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TZID:Asia/Hong_Kong
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TZOFFSETTO:+0800
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DTSTART:20240101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20251202T140000
DTEND;TZID=Asia/Hong_Kong:20251202T150000
DTSTAMP:20260510T224159
CREATED:20251125T032509Z
LAST-MODIFIED:20251125T032509Z
UID:114260-1764684000-1764687600@ece.hku.hk
SUMMARY:RPG Seminar – Ultrafast quantum sensing enabled by in-sensor computing
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/96428082165?pwd=Ra2k3v7nAa8r90G3Ntje8n6uspb3V4.1 \nAbstract\nNitrogen Vacancy (NV) center\, an optically addressable defect in diamond\, has been explored as a promising sensing platform\, due to its exceptional electronic spin properties at the room temperature. The widefield quantum sensing\, leveraging this special property\, allows for parallel readout of spatially resolved NV fluorescence\, and therefore offers enormous potential in diverse fields\, including temperature and magnetic field capturing. Conventional widefield quantum sensing method relying on traditional frame-based cameras\, however\, is usually limited in its sensing speed because it generates a massive amount of data in the form of image frames that needs to be transferred from the camera sensors for further processing. \nThis seminar will talk about a new method that realizes the ultrafast widefield quantum sensing by leveraging the bio-inspired in-sensor processing capability. The designed intelligent system mimics the working process of human eyes that merges signal detecting and processing together\, and the resonance frequencies then can be extracted during the sensing period while no redundant raw data needs to be transferred outside\, and thus an ultrashort sensing time (~ 10 µs in theory) can be achieved. \nSpeaker\nMr. Du Zhiyuan\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nDu Zhiyuan received his B.S. and M.S. degree from the School of Optics and Photonics at Beijing Institute of Technology (BIT)\, China in 2016 and 2019\, respectively. He is currently pursuing a Ph.D. degree at the Department of Electrical and Electronic Engineering under the supervision of Prof. Can Li. His research interests focus on in-sensor computing\, emerging memory device development\, and its application in intelligent quantum sensing. \nOrganiser\nProf. Can Li\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251202-2/
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:20251202T140000
DTEND;TZID=Asia/Hong_Kong:20251202T150000
DTSTAMP:20260510T224159
CREATED:20251113T062320Z
LAST-MODIFIED:20251126T063144Z
UID:113888-1764684000-1764687600@ece.hku.hk
SUMMARY:Seminar on Bi-Static Sensing for Next Generation Perceptive Communication Networks: Technologies and Applications
DESCRIPTION:The event time has been revised to start at 2:00 pm. \nAbstract\nIntegrated Sensing and Communications (ISAC) represents a paradigm shift from conventional communication-only networks toward systems that natively integrate radar-like sensing capabilities. It has become a foundational technology for next-generation wireless systems\, including Wi-Fi and 6G networks. \nBi-static sensing\, where a sensing receiver exploits signals transmitted by another node\, naturally aligns with the topology of communication networks. It circumvents the stringent full-duplex requirements of mono-static sensing and offers enhanced spatial sensing diversity. However\, clock (Local oscillating signal) asynchronism\, which inherently exists among spatially separated communication nodes\, poses a central and challenging problem. It can cause ranging ambiguities and disrupt coherent processing of discontinuous measurements\, such as those required for Doppler frequency estimation. If effectively resolved\, sensing could be seamlessly realised within existing communication infrastructures\, requiring minimal hardware or architectural modifications. \nThis talk explores advanced techniques for tackling clock asynchronism in bi-static sensing\, with a focus on efficient single-receiver-based solutions. The problem will first be introduced in the context of 6G perceptive mobile networks\, followed by a comprehensive overview of recent methods applicable to both multi-antenna and single-antenna configurations. I will then present our latest sensing applications developed using these techniques\, including moving-object tracking\, respiration and heartbeat monitoring\, behavior recognition\, and environmental sensing such as rainfall and water-level detection. The talk concludes by outlining key open challenges and future research directions in this rapidly evolving field. \nSpeaker\nProf. Andrew ZHANG\nUniversity of Technology Sydney \nSpeaker’s Biography\nProf. J. Andrew ZHANG (M’04-SM’11) is a Professor in the School of Electrical and Data Engineering\, University of Technology Sydney\, Australia. His research interests are in the area of signal processing for wireless communications and sensing. He has published more than 300 papers in leading Journals and conference proceedings\, and has won 7 best paper awards. He is a recipient of CSIRO Chairman’s Medal and the Australian Engineering Innovation Award for exceptional research achievements in multi-gigabit wireless communications. He is one of the pioneer researchers in ISAC. He initiated the concept of perceptive mobile network in 2017. Since then\, his team has published more than 70 top-tier journal papers on ISAC\, including several highly cited and review articles. In this field\, he has led or participated in multiple research projects with a total value of over AUD 8 million\, established a Joint Laboratory on Network Sensing with a mobile network operator\, developed multiple real-time ISAC demonstration systems\, and is currently advancing their commercialisation. Prof. Zhang co-organised a number of ISAC workshops at leading conferences and special issues in leading IEEE journals. He has also delivered multiple ISAC tutorials and numerous keynotes and invited talks. For details\, please refer to Prof. Zhang’s profile page: https://sites.google.com/view/andrewzhang \nOrganiser\nProf. Kaibin HUANG\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong\n\nAll are welcome!
