BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系 - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
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
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Hong_Kong
BEGIN:STANDARD
TZOFFSETFROM:+0800
TZOFFSETTO:+0800
TZNAME:HKT
DTSTART:20230101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20240531T150000
DTEND;TZID=Asia/Hong_Kong:20240531T160000
DTSTAMP:20260511T140113
CREATED:20240521T090117Z
LAST-MODIFIED:20250114T043757Z
UID:18572-1717167600-1717171200@ece.hku.hk
SUMMARY:Integrated Nonlinear Multimode Photonics for Information Processing and Computation
DESCRIPTION:Meeting ID:937 8072 8049\n \nAbstract\nPhotonics technology has become indispensable in communication\, imaging\, sensing\, metrology\, and more recently\, in signal processing and computation. These advancements are made possible through optical systems that leverage the control of multiple degrees of freedom of light\, e.g.\, in space/momentum\, time/frequency\, angle/orbital angular momentum and polarization. Recently\, the integration of table-top optical systems into photonic integrated circuits has not only miniaturized the form factor but also enhanced light-matter interaction. This offers enormous opportunities to explore fundamental nonlinear optical physics and unlock new functionalities in integrated photonics. \nThe first part of my talk focuses on the fundamental aspects of integrated nonlinear photonics. I will show how we optically induce the second-order ((2)) nonlinearity in silicon nitride photonics\, along with techniques to achieve quasi-phase-matching for the second-harmonic generation process. Moreover\, I will discuss the generalization of our approach to other (2) nonlinear processes\, such as sum-frequency generation\, backward second-harmonic generation\, and combined (2) and Kerr nonlinear effects for different participating spatial modes. \nThe second part of my talk is on the applications of integrated nonlinear multimode photonics for information processing and computing. I will discuss the use of integrated Kerr microcombs for microwave photonics\, showcasing the reconfigurable microwave filtering based on inherently rich soliton states from silicon nitride microresonators. I will also show how we combine second-harmonic generation and multiple light scattering in disordered lithium niobite (2) nanocrystals for various machine learning applications. Additionally\, I will introduce a new reservoir computing paradigm harnessing the massive spatial parallelism of light. \nSpeaker\nDr. Jianqi HU\nPostdoc Researcher\,\nÉcole Polytechnique Fédérale de Lausanne (EPFL)\, Switzerland \nBiography of the Speaker\nDr. Jianqi HU is currently a postdoc researcher in Prof. Tobias Kippenberg’s group at École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland. He received the B.E. from the University of Electronic Science and Technology of China in 2016\, and the Ph.D. in photonics from EPFL in 2021\, advised by Prof. Camille Brès. After completing the Ph.D.\, he continued his research as a postdoc at EPFL from 2021 to 2022\, and then he was an SNF postdoc fellow at Ecole Normale Supérieure\, France\, working with Prof. Sylvain Gigan from 2022 to 2023. His research interests include integrated nonlinear photonics\, frequency microcombs\, structured light\, and photonic computing. \nOrganizer\nProf. Kaibin HUANG \nAll are welcome! We look forward to seeing you!
URL:https://ece.hku.hk/events/20240531-3/
LOCATION:Online via Zoom
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/05/1280-2.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20240531T140000
DTEND;TZID=Asia/Hong_Kong:20240531T170000
DTSTAMP:20260511T140113
CREATED:20240521T083633Z
LAST-MODIFIED:20250114T043839Z
UID:18570-1717164000-1717174800@ece.hku.hk
SUMMARY:Real-time Twisting on a Chip
DESCRIPTION:Meeting ID: 939 1846 8563 \nAbstract\nOptical nanostructures and two-dimensional materials (2DM) have optical properties that are widely tunable via several approaches\, such as heating\, electrostatic gating\, and interfacial engineering such as twisting. Being able to tailor the interfacial properties in a similar real-time manner represents the next leap in our ability to modulate the underlying physics and build exotic photonics devices. We demonstrate the first on-chip platform designed for optical nanostructures and 2D materials with in situ tunable interfacial properties\, utilizing a microelectromechanical system (MEMS). Each of these compact\, cost-effective\, and versatile devices is a standalone micromachine that allows voltage-controlled approaching\, twisting\, and pressurizing of two sheets of materials with high accuracy. \nSpeaker\nDr. Haoning TANG\nHarvard Quantum Initiative Postdoctoral Fellow\,\nJohn A. Paulson School of Engineering Applied Science\,\nHarvard University \nBiography of the Speaker\nDr. Haoning TANG is the Harvard Quantum Initiative Postdoctoral Fellow at John A. Paulson School of Engineering Applied Science at Harvard University. She obtained bachelor’s degree at The Hong Kong University of Science and Technology\, and Ph.D. at Harvard. Her primary research interest is in the nonlinear and quantum optical properties of metamaterials and low-dimensional materials\, and the engineering of these properties through advanced nanotechnologies including micro-electricalmechanical systems (MEMS). \nOrganizer\nProf. Kaibin HUANG \nAll are welcome! We look forward to seeing you!
