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TZID:Asia/Hong_Kong
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DTSTART:20240101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250905T093000
DTEND;TZID=Asia/Hong_Kong:20250905T103000
DTSTAMP:20260509T201717
CREATED:20250902T061920Z
LAST-MODIFIED:20250902T091613Z
UID:113160-1757064600-1757068200@ece.hku.hk
SUMMARY:Seminar on Illuminating Life Sciences: Optical Engineering Technologies for Neuroscience and Biology
DESCRIPTION:Abstract\nIn this talk\, the speaker will introduce how optical technology can contribute to biological studies. Light has long been one of the most powerful tools for investigating living systems. Optical imaging techniques allow direct observation of dynamic cellular processes in vivo\, while optogenetics enables the precise modulation of neuronal circuits with high spatiotemporal resolution. \nIn Part One\, the speaker will present how digital micromirror device (DMD) equipped beam projection technology\, commonly used in movie theatres\, can be adapted for functional mapping of the mouse brain. In Part Two\, the speaker will describe how liquid crystal display (LCD) technology enables ultrafast optical recording of neuronal circuit activity. Finally\, in Part Three\, the speaker will outline my current and future research directions in immunophotonics\, driven by large-scale optical imaging\, deep learning–assisted automated cell tracking\, and skull transparency techniques for real-time visualisation of brain–immune system interactions. \nSpeaker\nDr. Seonghoon KIM\nSenior Research Scientist\,\nDepartment of Automation\,\nTsinghua University \nSpeaker’s Biography\nSeonghoon KIM is a Senior Research Scientist in the Department of Automation at Tsinghua University. He received his Ph.D. from the Korea Advanced Institute of Science and Technology (KAIST) in South Korea and subsequently completed postdoctoral training at Harvard Medical School\, Seoul National University\, and Tsinghua University. His research background is exceptionally broad\, spanning materials science\, optics\, neuroscience\, computational science\, and immunology. For example\, he has studied the oxidation of nanocrystal metal films\, developed implantable biomaterials\, developed photodynamic therapy for cancer treatment\, applied optogenetics with fMRI for functional mapping of the mouse brain\, and developed an ultrafast optical imaging system for neuronal voltage imaging. Recently\, he has published several papers in leading journals\, including Neuron\, Nature Communications\, Advanced Materials\, and Nature Methods. His current research focuses on investigating in vivo biological dynamics—particularly in neuroscience and immunology— through large-scale optical imaging systems integrated with computational imaging techniques.\n\nAll are welcome!
URL:https://ece.hku.hk/events/20250905-1/
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/2025/09/1280-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250815T110000
DTEND;TZID=Asia/Hong_Kong:20250815T120000
DTSTAMP:20260509T201717
CREATED:20250806T043442Z
LAST-MODIFIED:20250806T090812Z
UID:112883-1755255600-1755259200@ece.hku.hk
SUMMARY:Seminar on Towards Deep Learning MR Reconstruction with No Ground Truth and Fast Inference
DESCRIPTION:Abstract\nSince 2016\, deep learning techniques have been introduced to solve the inverse problem of MR image reconstruction from undersampled data from accelerated acquisitions. Since then\, the field has grown substantially. A wide range of machine learning methods have been developed\, translated into clinical practice and adopted as products by all major scanner vendors. In this talk\, after a general introduction to deep learning for MR image reconstruction\, I will focus on two open challenges in the field. First\, the application of deep learning reconstruction for dynamic contrast-enhanced imaging and abdominal imaging\, where no ground truth can be obtained for model training. Second\, the optimisation of network architectures towards computation time at inference for real-time imaging and clinical translation of instance-specific learning\, where trainings need to be performed during inference. \nSpeaker\nProf. Florian KNOLL\nProfessor and Head of the Computational Imaging Lab\,\nDepartment Artificial Intelligence in Biomedical Engineering (AIBE)\,\nFriedrich-Alexander-Universität Erlangen-Nürnberg \nSpeaker’s Biography\nProf. Florian KNOLL received his PhD in Electrical Engineering in 2011 from Graz University of Technology. From 2015 to 2021\, he was Assistant Professor for Radiology at the Center for Biomedical Imaging at NYU Grossman School of Medicine. Since 2021\, he has been Professor and Head of the Computational Imaging Lab at the Department Artificial Intelligence in Biomedical Engineering at Friedrich-Alexander-Universität Erlangen-Nürnberg. He currently holds four grants from the German Research Fund (DFG) and an R01 grant from the National Institutes of Health (NIH). His research interests include iterative MR image reconstruction\, parallel MR imaging\, compressed sensing and machine learning. \nOrganiser\nProf. Ed Xuekui WU\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250815-1/
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/2025/08/1280.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250814T160000
DTEND;TZID=Asia/Hong_Kong:20250814T173000
DTSTAMP:20260509T201717
CREATED:20250813T012141Z
LAST-MODIFIED:20250813T013434Z
UID:113006-1755187200-1755192600@ece.hku.hk
SUMMARY:Seminar on Voltage Stability Constrained Power System Optimization: A Constraint-learning Method
DESCRIPTION:Abstract\nHigh penetration of renewable energy poses severe challenges to power system voltage stability due to weakened voltage and reactive power support\, complex voltage stability mechanisms\, and highly diversified operation states. Traditional control strategies predefined based on limited operation states fail to maintain the required voltage stability margin. This report presents a constraint-learning-based framework for embedding accurate and efficient voltage stability constraints into power system optimization to improve voltage stability of the optimization results. The proposed framework represents the inherently nonlinear and nonconvex voltage stability constraints using multiple convex polyhedrons (MCPH). The learned constraints are linear\, sparse\, and embeddable in mixed-integer linear programming (MILP) formulations. Case studies demonstrate that integrating MCPH constraints into voltage stability constrained unit commitment improves voltage stability margin with reduced computational burden. Furthermore\, the framework is extended to generation expansion planning\, where physical ensemble constraint learning captures converter-driven stability requirements across diverse generation mix scenarios. The results confirm that the proposed approach effectively balances stability enhancement\, solution efficiency\, and scalability for high-renewable\, stability-constrained power system optimization. \nSpeaker\nMr. Hongyang Jia\nTsinghua University \nSpeaker’s Biography\nHongyang Jia received the B.S. degree in electrical engineering in 2021 from Tsinghua University\, Beijing\, China\, where he is currently pursuing the Ph.D. degree in Electrical Engineering under the supervision of Associate Prof. Ning Zhang. He was a Visiting Ph.D. Student at Imperial College London (Aug. 2024 – Feb. 2025) under Associate Prof. Fei Teng. His research interests center on trustworthy machine learning for science and engineering\, with specific applications in power system optimization under voltage stability constraints\, data-driven security/stability rule extraction and embedding\, and power system voltage stability assessment. \nOrganiser\nProf. Wang Yi \nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250814-1/
LOCATION:Room CB-601J\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=application/pdf:https://ece.hku.hk/wp-content/uploads/2025/08/August-14-2025-1-Web-banner.pdf
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250808T140000
DTEND;TZID=Asia/Hong_Kong:20250808T160000
DTSTAMP:20260509T201717
CREATED:20250801T021445Z
LAST-MODIFIED:20250804T020549Z
UID:112825-1754661600-1754668800@ece.hku.hk
SUMMARY:Seminar on Publishing in Nature Nanotechnology: An insider’s view of Nature journals
DESCRIPTION:Abstract\nDr. Lu will talk about the scope of Nature Nanotechnology and an overview of Nature journals. And she will share her experience on the editorial process (the workflow and the statistical data) and disclose the criteria of Nature journals from an insider’s view. Through the talk\, you will know better on what kind of papers a highly selective journal is looking for\, how the editors decide whether to send a paper out or not\, what they will do when the reviewers’ comments are contradictory. And Lu is happy to share with you her personal experience as an editor if you are interested together with some practical tips for writing and submitting a paper to a Nature journal. \nSpeaker\nDr. Lu Shi\nSenior Editor\nSpringer Nature \nSpeaker’s Biography\nDr. Lu Shi joined Nature Nanotechnology in April 2023 from Wiley\, where she was the Editor-in-Chief of Advanced Electronic Materials and Deputy Editor of Advanced Materials. Prior to her editorial career\, she worked on 2D materials growth and magnetoelectric transport behavior of van der Waals heterostructures. For her work\, she received a joint PhD in Materials Science and Physics from the Université Catholique de Louvain\, Belgium and Université Grenoble Alpes\, France. She obtained her BEng and MSc in Materials Science and Engineering from Shanghai Jiao Tong University and Shanghai Institute of Ceramics\, Chinese Academy of Sciences. She covers a broad range of topics across electronics and optoelectronics in the journal and is based in Shanghai. \nOrganiser\nProf. Can Li\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250808-1/
LOCATION:Tam Wing Fan Innovation Wing Two\, G/F\, Run Run Shaw Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/08/Can-Li_20250808-seminar-web-banner.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250724T143000
DTEND;TZID=Asia/Hong_Kong:20250724T153000
DTSTAMP:20260509T201717
CREATED:20250722T064431Z
LAST-MODIFIED:20250722T064800Z
UID:112757-1753367400-1753371000@ece.hku.hk
SUMMARY:Seminar on Symmetric Diffusers: Learning Discrete Diffusion on Finite Symmetric Groups
