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PRODID:-//Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系 - ECPv6.15.20//NONSGML v1.0//EN
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METHOD:PUBLISH
X-ORIGINAL-URL:https://ece.hku.hk
X-WR-CALDESC:Events for Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
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BEGIN:VTIMEZONE
TZID:Asia/Hong_Kong
BEGIN:STANDARD
TZOFFSETFROM:+0800
TZOFFSETTO:+0800
TZNAME:HKT
DTSTART:20230101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250515T150000
DTEND;TZID=Asia/Hong_Kong:20250515T160000
DTSTAMP:20260509T210636
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:20260509T210636
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
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:20250512T140000
DTEND;TZID=Asia/Hong_Kong:20250512T150000
DTSTAMP:20260509T210636
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
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:20250509T150000
DTEND;TZID=Asia/Hong_Kong:20250509T160000
DTSTAMP:20260509T210636
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
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:20250508T160000
DTEND;TZID=Asia/Hong_Kong:20250508T170000
DTSTAMP:20260509T210636
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:20260509T210636
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
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:20250507T140000
DTEND;TZID=Asia/Hong_Kong:20250507T150000
DTSTAMP:20260509T210636
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
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:20250507T100000
DTEND;TZID=Asia/Hong_Kong:20250507T110000
DTSTAMP:20260509T210636
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
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:20250506T140000
DTEND;TZID=Asia/Hong_Kong:20250506T150000
DTSTAMP:20260509T210636
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
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:20250506T110000
DTEND;TZID=Asia/Hong_Kong:20250506T120000
DTSTAMP:20260509T210636
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
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:20250506T100000
DTEND;TZID=Asia/Hong_Kong:20250506T110000
DTSTAMP:20260509T210636
CREATED:20250603T031739Z
LAST-MODIFIED:20250626T094107Z
UID:111541-1746525600-1746529200@ece.hku.hk
SUMMARY:A Diamond Heater-Thermometer Microsensor for Measuring Localized Thermal Conductivity: A Case Study in Gelatin Hydrogel
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/99573774863?pwd=rkA2rKib5AXIzhfMD5grx9darAa05B.1\nMeeting ID: 995 7377 4863\nPassword: 437351 \nAbstract\nUnderstanding the microscopic thermal effects of the hydrogel is important for its application in diverse fields\, including thermal-related studies in tissue engineering and thermal management for flexible electronic devices. In recent decades\, localized thermal properties\, such as thermal conductivity\, have often been overlooked due to technical limitations. To tackle this\, the study proposes a new hybrid diamond microsensor that is capable of simultaneous temperature control and readout in a decoupled manner. Specifically\, the sensor consists of a silicon pillar (heater) at ≈10 microns in length\, topped by a micron-sized diamond particle that contains silicon-vacancy (SiV) centers (thermometer) with 1.29 K/Hz^−0.5 temperature measurement sensitivity. Combining this innovative\, scalable sensor with a newly established simulation model that can transform heating-laser-induced temperature change into thermal conductivity\, an all-optical decoupled method is introduced with ≈0.05 W m−1 K−1 precision\, which can reduce laser crosstalk. For the first time\, the thermal conductivity change of hydrogels during the gelation process is tracked and the existence of variation is demonstrated. The study introduces a rapid\, undisturbed technique for measuring microscale thermal conductivity\, potentially serving as a valuable tool for cellular thermometry\, and highlights the idea that decoupling can reduce crosstalk from different lasers\, which is helpful for quantum sensing. \nSpeaker\nMr. Ma Linjie\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nSpeaker’s Biography\nLinjie Ma 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.S. degree in Physics from Nanjing University (NJU)\, China\, in 2020. His research focuses on nitrogen-vacancy (NV) centers\, diamond-based biosensing\, and mechanobiology. \nAll are welcome!
URL:https://ece.hku.hk/events/20250506-0/
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:20250430T110000
DTEND;TZID=Asia/Hong_Kong:20250430T120000
DTSTAMP:20260509T210636
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250429T110000
DTEND;TZID=Asia/Hong_Kong:20250429T120000
DTSTAMP:20260509T210636
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
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/04/1280-4.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250415T100000
DTEND;TZID=Asia/Hong_Kong:20250415T173000
DTSTAMP:20260509T210636
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
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/04/1280-3.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250415T093000
DTEND;TZID=Asia/Hong_Kong:20250415T103000
DTSTAMP:20260509T210636
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:20250407T093000
DTEND;TZID=Asia/Hong_Kong:20250407T100000
DTSTAMP:20260509T210636
CREATED:20250410T070207Z
LAST-MODIFIED:20250410T070207Z
UID:111086-1744018200-1744020000@ece.hku.hk
SUMMARY:Opportunities from Quantum Computing for Net-Zero Power System Optimisation
DESCRIPTION:Abstract\nOptimised power system planning and operation are core to delivering a low-cost and high-reliability transition path to net-zero carbon emissions. However\, power system optimisation problems are now posing challenges for even the largest exa-scale supercomputers. A new avenue for progress has been opened by recent breakthroughs in quantum computing. Quantum computing offers a fundamentally new computational infrastructure with different capabilities and trade-offs\, and is reaching a level of maturity where\, for the first time\, a practical advantage over classical computing is available for specific applications. The talk will present emerging opportunities where quantum computing can offer value for power system optimisation\, including combinatorial\, convex and machine learning-based optimisation problems. The talk will also discuss challenges for implementation and scale-up\, and outline promising directions for future research. \nSpeaker\nProfessor Thomas MORSTYN\nAssociate Professor in Power Systems\,\nDepartment of Engineering Science\,\nUniversity of Oxford \nBiography of the Speaker\nThomas MORSTYN is an Associate Professor in Power Systems with the Department of Engineering Science\, University of Oxford and he leads the Power Systems Architecture Lab. He is a Tutorial Fellow at Hertford College\, an Honorary Fellow at the University of Edinburgh\, Associate Editor of IEEE Transactions on Power Systems and Co-Chair of the IEEE Power & Energy Society Taskforce on Power System Operations and Control with Quantum Computing. His research is focused on power system digitalisation and market design as key interlinked enablers of the net-zero transition. He received the BEng (Hon.) degree from the University of Melbourne in 2011\, and the PhD degree from the University of New South Wales in 2016\, both in electrical engineering. Previously\, He was a lecturer at the University of Edinburgh and an EPSRC research fellow at the University of Oxford. Prior to undertaking his PhD\, he also worked as an electrical engineer in Rio Tinto’s Technology and Innovation group. \nOrganiser\nProf. Yi WANG\nAssistant Professor\,\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAcknowledgement\nThe seminar has been supported by the Postgraduate Students Conference/Seminar Grants of the Research Grants Council\, Hong Kong. \nAll are welcome!