URL:https://ece.hku.hk/events/20240531-2/
LOCATION:Online via Zoom
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/05/1280-3.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20240531T110000
DTEND;TZID=Asia/Hong_Kong:20240531T120000
DTSTAMP:20260511T140113
CREATED:20240506T014649Z
LAST-MODIFIED:20250114T043911Z
UID:18494-1717153200-1717156800@ece.hku.hk
SUMMARY:Localization of a Non-Cooperative Object Irrespective of the Range to Receivers
DESCRIPTION:Abstract\nPosition-related services and applications appear everywhere in our daily lives and precise positioning is indispensable.  For non-cooperative localization of an object by time difference of arrival (TDOA) such as in an integrated sensing and communications (ISAC) environment\, it requires the knowledge that the object is near to or far from the receivers.  If the object is close\, a near-field model is used to determine the unique coordinates of the object.  If it is distant\, a far-field model needs to be applied instead to obtain its direction.  Such knowledge\, however\, is seldom available in practice. This talk introduces a novel representation of the object position\, called the modified polar representation (MPR)\, which can eliminate the necessity of such knowledge.  MPR leads to a unified model that naturally yields the unique coordinates of the object if it is near and the direction if it is far.  Both the theory by the Cramer-Rao Lower Bound (CRLB) and the Hybrid Bhattacharyya-Barankin (HBB) Bound\, and the simulations by the Maximum Likelihood Estimator (MLE) support the effectiveness of MPR for TDOA localization. \nSpeaker:\nDr. Dominic K. C. HO\nProfessor\, University of Missouri\, USA \nBiography of the Speaker\nDr. Dominic K. C. HO was born in Hong Kong.  He received the BSc degree with First Class Honors and the PhD degree in Electronic Engineering\, both from the Chinese University of Hong Kong.  He was a research associate at the Royal Military College of Canada\, a member of scientific staff at the Bell-Northern Research\, and a faculty at the University of Saskatchewan\, Canada.  Since 1997\, he has been with the University of Missouri\, where he is a professor in the Electrical Engineering and Computer Science Department.  His research interests are in sensor array processing\, source localization\, subsurface object detection\, and wireless communications.  He has been active in the International Telecommunications Union (ITU) standard developments between 1995 and 2012.  He was the rapporteur of one recommendation and the editor of several others.  He was an Associate Editor of the IEEE Transactions of Signal Processing (2003-2006\, 2009-2013) and the IEEE Signal Processing Letters (2004-2008).  He served as the Chair of the IEEE Sensor Array Multichannel (SAM) Technical Committee from 2013 to 2014 and the Past Chair in 2015.  He was Technical Co-Chair of the IEEE International Conference on Acoustics\, Speech and Signal Processing (ICASSP) held in Shanghai\, in 2016.  He is an inventor of 22 patents in the United States\, Canada\, Europe and Asia on geolocation and signal processing for wireless communications.  He is a fellow of the IEEE. \nOrganizer\nProf. Y.C. WU \nAll are welcome! We look forward to seeing you!
URL:https://ece.hku.hk/events/20240531-1/
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/2024/05/1280-4.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20240524T150000
DTEND;TZID=Asia/Hong_Kong:20240524T163000
DTSTAMP:20260511T140113
CREATED:20240517T043054Z
LAST-MODIFIED:20250114T044042Z
UID:18564-1716562800-1716568200@ece.hku.hk
SUMMARY:EEE MasterClass (EEE 大師講堂) – Research Advances in Energy Storage Systems
DESCRIPTION:Abstract\nAs the penetration of variable renewable generation increases in power systems\, issues such as grid stiffness\, larger frequency deviations\, and grid stability are becoming more relevant. In this context\, Energy Storage Systems (ESSs) are proving to be effective in facilitating the integration of renewable resources\, and thus are being widely deployed in both microgrids and large power grids.  This talk will review several energy storage technologies\, particularly Compress Air Energy Storage (CAES)\, flywheels\, batteries\, and thermal energy systems\, and their modelling and applications for power systems.  An overview will be provided of the work being carried out by Prof. Canizares’ group at the University of Waterloo on all these energy storage systems\, focusing on novel models and applications in microgrids and distribution and transmission grids for system stability and control\, in particular for frequency regulation. \nSpeaker\nProf. Claudio Cañizares\nProfessor and Hydro One Endowed Chair\,\nElectrical and Computer Engineering (ECE) Department\,\nUniversity of Waterloo \nBiography of the Speaker\nProf. Claudio Cañizares is a University Professor and Hydro One Endowed Chair in the electrical and computer engineering (ECE) department at the University of Waterloo\, where he has held various academic and administrative positions since 1993. In 2021\, he was appointed the Executive Director of the Waterloo Institute for Sustainable Energy (WISE). He received an electrical engineering degree from the Escuela Politécnica Nacional (EPN) in Quito\, Ecuador in 1984\, where he held different academic and administrative positions between 1983 and 1993\, and his MSc (1988) and PhD (1991) degrees in electrical engineering from the University of Wisconsin-Madison\, in the USA. His research activities focus on the study of stability\, control\, optimization\, modelling\, simulation\, and computational issues in bulk power systems\, microgrids\, and energy systems in the context of competitive energy markets and smart grids. Professor Cañizares has collaborated with multiple industry and university researchers in Canada and abroad and supervised/co-supervised nearly 180 research fellows and students. He has authored/co-authored more than 370 publications with over 29\,000 citations and a 77 H index in Google Scholar and has been invited to deliver over 225 keynote speeches\, seminars\, tutorials\, and presentations at numerous institutions and conferences worldwide. He is the current Editor-In-Chief of the IEEE Transactions on Smart Grid\, a Fellow of the Institute of Electrical & Electronic Engineering (IEEE)\, a Fellow of the Royal Society of Canada\, and a Fellow of the Canadian Academy of Engineering. He is the recipient of the 2017 IEEE Power & Energy Society (PES) Outstanding Power Engineering Educator Award\, the 2016 IEEE Canada Electric Power Medal\, and multiple IEEE PES Technical Council and Committee awards and recognitions\, holding leadership positions in several IEEE-PES Committees\, Working Groups\, and Task Forces. In 2021 and 2022\, he received the Award for Excellence in Graduate Supervision from the University of Waterloo. \nOrganizer: Prof. Y. WANG \nAll are welcome! We look forward to seeing you!
