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X-ORIGINAL-URL:https://ece.hku.hk
X-WR-CALDESC:Events for Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
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TZID:Asia/Hong_Kong
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TZOFFSETFROM:+0800
TZOFFSETTO:+0800
TZNAME:HKT
DTSTART:20240101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250507T100000
DTEND;TZID=Asia/Hong_Kong:20250507T110000
DTSTAMP:20260509T211609
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
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250507T140000
DTEND;TZID=Asia/Hong_Kong:20250507T150000
DTSTAMP:20260509T211609
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:20250507T153000
DTEND;TZID=Asia/Hong_Kong:20250507T163000
DTSTAMP:20260509T211609
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
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