr. Yulin SHAO\nAssistant Professor\, State Key Laboratory of Internet of Things for Smart City\, University of Macau \nBiography of the Speaker\nDr. Yulin Shao is an Assistant Professor with the State Key Laboratory of Internet of Things for Smart City\, University of Macau\, and a Visiting Researcher with the Department of Electrical and Electronic Engineering\, Imperial College London. He received the B.S. and M.S. degrees in Communications and Information Engineering (Hons.) from Xidian University\, China\, in 2013 and 2016\, and the Ph.D. degree in Information Engineering from the Chinese University of Hong Kong in 2020. He was a Research Assistant with the Institute of Network Coding\, a Visiting Scholar with the Research Laboratory of Electronics at Massachusetts Institute of Technology\, a Research Associate with the Department of Electrical and Electronic Engineering at Imperial College London\, and a Lecturer in Information Processing with the University of Exeter. He was a Guest Lecturer at 5G Academy Italy and IEEE Information Theory Society Bangalore Chapter. \nDr. Shao’s research interests include coding and modulation\, machine learning\, and stochastic control. He is a Series Editor of IEEE Communications Magazine in the area of Artificial Intelligence and Data Science for Communications\, an Editor of IEEE Transactions on Communications in the area of Machine Learning and Communications\, and an Editor of IEEE Communications Letters. He received the Best Poster Award at CIE Information Theory Society 2024\, and the Best Paper Awards at IEEE International Conference on Communications (ICC) 2023 and IEEE Wireless Communications and Networking Conference (WCNC) 2024. \nOrganiser\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong
URL:https://ece.hku.hk/events/20250213-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/02/2342544.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250213T160000
DTEND;TZID=Asia/Hong_Kong:20250213T170000
DTSTAMP:20260512T161531
CREATED:20250210T095339Z
LAST-MODIFIED:20250211T042315Z
UID:108646-1739462400-1739466000@ece.hku.hk
SUMMARY:6G Waveforms-Perspectives on Throughput\, Reliability\, and ISAC
DESCRIPTION:Online via Zoom link: https://hku.zoom.us/j/2025021302 \nAbstract\nWith the commercialization of 5G technology\, research on 6G has emerged as a key focus in the field of wireless communications. In this talk\, we explore three candidate waveforms for 6G\, designed to meet its stringent requirements for throughput\, reliability\, and integrated sensing and communications (ISAC). \nWe begin by discussing faster-than-Nyquist (FTN) signaling\, a promising technique for enhancing communication spectral efficiency. The unique challenges associated with equalization and channel coding in FTN systems are highlighted\, along with novel solutions that are benchmarked against theoretical performance limits. \nNext\, we examine orthogonal time frequency space (OTFS) modulation\, which enhances communication reliability in dynamic wireless channels. We demonstrate that OTFS introduces a novel coupling mechanism between information symbols and the wireless channel\, enabling efficient equalization and robust MIMO transmissions by fully exploiting channel dynamics. \nFinally\, we focus on a communication-centric ISAC waveform\, evaluating its sensing performance through ambiguity functions. We analytically prove that OFDM is the optimal waveform for minimizing sidelobes in ranging\, while single-carrier waveforms are superior for Doppler sensing when using practical communication signals. \nThe talk concludes with a discussion of potential future research directions in 6G waveform design\, highlighting open challenges and opportunities in this evolving field. \nSpeaker\nDr. Shuangyang Li\nResearch Assistant\, Faculty of Electrical Engineering and Computer Science\, Technical University of Berlin \nBiography of the Speaker\nShuangyang Li (Member\, IEEE) received the B.S.\, M.S.\, and Ph.D. degrees from Xidian University\, China\, in 2013\, 2016\, and 2021\, respectively. He received his second Ph.D. degree from the University of New South Wales (UNSW)\, Australia\, in 2022. He is a recipient of the Marie Skłodowska-Curie Actions (MSCA) fellowship 2022 and is currently a research assistant at the Technical University of Berlin (TU-Berlin). Prior to that\, he was a research associate at the University of Western Australia (UWA). He received the Best Paper Award from IEEE ICC 2023\, and the Best Workshop Paper Award from IEEE WCNC 2023. He was listed in the World’s Top 2% Scientists by Stanford University for citation impact 2024 and is the recipient of the best young researcher award 2024 from the IEEE ComSoc EMEA region. He frequently serves as the organizer/chair for workshops and tutorials on related topics of orthogonal time frequency space (OTFS) in IEEE flagship conferences and is a founding member and currently the co-chair of the special interest group (SIG) on OTFS. He is now an editor of IEEE Transactions on Communications. His research interests include signal processing\, channel coding\, applied information theory\, and their applications to communication systems\, with a specific focus on waveform designs. \nOrganiser\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong
URL:https://ece.hku.hk/events/20250213-2/
LOCATION:Online via Zoom
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/02/23423556.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250214T150000
DTEND;TZID=Asia/Hong_Kong:20250214T160000
DTSTAMP:20260512T161531
CREATED:20250210T095554Z
LAST-MODIFIED:20250211T042315Z
UID:108650-1739545200-1739548800@ece.hku.hk
SUMMARY:Ubiquitous Sensing in 6G Cellular Networks
DESCRIPTION:Abstract\nRecently\, the International Telecommunication Union (ITU) has identified integrated sensing and communication (ISAC) as a primary usage scenario for the sixth-generation (6G) cellular networks in IMT-2030 Framework. As a result\, future cellular networks will provide not only communication services\, but also sensing services such as localization and tracking. However\, how to exploit the existing communication infrastructure to effectively achieve sensing functions remains an open problem for 6G. In this talk\, we will introduce the methodologies to leverage various types of communication nodes in cellular networks as anchors\, including base stations\, user equipments\, and intelligent reflecting surfaces\, to perform ubiquitous sensing. Specifically\, the advantages and disadvantages of each type of anchors will be listed\, and the efficient solutions to overcome these disadvantages will be outlined. Apart from theoretical works\, this talk will also present our latest achievements in building a 6G ISAC platform that operates at the millimeter-wave band. We will conclude this talk by discussing some promising future directions that will be beneficial to the transformation of the world’s largest communication network into the world’s largest sensing network. \nSpeaker\nDr. Liang LIU\nAssociate Professor\, Department of Electrical and Electronic Engineering\, The Hong Kong Polytechnic University \nBiography of the Speaker\nLiang Liu is currently an Associate Professor with the Department of Electrical and Electronic Engineering\, The Hong Kong Polytechnic University. He obtained his Ph.D. degree from National University of Singapore in 2014. His research interests lie in 5G/6G technologies\, including integrated sensing and communication (ISAC)\, massive Internet-of-Things (IoT) connectivity\, etc. Currently\, his project about 6G ISAC is supported by the RGC Collaborative Research Fund (CRF) Young Collaborative Research Grant. \nLiang Liu is an IEEE Communications Society (ComSoc) Distinguished Lecturer. He is a recipient of the 2021 IEEE Signal Processing Society (SPS) Best Paper Award\, the 2017 IEEE SPS Young Author Best Paper Award\, the Best Student Paper Award of 2022 IEEE International Conference on Acoustics\, Speech\, and Signal Processing (ICASSP)\, and the Best Paper Award of the 2011 International Conference on Wireless Communications and Signal Processing. He was recognized by Clarivate Analytics as a Highly Cited Researcher in 2018. He is an Editor of IEEE Transactions on Wireless Communications\, and was a Leading Guest Editor of IEEE Wireless Communications Special Issue on Massive Machine-Type Communications for IoT. He is a co-author of the book “Next Generation Multiple Access” published by Wiley-IEEE Press. \nOrganiser\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong
URL:https://ece.hku.hk/events/20250214-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250217T150000
DTEND;TZID=Asia/Hong_Kong:20250217T163000
DTSTAMP:20260512T161531
CREATED:20250211T013747Z
LAST-MODIFIED:20250211T042315Z
UID:108654-1739804400-1739809800@ece.hku.hk
SUMMARY:Personalized Federated Learning and Its Application in 360-degree Video Streaming
DESCRIPTION:Abstract\nFederated learning is a distributed artificial intelligence framework\, which allows multiple edge devices to train a single model collaboratively. In this talk\, we first introduce a personalized federated learning algorithm which can tackle the issues of data heterogeneity and device heterogeneity. Then\, we present a content-based viewport prediction framework for 360-degree video streaming\, wherein users’ head movement prediction models are trained using a personalized federated learning algorithm. The output of the viewport prediction framework corresponds to which video tiles to be transmitted. Finally\, we present an algorithm to determine the bitrate and beamforming matrices in a THz-enabled 360-degree video streaming system with multiple access points. \nSpeaker\nProf. Vincent Wong\nProfessor\nDepartment of Electrical and Computer Engineering\nUniversity of British Columbia\, Canada \nBiography of the Speaker\nVincent Wong is a Professor in the Department of Electrical and Computer Engineering at the University of British Columbia\, Vancouver\, Canada. His research areas include protocol design\, optimization\, and resource management of communication networks\, with applications to the Internet\, wireless networks\, smart grid\, mobile edge computing\, and Internet of Things. Dr. Wong is the Editor-in-Chief of the IEEE Transactions on Wireless Communications. He is a Fellow of the IEEE and the Engineering Institute of Canada. \nOrganiser\nProf. Kaibin Huang\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250217-1/