URL:https://ece.hku.hk/events/20240524-1/
LOCATION:Room CPD-3.01\, 3/F\, Run Run Shaw Tower\, The University of Hong Kong
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/05/1280-5.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20240523T143000
DTEND;TZID=Asia/Hong_Kong:20240523T180000
DTSTAMP:20260511T140113
CREATED:20240513T012814Z
LAST-MODIFIED:20250114T062539Z
UID:18505-1716474600-1716487200@ece.hku.hk
SUMMARY:Workshop on Frontiers of Legged Robotics 2024
DESCRIPTION:The Faculty of Engineering\, The University of Hong Kong is organizing the “Workshop on Frontiers of Legged Robotics 2024”. The workshop will be held on May 23\, 2024 (Thursday) from 2:30 pm to 6:00 pm at Lecture Theatre A\, G/F\, Chow Yei Ching Building\, HKU. \nKeynote Speaker for William Mong Distinguished Lecture:\n– Prof Abderrahmane Kheddar\, The French National Centre for Scientific Research (CNRS) \nSpeakers*: \n– Prof Hua Chen\, Zhejiang University and LimX Dynamics – Dr Zhicheng He\, Leju (Shenzhen) Robotics Co.\, Ltd – Dr Baiyu Pan\, UBTech Co.\, Ltd – Prof Peng Lu\, Department of Mechanical Engineering\, The University of Hong Kong – Prof Jiangcheng Chen\, Department of Industrial and Manufacturing Systems Engineering\, The University of Hong Kong \nRegister now: https://hkuems1.hku.hk/hkuems/ec_hdetail.aspx?guest=Y&ueid=93930 \nFor more details\, please visit: https://sites.google.com/view/workshop-legged-robotics \n*Speakers are in alphabetical order by surname.
URL:https://ece.hku.hk/events/20240523-1/
LOCATION:Lecture Theatre CB-A\, G/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/05/787584.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20240522T100000
DTEND;TZID=Asia/Hong_Kong:20240522T110000
DTSTAMP:20260511T140113
CREATED:20240520T042136Z
LAST-MODIFIED:20250114T062646Z
UID:18567-1716372000-1716375600@ece.hku.hk
SUMMARY:Optimization of Phase Change Memory for In-Memory Computing and Packaging for Small Computers
DESCRIPTION:Abstract\nThe need to shuttle data between processing and memory units has been a key performance bottleneck for conventional hardware implementations of artificial neural networks. Analog in-memory computing (AIMC) emerges as a promising solution to this challenge by directly performing computations within non-volatile memory devices\, such as phase change memory (PCM). However\, the utilization of PCMs for analog computing introduces nonidealities\, including resistance drift\, read noise\, limited memory window\, and various device failures. I will discuss our work on optimizing PCM devices to alleviate these nonidealities and mitigate their impact on AIMC. Additionally\, I will talk about the effort to packaging such computer chips with photovoltaic power conversion devices and optical communication devices for ultra-small form factor edge computing applications. \nSpeaker\nProf. Ning LI\nAssociate professor\,\nDepartment and the Material Research Institute\,\nPenn State University \nBiography of the Speaker\nProf. Ning LI obtained his B.S. and M.S. from Tsinghua University and his Ph.D. from the University of Texas at Austin\, TX. He was a Research Staff Member at IBM T. J. Watson Research Center\, Yorktown Heights\, NY\, from 2010 to 2022. His research work is related to memory devices and their applications in neuromorphic computing\, flexible and new form factor devices and systems\, optoelectronic devices for interconnects and communications\, heterogeneous integration\, organic electronics\, etc. He published ~100 research papers in scientific journals and peer reviewed conferences\, including Nature Photonics\, Nature Communications\, Advanced Materials\, IEDM\, VLSI\, OFC\, etc. He was awarded more than 250 US patents\, many IBM High Value Patent Awards\, IBM Invention Achievement Awards\, and Master Inventor Awards. His work has been featured on Nature Research Highlight\, Semiconductor Today\, etc.  He joined the Electrical Engineering Department and the Material Research Institute at Penn State University as an associate professor in 2024. \nOrganizer: Prof. Han WANG \nAll are welcome! We look forward to seeing you!
