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X-ORIGINAL-URL:https://ece.hku.hk
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:20241209T140000
DTEND;TZID=Asia/Hong_Kong:20241209T150000
DTSTAMP:20260512T170454
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
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/12/1280.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241218T103000
DTEND;TZID=Asia/Hong_Kong:20241218T113000
DTSTAMP:20260512T170454
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
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/12/1280-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241218T150000
DTEND;TZID=Asia/Hong_Kong:20241218T160000
DTSTAMP:20260512T170454
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:20260512T170454
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
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/02/3434343.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250120T110000
DTEND;TZID=Asia/Hong_Kong:20250120T120000
DTSTAMP:20260512T170454
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
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/01/1280-4.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250210T110000
DTEND;TZID=Asia/Hong_Kong:20250210T120000
DTSTAMP:20260512T170454
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:20260512T170454
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:20260512T170454
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:20260512T170454
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
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/02/22222355.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250217T150000
DTEND;TZID=Asia/Hong_Kong:20250217T163000
DTSTAMP:20260512T170454
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
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/02/1280.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250218T110000
DTEND;TZID=Asia/Hong_Kong:20250218T120000
DTSTAMP:20260512T170454
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
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:20250227T110000
DTEND;TZID=Asia/Hong_Kong:20250227T120000
DTSTAMP:20260512T170454
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
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250307T093000
DTEND;TZID=Asia/Hong_Kong:20250307T223000
DTSTAMP:20260512T170454
CREATED:20250304T072753Z
LAST-MODIFIED:20250306T015705Z
UID:110555-1741339800-1741386600@ece.hku.hk
SUMMARY:Revolutionizing Power Electronics with Heterogeneous Integration
DESCRIPTION:Abstract\nTraditional power electronic equipment has long relied on discrete active and passive components\, with performance enhancements often requiring trade-offs. Despite technological advancements\, manufacturing processes remain labor-intensive and largely unchanged for decades. \nThe advent of wide-bandgap (WBG) power semiconductor devices\, such as silicon carbide (SiC) and gallium nitride (GaN)\, has significantly reduced conduction and switching losses compared to silicon-based counterparts. However\, current design methodologies primarily follow a ‘plug-and-play’ approach\, yielding only incremental improvements in efficiency and power density without fully leveraging the transformative potential of these technologies. \nThis presentation explores the integration of matrix magnetics with WBG power devices to drive a fundamental shift in power electronics design and manufacturing through heterogeneous integration. This holistic approach enables simultaneous enhancements in efficiency\, power density\, cost\, and electromagnetic interference (EMI) performance. Additionally\, it streamlines traditionally labor-intensive manufacturing processes—particularly those involving magnetics and system assembly—through automation. \nThe discussion will feature multiple research examples demonstrating heterogeneous integration of matrix magnetics in power converters across diverse applications and power ranges. These include high-frequency power converters for artificial intelligence (AI) and high-performance computing systems\, battery chargers for electric vehicles\, and solid-state transformers for DC power distribution. \n\nSpeaker\nProf. Qiang LI\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. He is also a recipient of the U.S. National Science Foundation (NSF) Career Award. \nOrganiser\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250307-2/
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/2025/03/1280-2.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250307T100000
DTEND;TZID=Asia/Hong_Kong:20250307T110000
DTSTAMP:20260512T170454
CREATED:20250228T090613Z
LAST-MODIFIED:20250228T090613Z
UID:110553-1741341600-1741345200@ece.hku.hk
SUMMARY:RPG Seminar – Six Degrees of Freedom Tracking and Wireless Charging for Capsule Endoscopy
DESCRIPTION:Zoom \nLink: https://hku.zoom.us/j/95045238787?pwd=bAEbF49kv8uPbKPQG1n3B5I9bSphnT.1\nMeeting ID: 950 4523 8787\nPassword: 123456 \nAbstract\nCapsule endoscopy is an emerging technology providing a non-invasive approach to diagnose and monitor gastrointestinal diseases. However\, this technology has fundamental limitations of short-range of investigation due to limited battery capacity and uncertain position of the capsule inside the gastrointestinal tract. This research work attempts to address these two critical issues using a novel approach to seamlessly combine wireless battery charging\, and 6 degrees of freedom (6DoF) tracking of capsule endoscopy on a single apparatus. A full-scale complete prototype is presented in this paper\, wherein the receiving coil is integrated in a fully functional capsule\, and the transmitting coil is able to accommodate an adult human body. In the tracking operation\, a gradual DC magnetic field is generated in the transmitting coil. The magnetic field intensity surrounding the capsule is measured and consolidated with inertial measurement to identify the capsule’s position and orientation. In the evaluation\, the maximum mean absolute error for attitude angles and position are less than 3.80° and 2.07 mm\, respectively. A relatively uniform AC magnetic field will be generated while a low battery is detected. During wireless charging\, the capsule receives 376.08 mW of power\, resulting in an estimated charging rate of 2C for the capsule battery. \nSpeaker\nMr. ZHANG Heng\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nBiography of the Speaker\nHeng ZHANG received the B.Eng. degree in 2017\, and the M.Eng. degree in 2020\, both in Electrical Engineering and Automation\, from Taiyuan University of Technology\, Taiyuan\, Shanxi\, China. From 2021 to 2022\, he was a research assistant with the Chinese University of Hong Kong\, Hong Kong. He is currently working toward the Ph.D. degree in electrical and electronic engineering from the University of Hong Kong\, Hong Kong. His research interests include sensing technology\, medical robotic design and control\, wireless power transfer\, and power electronics. \nOrganiser\nProf. Chi-Kwan Lee \nAll are welcome.
