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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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X-WR-CALNAME:Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
X-ORIGINAL-URL:https://ece.hku.hk
X-WR-CALDESC:Events for Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
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BEGIN:VTIMEZONE
TZID:Asia/Hong_Kong
BEGIN:STANDARD
TZOFFSETFROM:+0800
TZOFFSETTO:+0800
TZNAME:HKT
DTSTART:20230101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20240802T110000
DTEND;TZID=Asia/Hong_Kong:20240802T120000
DTSTAMP:20260512T134938
CREATED:20240724T043745Z
LAST-MODIFIED:20250114T042912Z
UID:18932-1722596400-1722600000@ece.hku.hk
SUMMARY:High-Frame-Rate Ultrasound Imaging in the Deep Learning Era
DESCRIPTION:Please note the seminar venue is revised to Tam Wing Fan Innovation Wing Two\, The University of Hong Kong. \nAbstract\nUltrasound is undoubtedly a popular medical imaging modality and is becoming known for its high-frame-rate imaging capabilities. However\, high-frame-rate ultrasound has yet to flourish in point-of-care applications due to the lack of suitable portable hardware\, and its ability to offer time-resolved flow visualization is hampered by Doppler aliasing artifacts. Can we take advantage of deep learning to overcome bottlenecks in high-frame-rate system design? Can we design neural networks to resolve Doppler aliasing artifacts in real time? This seminar will introduce our laboratory’s quest to learn deep and learn smart about ultrasound imaging systems to make high-frame-rate ultrasound viable for portable use and flow estimation. We will demonstrate how deep learning solutions can be devised to resolve data transfer bottlenecks in ultrasound systems and\, in turn\, enable robust generation of high-frame-rate ultrasound images with data acquired from few array channels. We will also show how deep learning has enabled the design of advanced Doppler flow imaging platforms with lucid flow visualization performance. Related algorithms\, real-time engineering efforts\, and clinical applications will be presented throughout the presentation. \nSpeaker\nProf. Alfred C. H. YU\nAssistant Vice-President (Research and International)\,\nProfessor of Biomedical Engineering\,\nUniversity of Waterloo\, Canada \nBiography of the Speaker\nProf. Alfred C. H. YU is Assistant Vice-President (Research and International) and Professor of Biomedical Engineering at the University of Waterloo\, Canada. He leads the University of Waterloo’s research partnership portfolio and interdisciplinary research files\, and he is the Director of the NSERC Collaborative Research Program on “Next-Generation Innovations in Ultrasonics” in Canada. Alfred has a long-standing research interest in ultrasound imaging and therapeutics. He is a Fellow of IEEE\, American Institute of Ultrasound in Medicine\, Canadian Academy of Engineering\, and Engineering Institute of Canada. His research has been endorsed by many milestone prizes\, including the NSERC Steacie Memorial Fellowship\, the ISTU Frederic Lizzi Award\, the IEEE Ultrasonics Early Career Investigator Award\, the Ontario Early Researcher Award\, and various best paper awards. He is now the Editor-in-Chief of the IEEE Transactions on Ultrasonics\, Ferroelectrics\, and Frequency Control\, the Program Chair of 2023 and 2025 IEEE Ultrasonics Symposium\, and the Vice-Chair of the AIUM Basic Science and Instrumentation Group. \nOrganiser\nProf. W.-N. LEE \nCo-organisers\nIEEE EMB Hong Kong-Macau Joint Chapter\nTam Wing Fan Innovation Wing Two \nAll are welcome! We look forward to seeing you!