URL:https://ece.hku.hk/events/20240522-1/
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/2024/05/1280-6.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20240521T160000
DTEND;TZID=Asia/Hong_Kong:20240521T170000
DTSTAMP:20260511T140113
CREATED:20240514T041838Z
LAST-MODIFIED:20250114T062744Z
UID:18510-1716307200-1716310800@ece.hku.hk
SUMMARY:Energy Intelligent Computing Devices Based on 2D Materials
DESCRIPTION:Abstract\nDespite the long and crucial role of traditional solid-state physics for current silicon-based technologies\, next-generation neuromorphic\, non-volatile memory\, and energy devices that are key components in the era of the Internet of Things (IoT) require novel working principles with quantum physics emerging in low-dimensional materials. The main research direction for future devices is to realize ‘ultralow device operation energy’\, ‘ultrahigh device operation speed’\, and ‘large-scale device integration (up to 10)’\, which calls for exploring diverse quantum phenomena in low dimensional device components. In this talk\, I will present some of our recent efforts to establish new device physics for energy-intelligent devices\, which could be a milestone for promising future devices. In particular\, dynamic convolution neural network\, phase transition and other intriguing quantum physics in two-dimensional (2D) materials will be discussed along with logic device\, neuromorphic computing\, and energy device applications. \nSpeaker\nProf. Heejun YANG\nQuantum Energy Device (QED) Lab\nDepartment of Physics\,\nKorea Advanced Institute of Science and Technology (KAIST) \nBiography of the Speaker\nProf. Heejun YANG received B.S. in physics from KAIST in 2003 and a joint Ph.D. in physics from Seoul National University (Korea) and University Paris-Sud XI (France) in 2010. He was awarded the IUPAP Young Scientist Prize (YSP) in Semiconductor Physics 2018 for his outstanding contribution to novel interface devices based on structural\, electronic\, and quantum-state control with van der Waals layered materials. His Ph.D. subject was on graphene by scanning tunnelling microscopy and spectroscopy (STM/STS)\, and he experienced industrial device studies in Samsung Electronics from 2010 to 2012. Then\, he conducted his research on graphene spintronics in Albert Fert’s (2007 Novel laureate) group in CNRS/Thales as a postdoc from 2012 to 2014. Based on his research background on molecular and nanometer-scale studies (in Seoul and Paris) and electric and spintronic device physics (in Samsung and CNRS/Thales)\, he moved to Sungkyunkwan University (2014~2021) and KAIST (2021~) and started original device studies with phase engineering of low-dimensional materials. He has proposed novel and conceptual interface devices such as ‘Graphene Barristor’ and ‘Ohmic homojunction contact between semiconductor channel and metal electrodes’. In 2022\, he gave an invited talk at the IUPAP centenary symposium as a representative YSP winner. \nOrganizer\nProf. C.L. TAN \nAll are welcome! We look forward to seeing you!
URL:https://ece.hku.hk/events/20240521-1/
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/2024/05/1280-7.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20240520T140000
DTEND;TZID=Asia/Hong_Kong:20240520T150000
DTSTAMP:20260511T140113
CREATED:20240514T081548Z
LAST-MODIFIED:20250114T062839Z
UID:18512-1716213600-1716217200@ece.hku.hk
SUMMARY:EEE MasterClass (EEE 大師講堂) - Mixed-Dimensional Heterostructures for Electronic and Energy Technologies
DESCRIPTION:Abstract\nLayered two-dimensional (2D) materials interact primarily via van der Waals bonding\, which has created new opportunities for heterostructures that are not constrained by epitaxial lattice matching requirements. However\, since any passivated\, dangling bond-free surface interacts with another via non-covalent forces\, van der Waals heterostructures are not limited to 2D materials alone. In particular\, 2D materials can be integrated with a diverse range of other materials\, including those of different dimensionality\, to form mixed-dimensional van der Waals heterostructures. Furthermore\, chemical functionalization provides additional opportunities for tailoring the properties of 2D materials and the degree of coupling across heterointerfaces. In this manner\, a variety of optoelectronic and energy applications can be enhanced including photodetectors\, optical emitters\, supercapacitors\, and batteries. Furthermore\, mixed-dimensional heterostructures enable unprecedented electronic device function to be realized including neuromorphic memtransistors\, mixed-kernel heterojunction transistors\, and moiré synaptic transistors. In addition to technological implications for electronic and energy technologies\, this talk will explore several fundamental issues including band alignment\, doping\, trap states\, and charge/energy transfer across mixed-dimensional heterointerfaces. \nSpeaker\nProf. Mark C. HERSAM\nWalter P. Murphy Professor\,\nMaterials Science and Engineering\,\nNorthwestern University \nBiography of the Speaker\nProf. Mark C. HERSAM is the Walter P. Murphy Professor of Materials Science and Engineering\, Director of the Materials Research Center\, and Chair of the Materials Science and Engineering Department at Northwestern University. He also holds faculty appointments in the Departments of Chemistry\, Applied Physics\, Medicine\, and Electrical Engineering. He earned a B.S. in Electrical Engineering from the University of Illinois at Urbana-Champaign (UIUC) in 1996\, M.Phil. in Physics from the University of Cambridge (UK) in 1997\, and Ph.D. in Electrical Engineering from UIUC in 2000. His research interests include nanomaterials\, additive manufacturing\, nanoelectronics\, scanning probe microscopy\, renewable energy\, and quantum information science. Dr. Hersam has received several honors including the Presidential Early Career Award for Scientists and Engineers\, TMS Robert Lansing Hardy Award\, MRS Mid-Career Researcher Award\, AVS Medard Welch Award\, U.S. Science Envoy\, MacArthur Fellowship\, and eight Teacher of the Year Awards. Dr. Hersam has been repeatedly named a Clarivate Analytics Highly Cited Researcher with over 700 peer-reviewed publications that have been cited more than 70\,000 times with an h-index of 128. An elected member of the American Academy of Arts and Sciences\, National Academy of Engineering\, and National Academy of Inventors with over 170 issued and pending patents\, Dr. Hersam has founded two companies\, NanoIntegris and Volexion\, which are suppliers of nanoelectronic and battery materials\, respectively. Dr. Hersam is a Fellow of MRS\, ACS\, AVS\, APS\, AAAS\, SPIE\, and IEEE\, and also serves as an Executive Editor of ACS Nano. \nOrganizer\nProf. Han WANG \nAll are welcome! We look forward to seeing you!
