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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:20240101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250403T163000
DTEND;TZID=Asia/Hong_Kong:20250403T173000
DTSTAMP:20260511T235243
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:20260511T235243
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:20260511T235243
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
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/04/1280-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250409T110000
DTEND;TZID=Asia/Hong_Kong:20250409T120000
DTSTAMP:20260511T235243
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:20260511T235243
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
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:20250411T140000
DTEND;TZID=Asia/Hong_Kong:20250411T150000
DTSTAMP:20260511T235243
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:20260511T235243
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:20260511T235243
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:20260511T235243
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
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250416T140000
DTEND;TZID=Asia/Hong_Kong:20250416T170000
DTSTAMP:20260511T235243
CREATED:20250410T072506Z
LAST-MODIFIED:20250626T085202Z
UID:111109-1744812000-1744822800@ece.hku.hk
SUMMARY:Infineon Day
DESCRIPTION:Dear EEE Students\, \nYou are cordially invited to join us for the upcoming event “Infineon Day” on April 16\, 2025\, organised by Infineon and the Department of Electrical and Electronic Engineering\, The University of Hong Kong. Let you know more about the story of global semiconductor industry leader from Germany! Interesting interaction and prize-giving Q&A are waiting for you. Technical experts from Infineon will be on site to share the IOT\, Smart Campuses\, WBG technology and career development experience. \nJoin the talk and enjoy a fabulous gift! Don’t miss it! \nDate: 16 April 2025 (WED)\nTime: 2:00 pm – 5:00 pm\nVenue: Tam Wing Fan Innovation Wing Two\, G/F\, Run Run Shaw Building\, The University of Hong Kong \nTo register\, please fill in the registration form:\nhttps://hkuems1.hku.hk/hkuems/ec_hdetail.aspx?ueid=99743 \nSchedule:\n14:00-14:30 | Registration & Infineon Demo Exhibition\n14:30-14:40 | Opening Speech by HKU\n14:40-15:00 | Infineon Company Introduction\n15:00-15:20 | Leading the Future: Infineon’s Comprehensive Solutions Empower the Consumer\, Computing\, and Communications Industries\n15:20-15:40 | Infineon’s Automotive Solutions Accelerate Smart Vehicle Evolution\n15:40-16:00 | Greening the Future: Enable decarbonization across complete energy chain\n16:00-16:15 | The Exciting Career of Electronic Engineers\n16:15-16:30 | Infineon University Program and Career Development\n16:30-16:45 | Q&A\n16:45-17:00 | Luck Draw \nBest Regards\,\nDepartment of Electrical and Electronic Engineering
URL:https://ece.hku.hk/events/20250416-0/
LOCATION:Tam Wing Fan Innovation Wing Two\, G/F\, Run Run Shaw Building\, The University of Hong Kong
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250429T110000
DTEND;TZID=Asia/Hong_Kong:20250429T120000
DTSTAMP:20260511T235243
CREATED:20250424T014238Z
LAST-MODIFIED:20250424T014321Z
UID:111218-1745924400-1745928000@ece.hku.hk
SUMMARY:EEE MasterClass (EEE 大師講堂) – III-V Compounds on Si – Combining the Best of Both Worlds
DESCRIPTION:Abstract\nGaAs/InP and related alloys\, and III-nitrides are used for most high-performance device applications except CMOS logic. Optoeletronics\, high frequency (RF to THz) and power electronics are dominated by III-V compound semiconductors. We will discuss various factors of nature\, nurture\, and culture leading to today’s landscape. Photonic integrated circuits made with compound semiconductors on native substrates are costly and limited in wafer size and throughput. There is no universal formula for combining the best of both worlds –high performance and specific functionality of compound semiconductors with the efficiency and cost-effectiveness of Si integrated circuit manufacturability. Over the years\, intense efforts have been made to incorporate high-performance III-V active devices on silicon\, to be integrated with passive components and waveguides