URL:https://ece.hku.hk/events/20251202-1/
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/2025/11/1280-7.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20251202T110000
DTEND;TZID=Asia/Hong_Kong:20251202T120000
DTSTAMP:20260510T224159
CREATED:20251125T031958Z
LAST-MODIFIED:20251125T031958Z
UID:114255-1764673200-1764676800@ece.hku.hk
SUMMARY:RPG Seminar – Enhancing Ultrasound Shear Wave Elasticity Imaging Through Spectral Methods and Optimized Sparse Arrays
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/97206601239?pwd=ZZh3WRdq0GpwIlSkVT1UP19HuJD2XQ.1 \nAbstract\nShear wave elasticity imaging (SWEI) is a widely-used technique for quantifying the stiffness of biological tissues. Tissue stiffness varies with pathological processes\, for instance\, local tissue stiffening due to increased stromal density in cancer. However\, there still exist some challenges in SWEI. Specifically\, on the one hand\, the performance of current shear wave speed estimation methods still suffer from biased estimations or time-consuming computations\, and are prone to wave distortions in in vivo cases. On the other hand\, the 2-D nature of conventional SWEI leads to a lack of comprehensive analysis for 3-D shear wave propagation\, for instance\, in anisotropic tissues.  Hence\, for the first challenge\, we have proposed a parameter-free\, robust\, and efficient group SWS estimation method coined as Fourier energy spectrum centroid (FESC). The proposed FESC method is based on the center of mass in ω − k space. It has been evaluated on data from computer simulations with additive Gaussian noise\, a commercial elasticity phantom\, an ex vivo pig liver\, and in vivo biceps brachii muscles of three young healthy male subjects. The FESC method has been compared with four other benchmark methods. Statistical results showed that our FESC method exhibited excellent performance compared the other benchmark methods in terms of precision and computational efficiency. For the second challenge\, due to the instantaneity of shear wave propagation and the adverse effect of high sidelobes on shear wave imaging. We initially have designed an on-grid quasi-flatten side-lobe (Q-Flats) 2D sparse array with 252 activated elements\, which aims to achieve as high contrast performance as possible under the limits of resolution and maximum number of independent channels (i.e.\, 256). The imaging performance of the Q-Flats array has been evaluated using Field II simulations in a multi-angle steered diverging wave transmission scheme. It is demonstrated that the Q-Flats finds a good trade-off among resolution\, contrast\, and number of activated elements. \nSpeaker\nMr. Xi Zhang\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nXi Zhang received the B.S. degree in Electrical Engineering and its Automation from Huazhong University of Science and Technology in 2017 and the Master degree in Biomedical engineering  from Tsinghua university in 2020\, respectively. He is currently pursuing the Ph.D. degree in the Department of Electrical and Electronic Engineering at the University of Hong Kong\, Hong Kong. \nOrganiser\n Prof. Wei-Ning Lee\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251202/
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/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20251202T100000
DTEND;TZID=Asia/Hong_Kong:20251202T110000
DTSTAMP:20260510T224159
CREATED:20251125T033644Z
LAST-MODIFIED:20251125T033932Z
UID:114266-1764669600-1764673200@ece.hku.hk
SUMMARY:RPG Seminar – Stretchable\, Enhancement-mode PEDOT:PSS Organic Electrochemical Transistors
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/97481664242 \nAbstract\nThe rise of wearable and implantable bioelectronics necessitates stretchable electronic devices and systems to seamlessly integrate with soft biological environments. Stretchable organic electrochemical transistors (OECTs)\, based on conducting polymer poly (3\, 4-ethylenedioxythiophene) doped with polystyrene sulfonate (PEDOT: PSS)\, have emerged as a promising candidate because of their combined high stability and high transconductance. However\, a stretchable\, enhancement-mode PEDOT: PSS OECT (SE-OECT) is still missing\, limiting the development of complementary and low-power integration systems. In this Letter\, we report SE-OECTs. The devices showed typical enhancement-mode transistor behaviors with standby power as low as 0.1 μW while maintaining stable performance after 1000 cyclic tests within 50% strain. \nSpeaker\nMiss Yan Wang\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nYan Wang received her B.Sc in Chemistry from Nankai University. She is currently a Ph.D candidate in the WISE research group working on the processing of soft conducting polymers for high-performance soft OECTs. \nOrganiser\nProf. Shiming Zhang\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251202-3/
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:20251201T150000
DTEND;TZID=Asia/Hong_Kong:20251201T160000
DTSTAMP:20260510T224159
CREATED:20251124T035753Z
LAST-MODIFIED:20251124T035753Z
UID:114201-1764601200-1764604800@ece.hku.hk
SUMMARY:RPG Seminar – Efficient Learning for Image Restoration and Single-Photon Imaging without Clean Data
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/92646013468?pwd=lkoH511LkjLHtW43awHeBpEVnLfZ7b.1 \nAbstract\nSupervised deep learning has revolutionized computational imaging but relies heavily on vast datasets of clean\, ground-truth images\, which are often challenging to acquire in practice. This seminar presents a series of methods that break this dependency by embracing weakly-supervised and unsupervised learning\, directly addressing the challenge of learning without clean data. First\, I will introduce a Fourier-based statistical equivalence between learning with noisy targets and clean targets. Building on this\, I will present a weakly supervised framework for diverse image restoration tasks\, along with two unsupervised denoising methods specifically designed for pixel-wise and stripe-wise noise. Finally\, I will introduce a physics-informed unsupervised framework that can enable image restoration learning for single photon imaging with only the training data degraded by the blurring effect\, Poisson noise\, and readout noise. Collectively\, this seminar demonstrates powerful and flexible learning paradigms that advance the computational imaging for scenarios where clean data is unavailable. \nSpeaker\nMr. Haosen Liu\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nHaosen Liu received his B.Sc. and M.S. degrees from Huazhong University of Science and Technology\, and is currently a fourth-year Ph.D. candidate in the Department of Electrical and Electronic Engineering at The University of Hong Kong under the supervision of Prof. Edmund Y. Lam. His research interests mainly include data-efficient deep learning methods for image restoration and computational imaging. \nOrganiser\nProf. Edmund Y. Lam\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251201-2/
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:20251201T140000
DTEND;TZID=Asia/Hong_Kong:20251201T150000
DTSTAMP:20260510T224159
CREATED:20251119T033808Z
LAST-MODIFIED:20251119T033808Z
UID:113994-1764597600-1764601200@ece.hku.hk
SUMMARY:RPG Seminar – Brain-inspired Random Memristors Pruning for Input-aware Dynamic SNN
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/96497087839?pwd=5X1msaxhZNiH87SuzGTPgQHZILJmgi.1 \nAbstract\nMachine learning has advanced unprecedentedly\, exemplified by GPT-4 and SORA. However\, they cannot parallel human brains in efficiency and adaptability due to differences in signal representation\, optimization\, run-time reconfigurability\, and hardware architecture. To address these challenges\, we introduce PRIME—a pruning optimization for input-aware dynamic memristive spiking neural networks. PRIME leverages spiking neurons to emulate biological spiking mechanisms and optimizes the topology of random memristive SNNs\, mitigating memristor programming stochasticity. Additionally\, it employs an input-aware early-stop policy to reduce latency and memristive in-memory computing to alleviate the von Neumann bottleneck. Validated on a memristor-based macro\, PRIME achieves competitive classification accuracy and superior energy efficiency. \nSpeaker\nMr. Bo Wang\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nBo Wang received B.Eng. degree in Power Engineering\, Beihang University\, Beijing\, China\, in 2020\, and M.Eng. degree in Pattern Recognition and Intelligent Systems\, Beihang University\, Beijing\, China\, in 2022. He is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering under the supervision of Prof. Xiaojuan Qi. His research interests mainly include in-memory computing\, Embodied AI and software-hardware co-design. \nOrganiser\nProf. Xiaojuan Qi\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251201/
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:20251201T103000
DTEND;TZID=Asia/Hong_Kong:20251201T113000
DTSTAMP:20260510T224159
CREATED:20251125T033044Z
LAST-MODIFIED:20251125T033044Z
UID:114263-1764585000-1764588600@ece.hku.hk
SUMMARY:RPG Seminar – High-throughput Neuromorphic Computational Imaging
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/99712347936?pwd=P2oHpewBKizDaTJNY9m4YowNQLZfaP.1 \nAbstract\nHigh-throughput dynamic imaging must recover fine spatial structure under rapid motion\, yet no conventional sensor can fully overcome the trade-offs between spatial resolution\, temporal resolution\, and motion-induced degradation. Frame sensors inevitably blur fast dynamics due to global integration\, while event sensors\, although extremely fast and high-dynamic-range\, provide only local 1-bit temporal changes and lack global spatial context. These sensing limitations fundamentally constrain applications ranging from defect inspection to phase-flow analysis. In this seminar\, I will present a neuromorphic computational imaging paradigm\, Neuromorphic Super-Resolution (NeuroSR)\, that addresses these limitations through physics-informed spatio-temporal feature inference. NeuroSR unifies the complementary measurements of frames and events into a fully differentiable architecture\, enabling high space–time resolved reconstruction and direct inference of physical structure such as motion blur kernels or coherent wave propagation. To illustrate the generality of this paradigm\, I will also introduce Neuromorphic Wave-Normal Sensing (NeuroSH) as a representative white-box example. NeuroSH demonstrates how asynchronous event cues can recover large-gradient wavefront information and surpass classical spot-overlapping constraints in dynamic wavefront sensing systems. Together\, these results highlight a unified neuromorphic approach that transforms both dynamic imaging and physical-structure inference\, enabling ultrafast defect inspection\, large-gradient wavefront analysis\, and high-throughput computational imaging well beyond the limits of conventional sensors. \nSpeaker\nMr. Chutian Wang\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nChutian Wang received the B.S. degree from the University of Science & Technology Beijing in 2020\, and the M.S. degree at Imperial College London in 2021. He is currently working towards his Ph.D. degree with the Department of Electrical and Electronic Engineering\, the University of Hong Kong. His research interests include computational neuromorphic imaging\, wavefront sensing and digital holography. \nOrganiser\nProf. Edmund Y. Lam\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251201-3/