URL:https://ece.hku.hk/events/20250307-1/
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:20250312T100000
DTEND;TZID=Asia/Hong_Kong:20250312T110000
DTSTAMP:20260512T170454
CREATED:20250312T012704Z
LAST-MODIFIED:20250312T012704Z
UID:110582-1741773600-1741777200@ece.hku.hk
SUMMARY:2D Materials for Next-Generation Electronics: From Low-Power Logic to Monolithic Memory
DESCRIPTION:Abstract\nSilicon has been the dominant material for electronic computing for decades and very likely will stay dominant for the foreseeable future. However\, it is well-known that Moore’s law that propelled Silicon into this dominant position is long dead. Therefore\, a fervent search for (i) new semiconductors that could directly replace silicon or (ii) new architectures with novel materials/devices added onto silicon or (iii) new physics/state-variables or a combination of above has been the subject of much of the electronic materials and devices research of the past 2 decades. The above problem is further complicated by the changing paradigm of computing from arithmetic centric to data centric in the age of billions of internet-connected devices and artificial intelligence as well as the ubiquity of computing in ever more challenging environments. Therefore\, there is a pressing need for complementing and supplementing Silicon to operate with greater efficiency\, speed and handle greater amounts of data. This is further necessary since a completely novel and paradigm changing computing platform (e.g. all optical computing or quantum computing) remains out of reach for now. The above is however not possible without fundamental innovation in new electronic materials and devices. Therefore\, in this talk\, I will try to make the case of how novel layered two-dimensional (2D) chalcogenide materials1 and three-dimensional (3D) nitride materials might present interesting avenues to overcome some of the limitations being faced by Silicon hardware. I will start by presenting our ongoing and recent work on integration of 2D chalcogenide semiconductors with silicon2 to realize low-power tunnelling field effect transistors. In particular I will focus on In-Se based 2D semiconductors2 for this application and extend discussion on them to phase-pure\, epitaxial thin-film growth over wafer scales\,3 at temperatures low-enough to be compatible with back end of line (BEOL) processing in Silicon fabs. I will then switch gears to discuss memory devices from 2D materials when integrated with emerging wurtzite structure ferroelectric nitride materials4 namely aluminium scandium nitride (AlScN). First\, I will present on Ferroelectric Field Effect Transistors (FE-FETs) made from 2D materials when integrated with AlScN and make the case for 2D semiconductors in this application.5-7 Finally\, I will end with showing our most recent results on scaling 2D/AlScN FE-FETs\, achieving ultra-high carrier and current densities8 in ferroelectrically gated MoS2 and also demonstrate negative-capacitance FETs9 by engineering the AlScN/dielectric/2D interface. \nSpeaker\nProf. Deep Jariwala\nAssociate Professor\nPeter and Susanne Armstrong Distinguished Scholar\nElectrical and Systems Engineering Primary\nMaterials Science and Engineering\nThe University of Pennsylvania \nBiography of the Speaker\nDeep Jariwala is an Associate Professor and the Peter & Susanne Armstrong Distinguished Scholar in the Electrical and Systems Engineering as well as Materials Science and Engineering at the University of Pennsylvania (Penn). Deep completed his undergraduate degree in Metallurgical Engineering from the Indian Institute of Technology in Varanasi and his Ph.D. in Materials Science and Engineering at Northwestern University. Deep was a Resnick Prize Postdoctoral Fellow at Caltech before joining Penn to start his own research group. His research interests broadly lie at the intersection of new materials\, surface science and solid-state devices for computing\, opto-electronics and energy harvesting applications in addition to the development of correlated and functional imaging techniques. Deep’s research has been widely recognized with several awards from professional societies\, funding bodies\, industries as well as private foundations\, the most notable ones being the Optica Adolph Lomb Medal\, the Bell Labs Prize\, the AVS Peter Mark Memorial Award\, IEEE Photonics Society Young Investigator Award\, IEEE Nanotechnology Council Young Investigator Award\, IUPAP Early Career Scientist Prize in Semiconductors and the Alfred P. Sloan Fellowship. He has published over 150 journal papers with more than 21000 citations and holds several patents. He serves as the Associate Editor for ACS Nano Letters and has been appointed as a Distinguished Lecturer for the IEEE Nanotechnology Council for 2025. \nOrganisers\nProfessor Han Wang\, Department of Electrical and Electronic Engineering\, HKU\nIEEE ED/SSC Hong Kong Joint Chapter \nAll are welcome!
URL:https://ece.hku.hk/events/20250312-3/
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/2025/03/123213.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250312T110000
DTEND;TZID=Asia/Hong_Kong:20250312T120000
DTSTAMP:20260512T170454
CREATED:20250304T094302Z
LAST-MODIFIED:20250304T094302Z
UID:110564-1741777200-1741780800@ece.hku.hk
SUMMARY:RPG Seminar – Advancing Implicit Neural Representations for Efficiency and Robustness on Hardware
DESCRIPTION:This RPg seminar will be conducted in hybrid mode via Zoom link and in venue CB-601J. \nZoom Link: https://hku.zoom.us/j/9174230511 \nAbstract\nThis seminar presents advances in hardware-efficient Implicit Neural Representations (INRs)\, addressing the significant challenges in deploying these continuous signal representations on resource-constrained platforms. Our research spans three distinct hardware environments\, each with tailored solutions. For FPGAs\, we introduce Distribution-Aware Hadamard Quantization and QuadINR with hardware-friendly quadratic activations\, demonstrating substantial efficiency gains without compromising quality. For emerging compute-in-memory (CIM) architectures\, we present novel robustness techniques—gradient-based regularization and low-rank constraints—that maintain representation fidelity despite hardware-induced perturbations. For GPU acceleration\, we showcase training innovations including progressive resolution training and frequency-aware sampling that reduce training time. \nSpeaker\nMr. Zhou Wenyong\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nBiography of the Speaker\nWenyong Zhou received the B.E. degree in Electronic Science and Engineering from Tianjin University\, Tianjin\, China\, in 2019 and M.S. degree in Computer Engineering from Northwestern University\, Evanston\, US\, in 2021. He is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering\, The University of Hong Kong\, Hong Kong. His current research interests include implicit neural representation and efficient model design. \nOrganiser\nProf. Ngai Wong \nAll are welcome.