DESCRIPTION:Abstract\nFinite symmetric groups Sn are essential in fields such as combinatorics\, physics\, and chemistry. However\, learning a probability distribution over Sn poses significant challenges due to its intractable size and discrete nature. We introduce SymmetricDiffusers\, a novel discrete diffusion model that simplifies the task of learning a complicated distribution over Sn by decomposing it into learning simpler transitions of the reverse diffusion using deep neural networks. We identify the riffle shuffle as an effective forward transition and provide empirical guidelines for selecting the diffusion length based on the theory of random walks on finite groups. Additionally\, we propose a generalized Plackett-Luce (PL) distribution for the reverse transition\, which is provably more expressive than the PL distribution. We further introduce a theoretically grounded “denoising schedule” to improve sampling and learning efficiency. Extensive experiments show that our model achieves state-of-the-art or comparable performances on solving tasks including sorting 4-digit MNIST images\, jigsaw puzzles\, and traveling salesman problems. \nSpeaker\nProf. Renjie LIAO\nDepartment of Electrical and Computer Engineering\, and\nDepartment of Computer Science\,\nUniversity of British Columbia (UBC) \nSpeaker’s Biography\nRenjie Liao is an Assistant Professor in the Department of Electrical and Computer Engineering and an Associate Member of the Department of Computer Science at the University of British Columbia (UBC). He is also a faculty member at the Vector Institute and holds a Canada CIFAR AI Chair. Prior to joining UBC\, he was a Visiting Faculty Researcher at Google Brain\, working with Geoffrey Hinton and David Fleet. He received his Ph.D. in Computer Science from the University of Toronto in 2021\, under the supervision of Richard Zemel and Raquel Urtasun. During his Ph.D.\, he also worked as a Senior Research Scientist at Uber Advanced Technologies Group. He holds an M.Phil. in Computer Science from the Chinese University of Hong Kong (2015) and a B.Eng. in Automation from Beihang University (2011). His research interests span machine learning and its intersection with computer vision\, self-driving\, healthcare\, and beyond\, with a particular focus on probabilistic and geometric deep learning. \nOrganiser\nProf. Xiaojuan QI\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250724-1/
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;VALUE=DATE:20250719
DTEND;VALUE=DATE:20250720
DTSTAMP:20260509T201717
CREATED:20250326T030136Z
LAST-MODIFIED:20250625T063840Z
UID:110671-1752883200-1752969599@ece.hku.hk
SUMMARY:HKU-EEE RoboLeague 2025 港大電機電子工程：中學機械人競技聯盟2025
DESCRIPTION:🚀 Join the Excitement of Robotics! \nCompetition Details\nDate: 19 July 2025 (SAT)\nTime: Check-in begins at 9:00 AM\nVenue: Innovation Wing One\, G/F\, Hui Oi Chow Science Building\, The University of Hong Kong (HKU) (directions to the venue) \nCompetition Description\nEngage your autonomous robots in thrilling challenges:\n– Maze Navigation 迷宮探索 ​\n– Rescue Missions 搜救任務 ​\n– Soccer League – Standard Platform 足球標準平台組 ​\n– Soccer League – Open Platform 足球公開組 ​ \nWho Can Join?\nTarget Audience: Secondary School Students Team Formation: No more than 4 students ( any Forms ) per team from the same school \nKey Dates\n*Briefing Session: \n– Date: 26 April 2025 (SAT) @11:00AM \n– Venue: Room 603\, 6/F\, Chow Yei Ching Building\, HKU \n*Application Deadline: \n– Date: 30 April 2025 (WED) @11:59PM \nRegistration & Details\nhttps://linktr.ee/hkeeerl
URL:https://ece.hku.hk/events/20250426-1/
LOCATION:Tam Wing Fan Innovation Wing One\, G/F\, Hui Oi Chow Science Building\, The University of Hong Kong (HKU)
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/03/banner.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250715T160000
DTEND;TZID=Asia/Hong_Kong:20250715T170000
DTSTAMP:20260509T201717
CREATED:20250709T033608Z
LAST-MODIFIED:20250709T033659Z
UID:112605-1752595200-1752598800@ece.hku.hk
SUMMARY:Seminar on 6G Wireless Enabled Autonomous Driving and Transportation of Future
DESCRIPTION:Abstract\nThe forthcoming sixth generation (6G) wireless networks will be one of the galvanizing technologies for future advanced and autonomous transportation systems. 6G wireless will provide communication services with stringent requirements which are necessary for autonomous driving and on-flight Internet connectivity. Introduction of AI-empowered 6G would guarantee a more intelligent\, efficient and secure transportation system. In this talk\, some of the new usage scenarios and capabilities of the 6G compared with the existing cellular networks will be outlined\, followed by description of its potential and challenges for seamless and ubiquitous connectivity across the heterogeneous and multi-layer transportation systems. \nSpeaker\nProf. Abbas JAMALIPOUR\nPhD\, Fellow IEEE\, Fellow IEICE\, Fellow IEA\, Fellow AIIA\nChair Professor of Ubiquitous Mobile Networking\, The University of Sydney\nEditor-in-Chief\, IEEE Transactions on Vehicular Technology\nPast President\, IEEE Vehicular Technology Society \nSpeaker’s Biography\nProf. Abbas JAMALIPOUR is the Chair Professor of Ubiquitous Mobile Networking at The University of Sydney and the Editor-in-Chief\, IEEE Transactions on Vehicular Technology. He holds a PhD in Electrical Engineering from Nagoya University\, Japan; and is a Fellow of the IEEE\, IEICE\, Engineers Australia\, AIIA\, and a Visiting Fellow of the Royal Academy of Engineering. He has authored nine technical books\, eleven book chapters\, over 650 technical papers\, and five patents\, all in the field of wireless communications. He was the President (2020-21)\, Executive Vice-President (2018-19)\, and has been an elected voting member of the Board of Governors of the IEEE Vehicular Technology Society since 2014. Previously\, he served as the Editor-in-Chief IEEE Wireless Communications\, Vice President-Conferences\, and a member of Board of Governors of the IEEE Communications Society. He is on the editorial board of the IEEE Access\, member of the Advisory Board of IEEE Internet of Things Journal\, and an editor for several other journals. He is the recipient of several prestigious awards such as the IEEE ComSoc Harold Sobol Award\, the IEEE ComSoc Best Tutorial Paper Award\, as well as over fifteen Best Paper Awards. \nOrganiser\nProf. Hongyang DU\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong
URL:https://ece.hku.hk/events/20250715-1/
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:20250709T160000
DTEND;TZID=Asia/Hong_Kong:20250709T170000
DTSTAMP:20260509T201717
CREATED:20250707T114033Z
LAST-MODIFIED:20250708T020744Z
UID:112581-1752076800-1752080400@ece.hku.hk
SUMMARY:Seminar on AI for 6G Communications
DESCRIPTION:Abstract\nIntelligent reflecting surface (IRS) is envisioned to be a promising 6G technology which changes wireless communications from “adapting to wireless channels” to “changing wireless channels”. However\, current IRS configuration schemes\, consisting of sub-channel estimation and passive beamforming\, are model-based designs and are difficult to be realized in practical and complex radio environment. To create the smart radio environment\, we propose a model-free design of IRS control that is independent of the sub-channel channel state information (CSI) and requires the minimum interaction between IRS and the wireless communication system. We firstly model the control of IRS as a Markov decision process (MDP) and apply deep reinforcement learning (DRL) to perform real-time coarse phase control of IRS. Radio map technology offers a refined solution to reduce MIMO beamforming’s dependency on channel state information (CSI). We introduce a deep learning-based approach to generate radio maps directly from raw CSI data of MIMO systems\, presenting two baseline schemes—one predictive and another based on throughput. An end-to-end architecture\, tailored to MIMO beamforming vectors from location data\, is proposed to employ deep neural networks through a task-oriented design and a customized loss function. Our numerical results highlight the advantages of this approach\, suggesting the potential to replace MIMO CSI with location data. \nSpeaker\nProf. Wei ZHANG\nProfessor\,\nSchool of Electrical Engineering and Telecommunications\,\nThe University of New South Wales\nVice President\, IEEE Communications Society \nSpeaker’s Biography\nWei Zhang (F’15) is Vice President of IEEE Communications Society. He received the Ph.D. degree from the Chinese University of Hong Kong in 2005. Currently\, he is a professor at the School of Electrical Engineering and Telecommunications\, the University of New South Wales\, Sydney\, Australia. His current research interests include 6G communications. He has been an IEEE Fellow since 2015 and was an IEEE ComSoc Distinguished Lecturer in 2016-2017. Within the IEEE ComSoc\, he has taken many leadership positions including Chair of Wireless Communications Technical Committee (2019-2020)\, Vice Director of Asia Pacific Board (2016-2021)\, Editor-in-Chief of IEEE Wireless Communications Letters (2016-2019)\, Member-at-Large on the Board of Governors (2018-2020)\, Technical Program Committee Chair of APCC 2017 and ICCC 2019 and 2024\, Award Committee Chair of Asia Pacific Board and Award Committee Chair of Technical Committee on Cognitive Networks. \nOrganiser\nProf. Kaibin HUANG\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250709-1/
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:20250627T153000
DTEND;TZID=Asia/Hong_Kong:20250627T170000
DTSTAMP:20260509T201717
CREATED:20250619T015442Z
LAST-MODIFIED:20250619T015621Z
UID:111885-1751038200-1751043600@ece.hku.hk
SUMMARY:Seminars on Insights and Best Practices in UK-China Higher Engineering Education