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:20251129T143000
DTEND;TZID=Asia/Hong_Kong:20251129T150000
DTSTAMP:20260510T224159
CREATED:20251120T032427Z
LAST-MODIFIED:20251120T032427Z
UID:114026-1764426600-1764428400@ece.hku.hk
SUMMARY:RPG Seminar – Trustworthy data sharing in power systems via blockchain
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/99852061481?pwd=QAsfylVs3cR4U1q4B4fczaBQpbyzKl.1 \nAbstract\nWith the digitalization of smart grids\, data becomes vital for advanced applications like load forecasting\, energy management\, and demand response. To unlock its full potential\, the critical challenge becomes how to build a trustworthy data-sharing framework for diverse stakeholders. Blockchain stands out as a promising solution. In this report\, we introduce a comprehensive framework to support both direct and implicit sharing methods via blockchain. For direct sharing\, we introduce a blockchain based searchable encryption for secure data retrieval from the cloud. For implicit sharing\, we propose a blockchain assisted federated framework to achieve collaborated training. To realistically deploy blockchain within existing infrastructure\, an optimization approach for node deployment is proposed to ensure practical implementation. Through this series of framework constructions\, we demonstrate the significant potential of blockchain applications in building a secure and efficient data-sharing ecosystem for the next generation of smart grids. \n  \nSpeaker\nMr. Ruiyang Yao\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nRuiyang Yao received the integrated master’s degree in mathematics from University of Oxford in 2021. He received the MSc in computing from Imperial College London in 2022. He is currently pursuing the Ph.D. degree in electrical and electronic engineering with the University of Hong Kong. His current research interests include trustworthy data sharing in power systems. \nOrganiser\nProf. Yi Wang\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251129-3/
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:20251129T140000
DTEND;TZID=Asia/Hong_Kong:20251129T143000
DTSTAMP:20260510T224159
CREATED:20251112T083533Z
LAST-MODIFIED:20251112T083533Z
UID:113881-1764424800-1764426600@ece.hku.hk
SUMMARY:RPG Seminar – On the Understanding of Uncertainty in Load Forecasting
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/92603659701 \nAbstract\nIn the context of digital transformation of the energy system\, how to effectively manage and quantify the uncertainty in forecasting has become a key bottleneck that restricts its reliable operation. This report will introduce our systematic work in the field of probabilistic load forecasting\, dedicated to addressing this core challenge. Our research establishes a complete solution around uncertainty\, covering three key aspects: firstly\, data preprocessing\, aimed at reducing the uncertainty of raw data; Next is model construction\, which precisely quantifies the uncertainty of predictions through innovative deep learning models; Finally\, the model explanation provides a unified and clear explanation framework for the probabilistic forecasting model of the “black box”. Through this series of studies\, we have not only significantly improved forecasting accuracy\, but also developed an open-source toolkit aimed at promoting the practical application of high reliability load forecasting technology in future energy systems. \nSpeaker\nMr Zhixian Wang\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nZhixian Wang received the B.S. degree in Statistics from The University of Science and Technology of China in 2022. He is currently pursuing the Ph.D. degree in electrical and electronic engineering with the University of Hong Kong. His current research interests include application of AI techniques in power data analytics. \nOrganiser\nProf. Yi Wang\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251129-2/
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:20251129T110000
DTEND;TZID=Asia/Hong_Kong:20251129T120000
DTSTAMP:20260510T224159
CREATED:20251112T082828Z
LAST-MODIFIED:20251112T082828Z
UID:113878-1764414000-1764417600@ece.hku.hk
SUMMARY:RPG Seminar – Generative AI-empowered Time Series Synthesis in Smart Grids
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/94936719507?pwd=ceXhzS1htWuwj8oG0vJGQLS3JMpVwF.1 \nAbstract\nThe reliable operation and strategic planning of smart grids are critically dependent on high-fidelity time series data. However\, the increasing stochasticity of both energy supply and demand challenges conventional analytical methods\, exacerbated by potential extreme scenarios. This research posits Generative Artificial Intelligence (AI) as a transformative approach\, empowering not only the synthesis of realistic load/renewable energy time series\, but also their conditional generation for predictive analysis. This seminar will go through time series generation on both the supply and demand sides\, and then investigate the refinement for the generated data. Finally\, a Python library\, GenTS\, is constructed to provide a unified framework for benchmarking generative time series models under various tasks. \nSpeaker\nMr. Chenxi Wang\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nChenxi Wang received the B.S. degree in Electrical Engineering from South China University of Technology in 2022. He is currently pursuing the Ph.D. degree in electrical and electronic engineering with the University of Hong Kong. His current research interests include time series analytics and generative AI in smart grids. \nOrganiser\nProf. Yi Wang\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251129/
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:20251128T150000
DTEND;TZID=Asia/Hong_Kong:20251128T160000
DTSTAMP:20260510T224159
CREATED:20251112T081230Z
LAST-MODIFIED:20251112T081230Z
UID:113875-1764342000-1764345600@ece.hku.hk
SUMMARY:RPG Seminar – Collaborative Load Forecasting via Multi-Party Data Sharing
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/98873959228 \nAbstract\nAccurate load forecasting is fundamental to the stability and efficiency of modern power grids. While collaborative approaches that leverage multi-party data sharing can significantly enhance forecasting accuracy\, they also introduce complex challenges. Effective collaboration is often hindered by data heterogeneity across participants\, critical data privacy concerns\, and the lack of clear incentives for sharing. This seminar aims to bridge this gap by presenting a comprehensive framework for collaborative load forecasting via multi-party data sharing. The work focuses on three key areas: first\, handling data heterogeneity through personalization strategies; second\, enhancing data privacy with distributed learning techniques; and third\, fostering collaboration through an incentive-driven model trading mechanism. Ultimately\, this framework paves the way for a secure\, efficient\, and economically viable ecosystem for multi-party collaboration\, enabling more intelligent load forecasting paradigm. \nSpeaker\nMr. Dalin Qin\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nDalin Qin received the B.S. degree in electrical engineering and its automation from South China University of Technology in 2022. He is currently pursuing the Ph.D. degree in electrical and electronic engineering at the University of Hong Kong. His current research interests include data analytics and data sharing in smart grids. \nOrganiser\nProf. Yi Wang\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251128-2/
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:20251128T140000
DTEND;TZID=Asia/Hong_Kong:20251128T150000
DTSTAMP:20260510T224159
CREATED:20251124T040333Z
LAST-MODIFIED:20251124T040333Z
UID:114204-1764338400-1764342000@ece.hku.hk
SUMMARY:RPG Seminar – Tackling Instability and Redundancy in Diffusion-Based Generative Models
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/99109748447?pwd=JSHjhMjma2hylEHbOCcLr3fJRCOoJq.1 \nAbstract\nThis seminar presents novel solutions to tackle instability and redundancy in modern generative models. We first address the high-variance optimization challenges in Conditional Flow Matching (CFM) by introducing the Stable Velocity framework. This includes StableVM for robust training stability and StableVS\, a finetuning-free accelerator that doubles sampling speed. Second\, we target spatial redundancy in super-resolution via the Quadtree Diffusion Model (QDM). QDM utilizes a quadtree-guided masking strategy to focus computation solely on information-rich regions. Together\, these contributions pave the way for more stable\, efficient\, and scalable generative models. \nSpeaker\nMr. Donglin Yang\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nDonglin Yang is an MPhil student in the Department of Electrical and Electronic Engineering\, supervised by Prof. Xiaojuan Qi. He received his B.Eng. degree from Tsinghua University. His current research focuses on deep generative models\, with a particular emphasis on theoretical optimization for diffusion and flow-based models. \nOrganiser\nProf. Xiaojuan Qi\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251128-3/
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:20251128T110000
DTEND;TZID=Asia/Hong_Kong:20251128T120000
DTSTAMP:20260510T224159
CREATED:20251121T023324Z
LAST-MODIFIED:20251121T023324Z
UID:114064-1764327600-1764331200@ece.hku.hk
SUMMARY:RPG Seminar – Brain-Inspired Structural Optimization: Edge Pruning and Kernel Pruning Across Analog and Digital RRAM-Based Compute-in-Memory.