URL:https://ece.hku.hk/events/20240520-1/
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/2024/05/1280-8.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20240517T150000
DTEND;TZID=Asia/Hong_Kong:20240517T160000
DTSTAMP:20260511T140113
CREATED:20240418T012121Z
LAST-MODIFIED:20250114T062952Z
UID:18278-1715958000-1715961600@ece.hku.hk
SUMMARY:RPG Seminar – A Multi-Agent and Self-Adaptive Framework for Portfolio Management in Computational Finance
DESCRIPTION:Abstract:\nFinancial portfolio management (PM) is a very important topic in computational finance\, with its primary objective of achieving higher returns while reducing investment risks through dynamically allocating capital to different assets in a portfolio. Recently\, deep or reinforcement learning(DL/RL)-based PM approaches have been applied to capture the valuable opportunities from the underlying financial market yet the trade-off between returns and risks is definitely a great challenge. Accordingly\, this talk will consider the newly proposed Multi-Agent and Self-Adaptive (MASA) framework to dynamically balance the long-term portfolio profits and potential short-term risks. Through the close cooperation between the RL-based agent and solver-based agent\, the MASA framework continuously learns the profitable patterns of stock data from the concerned financial market\, monitors the current trends of financial markets\, and carefully evaluates the future risk exposures such that the newly balanced investment portfolios can adapt to the highly turbulent trading environments. Furthermore\, due to the high flexibility of the MASA framework\, its agent can be adaptively adjusted to satisfy a diversity of trading constraints and investor preferences. The reported results demonstrate the great potential of the proposed framework on PM tasks in various financial markets. Beyond the DL/RL-based PM approaches\, this talk will also introduce two novel meta-heuristic algorithms for solving continuous optimization problems with complex natures. The case studies show the significant advantages of the proposed algorithms on optimizing large-scale financial portfolios in such ever-changing financial markets. Most importantly\, the proposed frameworks shed light on many possible applications in computation finance like pair trading\, orderbook trading\, multi-factor model optimization\, etc. \nSpeaker: Mr. Zhenglong LI\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nBiography of the speaker:\nMr. Zhenglong LI received the B.Eng. degree in Electronic Information Science and Technology and B.Econ. degree in Financial Engineering both from Jinan University\, and the M.S. degree in Electrical and Electronics Engineering from the University of Hong Kong. He is currently pursuing the Ph.D degree with the Department of Electrical and Electronic Engineering at the University of Hong Kong\, under the supervision of Dr. Vincent Tam and Prof. Lawrence Yeung. His research interest lies in computational finance including portfolio optimization\, risk management\, pair trading\, and financial sentiment analysis. \nOrganizer:\nDr. Vincent TAM
URL:https://ece.hku.hk/events/20240517-1/
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:20240517T140000
DTEND;TZID=Asia/Hong_Kong:20240517T150000
DTSTAMP:20260511T140113
CREATED:20240510T013911Z
LAST-MODIFIED:20250114T063038Z
UID:18503-1715954400-1715958000@ece.hku.hk
SUMMARY:RPG Seminar – Trajectory Inference of T-Cell Activation from Label-free Single-cell Biophysical Morphologies with StaVia
DESCRIPTION:Abstract:\nBlood analysis is an indispensable clinical tool for human health and diseases. Specifically\, the overarching challenge in characterization of blood\, notably immune cells\, is to identify the cellular phenotypes at the single-cell precision to dissect the complex functional roles of different cell types/states. Strategies for phenotyping immune cells enable biological discovery and shed light on the immune system’s intricate mechanisms and the enormous heterogeneity of hematopoiesis. They are instrumental in the quality assessment and control of emerging immunotherapy methods. \nOften overlooked are the biophysical properties of the immune cell\, which simultaneously impact and are affected by its molecular signature. Defining biophysical markers\, which are label-free in nature\, could overcome the issues of scale and cost of analyzing numerous single cells. However\, deep biophysical profiling of single-cell requires both high-throughput and high-content that are not achievable or affordable with current technologies. Here we present a single-cell image-based trajectory inference strategy for tracking human T-cell activation based on the label-free biophysical morphology of T-cells. \nWe used our recently developed ultra-large-scale label-free imaging technology (up to 100\,000 cells/sec)\, multi-ATOM\, to extract high-resolution quantitative morphological and biophysical features (e.g. cell size\, shape\, dry mass density\, subcellular distributions) from the single-cell images. This is integrated with our unsupervised trajectory inference method StaVia to parameterize the morphology of each T-cell into a high-dimensional feature profile (> 90 dimensions) in order to uncover cellular dynamics of the underlying T-cell activation. We demonstrate that the integration of StaVia with multi-ATOM-derived single cells biophysical profiles reveal not only the overall T-cell activation process but also the subtle distinct morphological changes of CD4+ and CD8+ T-cells activations. We anticipate this work could spearhead further research in employing single-cell biophysical phenotypes as effective surrogate biomarkers of immune cell profiling in health and disease. Ultimately\, it could potentially inspire new cost-effective clinical diagnostic strategies in monitoring various immune-related disease/treatment progression. \nSpeaker\nMr. Kobashi MINATO\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nBiography of the speaker\nMr. Kobashi MINATO received his BEng degree in biological Engineering at The Hong Kong University of Science and Technology in 2022. He is currently a MPhil student supervised by Prof. Kevin K.M. Tsia in the department of electrical and electronic engineering. His research interest resides in the field of biomedical engineering and focusing on the analysis of biological data. \nOrganizer\nProf. Kevin K.M. TSIA\n \nAll are welcome.