URL:https://ece.hku.hk/events/20240802-1/
LOCATION:Tam Wing Fan Innovation Wing Two\, G/F\, Run Run Shaw Building\, The University of Hong Kong
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/07/1280-3.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20240808T140000
DTEND;TZID=Asia/Hong_Kong:20240808T170000
DTSTAMP:20260512T134938
CREATED:20240725T085244Z
LAST-MODIFIED:20250114T042822Z
UID:18938-1723125600-1723136400@ece.hku.hk
SUMMARY:MVSG-based Compact Models for GaN Devices
DESCRIPTION:Abstract\nGiven its high mobility\, high breakdown voltage and decent thermal conductivity\, GaN technologies have shown great promise for high-power high-frequency (HP-HF)\, rapidly rising as a front runner for mm-wave to THz analog/RF circuits for IoT and 5G/6G wireless communication. Meanwhile\, it is also heavily explored for power electronic applications for fast charging\, data center\, and electric vehicles. As GaN technology continues to improve\, challenges of high design cost and sub-optimal system performance emerge as bottlenecks preventing the technology from wide scale deployment. Accurate\, scalable and efficient compact model is key to overcome such challenges. \nThis presentation will provide a brief overview of the family of MVSG GaN compact model\, including models for GaN HEMT\, GaN multi-channel diodes and GaN transmission-line resistors.  The model formulation and various features will be introduced. Application examples will also be demonstrated\,  showing the potentials of this group of physics-based compact models. \nSpeaker\nProf. Lan WEI\nAssociate Professor\,\nUniversity of Waterloo\, Canada \nBiography of the Speaker\nProf. Lan WEI received her B.S. in Microelectronics from Peking University\, China (2001)\, M.S and Ph. D. in Electrical Engineering from Stanford University\, USA (2007 and 2010\, respectively). She is currently an Associate Professor at the University of Waterloo\, Canada. She has intensive experience in device physics-based compact modeling including silicon and GaN technologies\, device-circuit interactive design and optimization\, integrated nanoelectronic systems with low-dimensional materials\, cryogenic CMOS device modeling and circuit design for quantum computing.  She has authored/co-authored more than 90 peered reviewed publications and served on the technical program committees including IEDM\, ICCAD\, DATE\, ISQED\, BCICTS\, etc. \nOrganiser\nProf. H. Wang \nCo-organiser\nIEEE ED/SSC Hong Kong Joint Chapter\n\nAll are welcome! We look forward to seeing you!
URL:https://ece.hku.hk/events/20240808-1/
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/2024/07/1280-2.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20240813T143000
DTEND;TZID=Asia/Hong_Kong:20240813T153000
DTSTAMP:20260512T134938
CREATED:20240712T030813Z
LAST-MODIFIED:20250114T042722Z
UID:18871-1723559400-1723563000@ece.hku.hk
SUMMARY:Mechano-nanooncology
DESCRIPTION:Abstract\nNanomedicines are an important means of treating solid tumors\, but in clinical applications\, they can only reduce side effects and cannot significantly improve efficacy. The main reason is that the rapid clearance of the reticuloendothelial system (RES) and the abnormal mechanical microenvironment of solid tumors limit the delivery efficiency of nanomedicines.The RES blockade strategy can temporarily and reversibly delay liver clearance\, improve tumor enrichment and antitumor effects of nanomedicines\, and have good biological safety. However\, the large number of nanoparticles in the circulatory system still imposes an additional burden on RES\, making it particularly important to improve the efficiency of the RES blockade strategy. We systematically explored how to use the mechanical properties of nanogel to overcome the clearing effect of RES\, and proposed the treatment strategy of antitumor effect of nanomedicines with mechanical modulation for the first time. Crucially\, RES blockade strategy based on the mechanical properties of nanogel boosts antitumor efficacy of marketed nanomedicines\, such as Doxil® And Abraxane®. Therefore\, mechano-based RES blockade has broad universality and huge clinical application potential. We propose using hyperbaric oxygen and mild photothermal therapy to improve the abnormal mechanical microenvironment of solid tumors and enhance the antitumor effect of nanomedicines. For the first time\, we discovered that hyperbaric oxygen can overcome tumor hypoxia and inhibit tumor associated fibroblasts\, regulate the abnormal mechanical microenvironment of solid tumors\, as well as the structure and function of tumor blood vessels\, thereby selectively enhancing the commercialized nanomedicines\, e.g.\, Doxil® and Abraxane® and nanoscale biological macromolecules\, such as PD-1 antibodies. In addition\, we have confirmed that the mild photothermal effect of nanomedicines efficiently depletes tumor associated fibroblasts and extracellular matrix\, reduces solid stress and stiffness of tumors\, normalizes tumor vascular structure and function\, promotes subsequent nanomedicine and oxygen delivery\, damages the ecological niche of tumor stem cells\, eliminates cancer stem cells\, and augments the antitumor effect of nanomedicines. Our results indicate that mechano-mediated regulation strategies have the potential to enhance the antitumor effect of nanomedicines. Two prospective trials have been performed in bedside. \nSpeaker\nProf. Zifu LI\nFull Professor\,\nHuazhong University of Science and Technology \nBiography of the Speaker\nProf. Zifu LI received the B.S. degree from Huazhong University of Science and Technology in 2008 and the Ph.D. degree from the Chinese University of Hong Kong in 2012. In 2013 and 2015\, he worked as a postdoctoral fellow at the University of Alberta. He then joined Georgia Institute of Technology as a research scientist. Since 2016\, he has been a full professor at Huazhong University of Science and Technology. Professor Zifu Li’s lab is based on the National Engineering Research Center for Nanomedicine at Huazhong University of Science and Technology. His research lies at the interface of biomaterials\, drug delivery\, and cellular and molecular bioengineering to fundamentally understand and therapeutically target biological molecules\, cancer cells\, immune cells\, and cancer stem cells. He applies his research findings and the technologies developed to a range of human health applications\, particularly on cancer diagnosis and treatment. Current research projects include mechano-nanooncology\, smart nanomedicine and hyperbaric oxygen-enabled cancer therapy. \nOrganizer\nProf. Zhiqin CHU \nAll are welcome! We look forward to seeing you!
