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X-WR-CALNAME:Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
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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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TZID:Asia/Hong_Kong
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DTSTART:20230101T000000
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DTSTART;TZID=Asia/Hong_Kong:20241121T143000
DTEND;TZID=Asia/Hong_Kong:20241121T153000
DTSTAMP:20260512T104508
CREATED:20241115T035707Z
LAST-MODIFIED:20250114T032601Z
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SUMMARY:Low-coherence Biophotonics Imaging for the Brain
DESCRIPTION:Abstract\nThis talk will highlight our recent efforts to explore the brain using low-coherence light. We will discuss two key scenarios: intraoperative assessment of brain cancer infiltration in patients and real-time imaging of dynamic neural activities in freely behaving rodents. In the first scenario\, focused on clinical translation\, we developed a quantitative color-coded optical coherence tomography (OCT) technology. This provides neurosurgeons with direct visual cues based on the intrinsic optical properties of tissues\, enabling them to maximize cancer resection while minimizing damage to healthy brain tissue. Our results from over 50 patients demonstrate excellent specificity and sensitivity. For the second scenario\, which pertains to basic research\, we created the first all-fiber-optic\, head-mounted\, ultracompact (~2 mm diameter)\, and ultralight (<1 g) two-photon fiberscopy platform. This allows for high-resolution imaging of neuronal activity in freely walking or rotating mice. We will discuss recent advancements\, including a 15X increase in the area field of view through cascaded magnification\, facilitating the imaging of multiple neurons and their functional correlations within a single frame. Additionally\, we achieved a significant increase in imaging frame rates (10-30 times faster\, reaching video rates) through an improved scanner design and a two-stage deep learning strategy. Detailed neural imaging results will be presented at the seminar\, and if time permits\, we will also explore other applications of these technologies for noninvasive in vivo optical histology. \nSpeaker\nProf. Xingde Li\nDepartment of Biomedical Engineering\,\nJohns Hopkins University \nBiography of the Speaker\nXingde Li earned his PhD in Physics from the University of PENN in 1998. Following 3 years of postdoctoral training at MIT\, he began his academic career as Assistant Professor at the University of Washington. In 2009\, he joined the BME Department at Johns Hopkins as Associate Professor and later became a Full Professor in 2011. His research is centered around on biophotonics imaging technologies and their applications in translational and basic research. He has published about 150 journal papers\, with a total citation over 23\,000 and an H-index of 65 (Google Scholar). Beyond research endeavors\, he has actively participated in various committees for different societies\, chaired numerous international conferences\, and served on many proposal review panels. He has also taken on editorial roles as a topical editor\, associate editor for several journals and the lead founding EiC for a Science Partner Journal – BMEF. He has been elected Fellow of OPTICA\, SPIE\, and AIMBE. \nOrganiser\nProf. Kenneth K.Y. Wong\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20241121-2/
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/2024/11/1280-7.jpg
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241121T150000
DTEND;TZID=Asia/Hong_Kong:20241121T160000
DTSTAMP:20260512T104508
CREATED:20241115T035452Z
LAST-MODIFIED:20250114T032348Z
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SUMMARY:Data-Centric Architecture and Algorithm Co-design for Data-Intensive Modern Applications
DESCRIPTION:Mode: Online via Zoom\nZoom Link: https://hku.zoom.us/j/97278710788?pwd=VOGfxnayhDjXu3PTxj37IfWDzCGQ1i.1\nMeeting ID: 972 7871 0788\nPassword: 533887 \nAbstract\nIn today’s digital landscape\, the exponential growth of data has become the driving force behind modern applications\, such as genome analysis and machine learning applications\, revolutionizing our approach to healthcare and overall living quality. However\, this unprecedented deluge of data poses a formidable challenge to traditional von Neumann computer architectures. The inefficiencies arising from the constant data movement between processors and memory consume a substantial portion of both execution time and energy when running modern applications on conventional von Neumann computers. To reduce this significant data movement\, data-centric architectures\, particularly processing-in-memory accelerators\, emerge as a promising solution by enabling the processing of data directly where it resides. Nonetheless\, most existing data-centric architectures primarily focus on accelerating specific arithmetic operations\, inadvertently leaving a substantial gap between the architectural enhancements and the holistic needs of modern applications. Concurrently\, conventional software optimizations often treat the architecture as a black box\, which inherently limits the potential acceleration of applications. \nThis talk seeks to bridge the gaps between modern applications and data-centric architectures and revolutionize the landscape of data-centric acceleration. First\, this talk delves into the distinctive features of modern applications\, illustrating these features with a focus on genome analysis within the realm of bioinformatics. Second\, challenges arise when data-intensive modern applications are executed on conventional computers. The talk then transitions to a compelling remedy: the