URL:https://ece.hku.hk/events/20240517-3/
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:20240517T110000
DTEND;TZID=Asia/Hong_Kong:20240517T120000
DTSTAMP:20260511T140113
CREATED:20240510T040913Z
LAST-MODIFIED:20250114T063123Z
UID:18504-1715943600-1715947200@ece.hku.hk
SUMMARY:RPG Seminar – Scalable Optical Neural Network Based on Parametric Process
DESCRIPTION:Abstract\nIn the past decades\, with the rapidly increasing data\, AI technology\, including neural network (NN) shows more and more powerful ability. However\, the development of electronic hardware meets a dilemma because of the physical limitation\, which restricts the computation performance growth of NN. Thus\, developing the next-generation computation platform for NN is necessary. Since the optical system can also provide the solution to carry and process the information\, optical neural network (ONN) is developed\, and it shows high energy efficiency\, low crosstalk\, low latency\, and massive parallelism computation. Most ONNs are realized by direct spatial manipulation and observation with digital micromirror device\, spatial light modulator\, and camera. But those items can only provide a frame rate of several kHz\, which will limit the computation speed of ONN. \nThanks to the development of high-speed optical communication system\, superior optical manipulation and observation methods are available. The mode-locked laser can achieve ultrawide bandwidth for spectral encoding. By achieving wavelength-to-time mapping with temporal dispersion\, time-stretch method or we also called it as dispersive Fourier transform can be utilized for high-frame rate spectrum observation\, which is widely applied in microscopy\, and soliton dynamics observation. Here\, we applied to our ONN to achieve high computation frame rate. \nSpeaker\nMr. Xin DONG\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nBiography of the speaker\nMr. Xin DONG received the B.S. degree and the master’s degree from the Huazhong University of Science and Technology (HUST)\, Wuhan\, China\, in 2016 and 2019. He worked as a Research Scientist with the Wuhan National Laboratory for Optoelectronics\, and School of Optical and Electronic Information\, HUST from 2019 to 2020. He is currently a Ph.D. Candidate at the Department of Electrical and Electronic Engineering\, University of Hong Kong\, Hong Kong\, China. His research interests include fiber nonlinearities\, ultrafast spectroscopy\, fluorescence imaging\, structure illumination and optical neural network. \nOrganizer\nProf. Kenneth K. Y. WONG \nAll are welcome.
URL:https://ece.hku.hk/events/20240517-4/
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:20240517T100000
DTEND;TZID=Asia/Hong_Kong:20240517T110000
DTSTAMP:20260511T140113
CREATED:20240510T012737Z
LAST-MODIFIED:20250114T063205Z
UID:18502-1715940000-1715943600@ece.hku.hk
SUMMARY:RPG Seminar – Manipulating Light Scattering at the Nanoscale by Metasurface
DESCRIPTION:Abstract\nLight scattering is a fundamental optical process that accounts for many optical phenomena and applications. This process comes from the interaction between light and scattering particles\, or scatters. It greatly depends on parameters such as the scatters’ shapes and refractive index\, the polarization and wavelength of light. We will show that by arranging the specially designed nano scatters on a flat surface to form a metasurface\, the output light field can be manipulated at the nanoscale\, which will lead to many promising applications. \nTwo main topics will be discussed in this seminar. The first topic relates to tri-channel metalenses. Since it is difficult to encode three independent phase information at single-pixel or single-cell level\, most current designs use spatial multiplexing strategies including segmentation\, interleaving and multilayer integration\, which would result in large unit pixel sizes and limited performances. In this seminar\, we will present a single-celled design method to achieve tri-functional metalenses. Another topic relates to broadband antireflection by metasurfaces. We have proposed a quasi-random design method\, and developed a high-throughput nanofabrication method to fabricate the metasurfaces. \nSpeaker\nMr. Xudong GUO\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nBiography of the speaker\nMr. Xudong GUO received the B.Eng. degree in Optoelectronic Information Science and Engineering from Changchun University of Science and Technology\, Changchun\, in 2018. He is currently working toward the Ph.D. degree in electrical and electronic engineering with the Department of Electrical and Electronic Engineering\, The University of Hong Kong\, Hong Kong. His research interests include metasurface\, holography and imaging. \nOrganizer\nProf. Kenneth K. Y. WONG