URL:https://ece.hku.hk/events/20240813-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20240815T143000
DTEND;TZID=Asia/Hong_Kong:20240815T153000
DTSTAMP:20260512T134938
CREATED:20240716T092305Z
LAST-MODIFIED:20250114T042634Z
UID:18875-1723732200-1723735800@ece.hku.hk
SUMMARY:Developing of Stable Multimode Neural Interface for Brain Activity Detection and Modulation
DESCRIPTION:Speaker\nDr. Xiaoling WEI\nShanghai Institute of Microsystem and Information Technology\,\nChinese Academy of Sciences \nAbstract\nImplanted neural electrodes provide one of the most important neuro-techniques that are able to direct detect individual neuron electrical activities in the living brain. However\, there are three factors we need to consider for the further application of the electrodes for the Brain Machine Interface\, namely\, they are high-throughput\, low trauma and longevity. Ultraflexible neural electrodes have shown superior stability compared to rigid electrodes in long-term in vivo recordings\, owing to their low mechanical mismatch with brain tissue. To detect neurotransmitters as well as electrophysiology for months long is desirable in brain science. This talk I will cover our recent work on a novel stable electronic interface that can simultaneously detect neural electrical activity and dopamine concentration in deep brain. Also\, I will talk about some work related to silk fibroin-based bioelectronic devices for recording and modulation of neurons. \nSpeaker\nDr. Xiaoling WEI\nShanghai Institute of Microsystem and Information Technology\,\nChinese Academy of Sciences \nBiography of the Speaker\nDr. WEI received his B.Eng. degree in 2008 from the Department of Polymer Science and Engineering\, the University of Science and Technology of China and PhD degree in 2013 from the Department of Chemistry\, the Chinese University of Hong Kong. After one more year as a postdoctoral associate in the Hong Kong Polytechnic University\, he carried out his postdoctoral research (2014/11 – 2018/08) in Department of Biomedical Engineering\, the University of Texas at Austin (USA). Since September 2018\, he moved to Shanghai Institute of Microsystem and Information Technology\, the Chinese Academy of Sciences. He has authored and co-authored over more than 30 peer reviewed scientific articles\, including Science Advances\, Advanced Science\, Microsystem & Nanoengineering etc. His research interests are implantable devices\, neural interface and BioMEMS. \nOrganizer\nProf. Zhiqin CHU \nAll are welcome! We look forward to seeing you!