URL:https://ece.hku.hk/events/20250407-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250407T090000
DTEND;TZID=Asia/Hong_Kong:20250407T153000
DTSTAMP:20260509T210636
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:20250403T163000
DTEND;TZID=Asia/Hong_Kong:20250403T173000
DTSTAMP:20260509T210636
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250320T163000
DTEND;TZID=Asia/Hong_Kong:20250320T173000
DTSTAMP:20260509T210636
CREATED:20250304T090135Z
LAST-MODIFIED:20250304T090135Z
UID:110560-1742488200-1742491800@ece.hku.hk
SUMMARY:Shant Reactive Power Compensation Technologies
DESCRIPTION:Abstract\nReactive power compensation plays a critical role in improving power quality\, enhancing voltage stability\, and optimizing the efficiency of electrical power systems. This presentation will first highlight the main applications of shunt reactive power compensators and provide an overview of key technologies\, including Static Var Compensators (SVC)\, and Static Synchronous Compensators (StatComs). Then the focus will shift to StatCom\, which is considered state-of-the-art technology with superior performance. However\, the widespread adoption of high-power StatComs is hindered by cost constraints\, partly due to the large capacitor banks required in conventional Cascaded H-Bridge (CHB) multilevel converters. The presentation will discuss research advancements to highlight\, pros and cons of operating a CHB StatCom with low capacitance values. \nSpeaker\nProf. Glen FARIVAR\nNanyang Technological University (NUT Singapore) \nBiography of the Speaker\nGlen Farivar received PhD in Electrical Engineering from the University of NSW Australia in 2016. He was a Senior Research Fellow at the Energy Research Institute at the Nanyang Technological University (ERI@N) and a Co-director of the Power Electronics and Application Research Lab at Nanyang Technological University\, Singapore. Since 2023\, he has held a lecturer position\, leading power electronics research\, at the Department of Electrical and Electronic Engineering\, University of Melbourne. He is also a co-founder of SciLeap\, a platform dedicated to promoting research integrity\, accessibility\, and openness. He is a Senior Member of IEEE and co-authored over 130 papers in the areas of high-power multilevel converters and renewable energy systems. \nOrganiser\nProf. Yi WANG\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250320-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250313T110000
DTEND;TZID=Asia/Hong_Kong:20250313T113000
DTSTAMP:20260509T210636
CREATED:20250312T065738Z
LAST-MODIFIED:20250312T073813Z
UID:110586-1741863600-1741865400@ece.hku.hk
SUMMARY:On The Interplay of T and R in VCM-based 1T1R Structures
DESCRIPTION:Abstract\nRedox-based resistive switching random access memory (ReRAM) which is frequently discussed as a promising non-volatile memory as well as a central element in novel neuromorphic computing applications\, is typically integrated in 1-transistor-1-resistor (1T1R) structures. While the access transistor is required as a selective device and acts as an effective current compliance during SET\, it may hinder the RESET operation due to its series resistance. We showed that this may lead to a rare endurance failure. Furthermore\, the RESET speed is affected by the voltage divider of transistor and ReRAM cell\, where the initial cell resistance\, the gate voltage and the transistor geometry (i.e.\, width over length ratio w/L) are crucial. For both\, the HRS and LRS\, we demonstrate that the operation point of the 1T1R voltage divider can be shifted between the linear and the saturation regime of the transistor transfer characteristics. \nSpeaker\nDr. Stefan Wiefels\nPGI-7\, Forschungszentrum Jülich \nBiography of the Speaker\nStefan Wiefels was born in Grevenbroich\, Germany. He received the M.Sc. degree in materials science and the Ph.D. degree in electrical engineering and information technology from RWTH Aachen University\, Aachen\, Germany\, in 2016 and 2021\, respectively. His current research group is centered around the electrical characterization of memristive devices\, reaching from Redox-based resistive switches (ReRAM) to phase change memory (PCM) and from single cells (1R)\, via 1-transistor-1-resistor (1T1R) structures to arrays and circuits. A general focus lies on the automation of measurement schemes to generate significant statistics. This allows for understanding the intrinsic variability of resistive switching devices and is crucial to identify rare failure mechanisms. Further\, variability aware algorithms to program memristive devices are developed. A general target is emulating neuromorphic functionalities using external DAC and ADC before they are integrated on chip. \nOrganisers\n– Can Li\, Department of Electrical and Electronic Engineering\, The University of Hong Kong\n– Center for Advanced Semiconductor and Integrated Circuit \nAll are welcome!