URL:https://ece.hku.hk/events/20250312-1/
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:20250312T150000
DTEND;TZID=Asia/Hong_Kong:20250312T160000
DTSTAMP:20260512T170454
CREATED:20250310T012708Z
LAST-MODIFIED:20250310T012708Z
UID:110579-1741791600-1741795200@ece.hku.hk
SUMMARY:RPG Seminar – Personalized Video Fragment Recommendation
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/96780307210?pwd=1wIMCFlBwNOSDiVMsEFxnVgzm5kLnn.1\nMeeting ID: 967 8030 7210\nPassword: 943198 \nAbstract\nIn the current digital landscape\, capturing user attention for long video content is increasingly challenging due to diminishing attention spans. While video production is shifting towards shorter formats\, there remains significant value in leveraging the huge volume of extant long videos to meet fast-paced user consumption habits. Addressing this challenge\, our talk presents a novel framework designed to recommend specific video fragments from long videos that align with individual user profiles. Our approach is based on three key insights: the inter-fragment contextual effect\, which leverages the complex relationships among fragments within a long video; the intra-fragment contextual effect\, which models herding effects from multi-modal signals in a single video fragment; and video-level preferences\, which ensure consistency with users’ overarching interests. Extensive experiments demonstrate that our model achieves superior performance across multiple key metrics\, outperforming state-of-the-art methods. \nSpeaker\nMr. Wang Jiaqi\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nBiography of the Speaker\nJiaqi Wang received the B.E. degree in Computer Science and Technology from South China University of Technology. He is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering\, The University of Hong Kong\, Hong Kong. His current research interests include recommendation systems and deep learning. \nOrganiser\nProf. Edith Ngai \nAll are welcome.
URL:https://ece.hku.hk/events/20250312-2/
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:20250313T100000
DTEND;TZID=Asia/Hong_Kong:20250313T113000
DTSTAMP:20260512T170454
CREATED:20250211T081812Z
LAST-MODIFIED:20250212T083236Z
UID:108702-1741860000-1741865400@ece.hku.hk
SUMMARY:In-memory\, Mixed Analog-digital Architectures for Energy-efficient Computing Applications
DESCRIPTION:Abstract\nThere is simultaneously an interest for more energy-efficient hardware in challenging applications\, as well as a drive to overhaul the von Neumann architecture toward more brain-like architectures. Compute-in-Memory (CIM) is one emerging paradigm addressing key memory bottlenecks such as bandwidth limitations\, access latency\, and high data movement energy in conventional computing platforms.\nIn part one\, we use this paradigm to accelerate the solving of challenging optimization problems. We describe our in-memory\, hardware approach built around modified Hopfield neural networks to accelerate problem classes such as Boolean satisfiability (3-SAT). In an algorithm-hardware co-design process\, we have developed three different architectures steadily improving on the state-of-the-art. We discuss issues encountered in mapping native optimization problems to physical hardware\, precision demands\, and matching mixed analog-digital blocks to the algorithmic needs. Quantitative performance comparisons to competing approaches in both mature and emerging technologies will be presented.\nIn the second part of the talk\, we extend CIM beyond matrix-dominant workloads\, exploring its potential for novel and powerful models like Kolmogorov-Arnold Networks (KAN). Our new work\, called “KA-CIM\,” provides hardware acceleration for KANs. KANs offer significant parameter reduction over traditional neural networks for AI+Science applications but relies on computationally expensive non-linear functions. We present an innovative memory-centric design that enables energy-efficient and flexible computation of non-linear functions central to KAN\, while efficiently executing KAN inference.\nWe also discuss analog CIM using commodity DRAM architectures. Unlike SRAM- and emerging memory-based CIM accelerators\, DRAM-based CIM offers both high memory capacity and technological maturity\, making it an attractive candidate for large-scale AI workloads. This portion of the talk will present both the opportunities and constraints of using commodity DRAM for CIM. A novel analog CIM architecture will be presented\, which mitigates several of these constraints and demonstrates how area- and power-intensive ADCs can be efficiently integrated within an area-optimized commodity DRAM design. Furthermore\, a key feature of this architecture is its Dual-Mode functionality\, enabling it to seamlessly operate as both conventional main memory and an accelerator. \nSpeakers\nProf. John Paul Strachan\nProfessor\, RWTH Aachen University\, Aachen\, Germany\nHead\, Peter Grünberg Institute (PGI-14)\, Forschungszentrum Jülich\, Jülich\, Germany\n\nDr. Chirag Sudarshan\nPostdoctoral Researcher\nPeter Grünberg Institute (PGI-14)\, Forschungszentrum Jülich\, Jülich\, Germany \nBiography of the Speakers\nProf. John Paul Strachan directs the Peter Grünberg Institute on Neuromorphic Compute Nodes (PGI-14) at Forschungszentrum Jülich and is a Professor at RWTH Aachen.  Previously he led the Emerging Accelerators team as a Distinguished Technologist at Hewlett Packard Labs\, HPE. His teams explore novel types of hardware accelerators using emerging device technologies\, with expertise spanning materials\, device physics\, circuits\, architectures\, benchmarking and building prototype systems. Their interests span applications in machine learning\, network security\, and optimization. John Paul has degrees in physics and electrical engineering from MIT and a PhD in applied physics from Stanford University. He has over 60 patents\, has authored or co-authored over 100 peer-reviewed papers\, and been the PI in many USG research grants. He has previously worked on nanomagnetic devices for memory for which he was awarded the Falicov Award from the American Vacuum Society\, and has developed sensing systems for precision agriculture in a company which he co-founded. He serves in professional societies including IEEE IEDM ExComm\, the Nanotechnology Council ExComm\, and past program chair and steering member of the International Conference on Rebooting Computing.\n\nDr. Chirag Sudarshan is currently a Postdoctoral Researcher at the Peter Grünberg Institute on Neuromorphic Compute Nodes (PGI-14)\, Forschungszentrum Jülich\, working under the supervision of Prof. John Paul Strachan. He received his Master’s degree (2017) and Ph.D. (2023) in Electrical Engineering from the University of Kaiserslautern-Landau\, Germany. During his Ph.D.\, he extensively worked on novel DRAM architectural designs and is now developing innovative compute-in-memory architectures with emerging memory technologies for neuromorphic applications. His contributions have been recognized with a special academic achievement award from the Department of Electrical and Computer Engineering and WIPOTEC GmbH following his master’s studies. He has authored or co-authored 23 publications\, filed six patents\, and currently serves as a reviewer for journals such as Results in Engineering and Micromachines. His research interests include compute-in-memory architectures\, neuromorphic computing\, emerging memory technologies\, and DRAM architectures. \nOrganiser\n\nProf. Can Li\, Department of Electrical and Electronic Engineering\, The University of Hong Kong; and\nCenter for Advanced Semiconductor and Integrated Circuits\n\nAll are welcome! 