URL:https://ece.hku.hk/events/20240815-1/
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/2024/07/1280.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20240819T163000
DTEND;TZID=Asia/Hong_Kong:20240819T173000
DTSTAMP:20260512T134938
CREATED:20240813T061300Z
LAST-MODIFIED:20250114T042504Z
UID:18982-1724085000-1724088600@ece.hku.hk
SUMMARY:Variational Bayesian Inference for Sensing Over Wireless Networks
DESCRIPTION:Abstract\nFuture wireless networks are envisioned to provide ubiquitous sensing services\, driving a substantial demand for high-accuracy and low-complexity estimation algorithms. Variational Bayesian inference (VBI) provides a powerful tool for modeling complex estimation problems and leveraging prior information\, but poses a long-standing challenge on computing intractable posterior distributions. In this talk\, we propose two problem formulations that are suitable for different sensing scenarios. In the first formulation\, the sensing problem is modeled as a multi-dimensional non-convex parameter estimation. We propose a parallel stochastic particle VBI (PSPVBI) algorithm to solve this challenging problem. Due to innovations like particle approximation\, added updates of particle positions\, and parallel stochastic successive convex approximation (PSSCA)\, PSPVBI can flexibly drive particles to fit the posterior distribution with acceptable complexity\, yielding high-precision estimates of the target parameters. Furthermore\, additional speedup can be achieved by deep-unfolding this algorithm to obtain a learnable PSPVBI (LPSPVBI). In the second formulation\, the sensing problem is modeled as a structured compressive sensing with a dynamic grid. The state-of-the-art expectation maximization based compressed sensing (EM-CS) methods have a relatively slow convergence speed and each inner iteration in the E-step involves a high-dimensional matrix inverse in general. To better address this problem\, we propose an alternating estimation framework (called AE-SC-VBI) based on a novel subspace constrained VBI (SC-VBI) method\, in which the high-dimensional matrix inverse is replaced by a low-dimensional subspace constrained matrix inverse. We further prove the convergence of the SC-VBI to a stationary solution of the Kullback-Leibler divergence minimization problem. Finally\, we apply the LPSPVBI and AE-SC-VBI to solve several important sensing problems\, including multi-band WiFi sensing and TDD massive MIMO channel extrapolation. Simulations demonstrate that the proposed VBI-based algorithms can achieve a much better tradeoff between complexity per iteration\, convergence speed\, and performance compared to the state-of-the-art algorithms. \nSpeaker\nProf. An LIU\nAssociated Professor\,\nCollege of Information Science and Electronic Engineering\,\nZhejiang University \nBiography of the Speaker\nProf. An LIU (Senior Member\, IEEE) received the B.S. and Ph.D. degrees in electrical engineering from Peking University\, China\, in 2004 and 2011\, respectively. From 2008 to 2010\, he was a Visiting Scholar with the Department of ECEE\, University of Colorado at Boulder. He was a Post-Doctoral Research Fellow from 2011 to 2013\, a Visiting Assistant Professor in 2014\, and a Research Assistant Professor with the Department of ECE\, HKUST\, from 2015 to 2017. He is currently an Associated Professor with the College of Information Science and Electronic Engineering\, Zhejiang University. His research interests include wireless communications\, stochastic optimization\, compressive sensing\, and machine/deep learning for communications. He is serving an Editor for IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS and a member for the Signal Processing for Communications and Networking Technical Committee (SPCOM TC) of IEEE Signal Processing Society. He served as an Editor for IEEE TRANSACTIONS ON SIGNAL PROCESSING and IEEE WIRELESS COMMUNICATIONS LETTERS. \nOrganiser\nProf. Kaibin HUANG \nAll are welcome! We look forward to seeing you!
URL:https://ece.hku.hk/events/20240819-1/
LOCATION:Room CB-601J\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/08/1280-4.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20240826T093000
DTEND;TZID=Asia/Hong_Kong:20240826T124000
DTSTAMP:20260512T134938
CREATED:20240805T022655Z
LAST-MODIFIED:20250114T042419Z
UID:18974-1724664600-1724676000@ece.hku.hk
SUMMARY:A New Era of Emerging Microelectronics and Applications
DESCRIPTION:Seminar series under the 2022/23 Theme-based Research Scheme ReRACE: ReRAM Al Chips on the Edge. \nProgramme & Speakers \n\n\n\n09:30am–09:55am\nOpening & Briefing of ReRACE\nProf. Ngai Wong\n\n\n10:00am–10:40am\nEfficient & Secure Edge Al through ReRAM Compute-in-Memory (CIM) Co-Design\nProf. Ngai Wong\, Dr. Zhengwu Liu & Students\n\n\n10:45am–11:25am\nBuilding ReRAM Crossbars and Peripheral Circuitry for Efficient Edge Al\nProf.  Can Li & Students\n\n\n11:30am–12:10pm\nWearable\, Soft Electronics Merging Humans and Machines\nProf. Shiming Zhang & Students\n\n\n12:15pm–12:40pm\nFuture Directions\, Industrial Pointers & Wrap-up\nProf. Ngai Wong\n\n\n\nOrganizer\nProf. Ngai Wong \nAll are welcome! We look forward to seeing you!
