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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:20240101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250808T140000
DTEND;TZID=Asia/Hong_Kong:20250808T160000
DTSTAMP:20260511T191747
CREATED:20250801T021445Z
LAST-MODIFIED:20250804T020549Z
UID:112825-1754661600-1754668800@ece.hku.hk
SUMMARY:Seminar on Publishing in Nature Nanotechnology: An insider’s view of Nature journals
DESCRIPTION:Abstract\nDr. Lu will talk about the scope of Nature Nanotechnology and an overview of Nature journals. And she will share her experience on the editorial process (the workflow and the statistical data) and disclose the criteria of Nature journals from an insider’s view. Through the talk\, you will know better on what kind of papers a highly selective journal is looking for\, how the editors decide whether to send a paper out or not\, what they will do when the reviewers’ comments are contradictory. And Lu is happy to share with you her personal experience as an editor if you are interested together with some practical tips for writing and submitting a paper to a Nature journal. \nSpeaker\nDr. Lu Shi\nSenior Editor\nSpringer Nature \nSpeaker’s Biography\nDr. Lu Shi joined Nature Nanotechnology in April 2023 from Wiley\, where she was the Editor-in-Chief of Advanced Electronic Materials and Deputy Editor of Advanced Materials. Prior to her editorial career\, she worked on 2D materials growth and magnetoelectric transport behavior of van der Waals heterostructures. For her work\, she received a joint PhD in Materials Science and Physics from the Université Catholique de Louvain\, Belgium and Université Grenoble Alpes\, France. She obtained her BEng and MSc in Materials Science and Engineering from Shanghai Jiao Tong University and Shanghai Institute of Ceramics\, Chinese Academy of Sciences. She covers a broad range of topics across electronics and optoelectronics in the journal and is based in Shanghai. \nOrganiser\nProf. Can Li\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250808-1/
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/2025/08/Can-Li_20250808-seminar-web-banner.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250813T140000
DTEND;TZID=Asia/Hong_Kong:20250813T150000
DTSTAMP:20260511T191747
CREATED:20250807T044930Z
LAST-MODIFIED:20250808T013651Z
UID:112971-1755093600-1755097200@ece.hku.hk
SUMMARY:RPG Seminar – Incorporating Long-Term Costs of Renewable Energy Generation into Electricity Pricing
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/96780307210?pwd=1wIMCFlBwNOSDiVMsEFxnVgzm5kLnn.1 \n  \nAbstract\nRenewable energy generation has almost no variable costs and is no longer suitable for marginal cost pricing. New renewable power-generating facilities have a large amount of fixed costs\, which can be determined by long-term average cost to join the final electricity price setting in the electricity market. This paper designs a price offer method that let renewable energy generators use their corresponding Levelized Cost of Electricity (LCOE) as their price offer benchmarks to participate electricity price setting. The multi-time-period optimal power flow (OPF) model including price offer benchmark for renewable energy is proposed to study the effect of long-term average cost pricing.   Results indicate that the proposed price offer benchmark can help maintain reasonable wholesale electricity price in high renewable energy penetration and create incentives for renewable energy development. It provides a way to price the electricity of future green energy system. \nSpeaker\nSpeaker: Mr. Zhang Yanning\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nYanning Zhang received his B.Eng. and M.Eng. Degree from the University of New South Wales. He is currently pursing a PhD degree in the Department of Electrical and Electronic Engineering\, The University of Hong Kong. His research interests include electricity market\, renewable energy generation\, electricity long-term pricing. \nOrganiser\nProf. Jin Zhong \nAll are welcome.