URL:https://ece.hku.hk/events/20250313-2/
LOCATION:Lecture Theatre CB-A\, G/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:20250313T103000
DTEND;TZID=Asia/Hong_Kong:20250313T113000
DTSTAMP:20260512T170454
CREATED:20250102T023031Z
LAST-MODIFIED:20250211T042315Z
UID:19666-1741861800-1741865400@ece.hku.hk
SUMMARY:Quantum Technologies with Hexagonal Boron Nitride
DESCRIPTION:Abstract\nEngineering robust\, solid-state quantum systems is amongst the most pressing challenges to realise scalable quantum photonic circuitry. While several 3D systems (such as diamond or gallium arsenide) have been thoroughly studied\, solid state emitters in van der Waals (vdW) and two dimensional (2D) materials are still in their infancy. \nIn this presentation\, I will discuss the appeal of an emerging vdW crystal – hexagonal boron nitride (hBN). This unique system possesses a large bandgap of ~ 6 eV and can host single defects that can act as ultra-bright quantum light sources. In addition\, some of these defects exhibit spin dependent fluorescence that can be initialised and coherently manipulated. I will discuss in details various methodologies to engineer these defects and show their peculiar properties. Furthermore\, I will discuss how hBN crystals can be carefully sculpted into nanoscale photonic resonators to confine and guide light at the nanoscale. Taking advantage of the unique 2D nature of hBN\, I will also show promising avenues to integrate hBN emitters with silicon nitride photonic crystal cavities. \nAll in all\, hBN possesses all the vital constituents to become the leading platform for integrated quantum photonics. To this extent\, I will highlight the challenges and opportunities in engineering hBN quantum photonic devices and will frame it more broadly in the growing interest with 2D materials nanophotonics. \nSpeaker\nProf. Igor Aharonovich\nSchool of Mathematical and Physical Sciences\,\nFaculty of Science\,\nUniversity of Technology Sydney \nBiography of the Speaker\nIgor Aharonovich is an award-winning scientist working on cutting-edge research into quantum sources that are able to generate\, encode and distribute quantum information. A Professor in the School of Mathematical and Physical Sciences at UTS\, Igor investigates optically active defects in solids\, with the aim of identifying a new generation of ultra-bright solid state quantum emitters. He is a chief investigator at the ARC Centre of Excellence for Transformative Meta-Optical Materials (TMOS)\, and leads an international collaboration investigating the chemical structure of crystal imperfections\, or defects\, in the nanomaterial hexagonal boron nitride (hBN). \nIn 2016\, Igor and his team discovered the first quantum emitters in 2D materials that operate at room temperature based on defects in hBN. He has co-authored more than 200 peer-reviewed publications\, including one of the most cited reviews on diamond photonics. He has also written a road map for solid state single-photon sources. In 2019\, Igor co-founded the inaugural online photonics conference\, Photonics Online Meetup\, which attracted more than 1100 attendees from around the world\, and which was highlighted by top science outlets. The conference now runs twice a year. He has received several international awards including the Pawsey Medal (2017)\, the IEEE Photonics Young Investigator Award (2016) and in 2020 he was the recipient of the Kavli Foundation Early Career Lectureship in Materials Science from Materials Research Society. In 2021\, he became a Fellow of the Optical Society (OSA)\, and in 2024 elected as a fellow of SPIE.\nRead more about the speaker’s biography: https://profiles.uts.edu.au/Igor.Aharonovich \nOrganiser\nProf. Zhiqin Chu\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nSupported by\nTam Wing Fan Innovation Wing Two \nAll are welcome! \nDirection: https://innowings.engg.hku.hk/innowing2/visitors
URL:https://ece.hku.hk/events/20250313-1/
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:20250313T110000
DTEND;TZID=Asia/Hong_Kong:20250313T113000
DTSTAMP:20260512T170454
CREATED:20250312T065738Z
LAST-MODIFIED:20250312T073813Z
UID:110586-1741863600-1741865400@ece.hku.hk
SUMMARY:On The Interplay of T and R in VCM-based 1T1R Structures
DESCRIPTION:Abstract\nRedox-based resistive switching random access memory (ReRAM) which is frequently discussed as a promising non-volatile memory as well as a central element in novel neuromorphic computing applications\, is typically integrated in 1-transistor-1-resistor (1T1R) structures. While the access transistor is required as a selective device and acts as an effective current compliance during SET\, it may hinder the RESET operation due to its series resistance. We showed that this may lead to a rare endurance failure. Furthermore\, the RESET speed is affected by the voltage divider of transistor and ReRAM cell\, where the initial cell resistance\, the gate voltage and the transistor geometry (i.e.\, width over length ratio w/L) are crucial. For both\, the HRS and LRS\, we demonstrate that the operation point of the 1T1R voltage divider can be shifted between the linear and the saturation regime of the transistor transfer characteristics. \nSpeaker\nDr. Stefan Wiefels\nPGI-7\, Forschungszentrum Jülich \nBiography of the Speaker\nStefan Wiefels was born in Grevenbroich\, Germany. He received the M.Sc. degree in materials science and the Ph.D. degree in electrical engineering and information technology from RWTH Aachen University\, Aachen\, Germany\, in 2016 and 2021\, respectively. His current research group is centered around the electrical characterization of memristive devices\, reaching from Redox-based resistive switches (ReRAM) to phase change memory (PCM) and from single cells (1R)\, via 1-transistor-1-resistor (1T1R) structures to arrays and circuits. A general focus lies on the automation of measurement schemes to generate significant statistics. This allows for understanding the intrinsic variability of resistive switching devices and is crucial to identify rare failure mechanisms. Further\, variability aware algorithms to program memristive devices are developed. A general target is emulating neuromorphic functionalities using external DAC and ADC before they are integrated on chip. \nOrganisers\n– Can Li\, Department of Electrical and Electronic Engineering\, The University of Hong Kong\n– Center for Advanced Semiconductor and Integrated Circuit \nAll are welcome!