URL:https://ece.hku.hk/events/20250313-3/
LOCATION:Lecture Theatre CB-A\, G/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250313T103000
DTEND;TZID=Asia/Hong_Kong:20250313T113000
DTSTAMP:20260509T210636
CREATED:20250102T023031Z
LAST-MODIFIED:20250211T042315Z
UID:19666-1741861800-1741865400@ece.hku.hk
SUMMARY:Quantum Technologies with Hexagonal Boron Nitride
DESCRIPTION:Abstract\nEngineering robust\, solid-state quantum systems is amongst the most pressing challenges to realise scalable quantum photonic circuitry. While several 3D systems (such as diamond or gallium arsenide) have been thoroughly studied\, solid state emitters in van der Waals (vdW) and two dimensional (2D) materials are still in their infancy. \nIn this presentation\, I will discuss the appeal of an emerging vdW crystal – hexagonal boron nitride (hBN). This unique system possesses a large bandgap of ~ 6 eV and can host single defects that can act as ultra-bright quantum light sources. In addition\, some of these defects exhibit spin dependent fluorescence that can be initialised and coherently manipulated. I will discuss in details various methodologies to engineer these defects and show their peculiar properties. Furthermore\, I will discuss how hBN crystals can be carefully sculpted into nanoscale photonic resonators to confine and guide light at the nanoscale. Taking advantage of the unique 2D nature of hBN\, I will also show promising avenues to integrate hBN emitters with silicon nitride photonic crystal cavities. \nAll in all\, hBN possesses all the vital constituents to become the leading platform for integrated quantum photonics. To this extent\, I will highlight the challenges and opportunities in engineering hBN quantum photonic devices and will frame it more broadly in the growing interest with 2D materials nanophotonics. \nSpeaker\nProf. Igor Aharonovich\nSchool of Mathematical and Physical Sciences\,\nFaculty of Science\,\nUniversity of Technology Sydney \nBiography of the Speaker\nIgor Aharonovich is an award-winning scientist working on cutting-edge research into quantum sources that are able to generate\, encode and distribute quantum information. A Professor in the School of Mathematical and Physical Sciences at UTS\, Igor investigates optically active defects in solids\, with the aim of identifying a new generation of ultra-bright solid state quantum emitters. He is a chief investigator at the ARC Centre of Excellence for Transformative Meta-Optical Materials (TMOS)\, and leads an international collaboration investigating the chemical structure of crystal imperfections\, or defects\, in the nanomaterial hexagonal boron nitride (hBN). \nIn 2016\, Igor and his team discovered the first quantum emitters in 2D materials that operate at room temperature based on defects in hBN. He has co-authored more than 200 peer-reviewed publications\, including one of the most cited reviews on diamond photonics. He has also written a road map for solid state single-photon sources. In 2019\, Igor co-founded the inaugural online photonics conference\, Photonics Online Meetup\, which attracted more than 1100 attendees from around the world\, and which was highlighted by top science outlets. The conference now runs twice a year. He has received several international awards including the Pawsey Medal (2017)\, the IEEE Photonics Young Investigator Award (2016) and in 2020 he was the recipient of the Kavli Foundation Early Career Lectureship in Materials Science from Materials Research Society. In 2021\, he became a Fellow of the Optical Society (OSA)\, and in 2024 elected as a fellow of SPIE.\nRead more about the speaker’s biography: https://profiles.uts.edu.au/Igor.Aharonovich \nOrganiser\nProf. Zhiqin Chu\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nSupported by\nTam Wing Fan Innovation Wing Two \nAll are welcome! \nDirection: https://innowings.engg.hku.hk/innowing2/visitors
URL:https://ece.hku.hk/events/20250313-1/
LOCATION:Tam Wing Fan Innovation Wing Two\, G/F\, Run Run Shaw Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250313T100000
DTEND;TZID=Asia/Hong_Kong:20250313T113000
DTSTAMP:20260509T210636
CREATED:20250211T081812Z
LAST-MODIFIED:20250212T083236Z
UID:108702-1741860000-1741865400@ece.hku.hk
SUMMARY:In-memory\, Mixed Analog-digital Architectures for Energy-efficient Computing Applications