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/93194207095?pwd=se5Jt0b8jIM7nz3yy9YZdNrWJIm818.1 \nAbstract\nThis seminar introduces two complementary pruning strategies implemented directly on RRAM-based compute-in-memory hardware. The first approach uses the intrinsic randomness of analog RRAM electroforming to build an over-parameterized random-weight network\, where edge pruning selects an efficient sub-network without requiring precise conductance tuning. This enables robust topology optimization while minimizing programming complexity.\nThe second approach is realized on a fully digital reconfigurable RRAM logic architecture\, where in-memory XOR/AND operations measure kernel similarity and dynamically prune redundant convolution kernels during training. Together\, these two pruning mechanisms illustrate a unified hardware–algorithm co-design philosophy: pruning is not a post-processing step\, but a native in-memory operation that co-optimizes connectivity\, computation\, and resource efficiency. This synergy highlights a scalable path toward adaptive\, energy-efficient RRAM-based AI accelerators. \nSpeaker\nMr. Songqi Wang\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nSongqi Wang received his B.Sc. degree from Huazhong University of Science and Technology\, and is currently a fourth-year Ph.D. candidate in the Department of Electrical and Electronic Engineering at The University of Hong Kong under the supervision of Prof. Han Wang. His research interests mainly include RRAM-based compute-in-memory architectures\, secure and intelligent edge-computing systems\, and software–hardware co-design for differential-equation-based models. \nOrganiser\nProf. Han Wang\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251128/
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:20251128T110000
DTEND;TZID=Asia/Hong_Kong:20251128T120000
DTSTAMP:20260510T224159
CREATED:20251111T032230Z
LAST-MODIFIED:20251111T041157Z
UID:113860-1764327600-1764331200@ece.hku.hk
SUMMARY:RPG Seminar – Lightweight Learning for the Coordination of Distributed Energy Resources
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/8957840635?pwd=jB4IyfmX0hTbEjn9W0LVEs31VhDw0e.1&omn=97635631185 \nAbstract\nThe proliferation of distributed energy resources presents significant coordination challenges due to their scale and variability. While traditional centralized methods are hindered by high communication and computational costs\, resource-constrained edge devices struggle with conventional algorithms. This paper aims to bridge this gap by developing lightweight learning approaches for edge devices\, enabling scalable and efficient coordination of distributed resources. The work focuses on three key analyses: descriptive analysis (non-intrusive load monitoring)\, predictive analysis (load forecasting)\, and prescriptive analysis (energy management for market participation). Ultimately\, these lightweight algorithms are implemented on established hardware testbeds\, paving the way for low-cost\, high-efficiency coordination of massive\, distributed assets. \n  \nSpeaker\nMr. Yehui LI\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nYehui Li received the B.S. degree in electronic science and technology from Harbin Institute of Technology in 2022. He is currently pursuing the Ph.D. degree in electrical and electronic engineering with the University of Hong Kong. His current research interests include data analytics and edge intelligence in smart grids. \nOrganiser\nProf. Yi Wang\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251128-1/
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:20251127T150000
DTEND;TZID=Asia/Hong_Kong:20251127T160000
DTSTAMP:20260510T224159
CREATED:20251120T032944Z
LAST-MODIFIED:20251120T032944Z
UID:114030-1764255600-1764259200@ece.hku.hk
SUMMARY:RPG Seminar – Trustworthy Tree-based Machine Learning by MoS2 Flash-based Analog CAM with Inherent Soft Boundaries
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/96125660975?pwd=yg6g1tnX9xobocust8dUATRUcIan5q.1 \nAbstract\nThe rapid advancement of artificial intelligence has raised concerns regarding its trustworthiness\, especially in terms of interpretability and robustness. Tree-based models like Random Forest and XGBoost excel in interpretability and accuracy for tabular data\, but scaling them remains computationally expensive due to poor data locality and high data dependence. Previous efforts to accelerate these models with analog content addressable memory (CAM) have struggled\, due to the fact that the difficult-to-implement sharp decision boundaries are highly susceptible to device variations\, which leads to poor hardware performance and vulnerability to adversarial attacks. This work presents a novel hardware-software co-design approach using MoS2 Flash-based analog CAM with inherent soft boundaries\, enabling efficient inference with soft tree-based models. Our soft tree model inference experiments on MoS2 analog CAM arrays show this method achieves exceptional robustness against device variation and adversarial attacks while achieving state-of-the-art accuracy. Specifically\, our fabricated analog CAM arrays achieve 96% accuracy on Wisconsin Diagnostic Breast Cancer (WDBC) database\, while maintaining decision explainability. Our experimentally calibrated model validated only a 0.6% accuracy drop on the MNIST dataset under 10% device threshold variation\, compared to a 45.3% drop for traditional decision trees. This work paves the way for specialized hardware that enhances AI’s trustworthiness and efficiency. \nSpeaker\nMr. Bo Wen\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nBo Wen received his B.Eng. degree from the School of Materials Science and Engineering at Huazhong University of Science and Technology (HUST)\, China in 2015\, and his M.Eng. degree from the University of Chinese Academy of Sciences in 2020. He is currently pursuing a Ph.D. degree at the Department  of Electrical and Electronic Engineering under the supervision of Prof. Can Li. His research interests focus on in-memory computing\, analog content-addressable memory\, trustworthy machine learning and software-hardware co-design. \nOrganiser\nProf. Can Li\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251127/
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:20251127T143000
DTEND;TZID=Asia/Hong_Kong:20251127T153000
DTSTAMP:20260510T224159
CREATED:20251120T080746Z
LAST-MODIFIED:20251120T080746Z
UID:114042-1764253800-1764257400@ece.hku.hk
SUMMARY:RPG Seminar – Toward 6G Edge AI: The Optimization and Application of Movable Antenna and Fluid Antenna
DESCRIPTION:Zoom Link:https://hku.zoom.us/j/97594921448?pwd=0AyvpTWODP87uNjZhADkvcGRrXh3V7.1 \nAbstract\nThe recently emerged movable antenna (MA) and Fluid antenna (FA) show great potential in leveraging spatial degrees of freedom for enhancing the performance of wireless systems. In future AI-embedded 6G communication networks\, MA/FA has great potential to improve the quality of service of edge AI. However\, resource allocation in MA/FA-aided systems faces unique challenges due to the non-convex and coupled constraints on antenna positions. \nIn this talk\, we will systematically reveal the challenges brought by the minimum MA/FA separation constraints at first\, and propose a penalty framework for resource allocation under such new constraints in MA/FA-aided systems. \nFurthermore\, we will also address the challenge of edge AI inference for handling the trade-off problem of model accuracy and network latency. To guarantee the high-quality of users’ service\, the latency and peak signal-to-noise ratio (PSNR) of features are considered in the objective of optimization\, and we propose an efficient algorithm under the block coordinate descent framework to solve this trade-off problem.\n \nSpeaker\nMr. Yichen Jin\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nYichen Jin received the B.Eng. degree from the Faculty of Automation\, Nanjing University of Science and Technology\, Nanjing\, China\, and the MSc degree from the Department of Electrical and Electronic Engineering\, The University of Hong Kong\, Hong Kong\, in 2020 and 2022\, respectively. He is currently working toward the Ph.D. degree with the Department of Electrical and Electronic Engineering\, The University of Hong Kong\, Hong Kong. His research interests include wireless communication and edge AI. \nOrganiser\nProf. Yik-Chung Wu\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251127-2/