URL:https://ece.hku.hk/events/20250313-3/
LOCATION:Lecture Theatre CB-A\, G/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/03/20250313-3-03.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250320T163000
DTEND;TZID=Asia/Hong_Kong:20250320T173000
DTSTAMP:20260512T170454
CREATED:20250304T090135Z
LAST-MODIFIED:20250304T090135Z
UID:110560-1742488200-1742491800@ece.hku.hk
SUMMARY:Shant Reactive Power Compensation Technologies
DESCRIPTION:Abstract\nReactive power compensation plays a critical role in improving power quality\, enhancing voltage stability\, and optimizing the efficiency of electrical power systems. This presentation will first highlight the main applications of shunt reactive power compensators and provide an overview of key technologies\, including Static Var Compensators (SVC)\, and Static Synchronous Compensators (StatComs). Then the focus will shift to StatCom\, which is considered state-of-the-art technology with superior performance. However\, the widespread adoption of high-power StatComs is hindered by cost constraints\, partly due to the large capacitor banks required in conventional Cascaded H-Bridge (CHB) multilevel converters. The presentation will discuss research advancements to highlight\, pros and cons of operating a CHB StatCom with low capacitance values. \nSpeaker\nProf. Glen FARIVAR\nNanyang Technological University (NUT Singapore) \nBiography of the Speaker\nGlen Farivar received PhD in Electrical Engineering from the University of NSW Australia in 2016. He was a Senior Research Fellow at the Energy Research Institute at the Nanyang Technological University (ERI@N) and a Co-director of the Power Electronics and Application Research Lab at Nanyang Technological University\, Singapore. Since 2023\, he has held a lecturer position\, leading power electronics research\, at the Department of Electrical and Electronic Engineering\, University of Melbourne. He is also a co-founder of SciLeap\, a platform dedicated to promoting research integrity\, accessibility\, and openness. He is a Senior Member of IEEE and co-authored over 130 papers in the areas of high-power multilevel converters and renewable energy systems. \nOrganiser\nProf. Yi WANG\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250320-1/
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/2025/03/1280-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250403T163000
DTEND;TZID=Asia/Hong_Kong:20250403T173000
DTSTAMP:20260512T170454
CREATED:20250320T064619Z
LAST-MODIFIED:20250321T014233Z
UID:110629-1743697800-1743701400@ece.hku.hk
SUMMARY:Future of MRI: Can We Learn from the Past?
DESCRIPTION:Abstract \nIn this presentation\, the speaker will cover how MRI technologies have evolved from the early pioneering days till today. The presentation will discuss the technological challenges to be met in our current pursuit of broader MRI applications in healthcare and basic biomedical research. The speaker will also discuss different application scenarios for the huge range of magnetic field strengths available today. \nSpeaker \nProfessor Juergen HENNIG\nDepartment of Radiology and Medical Physics\nUniversity Medical Center FREIBURG\, Germany \nBiography of the Speaker \nProfessor Hennig is a pioneer in MRI technology development. His research interests include MRI methodological and technological developments and their applications in clinical medicine and basic science. He has made numerous and seminal contributions to MRI technology development since the inception of MRI several decades ago. Professor Hennig received numerous international awards including Gold Medal of International Society for Magnetic Resonance in Medicine (ISMRM)\, Max Planck Award\, Houndsfield Medal for Medical Imaging\, and Einstein Professorship of the Chinese Academy of Science. He was also the past President of ISMRM. \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/20250403-1/
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/2025/03/1280-3.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250407T090000
DTEND;TZID=Asia/Hong_Kong:20250407T153000
DTSTAMP:20260512T170454
CREATED:20250410T065951Z
LAST-MODIFIED:20250410T065951Z
UID:111079-1744016400-1744039800@ece.hku.hk
SUMMARY:Seminar on Low-Carbon and Digital Power Systems
DESCRIPTION:Click HERE to view the event poster.\n \nTime & Venue\n09:00 – 12:00: Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong (HKU);\n14:00 – 15:30: Room EH-101\, 1/F\, Eliot Hall\, HKU. \nSchedule & Speakers\n\n\n\nTime & Venue*:\nProgramme:\n\n\n09:00 – 09:20\nCB-603\nRegistration\n\n\n09:20 – 09:30\nCB-603\nOpening Remarks\n– Prof. Yi WANG\,\n  The University of Hong Kong\n\n\n09:30 – 10:00\nCB-603\nOpportunities from Quantum Computing for Net-Zero Power System Optimisation\n– Prof. Thomas MORSTYN\,\n  Oxford University\n\n\n10:00 – 10:30\nCB-603\nOnline EV Management in Smart Grids\n– Prof. Yue CHEN\,\n  The Chinese University of Hong Kong\n\n\n10:30 – 11:00\nCB-603\nUncertainty Quantification of Low-Carbon Power System Dynamics\n– Prof. Siqi BU\,\nThe Hong Kong Polytechnic University\n\n\n11:00 – 11:30\nCB-603\nLarge-scale Offshore Wind Farm Planning based on Complex Combinatorial Optimization\n– Prof. Xinwei SHEN\,\n  Tsinghua Shenzhen International Graduate School\n\n\n11:30 – 12:00\nCB-603\nData-Driven Operations for the Future Power Grid\n– Prof. Chenye WU\,\n  The Chinese University of Hong Kong\, Shenzhen\n\n\n12:00 – 14:00\nLunch and Break Time\n\n\n14:00 – 14:30\nEH-101\nSystem Strength as a Service in Large-scale Renewable Energy Projects\n– Prof. Yun LIU\,\nSouth China University of Technology\n\n\n14:30 – 15:00\nEH-101\nUrban Power System Optimization Considering Interaction with Building Clusters and Microclimates\n– Prof. Hongxun HUI\,\n  University of Macau\n\n\n15:00 – 15:30\nEH-101\nQ&A Session & Closing Remarks\n– Mr. Xueyuan CUI\,\n  The University of Hong Kong\n\n\n\n*Each 30-minute presentation should include about 25 minutes by the presenter\, and the rest is for a Q&A discussion. \nOrganisers\nProf. Yi WANG & Mr. Xueyuan CUI\nDepartment of Electrical and Electronic Engineering\, HKU \nAcknowledgement\nThe seminar has been supported by the Postgraduate Students Conference/Seminar Grants of the Research Grants Council\, Hong Kong. \nAll are welcome to join!