DESCRIPTION:Abstract\nThere is simultaneously an interest for more energy-efficient hardware in challenging applications\, as well as a drive to overhaul the von Neumann architecture toward more brain-like architectures. Compute-in-Memory (CIM) is one emerging paradigm addressing key memory bottlenecks such as bandwidth limitations\, access latency\, and high data movement energy in conventional computing platforms.\nIn part one\, we use this paradigm to accelerate the solving of challenging optimization problems. We describe our in-memory\, hardware approach built around modified Hopfield neural networks to accelerate problem classes such as Boolean satisfiability (3-SAT). In an algorithm-hardware co-design process\, we have developed three different architectures steadily improving on the state-of-the-art. We discuss issues encountered in mapping native optimization problems to physical hardware\, precision demands\, and matching mixed analog-digital blocks to the algorithmic needs. Quantitative performance comparisons to competing approaches in both mature and emerging technologies will be presented.\nIn the second part of the talk\, we extend CIM beyond matrix-dominant workloads\, exploring its potential for novel and powerful models like Kolmogorov-Arnold Networks (KAN). Our new work\, called “KA-CIM\,” provides hardware acceleration for KANs. KANs offer significant parameter reduction over traditional neural networks for AI+Science applications but relies on computationally expensive non-linear functions. We present an innovative memory-centric design that enables energy-efficient and flexible computation of non-linear functions central to KAN\, while efficiently executing KAN inference.\nWe also discuss analog CIM using commodity DRAM architectures. Unlike SRAM- and emerging memory-based CIM accelerators\, DRAM-based CIM offers both high memory capacity and technological maturity\, making it an attractive candidate for large-scale AI workloads. This portion of the talk will present both the opportunities and constraints of using commodity DRAM for CIM. A novel analog CIM architecture will be presented\, which mitigates several of these constraints and demonstrates how area- and power-intensive ADCs can be efficiently integrated within an area-optimized commodity DRAM design. Furthermore\, a key feature of this architecture is its Dual-Mode functionality\, enabling it to seamlessly operate as both conventional main memory and an accelerator. \nSpeakers\nProf. John Paul Strachan\nProfessor\, RWTH Aachen University\, Aachen\, Germany\nHead\, Peter Grünberg Institute (PGI-14)\, Forschungszentrum Jülich\, Jülich\, Germany\n\nDr. Chirag Sudarshan\nPostdoctoral Researcher\nPeter Grünberg Institute (PGI-14)\, Forschungszentrum Jülich\, Jülich\, Germany \nBiography of the Speakers\nProf. John Paul Strachan directs the Peter Grünberg Institute on Neuromorphic Compute Nodes (PGI-14) at Forschungszentrum Jülich and is a Professor at RWTH Aachen.  Previously he led the Emerging Accelerators team as a Distinguished Technologist at Hewlett Packard Labs\, HPE. His teams explore novel types of hardware accelerators using emerging device technologies\, with expertise spanning materials\, device physics\, circuits\, architectures\, benchmarking and building prototype systems. Their interests span applications in machine learning\, network security\, and optimization. John Paul has degrees in physics and electrical engineering from MIT and a PhD in applied physics from Stanford University. He has over 60 patents\, has authored or co-authored over 100 peer-reviewed papers\, and been the PI in many USG research grants. He has previously worked on nanomagnetic devices for memory for which he was awarded the Falicov Award from the American Vacuum Society\, and has developed sensing systems for precision agriculture in a company which he co-founded. He serves in professional societies including IEEE IEDM ExComm\, the Nanotechnology Council ExComm\, and past program chair and steering member of the International Conference on Rebooting Computing.\n\nDr. Chirag Sudarshan is currently a Postdoctoral Researcher at the Peter Grünberg Institute on Neuromorphic Compute Nodes (PGI-14)\, Forschungszentrum Jülich\, working under the supervision of Prof. John Paul Strachan. He received his Master’s degree (2017) and Ph.D. (2023) in Electrical Engineering from the University of Kaiserslautern-Landau\, Germany. During his Ph.D.\, he extensively worked on novel DRAM architectural designs and is now developing innovative compute-in-memory architectures with emerging memory technologies for neuromorphic applications. His contributions have been recognized with a special academic achievement award from the Department of Electrical and Computer Engineering and WIPOTEC GmbH following his master’s studies. He has authored or co-authored 23 publications\, filed six patents\, and currently serves as a reviewer for journals such as Results in Engineering and Micromachines. His research interests include compute-in-memory architectures\, neuromorphic computing\, emerging memory technologies\, and DRAM architectures. \nOrganiser\n\nProf. Can Li\, Department of Electrical and Electronic Engineering\, The University of Hong Kong; and\nCenter for Advanced Semiconductor and Integrated Circuits\n\nAll are welcome! 