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:20251127T140000
DTEND;TZID=Asia/Hong_Kong:20251127T150000
DTSTAMP:20260510T224159
CREATED:20251120T084558Z
LAST-MODIFIED:20251120T084558Z
UID:114048-1764252000-1764255600@ece.hku.hk
SUMMARY:RPG Seminar – A Continuous-Time Memristor-based Ising Solver for High-Efficiency Combinatorial Optimization
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/99378601502?pwd=bKeW5GqjRbFRaQFLBmZmTBJHPSdKBf.1 \nAbstract\nSolving complex combinatorial optimization problems is a fundamental challenge that pushes conventional digital computers to their limits. While some physics-based computing approaches offer a promising alternative\, many existing systems remain trapped in a hybrid digital-analog loop\, burdened by slow\, power-hungry iterations and data conversions. \nThis work presents a fully integrated memristor-based Ising machine chip that operates as a fully analog dynamic system\, solving these problems in a single shot. Its architecture embeds the entire optimization process into the continuous physical dynamics of the circuit. By encoding the problem’s couplings as memristor conductances\, the hardware directly minimizes the system’s Hamiltonian through a single\, continuous analog transient. \nExperimental results from a 96-spin integrated chip demonstrate the system’s capability to find high-quality solutions using a quantum-inspired annealing protocol. By eliminating digital overhead entirely\, the solver achieves a nearly 10x improvement in energy efficiency and a significant speed-up. This approach opens a new avenue for creating powerful and scalable hardware accelerators for the next generation of computing. \nSpeaker\nMs. Keyi Shan\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nKeyi Shan is a Ph.D. student in the Department of Electrical and Electronic Engineering\, supervised by Prof. Can Li. She received her B.E. degree in Automation from Xi’an Jiaotong University\, China in 2022. Her research focuses on in-memory computing\, Ising machine\, analog computing\, combinatorial optimization\, and energy-based neural networks. \nOrganiser\nProf. Can Li\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251127-3/
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:20251126T170000
DTEND;TZID=Asia/Hong_Kong:20251126T180000
DTSTAMP:20260510T224159
CREATED:20251121T024713Z
LAST-MODIFIED:20251121T024713Z
UID:114067-1764176400-1764180000@ece.hku.hk
SUMMARY:RPG Seminar – Split Learning: Empowering AI on Resource-Constrained Edge Devices
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/94531714904 \nAbstract\nThe next-generation mobile network aims to natively support distributed intelligence\, such as federated learning\, across massive wireless edge devices. Unfortunately\, in the era of large models\, the deployment of federated learning faces significant obstacles due to the limited resources on edge devices. In this talk\, I will briefly introduce split learning (SL) and elucidate how it overcomes resource limitations via device-server co-training\, which transforms next-generation edge AI. Then\, I will present our recent work on adaptive split federated learning (AdaptSFL) in resource-constrained edge networks. Specifically\, our work first provides a unified convergence analysis of split federated learning (SFL) to quantify the impact of model splitting and client-side model aggregation on the learning performance\, based on which the AdaptSFL framework is developed to adaptively control model splitting and client-side model aggregation to balance communication-computing latency and training convergence in SFL. Simulations results demonstrate the effectiveness of our approach in accelerating SFL under resource constraints. At last\, I will conclude the talk by discussing open problems and challenges in SL at the wireless edge.\n \nSpeaker\nMr. Zheng Lin\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nZheng Lin is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering\, The University of Hong Kong\, Hong Kong\, China.  His research interests include wireless networking\, edge intelligence\, and distributed machine learning. \nOrganiser\nProf. Xianhao Chen\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251126-4/
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:20251126T160000
DTEND;TZID=Asia/Hong_Kong:20251126T170000
DTSTAMP:20260510T224159
CREATED:20251120T033601Z
LAST-MODIFIED:20251120T033601Z
UID:114032-1764172800-1764176400@ece.hku.hk
SUMMARY:RPG Seminar – Parameter-sharing AI Model Caching\, Delivery\, and Inference at the Edge
DESCRIPTION:Zoom Link:  https://hku.zoom.us/j/94531714904 \nAbstract\nThe rapid proliferation of AI applications in the 6G era calls for efficient support of edge intelligence\, where models must be cached and executed at the network edge to deliver low-latency inference services. Unlike cloud data centers with abundant resources\, edge servers are constrained in both storage and computation\, creating new bottlenecks in AI service provisioning. Two critical yet underexplored challenges are storage efficiency in edge caching and model loading during edge inference\, both of which fundamentally determine the efficiency of delivering AI services at the network edge. \nThis talk will present recent advances on parameter-sharing AI model edge caching and inference. We first introduce TrimCaching\, a framework that leverages parameter sharing across models to improve storage efficiency in edge caching and significantly enhance model downloading performance. Building on this foundation\, we then discuss PartialLoading\, which reduces the dominant latency from repeatedly loading model parameters into GPU memory by strategically scheduling user requests to reuse shared parameters. Together\, these two works establish a unified perspective on exploiting parameter sharing to mitigate both edge caching and inference bottlenecks\, paving the way for scalable and efficient edge intelligence in next-generation networks.\n \nSpeaker\nMr. Guanqiao Qu\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nMr. Guanqiao Qu received his B.E. and M.E. degrees in Electronics and Information Engineering from Harbin Institute of Technology (HIT) in 2020 and 2022\, respectively. He is currently pursuing the Ph.D. degree in the Department of Electrical and Electronic Engineering at the University of Hong Kong (HKU). His research interests include edge intelligence\, wireless networking\, distributed learning\, and edge inference. \nOrganiser\nProf. Xianhao Chen\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251126-3/
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:20251126T150000
DTEND;TZID=Asia/Hong_Kong:20251126T160000
DTSTAMP:20260510T224159
CREATED:20251117T073459Z
LAST-MODIFIED:20251117T092027Z
UID:113908-1764169200-1764172800@ece.hku.hk
SUMMARY:RPG Seminar – A Novel Fabric Alignment System for Sewing
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/9706928305?omn=95929409545 \nAbstract\nAccurate fabric alignment is essential in garment manufacturing\, yet remains a challenging and labor-intensive task. This work presents a novel automated fabric alignment system that integrates a vision-guided robotic platform and a new Global Local Weighted Iterative Closest Point (GLW-ICP) algorithm. The system estimates the pose of wrinkle-free fabric panels—even under partial occlusion—by aligning global fabric edges and local sewing lines to CAD models. A roller-based end-effector then manipulates the fabric to achieve millimeter-level alignment accuracy. Unlike traditional ICP methods\, GLW-ICP introduces adaptive weighting and sparsity to enhance robustness against occlusion and unmatched points. Experiments with various fabric types\, including shirts and collars\, demonstrate consistent\, high-precision alignment. This system reduces operator dependency\, improves consistency\, and serves as a crucial step toward fully automated garment production workflows. \n  \nSpeaker\nMr. Wenbo Dong\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nWenbo Dong received his B.Sc. in Automation from Northeastern University\, China\, and M.Sc. degrees in Control Engineering from Harbin Institute of Technology and Mechanical Engineering from the University of California\, Riverside. He is currently pursuing a Ph.D. at the University of Hong Kong\, where he is affiliated with the JC STEM Lab of Robotics for Soft Materials. \nOrganiser\nProfessor Kazuhiro Kosuge\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251126-1/