DESCRIPTION:Seminar (1): Navigating Excellence in Transnational Engineering Education: The QMUL-BUPT Joint Programme Experience\nSpeaker: Prof. Michael CHAI\, Professor of Internet of Things\, Queen Mary University of London \nAbstract:\nAs global demand grows for engineering graduates equipped with both academic excellence and international perspectives\, transnational education (TNE) plays a vital role in redefining the boundaries of global higher education. This talk presents the strategic vision\, evolution\, and implementation of the Joint Programme (JP)/ Joint Educational Institute (JEI) between Queen Mary University of London and Beijing University of Posts and Telecommunications (QMUL-BUPT)\, a flagship model of collaborative engineering education. \nSince its inception\, the QMUL-BUPT JP has been committed to delivering a world-class\, research-informed curriculum that bridges Eastern and Western educational philosophies while upholding rigorous UK academic standards. The programme not only cultivates technical expertise but also fosters global competence\, bilingual communication skills\, and intercultural understanding. Central to its success are shared academic governance\, faculty exchange\, quality assurance mechanisms\, and ongoing innovation in teaching and assessment. \nThis talk will highlight how the programme has evolved to meet the changing needs of students and industry. Attendees will gain insights into the opportunities and challenges of sustaining excellence in TNE and the strategic leadership required to foster impactful global academic partnerships. \n******************************************************************* \nSeminar (2): Shaping AI-Ready Graduates through Higher Education Innovation\nSpeaker: Prof. Yue CHEN\, Professor of Telecommunications Engineering\, Queen Mary University \nAbstract:\nAs Artificial Intelligence (AI) transforms the global workforce and the landscape of higher education\, preparing students with AI competence has become both a strategic imperative and a pedagogical challenge. This talk introduces AICoDE\, a structured and human-centred framework\, recently developed and implemented at Queen Mary University of London and Beijing University of Posts and Telecommunications (QMUL-BUPT) joint transnational education programmes. \nAICoDE is designed to formally embed AI competence development into the engineering curriculum. It pursues three key objectives: to leverage AI’s transformative potential to enrich teaching and learning; to mitigate the risks associated with its misuse or unethical application; and to equip graduates with the critical knowledge and skills required to thrive in increasingly AI-driven professional environments. \nA distinctive feature of AICoDE is its 3A assessment strategy\, which scaffolds AI integration in education while safeguarding academic integrity and promoting critical thinking. This talk will also outline the framework’s implementation through curriculum and assessment mapping. Attendees will gain strategic insights and actionable guidance on embedding AI literacy and competence into higher education curricula\, with relevance across both institutional and international contexts. \n*******************************************************************\nSpeakers’ Biography: \nProf. Michael CHAI is Professor of Internet of Things at the School of Electronic Engineering and Computer Science\, Queen Mary University of London. He serves as the Queen Mary Director of TNE in partnership with BUPT in Beijing. His research began in smart Internet of Things and wireless communications\, and has since expanded to include pedagogical scholarship aimed at enhancing student learning and academic development in Higher Education and transnational contexts. \nProf. Yue CHEN is Professor of Telecommunications Engineering\, Director of Scholarship\, and Senior Advisor for Transnational Education at the School of Electronic Engineering and Computer Science\, Queen Mary University of London. She is an accomplished academic whose work bridges technical expertise and educational innovation\, with a focus on wireless communications\, mobile edge computing\, and the Scholarship of Teaching and Learning. Her current research and leadership activities centre on active learning\, curriculum enhancement\, the pedagogical integration of AI in higher education\, and the advancement of transnational education. \nOrganiser: Prof. Yuanwei LIU\, Professor\, Department of Electrical and Electronic Engineering\, HKU \nAll are welcome!
URL:https://ece.hku.hk/events/20250627-1/
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:20250529T103000
DTEND;TZID=Asia/Hong_Kong:20250529T113000
DTSTAMP:20260509T201717
CREATED:20250603T025752Z
LAST-MODIFIED:20250603T025757Z
UID:111512-1748514600-1748518200@ece.hku.hk
SUMMARY:Seminar on Terahertz Optoelectronics for Non-Invasive Imaging and Beyond
DESCRIPTION:Abstract\nTerahertz (THz) imaging technology is growing rapidly due to its potential applications in material exploration\, non-destructive evaluation\, industrial inspection\, and bioinformatics. However\, the practical feasibility of THz imaging systems is significantly constrained by the low efficiency of active THz devices\, long imaging acquisition time\, insufficient use of THz signal datasets\, and their bulky nature. In this talk\, I will present our recent research on high-precision THz imaging systems\, starting from material development\, THz optoelectronics designs\, and system integration toward image reconstruction modalities for on-site applications. As the image data quality and data acquisition speed highly rely on the brightness of THz sources\, we have developed high-performance THz plasmonic photoconductive sources generating mW-level radiating power over a several-THz spectral range\, which offers excellent time-resolved raw data for further image restoration and reconstruction. I will further introduce some of our image reconstruction approaches – equalized compressed sensing imaging\, multi-scale deep-learning fusion imaging\, and compressive hybrid neural network – that further speed up the data acquisition process and achieve significantly better reconstructed imaging quality compared with conventional THz CT modalities. This paves the way toward real-time\, hyperspectral THz 3D imagers in the near future\, which opens the door for various exciting applications in non-destructive sensing\, imaging\, and material inspection. \nSpeaker\nProf. Shang-Hua (Steve) YANG\nAssociate Professor\,\nDepartment of Electrical Engineering\,\nNational Tsing Hua University \nSpeaker’s Biography\nShang-Hua (Steve) YANG is an Associate Professor in the Department of Electrical Engineering at National Tsing Hua University. He is renowned for his significant contributions to THz optoelectronics\, communication\, imaging\, and innovative plasmonic photonics applications. His research findings are published in over 100 refereed papers in peer-reviewed journals and conference proceedings. He has received several prestigious awards\, including the IEEE Antennas and Propagation Society Doctoral Research Award\, MOST Young Scholar Fellowship\, NTHU Young Faculty Research Award\, Human Frontier Science Program Research Grant Award\, and Ta-You Wu Memorial Award (2024). He currently serves as the Director of the NTHU THz Optics & Photonics Center\, Taiwan’s first dedicated THz research center. He is a senior member of IEEE\, Optica\, and SPIE. \nOrganiser\nProf. Kenneth K.Y. WONG\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250529-1/
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:20250528T143000
DTEND;TZID=Asia/Hong_Kong:20250528T153000
DTSTAMP:20260509T201717
CREATED:20250603T025615Z
LAST-MODIFIED:20250626T083737Z
UID:111508-1748442600-1748446200@ece.hku.hk
SUMMARY:Seminar on Human-AI Ecosystems for Daily Health and Well-being
DESCRIPTION:Abstract\nAs the intelligence of everyday smart devices continues to evolve\, they can already monitor basic health behaviors such as physical activities and heart rates. The vision of an intelligent health monitoring and intervention pipeline seems to be within reach. How do we get there? \nIn this talk\, I will introduce a comprehensive pipeline that connects AI\, end-users\, and health experts. For end-users\, I will introduce our work that bridges behavior science theory-driven intervention designs and generalizable behavior models. I will also introduce my efforts on passive sensing datasets\, human-centered algorithms & large language models (LLMs)\, as well as a benchmark platform that drives the community toward more robust and deployable health systems for both end-users and experts. \nSpeaker\nProf. Xuhai (Orson) XU\nAssistant Professor\,\nColumbia University \nSpeaker’s Biography\nXuhai (Orson) XU is an Assistant Professor at Columbia University\, Department of Biomedical Informatics and Department of Computer Science (by courtesy)\, where he directs the SEA (Sense\, Empower\, and Augment) Lab. He is also a visiting faculty researcher at Google. He received his PhD at the University of Washington in 2023 and was a postdoc at MIT until 2024. Specializing in human-computer interaction\, applied machine learning\, and health\, Xu develops deployable behavior modeling algorithms to monitor various health and well-being conditions using everyday sensor data and health records. He further designs and deploys intelligent intervention & interaction techniques that help users achieve personal health and well-being goals and support health experts in making decisions. Xu has earned several awards\, including several Best Paper\, Best Paper Honorable Mention\, and Best Artifact awards. His research has been covered by media outlets such as the Washington Post and ACM News. He was recognized as the Outstanding Student Award Winner at UbiComp 2022\, the 2023 UW Distinguished Dissertation Award\, and the 2024 Innovation and Technology Award at the Western Association of Graduate Schools. \nSEA Lab is actively hiring PhD students with the background of HCI\, mobile sensing\, and applied AI\, with the focus in health applications. \nOrganiser\nProf. Edith C.H. NGAI\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250528-2/
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:20250527T150000
DTEND;TZID=Asia/Hong_Kong:20250527T160000
DTSTAMP:20260509T201717
CREATED:20250603T032119Z
LAST-MODIFIED:20250603T032140Z
UID:111548-1748358000-1748361600@ece.hku.hk
SUMMARY:Digital Over-the-Air Computation: Achieving High Reliability via Bit-Slicing