adoption of a data-centric architecture. Third\, this talk outlines the intricacies involved in designing a data-centric architecture for modern applications. It explores the challenges inherent in this process and concurrently offers potential solutions. Following this analysis\, the talk advances to put forth an innovative architecture specifically designed for genome analysis via algorithm-architecture co-design. In conclusion\, the talk wraps up with a summary and a glimpse into future avenues of exploration. \nSpeaker\nDr. Haiyu Mao\nLecturer (Assistant Professor)\,\nDepartment of Engineering\,\nKing’s College London \nBiography of the Speaker\nDr. Haiyu Mao is a Lecturer (Assistant Professor) in the Department of Engineering at King’s College London. Before that\, she was a postdoctoral researcher in the SAFARI Research group led by Prof. Onur Mutlu at ETH Zurich\, Switzerland\, since September 2020. In July 2020\, she received her Ph.D. degree in computer science from Tsinghua University\, China. Her research interests intersect between computer architecture\, memory systems\, data-centric acceleration\, bioinformatics\, machine learning accelerators\, non-volatile memory\, and secure memory. Visit Haiyu’s website for more info: https://hybol1993.github.io \nOrganiser\nProf. Kaibin Huang\nHead\,\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20241121-1/
LOCATION:Online via Zoom
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/1280-6.jpg
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20241121T160000
DTEND;TZID=Asia/Hong_Kong:20241121T170000
DTSTAMP:20260512T104508
CREATED:20241119T072932Z
LAST-MODIFIED:20250114T032246Z
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SUMMARY:Integrated Sensing and Communications: From Signal Processing to Prototype
DESCRIPTION:Abstract\nIntegrating sensing functionality into communication devices is emerging as a key feature of the 6G Radio access network. Dual-function radar communication (DFRC) systems implement both sensing and communication using the same hardware thus saving in power\, cost and spectral efficiency. In this talk\, we focus on some of the signal processing aspects of designing and implementing DFRC systems and discuss how the convergence of sensing and communication can be utilized to efficiently exploit congested resources and to communicatee intelligence via sensing.  In particular\, we begin by introducing several approaches to reduce hardware cost by exploiting sub-Nyquist principles and sparse arrays to sense and communicate jointly at low sampling and bit rates. We then introduce new hardware designs that allow continuous monitoring using event-based sampling and high dynamic range. We next consider several different approaches to waveform design and receive signal processing considering both radar detection mode and target localization including spectrum sharing\, joint precoder design\, and index modulation techniques. Our approaches allow design flexibility in trading off radar and communication performance\, while preserving the radar ambiguity function. We end by discussing future trends in DFRC systems including model-based AI for communication and radar under uncertain channels\, near-field communication and radar\, and hybrid RIS/DMA to create configurable radiation patterns for scalable and low power sensing and communication. Throughout the talk we will consider both the theory and hardware prototypes and show several demos of real-time DFRC systems\, low bit and low power ADCs\, and cognitive joint radio and radar systems. \nSpeaker\nProf. Yonina Eldar\nWeizmann Institute of Science\, Israel \nBiography of the Speaker\nYonina Eldar is a Professor in the Department of Mathematics and Computer Science\, Weizmann Institute of Science\, Rehovot\, Israel where she heads the Center for Biomedical Engineering and Signal Processing and holds the Dorothy and Patrick Gorman Professorial Chair. She is also a Visiting Professor at MIT\, a Visiting Scientist at the Broad Institute\, and an Adjunct Professor at Duke University and was a Visiting Professor at Stanford.  She is a member of the Israel Academy of Sciences and Humanities\, an IEEE Fellow and a EURASIP Fellow. She received the B.Sc. degree in physics and the B.Sc. degree in electrical engineering from Tel-Aviv University\, and the Ph.D. degree in electrical engineering and computer science from MIT\, in 2002. She has received many awards for excellence in research and teaching\, including the IEEE Signal Processing Society Technical Achievement Award (2013)\, the IEEE/AESS Fred Nathanson Memorial Radar Award (2014) and the IEEE Kiyo Tomiyasu Award (2016). She was a Horev Fellow of the Leaders in Science and Technology program at the Technion and an Alon Fellow. She received the Michael Bruno Memorial Award from the Rothschild Foundation\, the Weizmann Prize for Exact Sciences\, the Wolf Foundation Krill Prize for Excellence in Scientific Research\, the Henry Taub Prize for Excellence in Research (twice)\, the Hershel Rich Innovation Award (three times)\, and the Award for Women with Distinguished Contributions. She received several best paper awards and best demo awards together with her research students and colleagues\, was selected as one of the 50 most influential women in Israel\, and was a member of the Israel Committee for Higher Education. She is the Editor in Chief of Foundations and Trends in Signal Processing\, a member of several IEEE Technical Committees and Award Committees\, and heads the Committee for Promoting Gender Fairness in Higher Education Institutions in Israel. \nOrganiser\nProf. Kaibin Huang\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20241121-3/
LOCATION:Room CB-601J\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
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