URL:https://ece.hku.hk/events/20250813-1/
LOCATION:Online via Zoom
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250814T160000
DTEND;TZID=Asia/Hong_Kong:20250814T173000
DTSTAMP:20260511T191747
CREATED:20250813T012141Z
LAST-MODIFIED:20250813T013434Z
UID:113006-1755187200-1755192600@ece.hku.hk
SUMMARY:Seminar on Voltage Stability Constrained Power System Optimization: A Constraint-learning Method
DESCRIPTION:Abstract\nHigh penetration of renewable energy poses severe challenges to power system voltage stability due to weakened voltage and reactive power support\, complex voltage stability mechanisms\, and highly diversified operation states. Traditional control strategies predefined based on limited operation states fail to maintain the required voltage stability margin. This report presents a constraint-learning-based framework for embedding accurate and efficient voltage stability constraints into power system optimization to improve voltage stability of the optimization results. The proposed framework represents the inherently nonlinear and nonconvex voltage stability constraints using multiple convex polyhedrons (MCPH). The learned constraints are linear\, sparse\, and embeddable in mixed-integer linear programming (MILP) formulations. Case studies demonstrate that integrating MCPH constraints into voltage stability constrained unit commitment improves voltage stability margin with reduced computational burden. Furthermore\, the framework is extended to generation expansion planning\, where physical ensemble constraint learning captures converter-driven stability requirements across diverse generation mix scenarios. The results confirm that the proposed approach effectively balances stability enhancement\, solution efficiency\, and scalability for high-renewable\, stability-constrained power system optimization. \nSpeaker\nMr. Hongyang Jia\nTsinghua University \nSpeaker’s Biography\nHongyang Jia received the B.S. degree in electrical engineering in 2021 from Tsinghua University\, Beijing\, China\, where he is currently pursuing the Ph.D. degree in Electrical Engineering under the supervision of Associate Prof. Ning Zhang. He was a Visiting Ph.D. Student at Imperial College London (Aug. 2024 – Feb. 2025) under Associate Prof. Fei Teng. His research interests center on trustworthy machine learning for science and engineering\, with specific applications in power system optimization under voltage stability constraints\, data-driven security/stability rule extraction and embedding\, and power system voltage stability assessment. \nOrganiser\nProf. Wang Yi \nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250814-1/
LOCATION:Room CB-601J\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=application/pdf:https://ece.hku.hk/wp-content/uploads/2025/08/August-14-2025-1-Web-banner.pdf
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250815T110000
DTEND;TZID=Asia/Hong_Kong:20250815T120000
DTSTAMP:20260511T191747
CREATED:20250806T043442Z
LAST-MODIFIED:20250806T090812Z
UID:112883-1755255600-1755259200@ece.hku.hk
SUMMARY:Seminar on Towards Deep Learning MR Reconstruction with No Ground Truth and Fast Inference
DESCRIPTION:Abstract\nSince 2016\, deep learning techniques have been introduced to solve the inverse problem of MR image reconstruction from undersampled data from accelerated acquisitions. Since then\, the field has grown substantially. A wide range of machine learning methods have been developed\, translated into clinical practice and adopted as products by all major scanner vendors. In this talk\, after a general introduction to deep learning for MR image reconstruction\, I will focus on two open challenges in the field. First\, the application of deep learning reconstruction for dynamic contrast-enhanced imaging and abdominal imaging\, where no ground truth can be obtained for model training. Second\, the optimisation of network architectures towards computation time at inference for real-time imaging and clinical translation of instance-specific learning\, where trainings need to be performed during inference. \nSpeaker\nProf. Florian KNOLL\nProfessor and Head of the Computational Imaging Lab\,\nDepartment Artificial Intelligence in Biomedical Engineering (AIBE)\,\nFriedrich-Alexander-Universität Erlangen-Nürnberg \nSpeaker’s Biography\nProf. Florian KNOLL received his PhD in Electrical Engineering in 2011 from Graz University of Technology. From 2015 to 2021\, he was Assistant Professor for Radiology at the Center for Biomedical Imaging at NYU Grossman School of Medicine. Since 2021\, he has been Professor and Head of the Computational Imaging Lab at the Department Artificial Intelligence in Biomedical Engineering at Friedrich-Alexander-Universität Erlangen-Nürnberg. He currently holds four grants from the German Research Fund (DFG) and an R01 grant from the National Institutes of Health (NIH). His research interests include iterative MR image reconstruction\, parallel MR imaging\, compressed sensing and machine learning. \nOrganiser\nProf. Ed Xuekui WU\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAll are welcome!
URL:https://ece.hku.hk/events/20250815-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Highlights,Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2025/08/1280.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250827T140000
DTEND;TZID=Asia/Hong_Kong:20250827T150000
DTSTAMP:20260511T191747
CREATED:20250822T025105Z
LAST-MODIFIED:20250822T025217Z
UID:113079-1756303200-1756306800@ece.hku.hk
SUMMARY:RPG Seminar – Calibrationless Reconstruction of Uniformly Undersampled Multi-Channel MR Data with Deep Learning Estimated ESPIRiT Maps
DESCRIPTION:Zoom Link: https://us05web.zoom.us/j/82334736966?pwd=rKsbc5OlbKDaftQ5NbrkvvoohDw5ct.1 \nAbstract\nESPIRiT\, one commonly used parallel imaging reconstruction technique\, forms the images from undersampled MR data using ESPIRiT data using ESPIRiT maps that closely represent coil sensitivity information. Accurate estimation of ESPIRiT maps requires the acquisition of coil sensitivity calibration or autocalibration signals. We develop a U-Net based deep learning model to directly estimate the multi-channel ESPIRiT maps from uniformly undersampled multi-channel multi-slice 2D MR data. The model incorporates coil-subject geometry prior information. It is trained using fully sampled data from the same MR receiving coil system by minimizing a hybrid loss on ESPIRiT maps derived from each dataset with and without spatial alignment to the coil system. The performance of the approach was evaluated using publicly available gradient-echo T1-weighed brain data. \nSpeaker\nSpeaker: Junhao Zhang\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nJunhao Zhang obtained his Bachelor degree in Biomedical Engineering from Xi’an Jiaotong University in 2019. In 2021\, he obtained his Master degree in Biomedical Engineering from Columbia University in the City of New York. He is now pursuing a PhD degree with Prof Ed X Wu in the Department of Electrical and Electronic Engineering. His research focuses on the application of deep learning techniques to MRI reconstruction and dynamic MRI. \nOrganiser\nProf. Ed X Wu \nAll are welcome.