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/99273671426?pwd=bm3VeyFWXnLAlUIBBXJDGAmMfzoKJ5.1 \nAbstract\n6G mobile networks aim to realize ubiquitous intelligence at the network edge via distributed learning\, sensing\, and data analytics. Their common operation is to aggregate high-dimensional data\, which causes a communication bottleneck that cannot be resolved using traditional orthogonal multi-access schemes. A promising solution\, called over-the-air computation (AirComp)\, exploits channels’ waveform superposition property to enable simultaneous access\, thereby overcoming the bottleneck. Nevertheless\, its reliance on uncoded linear analog modulation exposes data to perturbation by noise and interference. Hence\, the traditional analog AirComp falls short of meeting the high-reliability requirement for 6G. Overcoming the limitation of analog AirComp motivates this work\, which focuses on developing a framework for digital AirComp. The proposed framework features digital modulation of each data value\, integrated with the bit-slicing technique to allocate its bits to multiple symbols\, thereby increasing the AirComp reliability. To optimally detect the aggregated digital symbols\, we derive the optimal maximum a posteriori detector that is shown to outperform the traditional maximum likelihood detector. Furthermore\, a comparative performance analysis of digital AirComp with respect to its analog counterpart with repetition coding is conducted to quantify the practical signal-to-noise ratio (SNR) regime favoring the proposed scheme. On the other hand\, digital AirComp is enhanced by further development to feature awareness of heterogeneous bit importance levels and its exploitation in channel adaptation. Lastly\, simulation results demonstrate the achivability of substantial reliability improvement of digital AirComp over its analog counterpart given the same channel uses. \nSpeaker\nMr. Jiawei LIU\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nSpeaker’s Biography\nJiawei Liu received the B.Eng. degree from the Southern University of Science and Technology\, Shenzhen\, China\, in 2020. He is currently pursuing the Ph.D. degree with the Department of Electrical and Electronics Engineering\, The University of Hong Kong (HKU)\, Hong Kong. His research interests include wireless communication system design\, in-memory computing hardware\, and edge intelligence systems. \nAll are welcome!
URL:https://ece.hku.hk/events/20250527-1/
LOCATION:Online via Zoom
CATEGORIES:Highlights,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:20250523T160000
DTEND;TZID=Asia/Hong_Kong:20250523T160000
DTSTAMP:20260509T201717
CREATED:20250603T032424Z
LAST-MODIFIED:20250603T032435Z
UID:111553-1748016000-1748016000@ece.hku.hk
SUMMARY:Security and Efficient Brain-inspired In-memory Computing
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/97430126742?pwd=ou6CUPNMjhlrmRbwUKRa8aTHi6PjYX.1\nMeeting ID: 974 3012 6742\nPassword: 967270 \nAbstract\nThe human brain operates as a sophisticated spiking neural network (SNN)\, capable of learning multimodal signals in a zero-shot manner by leveraging prior knowledge. Impressively\, it accomplishes this with minimal energy consumption\, relying on event-driven signals that travel through its intricate structure. However\, replicating the brain’s functionality in efficient neuromorphic hardware poses significant challenges in both hardware and software. Moreover\, training these algorithms demands extensive resources\, and effective security measures remain insufficient. \nBenefiting from the RRAM array inherit stochasticity\, we demonstrated an efficient analogue-digital system that can handle multi-modal spiking signals and possess zero-shot learning capability like a human. This reservoir accelerated system enables significant lower training overheads while maintaining comparable baseline utility. Since emerging brain-inspired computing raises security concerns\, we also share new methodologies insights onto these neuromorphic systems that can secure non-volatile CIM-based parameters without sacrificing latency and energy efficiency. \nThis presentation will delve into the development of a secure and efficient brain-inspired in-memory computing system\, achieved through the integrated co-design of algorithms\, circuits\, and devices. \nSpeaker\nMr. WONG Edwin Kwun Hang\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nSpeaker’s Biography\nMr. Edwin Kwun Hang Wong received the B.Eng. (EE) degree from The University of Hong Kong in 2023. He is currently working towards MPhil degree with The University of Hong Kong. His research interests include AI Security\, Brain-inspired computing\, and RRAM-based accelerator. \nAll are welcome!
URL:https://ece.hku.hk/events/20250523-1/
LOCATION:Online via Zoom
CATEGORIES:Highlights,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:20250522T143000
DTEND;TZID=Asia/Hong_Kong:20250522T160000
DTSTAMP:20260509T201717
CREATED:20250603T025446Z
LAST-MODIFIED:20250603T025451Z
UID:111502-1747924200-1747929600@ece.hku.hk
SUMMARY:Reimagining Edge AI and LLM Inference with Compute Memory Architectures
DESCRIPTION:Abstract\nRecent advances in artificial intelligence (AI)\, especially in large language models (LLMs)\, have dramatically increased model sizes and computational demands\, significantly straining computing system capabilities. This issue is particularly acute in resource-constrained edge AI scenarios\, where efficient hardware acceleration of compute-intensive tasks and optimization of data reuse to minimize costly data transfers are essential. Addressing these challenges\, this talk will explore various options for designing compute memory subsystems through innovative circuit-level and system-level approaches to enhance the efficiency of edge AI applications. Firstly\, we will introduce MAXWELL\, a near-SRAM co-design computing architecture specifically tailored for edge AI. MAXWELL optimizes performance and energy efficiency by leveraging the regular structure of memory arrays\, achieving high parallelization for both convolutional and fully connected layers\, while supporting fine-grained quantization for real-time image and video processing\, autonomous vehicles\, and Internet of Things (IoT) devices. Secondly\, we will delve into SLIM\, a complementary approach to MAXWELL for big data analytics\, scientific computing\, and financial modeling that provides a cost-effective solution for sparse LLM inference. SLIM leverages in-memory processing in DRAM to significantly reduce latency for large caching datasets in edge AI and utilizes near-storage in Solid State Drives (SSD) for large LLM models. Potential applications of SLIM include. This talk will demonstrate that these complementary approaches for compute memory subsystems can achieve up to 10x speed-ups compared to state-of-the-art edge AI accelerators that require data transfers at the boundaries of ML layers. Furthermore\, the proposed co-design approach can accelerate performance by up to 250x compared to pure software optimizations on the X-HEEP edge AI open-source platform\, which integrates MAXWELL and SLIM logic with a 32-bit RISC-V core. Notably\, these accelerator-specific components of computing memories account for less than 12% of the total memory area of X-HEEP. \nSpeaker\nProf. David ATIENZA\nFull Professor of Electrical and Computer Engineering\,\nHead of the Embedded Systems Laboratory (ESL)\,\nAssociate Vice President for Research Centers and Technology Platforms\,\nÉcole polytechnique fédérale de Lausanne (EPFL)\, Switzerland \nSpeaker’s Biography\nDavid Atienza is a Full Professor of Electrical and Computer Engineering at EPFL\, Switzerland\, where he heads the Embedded Systems Laboratory (ESL) and serves as the Associate Vice President for Research Centers and Technology Platforms. His research focuses on system-level design methodologies for energy-efficient multi-processor system-on-chip (MPSoC) architectures\, targeting next-generation computing servers\, data centers\, and edge AI embedded systems\, particularly smart wearables and medical devices in the Internet of Things (IoT) era. Dr. Atienza has co-authored over 450 publications\, holds 14 patents\, and has received numerous best paper awards at top conferences. He is the Editor-in-Chief of IEEE TCAD and ACM CSUR\, and has served as the Technical Program Chair of DATE 2015 and General Chair of DATE 2017. Among other awards\, he has received the 2024 Test-of-Time Best Paper Award at the IEEE/ACM CODES+ISSS Conference\, the IEEE/ACM ICCAD 10-Year Retrospective Most Influential Paper Award in 2020\, the DAC Under-40 Innovators Award in 2018\, and an ERC Consolidator Grant in 2016. He has also served as President of IEEE CEDA (2018-2019) and Chair of the EDAA (2022-2024). He is a Fellow of IEEE and ACM. \nOrganiser\nProf. Kaibin HUANG\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250522-1/
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/2025/06/1280-4.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250520T140000
DTEND;TZID=Asia/Hong_Kong:20250520T150000
DTSTAMP:20260509T201717
CREATED:20250603T032657Z
LAST-MODIFIED:20250603T032657Z
UID:111558-1747749600-1747753200@ece.hku.hk
SUMMARY:Understanding Complex-Valued Transformer for Modulation Recognition
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/95380440070 \nAbstract\nComplex-valued convolution neural networks (CVCNNs) have been recently applied for modulation recognition (MR)\, due to its ability to capture the relationship between the real and imaginary parts of the received signal. On the other hand\, the transformer model has been shown to be distinguished in MR by its superior capability to extract the correlation among high-dimensional signals compared to the CNN. It is a logical next step to ask whether a fully complex-valued transformer based neural network (CVTNN) can bring further performance gain? If so\, where the gain comes from? To answer these questions\, this letter designs the building blocks of the CVTNN for MR\, which is composed of a convolution embedding module\, a complete transformer encoder\, and a C2R classifier\, and establishes the estimation error bound of the proposed CVTNN from an inductive bias perspective. We theoretically prove that the estimation error bound of the proposed CVTNN is lower than that of the real-valued transformer based neural network (RVTNN) for MR. Simulation results further show that the proposed CVTNN outperforms the RVTNN and other benchmarks under different settings\, which corroborates the proposed theoretical analysis. \nSpeaker\nMr. Jingreng Lei\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nSpeaker’s Biography\nJingreng Lei received the B.Eng. degree from Sun Yat-sen University\, China\, in 2023. He is currently working towards MPhil degree with The University of Hong Kong\, Hong Kong. His research interests include complex-valued neural network\, distributed optimization and wireless communication. \nAll are welcome!