URL:https://ece.hku.hk/events/20250313-2/
LOCATION:Lecture Theatre CB-A\, G/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250307T093000
DTEND;TZID=Asia/Hong_Kong:20250307T223000
DTSTAMP:20260509T210636
CREATED:20250304T072753Z
LAST-MODIFIED:20250306T015705Z
UID:110555-1741339800-1741386600@ece.hku.hk
SUMMARY:Revolutionizing Power Electronics with Heterogeneous Integration
DESCRIPTION:Abstract\nTraditional power electronic equipment has long relied on discrete active and passive components\, with performance enhancements often requiring trade-offs. Despite technological advancements\, manufacturing processes remain labor-intensive and largely unchanged for decades. \nThe advent of wide-bandgap (WBG) power semiconductor devices\, such as silicon carbide (SiC) and gallium nitride (GaN)\, has significantly reduced conduction and switching losses compared to silicon-based counterparts. However\, current design methodologies primarily follow a ‘plug-and-play’ approach\, yielding only incremental improvements in efficiency and power density without fully leveraging the transformative potential of these technologies. \nThis presentation explores the integration of matrix magnetics with WBG power devices to drive a fundamental shift in power electronics design and manufacturing through heterogeneous integration. This holistic approach enables simultaneous enhancements in efficiency\, power density\, cost\, and electromagnetic interference (EMI) performance. Additionally\, it streamlines traditionally labor-intensive manufacturing processes—particularly those involving magnetics and system assembly—through automation. \nThe discussion will feature multiple research examples demonstrating heterogeneous integration of matrix magnetics in power converters across diverse applications and power ranges. These include high-frequency power converters for artificial intelligence (AI) and high-performance computing systems\, battery chargers for electric vehicles\, and solid-state transformers for DC power distribution. \n\nSpeaker\nProf. Qiang LI\nCenter for Power Electronics Systems (CPES)\,\nVirginia Tech \nBiography of the Speaker\nQiang Li received the B.S. and M.S. degrees from Zhejiang University\, China\, in 2003 and 2006\, respectively\, and the Ph.D. degree from Virginia Tech\, Blacksburg\, VA\, in 2011. He is currently a full professor in the Center for Power Electronics Systems (CPES) at Virginia Tech. His research interests include high-frequency power conversion and control\, high-density electronics packaging and magnetic integration\, as well as power solutions for high-performance computing\, data centers\, electric vehicles\, and energy storage systems. With over 300 peer-reviewed technical publications\, including 100 journal articles\, he has received eight prize paper awards and holds 26 U.S. patents. He currently serves as the Chair of Academic Affairs for the IEEE Power Electronics Society and is an associate editor for both the IEEE Transactions on Power Electronics and the IEEE Journal of Emerging and Selected Topics in Power Electronics. He is also a recipient of the U.S. National Science Foundation (NSF) Career Award. \nOrganiser\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250307-2/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250217T150000
DTEND;TZID=Asia/Hong_Kong:20250217T163000
DTSTAMP:20260509T210636
CREATED:20250211T013747Z
LAST-MODIFIED:20250211T042315Z
UID:108654-1739804400-1739809800@ece.hku.hk
SUMMARY:Personalized Federated Learning and Its Application in 360-degree Video Streaming
DESCRIPTION:Abstract\nFederated learning is a distributed artificial intelligence framework\, which allows multiple edge devices to train a single model collaboratively. In this talk\, we first introduce a personalized federated learning algorithm which can tackle the issues of data heterogeneity and device heterogeneity. Then\, we present a content-based viewport prediction framework for 360-degree video streaming\, wherein users’ head movement prediction models are trained using a personalized federated learning algorithm. The output of the viewport prediction framework corresponds to which video tiles to be transmitted. Finally\, we present an algorithm to determine the bitrate and beamforming matrices in a THz-enabled 360-degree video streaming system with multiple access points. \nSpeaker\nProf. Vincent Wong\nProfessor\nDepartment of Electrical and Computer Engineering\nUniversity of British Columbia\, Canada \nBiography of the Speaker\nVincent Wong is a Professor in the Department of Electrical and Computer Engineering at the University of British Columbia\, Vancouver\, Canada. His research areas include protocol design\, optimization\, and resource management of communication networks\, with applications to the Internet\, wireless networks\, smart grid\, mobile edge computing\, and Internet of Things. Dr. Wong is the Editor-in-Chief of the IEEE Transactions on Wireless Communications. He is a Fellow of the IEEE and the Engineering Institute of Canada. \nOrganiser\nProf. Kaibin Huang\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250217-1/
LOCATION:Room CB-601J\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250214T150000
DTEND;TZID=Asia/Hong_Kong:20250214T160000
DTSTAMP:20260509T210636
CREATED:20250210T095554Z
LAST-MODIFIED:20250211T042315Z
UID:108650-1739545200-1739548800@ece.hku.hk
SUMMARY:Ubiquitous Sensing in 6G Cellular Networks