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/2024/11/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20251126T140000
DTEND;TZID=Asia/Hong_Kong:20251126T150000
DTSTAMP:20260510T224159
CREATED:20251119T043408Z
LAST-MODIFIED:20251119T043408Z
UID:113999-1764165600-1764169200@ece.hku.hk
SUMMARY:RPG Seminar – Hardware-Adaptive and Superlinear-Capacity Memristor-based Associative Memory
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/99913066038?pwd=qqqBn1ojbFqbJJ4Koun6hucopMT2rJ.1 \nAbstract\nBrain-inspired computing aims to mimic cognitive functions like associative memory\, the ability to recall complete patterns from partial cues. Memristor technology offers promising hardware for such neuromorphic systems due to its potential for efficient in-memory analog computing. Hopfield Neural Networks (HNNs) are a classic model for associative memory\, but implementations on conventional hardware suffer from efficiency bottlenecks\, while prior memristor-based HNNs faced challenges with vulnerability to hardware defects due to offline training\, limited storage capacity\, and difficulty processing analog patterns. Here we introduce and experimentally demonstrate on integrated memristor hardware a new hardware-adaptive learning algorithm for associative memories that significantly improves defect tolerance and capacity\, and naturally extends to scalable multilayer architectures capable of handling both binary and continuous patterns. Our approach achieves 3x effective capacity under 50% device faults compared to state-of-the-art methods. Furthermore\, its extension to multilayer architectures enables superlinear capacity scaling (∝  for binary patterns) and effective recalling of continuous patterns (∝  scaling)\, as compared to linear capacity scaling for previous HNNs. It also provides flexibility to adjust capacity by tuning hidden neurons for the same-sized patterns. By leveraging the massive parallelism of the hardware enabled by synchronous updates\, it reduces energy by 8.8× and latency by 99.7% for 64-dimensional patterns over asynchronous schemes\, with greater improvements at scale. This promises the development of more reliable memristor-based associative memory systems and enables new applications research due to the significantly improved capacity\, efficiency\, and flexibility. \nSpeaker\nMr. Chengping He\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nChengping He received his B.Eng. and M.S. degrees from the Department of Physics at Nanjing University\, China\, in 2019 and 2022\, respectively. He is currently pursuing a Ph.D. in the Department of Electrical and Electronic Engineering under the supervision of Professor Can Li. His research focuses on in-memory computing\, analog computing\, associative memory\, and software-hardware co-design. \nOrganiser\nProf. Can Li\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251126/
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:20251126T140000
DTEND;TZID=Asia/Hong_Kong:20251126T150000
DTSTAMP:20260510T224159
CREATED:20251117T074555Z
LAST-MODIFIED:20251117T074555Z
UID:113912-1764165600-1764169200@ece.hku.hk
SUMMARY:RPG Seminar – Dynamic Motion Modeling and Planning of Fabric Piece
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/9706928305?omn=95929409545 \nAbstract\nUnlike rigid objects\, fabric pieces are difficult for robots to plan motion because they are deformable objects with infinite degrees of freedom\, and their states evolve during robot motion. Instead of using a detailed model\, we propose using an oriented bounding box to approximate the state of the fabric piece. The fabric piece motion is approximated by a Transformer-based neural network. A simple yet effective robot trajectory is designed based on the predicted future motion of the fabric piece. Experimental results on an industrial robot system with a fabric piece demonstrate that the fabric piece can avoid collisions with different obstacles and types of fabric. We then extend this approach to garment dynamic motion planning\, incorporating more complicated oriented bounding box modeling and trajectory design methods. \nSpeaker\nMr. Letian Li\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nLetian Li received the B. Eng. degree in detection\, guidance\, and control technology and the M. Eng. degree in instrumentation science and technology from the School of Instrumentation and Optoelectronic Engineering\, Beihang University\, Beijing\, China\, in 2019 and 2022\, respectively. He is currently pursuing the Ph.D. degree with JC STEM Lab of Robotics for Soft Materials\, Department of Electrical and Electronic Engineering\, Faculty of Engineering\, The University of Hong Kong\, Hong Kong SAR\, China. He is engaged in collaborative research with the Centre for Transformative Garment Production\, Hong Kong SAR\, China. His research interests include motion planning and learning. \nOrganiser\nProf. Kazuhiro Kosuge\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251126-2/
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/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20251125T160000
DTEND;TZID=Asia/Hong_Kong:20251125T170000
DTSTAMP:20260510T224159
CREATED:20251118T035502Z
LAST-MODIFIED:20251118T035502Z
UID:113919-1764086400-1764090000@ece.hku.hk
SUMMARY:RPG Seminar – Automated Straight-line Sewing of Stretchable Fabrics with Different Lengths
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/7425733217?omn=96993354197 \nAbstract\nDifferent Length Alignment Sewing (DLAS)\, which involves stretching the shorter fabric to match the longer one and sewing them together in a straight line\, is a challenging task that needs to satisfy several requirements when automating the sewing process. To address the challenges\, we propose a novel robotic sewing system\, Different Length Robotic Sewing System (DLRoSS)\, which consists of a roller type end-effector\, attached to a 6-DoF manipulator. The end-effector composed of active shorter and longer fabric rollers\, and a passive press-roller attached to the shorter-fabric roller. Assuming that one end of the two fabric layers are initially positioned under the sewing machine’s presser foot\, the system automates DLAS by operating in four distinct phases. (P1) Fabric wrapping: Individual fabric layers are picked\, held\, and wrapped from the other end onto the feed rollers. (P2) Sewing: During the sewing\, the shorter fabric is stretched and aligned with the longer fabric in real- time using roller velocity control based on the sewing speed and apriori known length ratio. (P3) Sewing completion: In the final sewing round on the fabric rollers\, the press roller is engaged to prevent the stretched fabric from slipping off due to internal tension. (P4) Sewing fabric release: At the end of sewing\, the fabric edge moves past the press roller\, and the fabric releases from the rollers. Experimental results demonstrate that DLRoSS achieves consistent\, high-quality sewing of stretchable fabrics of different materials and lengths. \n \nSpeaker\nMr. Bingchen Jin\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nBingchen Jin received his B.Sc. degree in Mechanics and Electronics Engineering from Jiangsu University\, China\, in 2015\, and his M.Sc. degree in Mechanical Engineering from Harbin Institute of Technology (Shenzhen)\, in 2018. From 2019 to 2021\, he was a research assistant in the Chinese University of Hong Kong (Shenzhen). He is currently towards his Ph.D. degree at the Department of Electrical and Electronic Engineering\, The University of Hong Kong\, Hong Kong SAR. He is involved in the Centre for Transformative Garment Production\, Hong Kong SAR. His research focuses on robotics manipulation\, and artificial intelligence. \nOrganiser\nProf. Kazuhiro Kosuge\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251125-3/