URL:https://ece.hku.hk/events/20250827-2/
LOCATION:Online via Zoom
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250827T150000
DTEND;TZID=Asia/Hong_Kong:20250827T160000
DTSTAMP:20260511T191747
CREATED:20250822T024243Z
LAST-MODIFIED:20250822T024728Z
UID:113076-1756306800-1756310400@ece.hku.hk
SUMMARY:RPG Seminar – Ultra-low-field magnetization transfer imaging with low specific absorption rate
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/93345117865?pwd=f9VJJMOyiUlAkwOwAe4E5rboouDJIP.1 \n  \nAbstract\nThe recent resurgence of ultra-low-field (ULF) magnetic resonance imaging (MRI) (i.e.\, below 0.1 T) is showing great promise for future clinical applications due to its low cost\, portability\, and accessibility. One significant advantage of ULF MRI is its extremely low specific absorption rate (SAR)\, which makes it possible to use highly flexible frequency (RF) pulses for strong magnetization transfer (MT) saturation and achieve tissue/lesion contrast enhancement. MT imaging provides insights into dipolar coupling and chemical exchange processes between the “bound pool” (i.e.\, macromolecular) and the “free pool” (i.e.\, bulk water)\, and has proven valuable in the diagnosis of demyelinating disease and the evaluation of articular cartilage degeneration and repair. In this seminar\, we present novel techniques for efficient MT imaging with strong MT saturation and extremely low SAR on ULF MRI scanners. \nSpeaker\nSpeaker: Shi Su\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nShi Su received his BEng and MEng degrees in Electrical and Electronic Engineering from Beihang University in 2011 and 2014\, respectively. He is now pursuing a PhD degree with Prof Ed X. Wu in the Department of Electrical and Electronic Engineering. His research focuses on the technical development and application of MRI. \nOrganiser\nProf. Ed X Wu \nAll are welcome.
URL:https://ece.hku.hk/events/20250827-1/
LOCATION:Online via Zoom
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20250827T160000
DTEND;TZID=Asia/Hong_Kong:20250827T170000
DTSTAMP:20260511T191747
CREATED:20250822T031939Z
LAST-MODIFIED:20250822T031939Z
UID:113084-1756310400-1756314000@ece.hku.hk
SUMMARY:RPG Seminar – Ultra-low-field Balanced Steady-state Free Precession MRI at 0.05 Tesla
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/94172535993?pwd=oTH8kZ6eiLvp9a61FDZ21omj70WVaA.1 \n  \nAbstract\nThe high cost and limited accessibility of MRI scanners remain significant barriers to their broader use in clinical settings. This study aims to demonstrate the feasibility of balanced steady-state free precession (bSSFP) imaging at ultra-low-field (ULF) on a highly simplified and low-cost 0.05 Tesla whole-body MRI scanner. The bSSFP protocols demonstrated reasonable image quality at 0.05 Tesla\, allowing visualization of various anatomical structures. The protocols provided a spatial resolution of 2×2×6 mm3 with approximately 5 minutes of scan time per protocol. Good soft tissue contrasts were shown\, facilitating the identification of major tissue types within each structure. This study demonstrates that imaging various anatomical structures with bSSFP at 0.05 Tesla is efficient and feasible. \nSpeaker\nSpeaker: Mr Ding Ye\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nDing Ye received his BEng in Electrical and Electronic Engineering from Liaoning University in 2018 and received his MEng degrees in Electrical and Electronic Engineering from Chongqing University in 2021\, respectively. He is now pursuing a PhD degree with Prof Ed X Wu in the Department of Electrical and Electronic Engineering. His research focuses on the technical development and application of ULF MRI. \nOrganiser\nProf. Ed X Wu \nAll are welcome.
URL:https://ece.hku.hk/events/20250827-3/
LOCATION:Online via Zoom
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
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