DESCRIPTION:Abstract\nRecently\, the International Telecommunication Union (ITU) has identified integrated sensing and communication (ISAC) as a primary usage scenario for the sixth-generation (6G) cellular networks in IMT-2030 Framework. As a result\, future cellular networks will provide not only communication services\, but also sensing services such as localization and tracking. However\, how to exploit the existing communication infrastructure to effectively achieve sensing functions remains an open problem for 6G. In this talk\, we will introduce the methodologies to leverage various types of communication nodes in cellular networks as anchors\, including base stations\, user equipments\, and intelligent reflecting surfaces\, to perform ubiquitous sensing. Specifically\, the advantages and disadvantages of each type of anchors will be listed\, and the efficient solutions to overcome these disadvantages will be outlined. Apart from theoretical works\, this talk will also present our latest achievements in building a 6G ISAC platform that operates at the millimeter-wave band. We will conclude this talk by discussing some promising future directions that will be beneficial to the transformation of the world’s largest communication network into the world’s largest sensing network. \nSpeaker\nDr. Liang LIU\nAssociate Professor\, Department of Electrical and Electronic Engineering\, The Hong Kong Polytechnic University \nBiography of the Speaker\nLiang Liu is currently an Associate Professor with the Department of Electrical and Electronic Engineering\, The Hong Kong Polytechnic University. He obtained his Ph.D. degree from National University of Singapore in 2014. His research interests lie in 5G/6G technologies\, including integrated sensing and communication (ISAC)\, massive Internet-of-Things (IoT) connectivity\, etc. Currently\, his project about 6G ISAC is supported by the RGC Collaborative Research Fund (CRF) Young Collaborative Research Grant. \nLiang Liu is an IEEE Communications Society (ComSoc) Distinguished Lecturer. He is a recipient of the 2021 IEEE Signal Processing Society (SPS) Best Paper Award\, the 2017 IEEE SPS Young Author Best Paper Award\, the Best Student Paper Award of 2022 IEEE International Conference on Acoustics\, Speech\, and Signal Processing (ICASSP)\, and the Best Paper Award of the 2011 International Conference on Wireless Communications and Signal Processing. He was recognized by Clarivate Analytics as a Highly Cited Researcher in 2018. He is an Editor of IEEE Transactions on Wireless Communications\, and was a Leading Guest Editor of IEEE Wireless Communications Special Issue on Massive Machine-Type Communications for IoT. He is a co-author of the book “Next Generation Multiple Access” published by Wiley-IEEE Press. \nOrganiser\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong
URL:https://ece.hku.hk/events/20250214-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250213T160000
DTEND;TZID=Asia/Hong_Kong:20250213T170000
DTSTAMP:20260509T210636
CREATED:20250210T095339Z
LAST-MODIFIED:20250211T042315Z
UID:108646-1739462400-1739466000@ece.hku.hk
SUMMARY:6G Waveforms-Perspectives on Throughput\, Reliability\, and ISAC
DESCRIPTION:Online via Zoom link: https://hku.zoom.us/j/2025021302 \nAbstract\nWith the commercialization of 5G technology\, research on 6G has emerged as a key focus in the field of wireless communications. In this talk\, we explore three candidate waveforms for 6G\, designed to meet its stringent requirements for throughput\, reliability\, and integrated sensing and communications (ISAC). \nWe begin by discussing faster-than-Nyquist (FTN) signaling\, a promising technique for enhancing communication spectral efficiency. The unique challenges associated with equalization and channel coding in FTN systems are highlighted\, along with novel solutions that are benchmarked against theoretical performance limits. \nNext\, we examine orthogonal time frequency space (OTFS) modulation\, which enhances communication reliability in dynamic wireless channels. We demonstrate that OTFS introduces a novel coupling mechanism between information symbols and the wireless channel\, enabling efficient equalization and robust MIMO transmissions by fully exploiting channel dynamics. \nFinally\, we focus on a communication-centric ISAC waveform\, evaluating its sensing performance through ambiguity functions. We analytically prove that OFDM is the optimal waveform for minimizing sidelobes in ranging\, while single-carrier waveforms are superior for Doppler sensing when using practical communication signals. \nThe talk concludes with a discussion of potential future research directions in 6G waveform design\, highlighting open challenges and opportunities in this evolving field. \nSpeaker\nDr. Shuangyang Li\nResearch Assistant\, Faculty of Electrical Engineering and Computer Science\, Technical University of Berlin \nBiography of the Speaker\nShuangyang Li (Member\, IEEE) received the B.S.\, M.S.\, and Ph.D. degrees from Xidian University\, China\, in 2013\, 2016\, and 2021\, respectively. He received his second Ph.D. degree from the University of New South Wales (UNSW)\, Australia\, in 2022. He is a recipient of the Marie Skłodowska-Curie Actions (MSCA) fellowship 2022 and is currently a research assistant at the Technical University of Berlin (TU-Berlin). Prior to that\, he was a research associate at the University of Western Australia (UWA). He received the Best Paper Award from IEEE ICC 2023\, and the Best Workshop Paper Award from IEEE WCNC 2023. He was listed in the World’s Top 2% Scientists by Stanford University for citation impact 2024 and is the recipient of the best young researcher award 2024 from the IEEE ComSoc EMEA region. He frequently serves as the organizer/chair for workshops and tutorials on related topics of orthogonal time frequency space (OTFS) in IEEE flagship conferences and is a founding member and currently the co-chair of the special interest group (SIG) on OTFS. He is now an editor of IEEE Transactions on Communications. His research interests include signal processing\, channel coding\, applied information theory\, and their applications to communication systems\, with a specific focus on waveform designs. \nOrganiser\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong
URL:https://ece.hku.hk/events/20250213-2/
LOCATION:Online via Zoom
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250213T143000
DTEND;TZID=Asia/Hong_Kong:20250213T153000
DTSTAMP:20260509T210636
CREATED:20250210T094926Z
LAST-MODIFIED:20250211T042315Z
UID:108642-1739457000-1739460600@ece.hku.hk