LOCATION:Room CB-601J\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250218T110000
DTEND;TZID=Asia/Hong_Kong:20250218T120000
DTSTAMP:20260512T161531
CREATED:20250213T031942Z
LAST-MODIFIED:20250213T031949Z
UID:109018-1739876400-1739880000@ece.hku.hk
SUMMARY:RPG Seminar – Decision-Dependent Resilience Enhancement for Distribution Systems Against Endogenous Wildfires
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/97816974267?pwd=YzdXMQaxBOpksF0QDz9Bh3Psl6rhrz.1\nMeeting ID: 978 1697 4267\nPassword: 401754 \nAbstract\nThe increasingly frequent wildfires pose significant threats to utility infrastructure and community safety. However\, existing methods that consider decision-dependent uncertainties (DDUs) in system hardening designs typically assume independent line failures\, neglecting the critical interdependencies between line damage statuses under endogenous wildfires ignited by faulted electrical components. These interdependencies render the related resilience enhancement strategies ineffective. To address this gap\, we propose a novel mathematical formulation that simultaneously quantifies various types of DDUs in endogenous wildfires. Furthermore\, we develop a two-stage wildfire-preventive decision-dependent resilience enhancement (WDDRE) model for distribution systems\, integrating the aforementioned DDUs. Numerical experiments demonstrate the effectiveness and superiority of the WDDRE model\, demonstrating its robustness in managing wildfire stochasticity and the DDUs related to planning decisions\, thereby significantly reducing potential wildfire risks. \nSpeaker\nMiss Chenxi HU\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nBiography of the Speaker\nChenxi Hu received the B.E. degree in electrical engineering and automation from Wuhan University\, Wuhan\, China\, in 2020. She is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering\, The University of Hong Kong\, Hong Kong. Her current research interests include resilient planning of renewable-dominated power systems and uncertainty modeling and quantification. \nOrganiser\nProf. Yunhe Hou \nAll are welcome.
URL:https://ece.hku.hk/events/20250213-3/
LOCATION:Online via Zoom
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250227T110000
DTEND;TZID=Asia/Hong_Kong:20250227T120000
DTSTAMP:20260512T161531
CREATED:20250220T062657Z
LAST-MODIFIED:20250220T062657Z
UID:109607-1740654000-1740657600@ece.hku.hk
SUMMARY:RPG Seminar – Configuration Strategy for Inertia Response and Primary Frequency Regulation
DESCRIPTION:Zoom link: https://hku.zoom.us/j/93192694458?pwd=S4PuTAT2tuI2idxEmbPCwEA0IU0ugx.1\nMeeting ID: 931 9269 4458\nPassword: 982082 \nAbstract\nThe energy storage(ES) systems controlled by Virtual Synchronous Generation (VSG) system provide inertia\, damping\, and enhance system stability. When transient overshoot in power and energy exceed the capacity constraints of the ES systems\, the output performance for inertia response (IR) and primary frequency regulation (PFR) oscillates instantly towards instability\, leading to shutdown. To optimize the configuration of ES systems\, we establishes an energy output model that responds to the variations of grid frequency based on IR and PFR. The expressions of power and energy demands are derived from the energy output model\, relating inertia\, damping\, and primary frequency regulation coefficient. Furthermore\, the optimal configuration approaches are summarized under different damping states\, considering the requirements of power and energy. The mentioned configuration approaches are demonstrated and validated by simulating ES models for single-unit and multiple-unit paralleled VSG inverters\, analyzing the transient characteristics and performance. \nSpeaker\nMiss Yuqing Cen\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nYuqing Cen received the B.E. degree in measurement control technology and instruments from Guangdong University of Technology\, Guangzhou\, China\, in 2022. She is currently pursuing the M.Phil. degree with the Department of Electrical and Electronic Engineering\, The University of Hong Kong\, Hong Kong. Her current research interests include the configuration and control of grid-connected energy storage systems\, as well as the optimization of shared energy storage systems. \nOrganiser\nProf. Yunhe Hou \nAll are welcome.
URL:https://ece.hku.hk/events/20250227-1/
LOCATION:Online via Zoom
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
END:VCALENDAR