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PRODID:-//Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系 - ECPv6.16.0//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
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X-WR-CALDESC:Events for Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
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TZID:Asia/Hong_Kong
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DTSTART:20230101T000000
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DTSTART;TZID=Asia/Hong_Kong:20240702T143000
DTEND;TZID=Asia/Hong_Kong:20240702T163000
DTSTAMP:20260512T152707
CREATED:20240619T020036Z
LAST-MODIFIED:20250114T043200Z
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SUMMARY:Magnetism-coupled Flexible and Wearable Devices
DESCRIPTION:Abstract\nThe emergence of flexible and wearable electronics is now leading a revolutionary era for real-time healthcare monitoring and human-machine interaction (HMI) in a more convenient and authentic manner. Sensors\, as the bridge between human beings and electrical terminals\, are playing an important role in facilitating interaction with the complex environment and promoting the healthy development of our society. It is thus crucial to develop high-performance flexible sensors for precise and effective conversion of multiple physiological signals from human beings. In this presentation\, we will introduce our recent studies of magnetism-coupled flexible sensors that aim to improve the sensing performance of flexible devices from linearity\, sensitivity\, to potential working range. Furthermore\, taking advantage of the intrinsic “divergence” and “curl” property of magnetic vector\, we will present that the coupling of magnetized component to flexible sensors can possibly enrich the function for future HMI and healthcare sensing. The design principle and optimization mechanism will be discussed in details. \nSpeaker\nProf. Bingpu ZHOU\nAssociate Professor\,\nInstitute of Applied Physics and Materials Engineering\,\nUniversity of Macau \nBiography of the Speaker\nProf. Bingpu ZHOU obtained his PhD degree from HKUST in 2015. He is currently an Associate Professor of Institute of Applied Physics and Materials Engineering in the University of Macau. He also serves as the Associate Department Head of Department of Physics and Chemistry in Faculty of Science and Technology\, and the Joint Associate Professor in Function Hub at HKUST (GZ). He is recipient of grants including FDCT (Macau SAR)\, GDST (Guangdong\, China) and FDCT-GDST joint projects. His group is mainly focusing on the optimization of flexible sensors with magnetism-mechanics-coupled effect\, and functional surface/interface analysis. He has authored/co-authored ~100 SCI papers. Some of the works have been published in Advanced Materials\, Advanced Functional Materials\, and ACS Nano\, etc. as first/corresponding author. \nOrganizer\nProf. Zhiqin CHU \nAll are welcome! We look forward to seeing you!
URL:https://ece.hku.hk/events/20240702-1/
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/2024/06/1280-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20240716T140000
DTEND;TZID=Asia/Hong_Kong:20240716T160000
DTSTAMP:20260512T152707
CREATED:20240702T075019Z
LAST-MODIFIED:20250114T043117Z
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SUMMARY:Introduction to Reinforcement Learning
DESCRIPTION:Abstract\nThis seminar aims to introduce reinforcement learning (RL) and its application to communication systems to graduate students\, although everyone is welcome to attend. RL has successfully been applied to many application domains ranging from control of communication and computer systems\, navigation of driverless vehicles\, robots and flying drones\, to guiding medical imaging and surgery\, to name a few. The speaker will first present the Markov Decision Process (MDP) – the mathematical foundation of RL. To solve the MDP\, the goal is to derive the optimal (action) policy that decides the optimal action for every given state of the system to maximize the long-term reward. As the underlying models for many application settings are unknown\, various model-free RL techniques have been developed\, including temporal difference learning\, SARSA\, Q-learning and policy gradient methods. The speaker will briefly describe these techniques. Furthermore\, as the system complexity increases\, neural networks are used to approximate the Q-values (rewards) and/or action policies as functions of system states and actions. This has led to deep RL where the neural-network parameters are “trained” or “learned” from processing the observed data from practical systems. For illustration purposes\, deep RL is used to manage communication infrastructures. New techniques will be highlighted to overcome issues of model complexity and long training time.  