URL:https://ece.hku.hk/events/20240517-2/
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:20240516T143000
DTEND;TZID=Asia/Hong_Kong:20240516T153000
DTSTAMP:20260511T140113
CREATED:20240507T082448Z
LAST-MODIFIED:20250114T063241Z
UID:18500-1715869800-1715873400@ece.hku.hk
SUMMARY:RPG Seminar – Exploration of Novel Operators with Memristor Arrays Towards Efficient and Robust In-memory Computing
DESCRIPTION:Abstract\nThe past decade of escalated development in deep learning (DL) has achieved unprecedented success in engineering fields. In particular\, deep neural networks (DNNs) via deep learning have achieved remarkable success across various applications. However\, challenges remain in the hardware implementation of these software-oriented AI algorithms\, primarily due to the reliance on traditional von Neumann computing architectures which are inefficient and lead to high power usage and latency particularly at the edge computing level. To address these issues\, compute-in-memory (CIM) using non-volatile memristive devices presents a promising solution. CIM leverages in-memory data processing to reduce data movement\, thereby improving efficiency. Therefore\, a core issue in artificial intelligence-related fields lies in leveraging hardware practice experience to explore and develop neuron models and operational operators. In the upcoming talk\, an innovative memristive unit cell based on the arithmetic unit model will be introduced\, aiming to explore its performance and robustness in emerging operational networks within AI fields. \nSpeaker\nMr. Yuan REN\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nBiography of the speaker\nMr. Yuan REN received the M.S. degree in electrical and computer engineering from the University of Macau (UM)\, Macao. He then joined the SoC Key Laboratory\, Peking University Shenzhen Institute and PKU-HKUST Shenzhen-Hong Kong Institution\, Guangdong\, China. He is currently pursuing the Ph.D. degree in electrical and electronic engineering from the University of Hong Kong (HKU)\, under the supervision of Dr. Ngai Wong. His research focuses on algorithm-hardware co-design for AI accelerator and memristor-based compute-in-memory integrated circuits. \nOrganizer\nProf. Ngai WONG \nAll are welcome.
URL:https://ece.hku.hk/events/20240516-2/
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:20240516T110000
DTEND;TZID=Asia/Hong_Kong:20240516T120000
DTSTAMP:20260511T140113
CREATED:20240507T081518Z
LAST-MODIFIED:20250114T063319Z
UID:18499-1715857200-1715860800@ece.hku.hk
SUMMARY:RPG Seminar – Domain-Specific Efficient Neural Network Architecture Design
DESCRIPTION:Abstract\nAI models significantly impact our daily lives\, but their high performance brings the challenge of model complexity. Deploying these models on edge devices poses additional challenges\, including power consumption\, memory storage and latency constraints. In this seminar\, we will delve into designing efficient neural network architectures for various domains\, including low-level computer vision and neural fields. We will start by discussing the latest Lookup Table (LUT)-based approach for Single-Image Super-Resolution (SISR). Our proposed Hundred-Kilobyte LUT (HKLUT) requires only 100KB\, 10X less than the second smallest LUT-based method\, and delivers superior performance. Moreover\, we will explore the field of Implicit Neural Representation (INR)\, where inference efficiency is often overlooked. We propose the Activation-Sharing Multi-Resolution (ASMR) coordinate network to enhance INR’s rendering efficiency. By sharing activations across data grids\, ASMR can reduce its Multiply-Accumulate (MAC) operations by up to 500x and improve reconstruction quality. \nSpeaker\nMr. Jason Chun Lok LI\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nBiography of the speaker\nMr. Jason Chun Lok LI holds a BEng degree from the Department of Electrical and Electronic Engineering at The University of Hong Kong\, obtained in 2020. He is currently continuing his studies at the same institution\, working towards a PhD. His research interest lies in the development of domain-specific techniques for efficient deep learning on edge devices. \nOrganizer\nProf. Ngai WONG \nAll are welcome.