URL:https://ece.hku.hk/events/20250407-2/
LOCATION:Room CB-603 / EH-101
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/04/1280.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250407T093000
DTEND;TZID=Asia/Hong_Kong:20250407T100000
DTSTAMP:20260512T170454
CREATED:20250410T070207Z
LAST-MODIFIED:20250410T070207Z
UID:111086-1744018200-1744020000@ece.hku.hk
SUMMARY:Opportunities from Quantum Computing for Net-Zero Power System Optimisation
DESCRIPTION:Abstract\nOptimised power system planning and operation are core to delivering a low-cost and high-reliability transition path to net-zero carbon emissions. However\, power system optimisation problems are now posing challenges for even the largest exa-scale supercomputers. A new avenue for progress has been opened by recent breakthroughs in quantum computing. Quantum computing offers a fundamentally new computational infrastructure with different capabilities and trade-offs\, and is reaching a level of maturity where\, for the first time\, a practical advantage over classical computing is available for specific applications. The talk will present emerging opportunities where quantum computing can offer value for power system optimisation\, including combinatorial\, convex and machine learning-based optimisation problems. The talk will also discuss challenges for implementation and scale-up\, and outline promising directions for future research. \nSpeaker\nProfessor Thomas MORSTYN\nAssociate Professor in Power Systems\,\nDepartment of Engineering Science\,\nUniversity of Oxford \nBiography of the Speaker\nThomas MORSTYN is an Associate Professor in Power Systems with the Department of Engineering Science\, University of Oxford and he leads the Power Systems Architecture Lab. He is a Tutorial Fellow at Hertford College\, an Honorary Fellow at the University of Edinburgh\, Associate Editor of IEEE Transactions on Power Systems and Co-Chair of the IEEE Power & Energy Society Taskforce on Power System Operations and Control with Quantum Computing. His research is focused on power system digitalisation and market design as key interlinked enablers of the net-zero transition. He received the BEng (Hon.) degree from the University of Melbourne in 2011\, and the PhD degree from the University of New South Wales in 2016\, both in electrical engineering. Previously\, He was a lecturer at the University of Edinburgh and an EPSRC research fellow at the University of Oxford. Prior to undertaking his PhD\, he also worked as an electrical engineer in Rio Tinto’s Technology and Innovation group. \nOrganiser\nProf. Yi WANG\nAssistant Professor\,\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAcknowledgement\nThe seminar has been supported by the Postgraduate Students Conference/Seminar Grants of the Research Grants Council\, Hong Kong. \nAll are welcome!
URL:https://ece.hku.hk/events/20250407-1/
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:20250409T110000
DTEND;TZID=Asia/Hong_Kong:20250409T120000
DTSTAMP:20260512T170454
CREATED:20250410T070435Z
LAST-MODIFIED:20250410T070813Z
UID:111088-1744196400-1744200000@ece.hku.hk
SUMMARY:Efficient Fine-Tuning and Compression of Large Language Models: Towards Low-bit and Ultra-Low Parameter Solutions
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/5995074181?omn=91841905345 \nAbstract\nEfficient fine-tuning of Large Language Models (LLMs) is crucial due to their substantial memory and computational demands. This seminar discusses recent advancements in techniques aimed at significantly reducing these costs\, enabling effective adaptation of large-scale models even on resource-constrained hardware. The talk will begin with an overview of current challenges and mainstream approaches to compressing and fine-tuning LLMs\, highlighting trade-offs between model size\, accuracy\, and efficiency. Subsequently\, the speaker will introduce novel approaches that enable fine-tuning at extremely low precision and ultra-low parameter regimes\, significantly reducing memory requirements without compromising performance. Finally\, the discussion will cover recent progress and future directions for achieving efficient deployment of LLMs in real-world applications. \nSpeaker\nMr. Jiajun Zhou\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nJiajun Zhou is currently a Ph.D. student in the Department of Electrical and Electronic Engineering at the University of Hong Kong (HKU)\, supervised by Prof. Ngai Wong\, and a visiting scholar at the University of California\, Santa Barbara (UCSB). He received his Master’s degree in IC Design Engineering from the Hong Kong University of Science and Technology (HKUST) in 2019 and a Bachelor’s degree in Integrated Circuit Design and Integrated Systems from National Huaqiao University\, China\, in 2018. He previously worked as a Research Assistant at the Chinese University of Hong Kong (CUHK). His research primarily focuses on developing innovative frameworks for efficient training and inference of Large Language Models (LLMs)\, particularly through quantization\, low-bit optimization\, and tensor decomposition. He has published extensively in AI and hardware acceleration venues\, including ACL\, NAACL\, IEEE FCCM\, and IEEE TCAD. \nOrganiser\nProf. Ngai Wong\nDepartment of Electrical and Electronic Engineering\, The University of Hong KongAll are welcome!