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/rpg-seminar.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20251125T150000
DTEND;TZID=Asia/Hong_Kong:20251125T160000
DTSTAMP:20260510T224159
CREATED:20251119T064431Z
LAST-MODIFIED:20251119T064939Z
UID:114022-1764082800-1764086400@ece.hku.hk
SUMMARY:RPG Seminar – Diffusion Model Acceleration with RRAM-based In-memory Neural Differential Equation Solver
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/93740801215 \nAbstract \nDiffusion models generate high-quality images and videos\, closely mirroring the imagination of human brain. Specifically\, score-based diffusion models generate by solving neural differential equations. However\, their digital computer implementations are discrete in time and inherently digital\, with energy efficiency constrained by the von Neumann architecture. Herein\, we firstly demonstrate a chip-level solution that embodies the implementation of time-continuous and analog conditional score-based diffusion using a Resistive Random Access Memory (RRAM) in-memory neural differential equation solver. Notably\, the score-based diffusion process is intrinsically robust to analog computing noise. We validate our solution on a conditional diffusion task. Our in-memory neural differential equation solver opens a brand-new hardware solution for edge generative AI. \nSpeaker\nMr. Jichang Yang\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nJichang Yang received both his B.Sc. and M.Sc. from the School of Electrical and Electronic Engineering at Huazhong University of Science and Technology\, Wuhan\, China\, in 2019 and 2022\, respectively. He is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering under the supervision of Prof. Han Wang. His research interests mainly include in-memory computing\, diffusion models\, and software-hardware co-design. \nOrganiser\nProf. Han Wang\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251125-4/
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:20251125T150000
DTEND;TZID=Asia/Hong_Kong:20251125T160000
DTSTAMP:20260510T224159
CREATED:20251118T034820Z
LAST-MODIFIED:20251118T035127Z
UID:113916-1764082800-1764086400@ece.hku.hk
SUMMARY:RPG Seminar – Seam-Informed Garment Handling Using Bimanual Manipulator
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/7425733217?omn=96993354197 \nAbstract\nSeams are information-rich components of garments. The presence and combination of different types of seam help to estimate the state of a garment. We introduce a novel Seam-Informed Strategy (SIS) for garment state estimation and planning for garment handling.  In this talk\, we will consider a problem to flatten a T-shirt which is randomly placed on a flat surface and demonstrate how SIS effectively estimate the garment state to facilitate grasp and unfold action. \nSeams are extracted from visual information. The Seam Feature Extraction Method is proposed to formulate seam extraction as an oriented object detection problem. The extracted seams provide an implicit representation of the garment’s structure and are used as grasping point candidates for bimanual flinging to unfold the garment. The Decision Matrix Iteration Method is proposed to select a pair of grasping points from the grasping point candidates. The decision matrix is initialized based on human demonstrations\, then updated using the robot’s execution results to improve its grasping and unfolding policy. Experimental results demonstrate the effectiveness and generalization ability of the proposed strategy. \nSpeaker\nMr. Xuzhao Huang\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nXuzhao Huang received the B.Eng. degree in Mechanical Design\, Manufacturing\, and Automation from Xiamen University\, China\, in 2018\, and the M.Eng. degree in Mechatronics Engineering from the Harbin Institute of Technology\, Shenzhen\, in 2021. He is currently pursuing the Ph.D. degree in Engineering with The University of Hong Kong. His research interests include visual perception and robotic manipulation of deformable objects. \nOrganiser\nProf. Kazuhiro Kosuge\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251125-2/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
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DTSTART;TZID=Asia/Hong_Kong:20251125T140000
DTEND;TZID=Asia/Hong_Kong:20251125T150000
DTSTAMP:20260510T224159
CREATED:20251117T063039Z
LAST-MODIFIED:20251117T063039Z
UID:113905-1764079200-1764082800@ece.hku.hk
SUMMARY:RPG Seminar – Robot Learning and Control for Fabric Manipulation and Fixture-Free Automated Sewing
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/7425733217?omn=96993354197 \nAbstract\nAutomating garment production requires production‑grade precision for each production process across diverse fabrics\, which remains challenging for conventional model-based methods. While model-based control scheme is effective for automating rigid-body handling processes\, it struggles with fabrics’ effectively infinite degrees of freedom\, nonlinear dynamics\, and frequent self-occlusions from wrinkles and folds. End‑to‑end deep learning offers rich representational power to capture fabric states and dynamics for policy learning\, yet existing methods lack the precision\, stability\, and safety guarantees demanded by industrial deployment. \nTo address this challenge\, we present a new robot learning and control paradigm for fabric manipulation in which learning expands the boundary of achievable tasks\, while control guarantees system stability and performance. The paradigm comprises: (i) multi-level perception\, (ii) feedback-structured policy learning\, and (iii) convergence-and-stability assurance. \nWe instantiate this paradigm in fixture-free sewing with a dual-arm manipulator and an ordinary industrial sewing machine. \n\nThe multi-level perception comprises global fabric state estimation using a mesh-based representation with a Graph Attention Network (GAT) and local\, real-time edge detection using High-speed Fabric Edge Detection System (Hi-FEDS)\, enabling global pose tracking\, wrinkle-aware state representation\, and precise seam estimation for real-time sewing.\nThe feedback‑structured policy learning—implemented via Imitation Learning (IL) with the Mesh Action Chunking Transformer (MACT)—operates in a closed‑loop\, error‑driven fashion to drive random wrinkled fabrics toward wrinkle‑free target states.\nOnce the fabrics reach control‑ready initial states\, a model‑based nonlinear controller—using nonholonomic sewing dynamics and time scaling—guarantees exponential convergence and sub‑millimeter steady‑state sewing error. Dual‑arm impedance control regulates internal wrenches applied to the fabric and the external wrenches of the system\, ensuring stability when interacting with passive environments.\n\nSpeaker\nMr. Kai Tang\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nKai Tang received his B.Sc. in Process Equipment and Control Engineering from South China University of Technology in 2020\, and M.Sc. (Distinction) in Control and Optimisation from Imperial College London in 2021. He is currently pursuing Ph.D. in robotics at JC STEM Lab of Robotics for Soft Materials\, the Department of Electrical and Electronic Engineering\, The University of Hong Kong. He is involved in the Centre for Transformative Garment Production\, Hong Kong SAR. His research focuses on robotic fabric manipulation and fixture-free automated sewing using control and deep learning. \nOrganiser\nProf. Kazuhiro Kosuge\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251125/
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:20251125T080000
DTEND;TZID=Asia/Hong_Kong:20251125T090000
DTSTAMP:20260510T224159
CREATED:20251120T081820Z
LAST-MODIFIED:20251120T081820Z
UID:114045-1764057600-1764061200@ece.hku.hk
SUMMARY:RPG Seminar – An Acoustic-responsive Hydrogel Electrode for Wearable Deep Brain Stimulation