URL:https://ece.hku.hk/events/20240826-1/
LOCATION:Lecture Theatre CB-A\, G/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/08/1280-3.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20240827T133000
DTEND;TZID=Asia/Hong_Kong:20240827T143000
DTSTAMP:20260512T134938
CREATED:20240826T065927Z
LAST-MODIFIED:20250114T042326Z
UID:19033-1724765400-1724769000@ece.hku.hk
SUMMARY:IEEE EDS Distinguished Lecturer Seminar: Key Considerations for Obtaining High Performance Contact-controlled Thin-film Transistors
DESCRIPTION:Abstract\nSource-gated transistors (SGTs) have a relatively long history of development but only recently have mainstream technologies allowed for their effective implementation at scale. This talk is addressed to those interested in efficient analog and mixed signal design with advanced thin-film transistors. They provide a development progression with a forward look toward SGT application to future edge processing of sensor data\, signal conditioning\, and current-mode driving. Crucially\, the concept can be applied in practically any material system. As such\, the talk will present the fundamentals of contact effect engineering and modelling\, design rules for successful SGT implementation\, specifics of performance optimisation in thin-film silicon\, organic\, and oxide semiconductors\, and structural evolutions for additional functionality. Finally\, the next step in the evolution of contact-controlled thin-film transistor\, the multimodal transistor (MMT) will be briefly introduced. \nSpeaker\nProf. Radu A. Sporea\nAssociate Professor in Semiconductor Devices\,\nUniversity of Surrey \nBiography of the Speaker\nProf. Radu Sporea is Associate Professor in Semiconductor Devices at the University of Surrey\, and holds an EPSRC Early Career Fellowship (2021-2026). He was RAEng Research Fellow (2011-2016)\, EPSRC PhD+ Fellow (2010-2011) and PhD researcher (2006 – 2010). Radu studied Computer Systems Engineering at “Politehnica” University\, Bucharest\, and worked as Design Engineer for Catalyst Semiconductor Romania on ultra-low-power CMOS analog circuits. Radu was named EPSRC Rising Star in 2014 and received the I K Brunel Award for Engineering in 2015\, the Vice Chancellor’s award for Early Career Teaching in 2017 and the Tony Jeans Inspirational Teaching distinction in 2018. In 2021\, he was a finalist for Innovator of the Year prize at Surrey. His research focuses on advanced thin-film transistors for improved manufacturability\, large area sensors and sensor arrays for smart environments\, and paper-based electronics and physical-digital interaction. He is chair of the IEEE EDS UK and Ireland chapter. \nOrganiser\nProf. Han Wang\nProfessor\,\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nCo-organiser\nIEEE EDS Hong Kong Chapter \nAll are welcome! We look forward to seeing you!
URL:https://ece.hku.hk/events/20240827-2/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/08/1280-2.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20240827T160000
DTEND;TZID=Asia/Hong_Kong:20240827T173000
DTSTAMP:20260512T134938
CREATED:20240826T065135Z
LAST-MODIFIED:20250114T042239Z
UID:19031-1724774400-1724779800@ece.hku.hk
SUMMARY:Asymptotic Capacity of 1-Bit MIMO Channels: From Bayesian Statistics to Large-Scale MIMO Communications
DESCRIPTION:Abstract\nLarge-scale MIMO systems utilizing low-resolution analog-to-digital converters (ADCs) have emerged as a cost-effective and energy-efficient solution for future wireless communication networks. While extended research has been conducted on signal processing and transceiver design in these systems\, the fundamental Shannon capacity limit remains elusive. In this talk\, we introduce a novel approach that leverages information-theoretic asymptotics from Bayesian statistics to derive the Shannon capacity of such systems. We reveal the critical role of the Fisher information and Jeffreys’ prior in this characterization\, and demonstrate how to apply this method to derive the asymptotic capacity of 1-bit MIMO channels in the Gaussian and the (coherent and non-coherent) fading cases. \nSpeaker\nProf. Sheng Yang\nFull Professor\,\nParis-Saclay University \nBiography of the Speaker\nProf. Sheng Yang received the B.E. degree in electrical engineering from Jiaotong University\, Shanghai\, China\, in 2001\, and both the engineer degree and the M.Sc. degree in electrical engineering from Telecom Paris\, France\, in 2004\, respectively. In 2007\, he obtained his Ph.D. from Université de Pierre et Marie Curie (Paris VI). From October 2007 to November 2008\, he was with Motorola Research Center in Gif-sur-Yvette\, France\, as a senior staff research engineer. Since December 2008\, he has joined CentraleSupélec\, Paris-Saclay University\, where he is currently a full professor. He has also hold visiting professorships in the University of Hong Kong (2015\, 2016) and the Hong Kong University of Science and Technology (2023\, 2024). He received the 2015 IEEE ComSoc Young Researcher Award for the Europe\, Middle East\, and Africa Region (EMEA). He was an associate editor of the IEEE transactions on wireless communications from 2015 to 2020. He is currently an associate editor of the IEEE transactions on information theory. \nOrganiser\nProf. Kaibin Huang \nAll are welcome! We look forward to seeing you!