URL:https://ece.hku.hk/events/20250409-1/
LOCATION:Online via Zoom
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250410T163000
DTEND;TZID=Asia/Hong_Kong:20250410T173000
DTSTAMP:20260512T170454
CREATED:20250410T071105Z
LAST-MODIFIED:20250410T071105Z
UID:111090-1744302600-1744306200@ece.hku.hk
SUMMARY:Semantic-Relevance Based Sensor Selection for Edge-AI Empowered Sensing Systems
DESCRIPTION:Abstract\nThe sixth-generation (6G) mobile network is envisioned to incorporate sensing and edge artificial intelligence (AI) as two key functions. Their natural convergence leads to the emergence of Integrated Sensing and Edge AI (ISEA)\, a novel paradigm enabling real-time  acquisition and understanding of sensory information at the network edge. However\, ISEA faces a communication bottleneck due to the large number of sensors and the high dimensionality of sensory features. Traditional approaches to communication-efficient ISEA lack awareness of semantic relevance\, i.e.\, the level of relevance between sensor observations and the downstream task. In this seminar\, I will introduce a novel framework for semantic-relevance-aware sensor selection to achieve optimal end-to-end (E2E) task performance under heterogeneous sensor relevance and channel states. E2E sensing accuracy analysis is provided to characterize the sensing task performance in terms of selected sensors’ relevance scores and channel states. Building on the results\, the sensor-selection problem for accuracy maximization is formulated as an integer program and solved through a tight approximation of the objective. The optimal solution exhibits a priority-based structure\, which ranks sensors based on a priority indicator combining relevance scores and channel states and selects top-ranked sensors. Experimental results on both synthetic and real datasets show substantial accuracy gain achieved by the proposed selection scheme compared to existing benchmarks. \nSpeaker\nLIU Zhiyan\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nZhiyan Liu received the B.Eng. degree from the Dept. of Electronic Engineering\, Tsinghua University\, Beijing\, in 2021. He is currently working towards the Ph.D. degree with Dept. of Electrical and Electronic Engineering\, The University of Hong Kong (HKU)\, Hong Kong. His research interests include edge intelligence and distributed sensing in 6G wireless networks. \nOrganiser\nProf. Kaibin Huang\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong\n\nAll are welcome!
URL:https://ece.hku.hk/events/20250410-1/
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:20250411T140000
DTEND;TZID=Asia/Hong_Kong:20250411T150000
DTSTAMP:20260512T170454
CREATED:20250410T071246Z
LAST-MODIFIED:20250410T071251Z
UID:111094-1744380000-1744383600@ece.hku.hk
SUMMARY:Mirror-Symmetrical Dijkstra’s Algorithm-Based Deep Reinforcement Learning for Dynamic Wireless Charging Navigation of Electric Vehicles
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/91849018634?pwd=YgyqXnIIfUsd8YGU2YNaSa5aj3uWou.1\nMeeting ID: 918 4901 8634\nPassword: 038419 \nAbstract\nThe dynamic wireless charging (DWC) system based on wireless charging lanes (WCLs) is an important component of smart cities\, allowing electric vehicles (EVs) to charge while moving. It is necessary to establish a user-oriented real-time DWC navigation system to achieve the joint optimization of EV routing and charging. However\, the modeling characteristics of DWC and the risk preferences of EV owners towards congested WCLs are completely different from those in traditional wired charging. Furthermore\, optimal EV charging navigation is always challenging without prior knowledge of uncertainty in electricity prices and traffic conditions. This work first proposes a novel dynamic charging routing model for individual EVs to minimize travel and charging costs\, and reformulates it as a two-step optimization problem to facilitate feature extraction. Then\, mirror-symmetrical Dijkstra’s algorithm (MSDA) is proposed to solve the reformulated model in linear time and extract advanced features from the stochastic information. By feeding the system state containing extracted features into the deep Q network (DQN) in an event-triggered manner\, the near-optimal charging navigation strategy is finally obtained. The proposed MSDA-DQN approach not only efficiently extracts low-dimensional interpretable input features\, but also adaptively learns the unknown dynamics of system uncertainty. Numerical results based on simulated and real-world data validate the proposed approach. \nSpeaker\nMiss Chaoran Si\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nChaoran Si received her bachelor degree from Tianjin University in 2018 and her master degree from Zhejiang University in 2021\, both in electrical engineering. She is currently working toward the Ph.D. degree in electrical and electronic engineering in the Department of Electrical and Electronic Engineering at the University of Hong Kong. Her current research interests include power-transportation systems\, wireless charging of electric vehicles\, and deep reinforcement learning. \nOrganiser\nProf. Yunhe Hou\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250411-1/
LOCATION:Online via Zoom
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250411T150000
DTEND;TZID=Asia/Hong_Kong:20250411T160000
DTSTAMP:20260512T170454
CREATED:20250410T071430Z
LAST-MODIFIED:20250410T071430Z
UID:111097-1744383600-1744387200@ece.hku.hk
SUMMARY:Wireless Permanent-Magnet Brushless DC Motor Using Contactless Feedback