DESCRIPTION:Zoom Link: https://hku.zoom.us/j/94612024859?pwd=Sqldxnoy6vEOP3HqfBBLs1oMxAQvFx.1 \nAbstract\nDeep brain stimulation (DBS) is a powerful therapy for neurological disorders\, yet conventional systems rely on finite-lifetime batteries and rigid implants that necessitate repeated surgeries and pose long-term risks. This seminar presents EchoGel\, a brain-compatible\, acoustic-responsive\, and conductive hydrogel electrode platform designed to enable fully wearable\, battery-free DBS. Once implanted in the brain\, EchoGel harvests external acoustic waves to enable wireless energy transfer and eliminate tethered connections. Its flexible\, needle-shaped hydrogel electrodes then provide stable and long-lasting stimulation. When integrated with a miniaturized wearable acoustic generator\, the system delivers deeper and more durable neuromodulation in freely behaving animals. Together\, these advances establish a path toward safer\, minimally invasive\, and long-lasting DBS technologies. \nSpeaker\nMs. Yilin Yang\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nYilin Yang received her B.Eng. and M.Eng\, both in Biomedical Engineering from Sun Yat-sen University. She is currently a Ph.D. student in the WISE Research Group working on brain-machine interfacing with soft and implantable bioelectronic systems. She is interested in the design\, fabrication\, and characterization of wearable neuromodulation and recording system based on novel soft materials. \nOrganiser\nProf. Shiming Zhang\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251125-5/
LOCATION:Online via Zoom
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20251124T150000
DTEND;TZID=Asia/Hong_Kong:20251124T160000
DTSTAMP:20260510T224159
CREATED:20251118T042520Z
LAST-MODIFIED:20251118T042520Z
UID:113922-1763996400-1764000000@ece.hku.hk
SUMMARY:RPG Seminar – Event-augmented 3D Geometry Estimation for Extreme Conditions
DESCRIPTION:Zoom Link: https://hku.zoom.us/meetings/92032873265/invitations?signature=ZlnhYyZi056expgN41HYZdcENW6INTs0MyPEhyhl7r8 \nAbstract\nRobust 3D geometry estimation from videos is essential for autonomous navigation\, SLAM\, and 3D reconstruction. While recent pointmap-based methods such as DUSt3R enable accurate pose-free reconstruction\, RGB-only approaches remain fragile under dynamic scenes and extreme illumination. We introduce a geometry estimation framework that augments pointmap reconstruction with asynchronous event data. It features: (1) a retinex-inspired enhancement module and a lightweight event adapter with SNR-aware fusion for adaptive RGB–event integration; and (2) an event-based photometric consistency loss that enforces spatiotemporal coherence during global optimization. Our method delivers robust geometry estimation in dynamic\, low-light environments without night-time retraining\, achieving substantial gains over state-of-the-art RGB-only baselines on monocular depth\, video depth\, and pose tracking.\n \nSpeaker\nMr. Yifei YU\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nYU Yifei received B.E degree from School of Information Science and Technology\, Fudan University\, Shanghai\, China\, in 2022. He is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering under the supervision of Prof. Xiaojuan Qi. His research interests mainly include in-memory computing\, 3D vision\, neuromorphic computing\, and software-hardware co-design. \nOrganiser\nProf. Xiaojuan Qi\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251124/
LOCATION:Online via Zoom
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20251121T143000
DTEND;TZID=Asia/Hong_Kong:20251121T150000
DTSTAMP:20260510T224159
CREATED:20251119T032920Z
LAST-MODIFIED:20251119T032920Z
UID:113987-1763735400-1763737200@ece.hku.hk
SUMMARY:RPG Seminar – Handling collaborative eavesdroppers in secure cell-free system
DESCRIPTION:Zoom Link: https://hku.zoom.us/meetings/93247207941/invitations?signature=9mw43b9u1DETwxS1FU3ze_f2GpaMXc_Qr8OHnU4L4c8 \nAbstract\nIn wireless communication system\, physical layer security is an important issue to ensure the data transmission of the communication users. In previous physical layer security problem\, eavesdroppers are considered wiretapping the target signal independently. However\, with the development of intelligent devices\, eavesdroppers can wiretap the signal collaborately. Combined with the covert and passive nature of eavesdroppers\, mitigating the adverse effect of the collaborative eavesdroppers becomes ultimately significant. \nIn this talk\, we try to maximize the secrecy rate of the communication users while restricting the outage probability by eavesdroppers within a limited threshold. In particular\, we provide an asymptotically equivalent transformation of the outage probability under passive and collaborative eavesdroppers. Furthermore\, a zeroth-order algorithm is proposed to handle the resultant optimization problem. \nSpeaker\nMr. Hancheng Zhu\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the Speaker\nHancheng Zhu received the B.Eng. degree from the Faculty of Computer Science and Technology\, Nanjing Tech University\, Nanjing\, China\, and the M.Eng. degree from the Faculty of Information Science and Engineering\, Southeast University\, Nanjing\, China\, in 2015 and 2018\, respectively. He is currently working toward the Ph.D. degree with the Department of Electrical and Electronic Engineering\, The University of Hong Kong\, Hong Kong. His research interests include first-order optimization\, and wireless communication. \nOrganiser\nProf. Yik-Chung Wu\nDepartment of Electrical and Electronic Engineering\, The University of Hong Kong \nAll are welcome.
URL:https://ece.hku.hk/events/20251121/
LOCATION:Online via Zoom
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20251121T110000
DTEND;TZID=Asia/Hong_Kong:20251121T120000
DTSTAMP:20260510T224159
CREATED:20251113T061708Z
LAST-MODIFIED:20251113T061708Z
UID:113884-1763722800-1763726400@ece.hku.hk
SUMMARY:Seminar on Probing Arousal Modulation of Brain Networks Using Multimodal Functional MRI in Awake Rodents and Non-human Primates
DESCRIPTION:Abstract\nArousal fluctuation is known to contribute to fMRI based functional dynamics\, but its detailed mechanism is largely unclear. Combining invasive neural recording (electrophysiological recording and fiber photometry) and manipulation (optogenetics and chemogenetics) techniques with awake\, unanesthetized animal fMRI provides unique opportunities to unravel the arousal contribution. Highly optimized unanesthetized mouse and marmoset fMRI setups allowed a wide range of arousal states from high alertness to NREM and REM sleep\, which was identified through simultaneous electrophysiological recording. Dynamic functional connectivity analysis revealed an inverted U-shape modulation of global functional connectivity strength and functional gradient from low to high arousal level. Further combined with simultaneous fiber photometry\, our multimodal fMRI revealed direct relationship between Locus Coeruleus Norepinephrine (LC-NE) system and such modulation. Direct neuronal manipulation using optogenetics/chemogenetics simultaneously with awake mouse fMRI confirmed the causal contribution of LC-NE system to inverted u-shape modulation. In conclusion\, multimodal fMRI in awake rodent and non-human primate revealed arousal modulated inverted U-shaped functional connectivity dynamics\, which can be driven by LC-NE activity. \nSpeaker\nDr. Zhifeng LIANG\nSenior Investigator\,\nDirector of the Brain Imaging Center\,\nInstitute of Neuroscience\,\nChinese Academy of Sciences\, Shanghai \nSpeaker’s Biography\nZhifeng LIANG obtained his Bachelor of Science in Life Sciences from Fudan University and PhD in Neuroscience from the University of Massachusetts Medical School. He conducted his postdoc training at the Department of Biomedical Engineering\, Pennsylvania State University\, before joining the Institute of Neuroscience (ION)\, Chinese Academy of Sciences as an Investigator and director of 9.4T animal MRI facility. He is now Senior Investigator and Director of the Brain Imaging Center at the Institute of Neuroscience\, with a research focus on multimodal fMRI techniques and applications in neuroscience. \nOrganiser\nDr. Alex Tze Lun LEONG\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAcknowledgement\nTam Wing Fan Innovation Wing Two\n\nAll are welcome!
URL:https://ece.hku.hk/events/20251121-1/
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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