URL:https://ece.hku.hk/events/20250520-3/
LOCATION:Online via Zoom
CATEGORIES:Highlights,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:20250520T100000
DTEND;TZID=Asia/Hong_Kong:20250520T110000
DTSTAMP:20260509T201717
CREATED:20250603T024946Z
LAST-MODIFIED:20250603T025050Z
UID:111488-1747735200-1747738800@ece.hku.hk
SUMMARY:Keynode-Driven Dynamic Mesh Compression
DESCRIPTION:Abstract\n3D Dynamic Meshes can deliver engaging experiences in various applications\, but the storage and transmission demands associated with these data structures can be prohibitive. We address this challenge with an efficient compression technique leveraging embedded key nodes. The temporal motion of each vertex is formulated as a distance-weighted combination of transformations from neighboring key nodes\, requiring the transmission of solely the key nodes’ transformations. Through extensive experiments\, we demonstrate the effectiveness of our method in significantly reducing storage requirements while preserving the immersive quality of the visual content. \nSpeaker\nProf. Truong NGUYEN\nElectrical and Computer Engineering\,\nUniversity of California San Diego \nBiography of the Speaker\nTruong Nguyen received his B.S.\, M.S. and Ph.D. in Electrical Engineering at California Institute of Technology in 1985\, 1986 and 1989. His current research areas include 3D Human Mesh segmentation and coding as well as machine learning for medical image analysis. During his academic career\, he has published over 200 peer-reviewed journal papers\, over 380 peer-reviewed conference papers\, 1 textbook\, 3 book chapters\, and 15 issued patents. His Google H-index is 72 with over 31K citations. He received the NSF Career Award in 1995\, and IEEE Signal Processing Society’s 1992 Paper Award (student author) for the 1990 paper “Structures for M-Channel Perfect-Reconstruction FIR QMF Banks Which Yield Linear-Phase Analysis Filters” (IEEE Trans. ASSP). He received the Distinguished Teaching Award at UCSD in 2019 and three teaching awards from the ECE Dept. at UCSD in 2006\, 2008 and 2010. He is a Fellow of IEEE. \nOrganiser\nProf. S.C. CHAN\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nCo-organiser\nIEEE Signal Processing Society Hong Kong Chapter \nAll are welcome!
URL:https://ece.hku.hk/events/20250520-1/
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:20250519T160000
DTEND;TZID=Asia/Hong_Kong:20250519T170000
DTSTAMP:20260509T201717
CREATED:20250603T024619Z
LAST-MODIFIED:20250603T041708Z
UID:111476-1747670400-1747674000@ece.hku.hk
SUMMARY:Strategies on Perovskite Nanocrystals for Achieving High-Performance Light Emitting Devices
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/92719085356?pwd=09aQ3vjvg9bhXcObBYxjNtj4UVx5V4.1 \n\nAbstract\nMixed-chloride/bromide perovskite nanocrystals (PeNCs) are known for their advantages in pure blue emission\, but often suffer from halogen segregation. This study investigates the ligand exchange process with different ion pair combinations to improve stability. Surprisingly\, altering the ligand ion combinations leads to a deviation from pure blue emission in CsPbBrxCl3-x nanocrystals due to halogen redistribution influenced by solubility principles in a non-polar environment. Furthermore\, a novel approach is demonstrated in this study by capping p-type cuprous sulfide (Cu2S) over Cs3Cu2I5 to enhance hole mobility in Cu-based perovskite nanocrystals. The resulting Cs3Cu2I5/Cu2S nanocrystals exhibit improved hole mobility and photoluminescence quantum yield\, leading to enhanced electroluminescent performance in white perovskite light-emitting diodes (W-PeLEDs). \nSpeaker\nMr. LI Dongyu\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nSpeaker’s Biography\nLI Dongyu received the M.S. degree from Jilin University in 2019. He is currently pursuing his Ph.D. degree with the Department of Electrical and Electronic Engineering\, The University of Hong Kong. His research interest is semiconductor materials and their application in light-emitting devices. \nAll are welcome!
URL:https://ece.hku.hk/events/20250519-01/
LOCATION:Online via Zoom
CATEGORIES:Highlights,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:20250519T153000
DTEND;TZID=Asia/Hong_Kong:20250519T163000
DTSTAMP:20260509T201717
CREATED:20250603T034323Z
LAST-MODIFIED:20250603T034323Z
UID:111564-1747668600-1747672200@ece.hku.hk
SUMMARY:Distributed Mixture-of-Expert Systems at the Wireless Edge
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/93486553339 \nAbstract\nThe emergence of distributed Mixture-of-Experts (DMoE) systems\, which deploy expert models at edge nodes\, offers a pathway to achieving connected intelligence in sixth-generation (6G) mobile networks and edge artificial intelligence (AI). However\, current DMoE systems lack an effective expert selection algorithm to address the simultaneous task-expert relevance and channel diversity inherent in these systems. Traditional AI or communication systems focus on either performance or channel conditions\, and direct application of these methods leads to high communication overhead or low performance. To address this\, we propose the DMoE protocol to schedule the expert inference and inter-expert transmission. This protocol identifies expert selection and subcarrier allocation as key optimization problems. We formulate an expert selection problem by incorporating both AI performance and channel conditions\, and further extend it to a Joint Expert and Subcarrier Allocation (JESA) problem for comprehensive AI and channel management within the DMoE framework. For the NP-hard expert selection problem\, we introduce the Dynamic Expert Selection (DES) algorithm\, which leverages a linear relaxation as a bounding criterion to significantly reduce search complexity. For the JESA problem\, we discover a unique structural property that ensures asymptotic optimality in most scenarios. We propose an iterative algorithm that addresses subcarrier allocation as a subproblem and integrates it with the DES algorithm. The proposed framework effectively manages the tradeoff between task relevance and channel conditions through a tunable importance factor\, enabling flexible adaptation to diverse scenarios. Numerical experiments validate the dual benefits of the proposed expert selection algorithm: high performance and significantly reduced cost. JESA consistently achieves higher accuracy compared to homogeneous expert selection and lowers the cost by up to 50% compared to Top-k scheduling. \nSpeaker\nMr. Shengling Qin\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nSpeaker’s Biography\nShengling Qin received the B.Eng. degree from Tsinghua University\, China\, in 2023. He is currently working towards MPhil degree with The University of Hong Kong\, Hong Kong. His research interests include mixture-of-experts\, large language models and distributed training. \n\n\n\nAll are welcome!