URL:https://ece.hku.hk/events/20240827-1/
LOCATION:Room CB-601J\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/08/1280-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20240829T100000
DTEND;TZID=Asia/Hong_Kong:20240829T150000
DTSTAMP:20260512T134938
CREATED:20240814T071715Z
LAST-MODIFIED:20250114T042119Z
UID:18984-1724925600-1724943600@ece.hku.hk
SUMMARY:Unveiling Neural Activities through Advanced Microscopic Technologies
DESCRIPTION:This workshop will include notable speakers from various universities in Hong Kong and we shall further discuss regarding the potential and advancement of neuroimaging technologies. \n \nProgramme and Speakers:\n \n\n\n\n\nTime\n\n\nActivity\n\n\n\n\n09:30 am – 10:00 am\n\n\nRegistration\n\n\n\n\n10:00 am – 10:15 am\n\n\nOpening\, Welcome Speech\, and Photo Sessions\n– Prof. Kenneth Kin-Yip Wong\, HKU\n\n\n\n\n10:15 am – 10:40 am\n\n\nTalk 1: Pushing the Limit of kHz Multiphoton FACED Imaging\n– Prof. Kevin Tsia\, HKU\n\n\n\n\n10:40 am – 11:05 am\n\n\nTalk 2: Counteracting Brain Aging: Recent Progress and Potential Further Applications of In Vivo Imaging\n– Dr. Junzhe Huang\, CUHK\n\n\n\n\n11:05 am – 11:30 am\n\n\nTalk 3: Empowering Multimode Fiber for Minimally Invasive Deep-brain Imaging with Wavefront Shaping\n– Prof. Puxiang Lai\, HKPolyU\n\n\n\n\n11:30 am – 11:55 am\n\n\nTalk 4: Volumetric Multiphoton Microscopy with Non-diffracting Beams\n– Dr. Hongsen He\, HKU\n\n\n\n\n11:55 am – 14:00 pm\n\n\nLunch Break\n\n\n\n\n02:00 pm – 02:25 pm\n\n\nTalk 5: Intravital Imaging in Learning and Memory\n– Prof. Cora Sau Wan Lai\, HKU\n\n\n\n\n02:25 pm – 02:50 pm\n\n\nTalk 6: Illuminating Links Between Neural Circuit Activity and Behaviour\n– Prof. Michael Hausser\, HKU\n\n\n\n\n02:50 pm – 03:00 pm\n\n\nClosing Remarks\n– Prof. Kenneth Kin-Yip Wong\, HKU\n\n\n\n\nOrganiser\nProf. Kenneth Kin-Yip Wong\nElectrical and Electronic Engineering Department\,\nThe University of Hong Kong (HKU) \nCo-organisers\nChinese University of Hong Kong (CUHK)\nHong Kong Polytechnic University (HKPolyU) \nFunded by\nCollaborative Research Fund (C7074-21G) \nAll are welcome! Save the date and we look forward to seeing you soon.
URL:https://ece.hku.hk/events/20240829-1/
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/2024/08/1280.jpg
END:VEVENT
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