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/94707594608?pwd=oSy6rRXxpXyd6bapdlmVDCtv9GRPrd.1\nMeeting ID: 947 0759 4608\nPassword: 566410 \nAbstract\nWireless power transfer (WPT) is a fast-developing technology in industrial and domestic applications. It achieves electrical and physical isolation between the power supply and load\, bringing high flexibility and safety. Based on WPT\, wireless motors allow electric motors to work in sealed environments\, however\, when using a single controller at the transmitter side\, acquiring the feedback of wireless motors is challenging because of the contactless structure. We propose a wireless permanent-magnet brushless DC motor with contactless feedback\, the rotor position measured by a wireless-powered Hall effect sensor is modulated and sensed at the transmitter side for commutation and precise speed control. Also\, the proposed system adopts hybrid modulation including PWM and sigma-delta modulated PFM (Σ-Δ PFM) to reduce the switching loss in the whole control process. Compared with existing wireless motor systems\, the proposed system realizes both power and control in the fully wireless approach and keeps good dynamic performance.  \nSpeaker\nMr. Songtao LI\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nSongtao Li received the B.Eng and M.Eng degrees in instrument science and technology from Southeast University\, Nanjing\, China in 2018 and 2021\, respectively. He is currently working toward the Ph.D. degree in electrical and electronic engineering in the University of Hong Kong\, Hong Kong\, China. His current research interests include power electronics\, wireless power transfer\, and electric vehicle technologies. \nOrganiser\nProf. Yunhe Hou\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome!\n\n——-
URL:https://ece.hku.hk/events/20250411-2/
LOCATION:Online via Zoom
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250415T093000
DTEND;TZID=Asia/Hong_Kong:20250415T103000
DTSTAMP:20260512T170454
CREATED:20250410T071656Z
LAST-MODIFIED:20250410T071700Z
UID:111099-1744709400-1744713000@ece.hku.hk
SUMMARY:Supporting Drone-based Autonomous System with Mobile-and-Edge\, Software-and-Hardware Co-Design
DESCRIPTION:Mode: Online via Zoom\nZoom Link: https://hku.zoom.us/j/93338747328\nMeeting ID: 933 3874 7328 \n \nAbstract\nDrones are among the most disruptive innovations in the past few years\, spawning many novel applications including aerial imaging\, instant delivery\, sky networking\, and industrial inspection. Building system supports for drone-based autonomous applications is critical to simultaneously enhance accuracy\, efficiency\, and minimize resource overhead. In this talk\, I will present my recent research focused on developing such system supports\, guided by a research motto “working codes on flying drones trump all hypes.” First\, I will discuss how to create “working codes suitable for flying drones”\, using GPS-denied localization as a case study to demonstrate improved system performance through algorithmic innovation. Second\, I will explore enabling “flying drones to effectively run working codes” by leveraging edge-based computational platforms\, illustrated through a collaborative drone system for industrial inspection. Third\, I will move beyond isolated algorithm design and computational platform optimizations\, discussing advancements achieved through software-hardware co-design. Finally\, I will outline future research directions aimed at advancing system performance and facilitating system deployment\, including (1) data reuse among drone control-computing-communication modules\, and (2) resource virtualization across mobile-edge-cloud infrastructures. \nSpeaker\nDr. Jingao XU\nPostdoctoral Research Associate\nCarnegie Mellon University \nBiography of the Speaker\nDr. Jingao Xu is a postdoctoral research associate at Carnegie Mellon University\, working with Prof. Mahadev Satyanarayanan. He completed his Ph.D. in Tsinghua University advised by Prof. Yunhao Liu and Prof. Zheng Yang. His research focuses on edge computing\, drone-based mobile computing and visual SLAM. He has published over 40 works in top-tier conferences and journals including NSDI\, MobiCom\, MobiSys\, Sensys\, ToN\, and TMC. He received the Honored Doctoral Dissertation Award from ACM SIGCOMM China 2022\, the Best Artifact Award at ACM MobiCom 2024. \nOrganiser\nProf. Kaibin HUANG\nProfessor & Head of Department\,\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250415-2/
LOCATION:Online via Zoom
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250415T100000
DTEND;TZID=Asia/Hong_Kong:20250415T173000
DTSTAMP:20260512T170454
CREATED:20250410T072025Z
LAST-MODIFIED:20250410T072141Z
UID:111103-1744711200-1744738200@ece.hku.hk
SUMMARY:Workshop on Frontiers of Image Science and Visual Computing 2025
DESCRIPTION:You are cordially invited to join us for the upcoming workshop on “Frontiers of Image Science and Visual Computing 2025” on April 15\, 2025. For the most updated details of the workshop and registration\, please visit the event website: https://hku.welight.fun/events/workshop_25Apr \n \n \nDate: April 15\, 2025 (TUE)\nTime: 10:00 – 17:30\nVenue: Multi-purpose Zone Room\, 3/F\, Main Library\, The University of Hong Kong (HKU)\, Hong Kong SAR\nChair: Prof. Evan Yifan PENG\, HKU EEE x CS\nOrganisation: HKU WeLight Lab \nSpeakers/Guests:\n \n• David FORSYTH\, ACM Fellow\, IEEE Fellow\, University of Illinois Urbana-Champaign (UIUC)\n• Yinqiang ZHENG\, The University of Tokyo (UTokyo)\n• Seung-Hwan BEAK\, Pohang University of Science and Technology (POSTECH)\n• Yuanmu YANG\, Tsinghua University (THU)\n• He SUN\, Peking University (PKU)\n• Hongzhi WU\, Zhejiang University (ZJU)\n• Hongbo FU\, Hong Kong University of Science and Technology (HKUST)\n• Ping TAN\, Hong Kong University of Science and Technology (HKUST)\n• Tianfan XUE\, The Chinese University of Hong Kong (CUHK)\n• Wenzheng CHEN\, Peking University (PKU)\n• Xiaojuan QI\, The University of Hong Kong (HKU) \nBrown bag light lunch & tea reception will be provided. \nDetails of the workshop and registration: https://hku.welight.fun/events/workshop_25Apr\n\nLooking forward to welcoming you at the event on April 15\, 2025 (TUE).
URL:https://ece.hku.hk/events/20250415-1/
LOCATION:Multi-purpose Zone Room\, 3/F\, Main Library\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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END:VEVENT
END:VCALENDAR