SUMMARY:Bridging Minds\, Not Just Devices: Semantic and Goal-Oriented Communication for the Internet of Intelligent Things
DESCRIPTION:Abstract\nThe next frontier of the Internet of Things (IoT) lies in transforming today’s smart devices into collaborative cognitive agents – an ecosystem termed the Internet of Intelligent Things (IoIT). While current IoT systems center on raw data exchange\, they fall short of enabling true collaboration: devices cannot share meaningful insights or align their objectives across dynamic\, real-world tasks. This talk presents a paradigm shift – semantic and goal-oriented communication – as the critical enabler for IoIT. I will introduce a theoretical framework that conceptualizes semantic communication through two key challenges: language exploitation and language design. The language exploitation problem focuses on optimizing the encoding and decoding of semantics to minimize distortion without modifying the underlying semantic language. In contrast\, the language design problem seeks to co-optimize both the encoder and decoder through joint source-channel coding\, particularly leveraging deep learning-based approaches. The talk will also explore the role of large language models in learning adaptive semantic representations\, making communication systems more resilient and context-aware. Finally\, I will discuss how the goal-oriented principle broadens classical Shannon theory by integrating decision-making objectives into communication system design. By framing communication as a meaning-driven\, goal-aware process\, we usher in a new era of collective intelligence – one where smart devices evolve into collaborative cognitive agents capable of shared understanding and coordinated action. \nSpeaker\nDr. Yulin SHAO\nAssistant Professor\, State Key Laboratory of Internet of Things for Smart City\, University of Macau \nBiography of the Speaker\nDr. Yulin Shao is an Assistant Professor with the State Key Laboratory of Internet of Things for Smart City\, University of Macau\, and a Visiting Researcher with the Department of Electrical and Electronic Engineering\, Imperial College London. He received the B.S. and M.S. degrees in Communications and Information Engineering (Hons.) from Xidian University\, China\, in 2013 and 2016\, and the Ph.D. degree in Information Engineering from the Chinese University of Hong Kong in 2020. He was a Research Assistant with the Institute of Network Coding\, a Visiting Scholar with the Research Laboratory of Electronics at Massachusetts Institute of Technology\, a Research Associate with the Department of Electrical and Electronic Engineering at Imperial College London\, and a Lecturer in Information Processing with the University of Exeter. He was a Guest Lecturer at 5G Academy Italy and IEEE Information Theory Society Bangalore Chapter. \nDr. Shao’s research interests include coding and modulation\, machine learning\, and stochastic control. He is a Series Editor of IEEE Communications Magazine in the area of Artificial Intelligence and Data Science for Communications\, an Editor of IEEE Transactions on Communications in the area of Machine Learning and Communications\, and an Editor of IEEE Communications Letters. He received the Best Poster Award at CIE Information Theory Society 2024\, and the Best Paper Awards at IEEE International Conference on Communications (ICC) 2023 and IEEE Wireless Communications and Networking Conference (WCNC) 2024. \nOrganiser\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong
URL:https://ece.hku.hk/events/20250213-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250210T110000
DTEND;TZID=Asia/Hong_Kong:20250210T120000
DTSTAMP:20260509T210636
CREATED:20250210T094548Z
LAST-MODIFIED:20250211T042315Z
UID:108637-1739185200-1739188800@ece.hku.hk
SUMMARY:Advanced Photonic Thin Films and Nanostructures for Next Generation Optoelectronic Systems
DESCRIPTION:Abstract\nPhotonic materials are the backbones of optical communication\, sensing and imaging systems. The advent of artificial intelligence\, internet of things and human-machine interfaces require optical information perception\, data communication and storage with a much higher bandwidth\, smaller footprint yet extremely low power consumption. Bulk materials can no longer support these tasks. Development of advanced photonic thin films and nanostructures becomes the key challenge. In this report\, I will introduce our recent progress on advanced photonic thin films and nanostructures for silicon photonic and free-space optoelectronic systems. I will cover two topics. First\, magneto-optical nonreciprocal photonics for silicon photonics\, including the development of wafer-scale high quality MO thin films\, nanophotonic structures\, nonreciprocal photonic devices and their application in laser module\, silicon photonic FMCW LiDAR systems. Second\, active optical metasurfaces\, including the development of phase change materials\, ferroelectric thin films and optical metasurfaces for optical switching and imaging applications.\n \nSpeaker\nProf. Lei Bi\nProfessor\, Department of Electronic Science and Engineering\,\nUniversity of Electronic Science and Technology of China (UESTC) \nBiography of the Speaker\nLei Bi is a professor in the department of Electronic Science and Engineering of University of Electronic Science and Technology of China (UESTC). He received his B.S. and M.S. degrees in Tsinghua University in 2004 and 2006 respectively\, both majored in materials science. He received his Ph.D. degree in MIT in 2011\, majored in materials science and engineering. He joined UESTC as a professor in 2013. His research interest includes nonreciprocal photonics\, magneto-photonics and optical metasurface. He has authored or co-authored more than 150 papers in peered-viewed journals. He is a senior member of IEEE\, and a member of Optica and SPIE. \nOrganisers\nProf. Han Wang\, Department of Electrical and Electronic Engineering\, HKU\nCenter for Advanced Semiconductors and Integrated Circuits \nAll are welcome!