Open research issues on RL will also be briefly discussed. \nSpeaker\nProf. Kin K. LEUNG\nTanaka Chair Professor\,\nElectrical and Electronic Engineering\, and Computing Departments\,\nImperial College\, London \nBiography of the Speaker\nProf. Kin K. LEUNG received his B.S. degree from the Chinese University of Hong Kong\, and his M.S. and Ph.D. degrees from University of California\, Los Angeles. He worked at AT&T Bell Labs and its successor companies in New Jersey from 1986 to 2004. Since then\, he has been the Tanaka Chair Professor in the Electrical and Electronic Engineering (EEE)\, and Computing Departments at Imperial College in London. He also served as the Head of Communications and Signal Processing Group in the EEE Department at Imperial from 2009 to 2024. His current research focuses on optimization and machine learning for system design and control of large-scale communications\, computer and quantum networks. He also works on multi-antenna and cross-layer designs for wireless networks. \nHe is a Fellow of the Royal Academy of Engineering\, IEEE Fellow\, IET Fellow\, and member of Academia Europaea. He received the Distinguished Member of Technical Staff Award from AT&T Bell Labs and the Royal Society Wolfson Research Merits Award. Jointly with his collaborators\, he received the IEEE Communications Society (ComSoc) Leonard G. Abraham Prize (2021)\, the IEEE ComSoc Best Survey Paper Award (2022)\, the U.S.–UK Science and Technology Stocktake Award (2021)\, the Lanchester Prize Honorable Mention Award (1997)\, and several best conference paper awards. He was an IEEE ComSoc Distinguished Lecturer. In 2012-15\, he chaired the IEEE Fellow Evaluation Committee for ComSoc. He has served as an editor for 10 IEEE and ACM journals and chaired the Steering Committee for the IEEE Transactions on Mobile Computing. Currently\, he is an editor for the ACM Computing Survey and International Journal of Sensor Networks. \nAll are welcome! We look forward to seeing you!
URL:https://ece.hku.hk/events/20240716-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/07/1280-4.jpg
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BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20240718T150000
DTEND;TZID=Asia/Hong_Kong:20240718T160000
DTSTAMP:20260512T152707
CREATED:20240529T090712Z
LAST-MODIFIED:20250114T043030Z
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SUMMARY:Quantum Dot-based Opto and Nano Electronic Devices
DESCRIPTION:Abstract\nColloidal quantum dots (QDs) have been of great interest owing to the unique optical and electrical properties\, such as size-dependent band gap tunability\, wide absorption and narrow emission spectra\, and controllable surface properties. Recently\, several types of opto- and nano-electronic devices utilizing QDs have been reported for future optoelectronics. The QD-based light-emitting diodes\, QLEDs\, are one of the most promising devices for future full-color displays and new types of light sources. However\, fundamental mechanisms\, such as charge injection into QDs\, exciton recombination\, and operational stability\, should be understood and improved more to commercialize the QLED displays. In this talk\, I will mainly present our recent research progress on QLEDs\, including device design to improve device performance and to understand operational mechanisms. Also\, I will briefly introduce the QD-based memristors for neuromorphic computing. \nSpeaker\nProf. Jeonghun KWAK\nAssociate Professor\,\nDepartment of Electrical and Computer Engineering\,\nSeoul National University \nBiography of the Speaker\nProf. Jeonghun KWAK received his B.S. (2005) and Ph.D. (2010) degrees in Electrical Engineering from Seoul National University (SNU)\, Korea. After working as a postdoctoral researcher at SNU for one year\, he worked as an assistant/associate professor at Dong-A University\, Korea (2011–2015) and at the University of Seoul\, Korea (2015–2019). Since March 2019\, he has been an associate professor at the Department of Electrical and Computer Engineering\, SNU. His current research interests focus on opto- and nano-electronic devices\, such as QLEDs\, organic thermoelectric devices\, and neuromorphic devices based on organic molecules and low-dimensional materials. \nOrganizer\nProf. Leo Tianshuo ZHAO \nAll are welcome! We look forward to seeing you!
URL:https://ece.hku.hk/events/20240718-1/
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/2024/05/1280.jpg
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