URL:https://ece.hku.hk/events/20250519-2/
LOCATION:Online via Zoom
CATEGORIES:Highlights,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:20250519T110000
DTEND;TZID=Asia/Hong_Kong:20250519T120000
DTSTAMP:20260509T201717
CREATED:20250603T024808Z
LAST-MODIFIED:20250603T025046Z
UID:111483-1747652400-1747656000@ece.hku.hk
SUMMARY:Symmetric Diffusers: Learning Discrete Diffusion on Finite Symmetric Groups
DESCRIPTION:We regret to inform you that the event has been cancelled and will be postponed to a later date.  \nAbstract\nFinite symmetric groups Sn are essential in fields such as combinatorics\, physics\, and chemistry. However\, learning a probability distribution over Sn poses significant challenges due to its intractable size and discrete nature. We introduce SymmetricDiffusers\, a novel discrete diffusion model that simplifies the task of learning a complicated distribution over Sn by decomposing it into learning simpler transitions of the reverse diffusion using deep neural networks. We identify the riffle shuffle as an effective forward transition and provide empirical guidelines for selecting the diffusion length based on the theory of random walks on finite groups. Additionally\, we propose a generalized Plackett-Luce (PL) distribution for the reverse transition\, which is provably more expressive than the PL distribution. We further introduce a theoretically grounded “denoising schedule” to improve sampling and learning efficiency. Extensive experiments show that our model achieves state-of-the-art or comparable performances on solving tasks including sorting 4-digit MNIST images\, jigsaw puzzles\, and traveling salesman problems. \nSpeaker\nProf. Renjie LIAO\nDepartment of Electrical and Computer Engineering\, and\nDepartment of Computer Science\,\nUniversity of British Columbia (UBC) \nSpeaker’s Biography\nRenjie Liao is an Assistant Professor in the Department of Electrical and Computer Engineering and an Associate Member of the Department of Computer Science at the University of British Columbia (UBC). He is also a faculty member at the Vector Institute and holds a Canada CIFAR AI Chair. Prior to joining UBC\, he was a Visiting Faculty Researcher at Google Brain\, working with Geoffrey Hinton and David Fleet. He received his Ph.D. in Computer Science from the University of Toronto in 2021\, under the supervision of Richard Zemel and Raquel Urtasun. During his Ph.D.\, he also worked as a Senior Research Scientist at Uber Advanced Technologies Group. He holds an M.Phil. in Computer Science from the Chinese University of Hong Kong (2015) and a B.Eng. in Automation from Beihang University (2011). His research interests span machine learning and its intersection with computer vision\, self-driving\, healthcare\, and beyond\, with a particular focus on probabilistic and geometric deep learning. \nOrganiser\nProf. Xiaojuan QI\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250519-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250515T150000
DTEND;TZID=Asia/Hong_Kong:20250515T160000
DTSTAMP:20260509T201717
CREATED:20250603T035131Z
LAST-MODIFIED:20250603T035131Z
UID:111572-1747321200-1747324800@ece.hku.hk
SUMMARY:End-to-end High-quality Posterior Ocular Shape Reconstruction in Ophthalmology
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/98147018160?pwd=KEuU1XQtISq3HQpWJyF6itZMJ1hYY5.1\nMeeting ID: 981 4701 8160\nPassword: 294231 \nAbstract\nAccurately estimating morphological changes of the Posterior Eyeball Shape (PES) is a critical task in ophthalmology\, since the PES is a crucial factor in many clinical applications\, such as myopia prevention\, surgical planning\, and disease screening. However\, existing imaging devices are constrained by limited field-of-view (FOV) and insufficient resolution\, thus providing insufficient diagnostic information for surgeons to make accurate decisions. Previous segment-based reconstruction methods suffer from two main drawbacks: first\, common imaging modalities can’t provide intact enough shape details as constraints\, thus requiring intricate pre- and post-processing; second\, existing data representations struggle to trade-off between computational efficiency and reconstruction quality\, thus hindering end-to-end reconstruction with fine-grained shape details. \nBenefiting from our more efficient 2D representation in polar coordinate\, we propose a novel task of reconstructing intact 3D PES based on purely small-FOV OCT scans and introduces a novel Posterior Eyeball Shape Network (PESNet) to accomplish this task. The proposed PESNet equips the Siamese structure that incorporates anatomical information of the eyeball as guidance. To capture more detailed information\, we introduce a Polar Voxelization Block (PVB) that transfers sparse input point clouds to a dense representation. Furthermore\, we propose a Radius-wise Fusion Block (RFB) that fuses correlative hierarchical features from the two branches. Finally\, this high-cost reconstruction task is compressed into the 2D surface map regression task. The experiments indicate that our method achieves state-of the-art performance\, providing a well-represented complete posterior eyeball shape on both healthy and patient cases. This result demonstrates that our method offers a significant improvement over existing methods in accurately reconstructing the complete 3D posterior eyeball shape. This achievement has important implications for clinical applications. \nSpeaker\nMr. ZHANG Jiaqi\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nSpeaker’s Biography\nMr. Jiaqi Zhang obtained his B.Sc. degree from Northeastern University (NEU) in China and his M.Res. degree from the National University of Singapore (NUS) in Singapore. He is currently pursuing the Ph.D. in the Department of Electrical and Electronic Engineering at the University of Hong Kong (HKU)\, under the supervision of Prof. Xiaojuan Qi. His research focuses on medical image processing\, 3D reconstruction\, AIGC\, and representation learning. \n\nAll are welcome!
URL:https://ece.hku.hk/events/20250515-2/
LOCATION:Online via Zoom
CATEGORIES:Highlights,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:20250515T150000
DTEND;TZID=Asia/Hong_Kong:20250515T160000
DTSTAMP:20260509T201717
CREATED:20250603T034632Z
LAST-MODIFIED:20250603T034632Z
UID:111567-1747321200-1747324800@ece.hku.hk
SUMMARY:Distributed Mixture-of-Expert Systems at the Wireless Edge (Duplicate)
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/91757354553?pwd=tHpInMTglaIVMJLek0ydP0vddHihh8.1 \nMeeting ID: 917 5735 4553\nPasscode: 587193 \nAbstract\nExisting Video-to-Audio (V2A) models typically generate sound based solely on visual input\, offering limited user control. To address this limitation\, we propose a multimodal controllable V2A system that conditions audio generation on a variety of user inputs– such as text\, images\, or audio– in addition to video. Our approach leverages the ImageBind model to align these diverse input modalities into a shared representation\, which is then used to guide audio generation. \nDue to the lack of multimodal datasets for audio generation\, we constructed a training dataset comprising large-scale text-audio data complemented with a limited amount of video-audio data to enable controllable and context-aware audio generation. To further enhance generative quality\, we introduce several data ensemble strategies: (1) Source Balancing\, which maintains a trade-off between concept diversity and sample diversity\, and (2) two synchronization techniques– Audio Feature Selector (AFS) and Audio Peak IoU Matching (APIM)– to improve temporal alignment between video and generated audio. \nOur system enables flexible and precise audio generation that aligns closely with multimodal user intent. Finally\, we introduce a novel benchmark with a cross-sample evaluation framework\, designed to standardize assessments of multimodal V2A systems by evaluating consistency and diversity across input-output combinations. Our method achieves state-of-the-art performance\, demonstrating significant improvements in audio quality\, synchronization\, and input-condition alignment. \nSpeaker\nMr. HE Ruifei\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nSpeaker’s Biography\nMr. Ruifei He is a final-year Ph.D. student in Department of Electrical and Electronic Engineering at the University of Hong Kong. He obtained his B.Eng. degree in the Department of Automation at Zhejiang University. His research focuses on data-centric/efficient learning (e.g. synthetic/generative data\, mixing multi-modal data\, and semi-supervised learning) for computer vision tasks. \n\nAll are welcome!
URL:https://ece.hku.hk/events/20250515-1/
LOCATION:Online via Zoom
CATEGORIES:Highlights,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:20250513T143000
DTEND;TZID=Asia/Hong_Kong:20250513T153000
DTSTAMP:20260509T201717
CREATED:20250603T041854Z
LAST-MODIFIED:20250603T041854Z
UID:111581-1747146600-1747150200@ece.hku.hk
SUMMARY:Mixture of Experts-augmented Deep Unfolding for Activity Detection
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/95300634244 \nAbstract\nIn the realm of activity detection for massive machine-type communications\, intelligent reflecting surfaces (IRS) have shown significant potential in enhancing coverage for devices lacking direct connections to the base station (BS). However\, traditional activity detection methods are typically designed for a single type of channel model\, which does not reflect the complexities of real-world scenarios\, particularly in systems incorporating IRS. To address this challenge\, this paper introduces a novel approach that combines model-driven deep unfolding with a mixture of experts (MoE) framework. By automatically selecting one of three expert designs and applying it to the unfolded projected gradient method\, our approach eliminates the need for prior knowledge of channel types between devices and the BS. Simulation results demonstrate that the proposed MoE-augmented deep unfolding method surpasses the traditional covariance-based method and black-box neural network design\, delivering superior detection performance under mixed channel fading conditions. \nSpeaker\nMr. REN Zeyi\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nSpeaker’s Biography\nZeyi Ren received the B.Eng. degree from Beijing Institute of Technology\, Beijing\, China\, in 2023. He is currently working toward the M.Phil. degree with The University of Hong Kong\, Hong Kong. His research interests include model driven deep learning and wireless communications. \nAll are welcome!