URL:https://ece.hku.hk/events/20250210-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250120T110000
DTEND;TZID=Asia/Hong_Kong:20250120T120000
DTSTAMP:20260509T210636
CREATED:20250116T025553Z
LAST-MODIFIED:20250211T042315Z
UID:108129-1737370800-1737374400@ece.hku.hk
SUMMARY:Edges Empowering AI\, Embracing LLMs: Issues\, Technologies\, and Applications
DESCRIPTION:Abstract\nAccelerated by the rapid advancements in AI and IoT technologies\, there is an urgent need to extend AI capabilities to the network edge to fully harness the potential of big data. To address this demand\, edge Intelligence has emerged as a promising paradigm for enabling distributed\, computation-intensive AI applications on edge devices. This talk explores the key dimensions of edge intelligence: data\, models\, and systems. It involves data evaluation by analyzing its contribution to model performance and investigates strategies for optimizing edge models in dynamic edge environments. Special attention is given to technologies such as distributed training (federated edge learning as an example)\, inference acceleration\, and model compression tailored for edge deployments. Furthermore\, with the advent of large language models (LLMs) and their overwhelming computational requirements\, the talk examines the evolving role of edge intelligence. Some open questions remain: e.g.\, how edge systems can integrate with and complement these foundational models\, addressing challenges such as resource constraints and latency while exploring potential synergies in hybrid edge-cloud architectures. \nSpeaker\nProf. Yinglei Teng\nProfessor\,\nBeijing University of Posts and Telecommunications \nBiography of the Speaker\nYinglei Teng\, a professor at Beijing University of Posts and Telecommunications\, specializes in wireless communications\, stochastic optimization and edge intelligence. She received funding from renowned programs\, including the NSFC\, National Key R&D Young Scientist Project\, Huawei\, China Mobile\, etc. She authored over 30 high-quality SCI papers\, holds more than 80 invention patents\, and contributed to 8 industry standards. She was recognized with honors such as the China Association for Science and Technology Special Award and the Beijing Science and Technology Award. Her recent research focuses on edge intelligence\, ML/AI for PHY\, and millimeter-wave technologies\, etc. \nOrganisers\nProf. Kaibin Huang & Prof. Xianhao Chen\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome! 
URL:https://ece.hku.hk/events/20250120-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241218T150000
DTEND;TZID=Asia/Hong_Kong:20241218T160000
DTSTAMP:20260509T210636
CREATED:20241128T022442Z
LAST-MODIFIED:20250211T042315Z
UID:19513-1734534000-1734537600@ece.hku.hk
SUMMARY:Toward Scalable Generative AI via Mixture of Experts in Mobile Edge Networks
DESCRIPTION:Abstract\nThe evolution of generative artificial intelligence (GAI) has driven revolutionary applications like ChatGPT. The proliferation of these applications is underpinned by the mixture of experts (MoE)\, which contains multiple experts and selectively engages them for each task to lower operation costs while maintaining performance. Despite MoE’s efficiencies\, GAI still faces challenges in resource utilization when deployed on local user devices. Therefore\, we first propose mobile edge networks supported MoE-based GAI. Rigorously\, we review the MoE from traditional AI and GAI perspectives\, scrutinizing its structure\, principles\, and applications. Next\, we present a new framework for using MoE for GAI services in Metaverse. Moreover\, we propose a framework that transfers subtasks to devices in mobile edge networks\, aiding GAI model operation on user devices. Moreover\, we introduce a novel approach utilizing MoE\, augmented with Large Language Models (LLMs)\, to analyze user objectives and constraints of optimization problems based on deep reinforcement learning (DRL) effectively. This approach selects specialized DRL experts\, and weights each decision from the participating experts. In this process\, the LLM acts as the gate network to oversee the expert models\, facilitating a collective of experts to tackle a wide range of new tasks. Furthermore\, it can also leverage LLM’s advanced reasoning capabilities to manage the output of experts for joint decisions. Lastly\, we insightfully identify research opportunities of MoE and mobile edge networks. \nSpeaker\nProf. Dusit Niyato\nPresident’s Chair Professor\,\nCollege of Computing & Data Science (CCDS)\,\nNanyang Technological University\, Singapore \nBiography of the Speaker\nDusit Niyato is a President’s Chair Professor in the College of Computing & Data Science (CCDS)\, Nanyang Technological University\, Singapore. Dusit’s research interests are in the areas of mobile generative AI\, edge intelligence\, quantum computing and networking\, and incentive mechanism design. Dusit won the IEEE Vehicular Technology Society Stuart Meyer Memorial Award. Dusit won the IEEE Vehicular Technology Society Stuart Meyer Memorial Award. Currently\, Dusit is serving as Editor-in-Chief of IEEE Communications Surveys and Tutorials (impact factor of 34.4 for 2023) and will serve as the Editor-in-Chief of IEEE Transactions on Network Science and Engineering (TNSE) from 2025. He is also an area editor of IEEE Transactions on Vehicular Technology (TVT)\, topical editor of IEEE Internet of Things Journal (IoTJ)\, lead series editor of IEEE Communications Magazine\, and associate editor of IEEE Transactions on Wireless Communications (TWC)\, IEEE Transactions on Mobile Computing (TMC)\, IEEE Wireless Communications\, IEEE Network\, IEEE Transactions on Information Forensics and Security (TIFS)\, IEEE Transactions on Cognitive Communications and Networking (TCCN)\, IEEE Transactions on Services Computing (TSC)\, and ACM Computing Surveys. Dusit is the Members-at-Large to the Board of Governors of IEEE Communications Society for 2024-2026. He was named the 2017-2023 highly cited researcher in computer science. He is a Fellow of IEEE and a Fellow of IET. \nOrganiser\nProf. Hongyang Du\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20241218-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
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END:VEVENT
END:VCALENDAR