of Si photonics. Heterogeneous integration techniques such as wafer bonding and die bonding (transfer printing) have been developed for this purpose. We have used such approaches to demonstrate and commercialize high-resolution micro-LED micro-displays. To efficiently couple light between active and passive components for Si photonics\, we recently developed a unique growth scheme – Lateral Aspect Ratio Trapping” (LART) to enable lateral selective epitaxy of device quality III-V materials right on top of the buried oxide layer of patterned silicon-on-insulator (SOI) wafers by metal organic chemical vapor deposition (MOCVD). For fully vertical GaN trench MOSs grown on Si\, balancing all the tradeoffs in terms of device structure\, performance\, process complexity and throughput is being considered. \nSpeaker\nProf. Kei May LAU\nHong Kong University of Science & Technology \nBiography of the Speaker\nKei May LAU is a Research Professor at the Hong Kong University of Science & Technology (HKUST). She received her degrees from the University of Minnesota and Rice University and served as a faculty member at the University of Massachusetts/Amherst before joining HKUST in 2000. Lau is an elected member of the US National Academy of Engineering\, a Fellow of IEEE\, Optica (formerly OSA)\, and the Hong Kong Academy of Engineering Sciences. She was also a recipient of the IPRM award\, IET J J Thomson medal for Electronics\, Optica Nick Holonyak Jr. Award\, IEEE Photonics Society Aron Kressel Award\, and Hong Kong Croucher Senior Research Fellowship. She was an Editor of the IEEE Transactions on Electron Devices and Electron Device Letters\, and an Associate Editor for the Journal of Crystal Growth and Applied Physics Letters. \nOrganiser\nProf. Han WANG\nProfessor & Associate Head (New Initiative)\,\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250429-1/
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:20250430T110000
DTEND;TZID=Asia/Hong_Kong:20250430T120000
DTSTAMP:20260511T235243
CREATED:20250603T023059Z
LAST-MODIFIED:20250603T023149Z
UID:111451-1746010800-1746014400@ece.hku.hk
SUMMARY:Wide Field-of-View Imaging with Efficient Off-Axis Modeling and Encoding
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/96308127770?pwd=EPYwEx3OHCPhiFDRaY9mNKFvbrtYMA.1\nMeeting ID: 963 0812 7770\nPassword: 254774 \nAbstract\nComputational optics is emerging as a transformative field to overcome challenges in achieving high-fidelity imaging across wide fields of view (FoV). However\, existing methods struggle with computational inefficiency for simulating off-axis diffraction and maintaining imaging quality at wide-FoV due to limited wavefront control. In this seminar\, I will present two synergistic advances addressing these limitations. First\, I introduce a universal angular spectrum method with optimized least-sampling criteria for off-axis diffraction modeling. Experimental results demonstrate substantial acceleration in computational speed while enabling high accuracy for ultra-wide-angle diffraction simulations. Second\, I present an end-to-end optimized framework that synergizes optical engineering and computational algorithms to transcend prior wide-FoV imaging constraints. By strategically positioning diffractive optical elements off-aperture and integrating hybrid refractive-diffractive optics with decoding multi-task networks\, we prototype two compact cameras that demonstrate high-fidelity color and depth imaging in real indoor and outdoor scenes. \nSpeaker\nMiss Haoyu Wei\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \n Speaker’s Biography\nHaoyu Wei received the M.S. degree from the Dept. of Computer Science\, Northwestern University\, Evanston\, USA\, in 2021 and B.Eng. degree from the Dept. of Computer Science\, Sichuan University\, Chengdu\, in 2019. She is currently working towards the Ph.D. degree with Dept. of Electrical and Electronic Engineering\, The University of Hong Kong (HKU)\, Hong Kong\, supervised by Prof. Edmund Y. Lam and Dr. Evan Peng. Her research interests include deep imaging systems and numerical simulations. \nAll are welcome!
URL:https://ece.hku.hk/events/20250430-1/
LOCATION:Online via Zoom
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
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