URL:https://ece.hku.hk/events/20250513-1/
LOCATION:Online via Zoom
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250512T140000
DTEND;TZID=Asia/Hong_Kong:20250512T150000
DTSTAMP:20260509T201717
CREATED:20250603T035657Z
LAST-MODIFIED:20250603T035719Z
UID:111583-1747058400-1747062000@ece.hku.hk
SUMMARY:Towards Federated and Annotation-efficient Deep Learning for Medical Image Analysis
DESCRIPTION:Zoom Link : https://hku.zoom.us/j/91765972342?pwd=CHXoKEknnfPc6zbhHCADi7A1abVUyI.1 \nAbstract\nAs deep learning is increasingly applied in medical image analysis\, developing efficient and accurate models has become crucial. However\, traditional deep learning methods usually require large amounts of annotated data\, posing a significant challenge in medical imaging due to complex data collection and high annotation costs. Furthermore\, privacy and security concerns restrict data sharing and collaboration between institutions.Federated learning (FL) and annotation-efficient techniques have emerged to address these issues. This seminar explores how combining federated learning with annotation-efficient methods can advance intelligent medical image analysis. The key topics include: 1. Annotation-efficient strategies: Discussing self-supervised and weakly-supervised learning methods to enhance training efficiency and model performance when labeled data is limited; 2. Federated learning applications: Exploring how federated learning enables distributed model training across multiple institutions without data sharing\, thereby protecting data privacy; 3. Practical applications and challenges: Analyzing specific scenarios such as disease diagnosis and organ segmentation\, discussing the strengths and limitations of federated learning and annotation-efficient techniques\, and forecasting future developments. \nSpeaker\nMr. LIN Li\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nSpeaker’s Biography\nLi LIN received his B.S. in Telecommunication Engineering from South China Normal University in 2018 and the M.S. in Information and Communication Engineering from Sun Yat-sen University in 2021. He is currently pursuing the Ph.D. degree in the Department of Electrical and Electronic Engineering at the University of Hong Kong\, Hong Kong. \nAll are welcome!
URL:https://ece.hku.hk/events/20250512-1/
LOCATION:Online via Zoom
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250509T150000
DTEND;TZID=Asia/Hong_Kong:20250509T160000
DTSTAMP:20260509T201717
CREATED:20250603T035916Z
LAST-MODIFIED:20250603T035916Z
UID:111587-1746802800-1746806400@ece.hku.hk
SUMMARY:WireLightning: Harnessing Capacitances for In-Transit Massively Parallel Matrix Multiplication
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/3837289217?omn=95617077246 \nAbstract\nAnalog computing-in-memory accelerators promise ultra-low-power\, on-device AI by reducing data transfer and energy usage. Yet inherent device variations and high energy consumption for analog-digital conversion continue to hinder their wide-scale adoption in mainstream systems. To address these issues\, this presentation will introduce WireLightning\, a novel capacitive-computing accelerator featuring a mixed-signal architecture that rethinks analog AI acceleration. Unlike conventional analog crossbars that encode weights in programmable devices\, WireLightning exploits intrinsic charge dynamics in passive capacitors\, encoding matrix multiplication through spike amplitude and timing. This design addresses critical limitations such as weight drift\, stochasticity\, and power-intensive ADC bottlenecks. Key innovations include: amplitude-temporal dual encoding that enables constant-time analog dot-products; time-based decoding scheme that significantly reduces reliance on power-intensive ADCs; row-wise parallel architecture for concurrent dot-product calculations across multiple rows to enhance throughput; and value repetition exploitation in low-bit quantized vectors to reduce multiplications to constant time complexity. A PCB prototype achieved higher accuracies than leading RRAM crossbar and PCM crossbar implementations. Implemented in a 40-nm CMOS technology\, WireLightning demonstrate superior potential in power efficiency\, while maintaining high precision. By integrating algorithm-circuit co-design with physical computing\, this work establishes capacitive computing as a promising path toward combining digital precision and analog efficiency in next-generation edge AI. \nSpeaker\nSpeaker: Mr. WANG Song\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nSpeaker’s Biography\nSong Wang received the B.Eng. degree in the Department of Automated Test and Control at Harbin Institute of Technology\, and M.Phil. degree in the Department of Mechanical Engineering at the University of Hong Kong. He is currently pursuing the Ph.D. degree in the Department of Electrical and Electronic Engineering at the University of Hong Kong\, under the supervision of Prof. Hayden So. His research interests include AI chip\, computer architecture\, and reconfigurable computing. \n\nAll are welcome!
URL:https://ece.hku.hk/events/20250509-1/
LOCATION:Online via Zoom
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250508T160000
DTEND;TZID=Asia/Hong_Kong:20250508T170000
DTSTAMP:20260509T201717
CREATED:20250603T024218Z
LAST-MODIFIED:20250603T025042Z
UID:111464-1746720000-1746723600@ece.hku.hk
SUMMARY:Trustworthy Image Semantic Communication with GenAI: Explainability\, Controllability\, and Efficiency
DESCRIPTION:Abstract\nImage semantic communication (ISC) has garnered significant attention for its potential to achieve high efficiency in visual content transmission. However\, existing ISC systems based on joint source-channel coding face challenges in interpretability\, operability\, and compatibility. To address these limitations\, we propose a novel trustworthy ISC framework. This approach leverages text extraction and segmentation mapping techniques to convert images into explainable semantics\, while employing Generative Artificial Intelligence (GenAI) for multiple downstream inference tasks. We also introduce a multi-rate ISC transmission protocol that dynamically adapts to both the received explainable semantic content and specific task requirements at the receiver. Simulation results based on a real-world demo demonstrate that our framework achieves explainable learning\, decoupled training\, and compatible transmission in various application scenarios. Finally\, some intriguing research directions and application scenarios are identified. \nSpeaker\nDr. Chenyuan FENG\nMarie Skłodowska-Curie Scholar\,\n6G Star Young Scientist\,\nResearch Fellow at the University of Exeter \nSpeaker’s Biography\nDr. Chenyuan FENG\, Marie Skłodowska-Curie Scholar\, 6G Star Young Scientist. Dr. Feng earned the Ph.D. degree from the Singapore University of Technology and Design. Currently\, Dr. Feng is a Research Fellow at the University of Exeter\, U.K. Her research interests include edge intelligence and AI for communication and network. Dr. Feng has published over 40 papers\, including one ESI top 1% highly cited paper and 3 IEEE conference best papers. Moreover\, Dr. Feng has obtained five Chinese national invention patents and three edited book; earned the First Prize in International Postdoctoral Innovation and Entrepreneurship Competition\, one Gold and one Silver Awards in Chinese Internet+ Innovation and Entrepreneurship Competition; presided one EU horizon project and several National Natural Science Foundation project and national key R&D sub-project\, as well as one Enterprise Start-up Grant for Intelligent Unmanned Systems R & D Project (funded by Merchant & Investment Bureau in Chengdu Government\, China\, 5 million RMB\, as a Co-founder) and one Enterprise Start-up Grant for AI-RAN. She has served as a TPC member in numerous international conferences\, and an Associate Editor for IEEE IoTJ and IEEE OJ-COMS. \nOrganiser\nProf. Hongyang DU\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250508-1/
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/2025/06/1280-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250507T153000
DTEND;TZID=Asia/Hong_Kong:20250507T163000
DTSTAMP:20260509T201717
CREATED:20250603T025756Z
LAST-MODIFIED:20250603T041929Z
UID:111506-1746631800-1746635400@ece.hku.hk
SUMMARY:Rank-Revealing Bayesian Block-Term Tensor Completion with Graph Information
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/97776760951?pwd=zaNBWC786IgVZjQ7NU8SNDJdEeIorn.1 \nAbstract\nBlock-term decomposition (BTD)\, particularly its rank-(L_r\,L_r\,1) special case\, is widely used in signal processing. Traditional methods for computing BTD from fully observed tensors either unrealistically assume the tensor rank and block-term ranks are known or require exhaustive tuning of these parameters. While sparsity-promoting regularization has been introduced to estimate ranks more efficiently\, it still requires regularization parameter tuning. Bayesian learning addresses these issues by employing sparsity-promoting priors\, but so far is limited to fully observed BTD tensors. To process incomplete BTD tensors\, only a few optimization-based methods have been proposed\, and they continue to suffer from time-consuming tuning. To enable tuning-free BTD completion\, a novel prior is proposed here within the Bayesian framework\, and it is proved theoretically that the proposed prior induces the desired dual-level sparsity as well as graph information in the BTD model. A mean-field design is further proposed to develop a closed-form updating variational inference (VI) algorithm without loss of graph information. Extensive experiments on both synthetic datasets and real-world datasets demonstrate the superiority of the proposed method in terms of rank learning\, tensor recovery\, and factor recovery. \nSpeaker\nMr. Zhongtao Chen\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nSpeaker’s Biography\nZhongtao Chen received the B.Eng. degree from The Chinese University of Hong Kong\, Shenzhen\, China\, in 2021. He is currently working toward the Ph.D. degree with The University of Hong Kong\, Hong Kong. His research interests include signal processing and machine learning using Bayesian methods. \n\n\nAll are welcome!
URL:https://ece.hku.hk/events/20050507-3/
LOCATION:Online via Zoom
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250507T140000
DTEND;TZID=Asia/Hong_Kong:20250507T150000
DTSTAMP:20260509T201717
CREATED:20250603T024130Z
LAST-MODIFIED:20250603T024304Z
UID:111460-1746626400-1746630000@ece.hku.hk
SUMMARY:A Novel Training Framework for Physics-informed Neural Networks: Towards Real-time Applications in Ultrafast Ultrasound Blood Flow Imaging
DESCRIPTION:Abstract\nUltrafast ultrasound blood flow imaging is a state-of-the-art technique for depiction of complex blood flow dynamics in vivo through thousands of full-view image data (or\, timestamps) acquired per second. Physics-informed Neural Network (PINN) is one of the most preeminent solvers of the Navier-Stokes equations\, widely used as the governing equation of blood flow. However\, that current approaches rely on full Navier-Stokes equations is impractical for ultrafast ultrasound. We hereby propose a novel PINN training framework for solving the Navier-Stokes equations. It involves discretizing Navier-Stokes equations into steady state and sequentially solving them with test-time adaptation. The novel training framework is coined as SeqPINN. Upon its success\, we propose a parallel training scheme for all timestamps based on averaged constant stochastic gradient descent as initialization. Uncertainty estimation through Stochastic Weight Averaging Gaussian is then used as an indicator of generalizability of the initialization. This algorithm\, named SP-PINN\, further expedites training of PINN while achieving comparable accuracy with SeqPINN. The performance of SeqPINN and SP-PINN was evaluated through finite-element simulations and in vitro phantoms of single-branch and trifurcate blood vessels. The successful implementation of SeqPINN and SP-PINN open the gate for real-time training of PINN for Navier-Stokes equations and subsequently reliable imaging-based blood flow assessment in clinical practice.\nSpeaker\nMr. Haotian Guan\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong\nSpeaker’s Biography\n\nHaotian Guan received his B.S. in Applied Mathematics from The University of New Hampshire in 2019 and the M.S. in Data Science from New York University in 2021. He is currently pursuing the Ph.D. degree in the Department of Electrical and Electronic Engineering at the University of Hong Kong\, Hong Kong.\n\n\nAll are welcome!
URL:https://ece.hku.hk/events/20250507-1/
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:20250507T100000
DTEND;TZID=Asia/Hong_Kong:20250507T110000
DTSTAMP:20260509T201717
CREATED:20250603T025022Z
LAST-MODIFIED:20250603T025022Z
UID:111484-1746612000-1746615600@ece.hku.hk
SUMMARY:Towards Ubiquitous Radio Access Using Nanodiamond Based Quantum Receivers
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/5098778281?pwd=wMZ3GQvpRdxkCjv8p79h3JN1xdgOJe.1\nMeeting ID: 509 877 8281\nPassword: 670951\nAbstract\nThe development of sixth-generation wireless communication systems demands innovative solutions to address challenges in the deployment of a large number of base stations and the detection of multi-band signals. Quantum technology\, specifically nitrogen-vacancy centers in diamonds\, offers promising potential for the development of compact\, robust receivers capable of supporting multiple users. Here we propose a multiple access scheme using fluorescent nanodiamonds containing nitrogen-vacancy centers as nano-antennas. The unique response of each nanodiamond to applied microwaves allows for distinguishable patterns of fluorescence intensities\, enabling multi-user signal demodulation. We demonstrate the effectiveness of our nanodiamonds-implemented receiver by simultaneously transmitting two uncoded digitally modulated information bit streams from two separate transmitters\, achieving a low bit error ratio. Moreover\, our design supports tunable frequency band communication and reference-free signal decoupling\, reducing communication overhead. Furthermore\, we implement a miniaturized device comprising all essential components\, highlighting its practicality as a receiver serving multiple users simultaneously. This approach enables the integration of quantum sensing technologies into future wireless communication networks.\nSpeaker\nMr. Zhang Jiahua\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong\nSpeaker’s Biography\n\nJiahua Zhang is currently pursuing a Ph.D. degree in the Department of Electrical and Electronic Engineering at The University of Hong Kong\, under the supervision of Prof. Zhiqin Chu. He received his B.Eng. and M.Eng. degree in Optical Engineering from Harbin Institute of Technology (HIT)\, China\, in 2019 and 2021. His research focuses on nitrogen-vacancy (NV) centers\, diamond-based biosensing\, and thermometry.\n\nAll are welcome!
URL:https://ece.hku.hk/events/20250507-2/
LOCATION:Online via Zoom
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250506T140000
DTEND;TZID=Asia/Hong_Kong:20250506T150000
DTSTAMP:20260509T201717
CREATED:20250603T023635Z
LAST-MODIFIED:20250603T023709Z
UID:111455-1746540000-1746543600@ece.hku.hk
SUMMARY:Highly Integrated Wireless Direct Drive Motor System for Fully Enclosed Environments
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/95286760379?pwd=3C7A8QPlmXLJAQZ5IsYbEWdDXXvWk1.1\nMeeting ID: 952 8676 0379\nPassword: 492840\nAbstract\nContemporary global motor systems predominantly rely on cable-based and battery-powered energy transmission mediums\, both of which exhibit fundamental structural limitations. Wired systems face challenges such as installation and maintenance complexity\, mobility constraints\, and safety vulnerabilities\, particularly in confined spaces or high-precision applications. On the other hand\, battery-dependent systems struggle with energy density limitations\, thermal sensitivity\, and a mass penalty\, which can increase operational downtime and affect performance. These systemic deficiencies underscore the urgent need to develop next-generation motor systems that integrate contactless power transfer technologies\, wireless direct-drive control\, and passive intelligent control to overcome conventional electromechanical constraints. This study introduces a highly integrated wireless ultrasonic motor system featuring three fundamental innovations. First\, an integrated magnetic coupler is designed to realize independent decoupling control of two-phase high-frequency magnetic fields\, eliminating the dependence on cables and batteries at the receiving side. In addition\, the structural design of the receiving side is simplified to realize the synchronous transmission of wireless energy and wireless drive signals\, enabling the high-frequency electromagnetic energy (40 kHz) induced at the receiving side can directly drive the motor\, which breaks through the elimination of the rectifier and inverter link. Furthermore\, an intelligent passive control is proposed\, whereby the motor side realizes the complete elimination of components such as controllers\, sensors\, compensation capacitors\, semiconductor switches\, rectifiers\, etc.\, and the precise control of rotational speed and direction can be accomplished through the electromagnetic field modulation at the transmitter side\, resulting in a significant reduction of system complexity and cost.\nSpeaker\nMr. Zhiwei Xue\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong\nSpeaker’s Biography\nZhiwei Xue is currently working toward the Ph.D. degree in electrical and electronic engineering with the Department of Electrical and Electronic Engineering at the University of Hong Kong\, Hong Kong\, China. From 2021 to 2022\, he was a Research Assistant at the Department of Electrical Engineering\, The Hong Kong Polytechnic University\, Hong Kong\, China. His research interests include wireless power transfer\, electrical machine drives\, and power electronics. \nAll are welcome!
URL:https://ece.hku.hk/events/20250506-2/
LOCATION:Online via Zoom
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250506T110000
DTEND;TZID=Asia/Hong_Kong:20250506T120000
DTSTAMP:20260509T201717
CREATED:20250603T025307Z
LAST-MODIFIED:20250603T025832Z
UID:111497-1746529200-1746532800@ece.hku.hk
SUMMARY:Progressive End-to-End Object Detection in Crowded Scenes
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/92481644796?pwd=2CJcxbzAimj87HfFHoBMvUr9oCnOUZ.1\nAbstract\nCrowded object detection is a practical yet challenging research field in computer vision. Many research efforts have been made and achieved impressive progress in the last few decades. However\, most of them require handcraft components\, e.g. anchor settings and post-processing\, resulted in sub-optimal performance in handling scenes. In this work\, we propose a new query-based detection framework for crowd detection. Previous query-based detectors suffer from two drawbacks: first\, multiple predictions will be inferred for a single object\, typically in crowded scenes; second\, the performance saturates as the depth of the decoding stage increases. Benefiting from the nature of the one-to-one label assignment rule\, we propose a progressive predicting method to address the above issues. Specifically\, we first select accepted queries prone to generate true positive predictions\, then refine the rest noisy queries according to the previously accepted predictions. Experiments show that our method can significantly boost the performance of query-based detectors in crowded scenes. Moreover\, the proposed method\, robust to crowdedness\, can still obtain consistent improvements on moderately and slightly crowded datasets\, such as CityPersons and COCO. \nSpeaker\nMr. Zheng Anlin\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nSpeaker’s Biography\nMr. Anlin Zheng received the M.S. degree in computer science and technology from Beihang University\, China. He then joined Beijing Megvii Technology Co.\, Ltd. He is currently pursuing the Ph.D. degree in electrical and electronic engineering from the University of Hong Kong (HKU)\, under the supervision of Dr. Xiaojuan Qi. His research focuses on applying deep learning technology to computer vision\, including object detection and AIGC. \nAll are welcome!
URL:https://ece.hku.hk/events/20250506-3/
LOCATION:Online via Zoom
CATEGORIES:Highlights,Seminar
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END:VEVENT
END:VCALENDAR