URL:https://ece.hku.hk/events/20240516-1/
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:20240515T160000
DTEND;TZID=Asia/Hong_Kong:20240515T170000
DTSTAMP:20260511T140113
CREATED:20240304T071147Z
LAST-MODIFIED:20250114T072111Z
UID:17971-1715788800-1715792400@ece.hku.hk
SUMMARY:Microneedle-based Skin Patch for Transdermal Drug Delivery and Biosensing
DESCRIPTION:Microneedles (MNs) are an emerging platform for transdermal applications including drug delivery\, insulin delivery\, vaccination\, biosensing\, disease diagnosis\, and cosmeceutical industry. Their advantages lie in their easy-to-use\, pain-free\, minimally invasive\, and self-administrable features. This overcomes the skin barrier to enhance transdermal delivery of drugs and biomolecules with different physicochemical properties in vitro\, ex vivo and in vivo. In this talk\, Prof. Xu will share microneedle technologies developed in his lab for meeting a wide range of medical needs\, including keloid treatment and prevention\, obesity treatment\, dental and eye disease treatment\, and immune therapies. He will also present his envision in utilizing MN platform for the in-situ monitoring of physiological signals. \nBiography of the speaker: \nProf. Chenjie XU got his PhD\, Master\, and BS from Brown University (2009)\, HKUST (2004)\, Nanjing University (2002) respectively. He had conducted research at Stanford University (2005)\, Brigham and Women’s Hospital (2009-2012)\, and Nanyang Technological University (2012-2019). Currently\, he is an associate professor of biomedical engineering at the City University of Hong Kong. Prof. XU is dedicated to the development of transdermal drug delivery formulations and devices (especially nucleic acid-based nanoparticles and microneedle-based skin patches). He is well known for the development of skin patch for keloid treatment\, anti-obese skin patch\, skin patch for skin interstitial fluid extraction etc. He has published more than 140 peer-reviewed articles (citation is 11k with H index of 45)\, edited two books\, holding 10 international patents\, and found two spin-offs. His research is supported by a wide range of public and private foundations including Singapore Minister of Education\, Singapore A*Star\, Continental Corp (German)\, Bill & Melinda Gates Foundation\, Hong Kong University Grants Committee\, National Natural Science Foundation of China\, etc. \nAll are welcome
URL:https://ece.hku.hk/events/20240315/
LOCATION:Room CB-601J\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/03/20240315-banner.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20240514T140000
DTEND;TZID=Asia/Hong_Kong:20240514T150000
DTSTAMP:20260511T140113
CREATED:20240506T013049Z
LAST-MODIFIED:20250114T063519Z
UID:18492-1715695200-1715698800@ece.hku.hk
SUMMARY:RPG Seminar – Memristor Enabling Efficient Combinatorial Optimization with Quantum-inspired Parallel Annealing
DESCRIPTION:Abstract\nCombinatorial optimization problems are prevalent in various fields\, but obtaining exact solutions remains challenging due to the combinatorial explosion with increasing problem size. Special-purpose hardware such as Ising machines\, particularly memristor-based analog Ising machines\, have emerged as promising solutions. However\, existing simulate-annealing-based \nimplementations have not fully exploited the inherent parallelism and analog storage/processing features of memristor crossbar arrays. This work proposes a quantum-inspired parallel annealing method that enables full parallelism and improves solution quality\, resulting in significant speed and energy improvement when implemented in analog memristor crossbars. We experimentally solved tasks\, including unweighted and weighted Max-Cut and traveling salesman problem\, using our integrated memristor chip. The quantum inspired parallel annealing method implemented in memristor-based hardware has demonstrated significant improvements in time- and energy- efficiency compared to previously reported simulated annealing and Ising machine implemented on other technologies. This is because our approach effectively exploits the natural parallelism\, analog conductance states\, and all-to-all connection provided by memristor technology\, promising its potential for solving complex optimization problems with greater efficiency. \nSpeaker\nMr. Mingrui JIANG\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nBiography of the speaker\nMr. Mingrui JIANG received the B.E. degree from School of Optical and Electronic Information\, Huazhong University of Science and Technology\, Wuhan\, China\, in 2020. He is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering\, The University of Hong Kong\, Hong Kong SAR. His research interests include analog signal processing\, analog in-memory computing and neuromorphic computing based on emerging memory devices (e.g.\, memristors). \nOrganizer\nProf. Can LI \nAll are welcome.
URL:https://ece.hku.hk/events/20240514-3/
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:20240514T140000
DTEND;TZID=Asia/Hong_Kong:20240514T150000
DTSTAMP:20260511T140113
CREATED:20240503T094824Z
LAST-MODIFIED:20250114T063447Z
UID:18490-1715695200-1715698800@ece.hku.hk
SUMMARY:RPG Seminar – Image Augmented Multimodal Autolabeller for 3D Object Detection
DESCRIPTION:Abstract\nRecently deep learning methods have gained groundbreaking success in many areas\, including autonomous driving and 3D object detection. Powerful neural networks are proposed and yield human-comparable ability after being trained from large datasets. Nonetheless\, the annotation procedure is time-consuming and tedious. To automate the annotation process\, we proposed two methods called MAP-Gen and MTrans\, respectively. Leveraging both image and point cloud modalities\, the two methods can effectively alleviate the sparsity problem of point clouds and hence generate high-quality pseudo labels. \nSpeaker\nMr. Liu Chang\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nBiography of the speaker\nMr. Liu Chang received his B.Eng. degree in Computer Engineering from the University of Hong Kong. He is currently a Ph.D. student supervised by Dr. N. Wong and Prof. Edmund Y. Lam\, at the Department of Electrical and Electronic Engineering\, University of Hong Kong. His current research interests include 3D Vision\, Point Cloud\, Multi-modal Neural Networks\, and NLP. \nOrganizer\nProf. N. WONG
URL:https://ece.hku.hk/events/20240514-2/
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:20240514T140000
DTEND;TZID=Asia/Hong_Kong:20240514T150000
DTSTAMP:20260511T140113
CREATED:20240503T092811Z
LAST-MODIFIED:20250114T063407Z
UID:18489-1715695200-1715698800@ece.hku.hk
SUMMARY:RPG Seminar – Complex-valued Transformer for Wireless Communications
DESCRIPTION:Meeting ID: 892 6022 4678\nPassword: n9QxYg \nAbstract\nIn recent years\, attention-based models\, particularly those employing the transformer structure\, have exhibited exceptional performance in tasks such as natural language processing\, computer vision\, and wireless communication\, among others. Notably\, most of these transformer frameworks rely on real-valued operations\, where inputs\, outputs\, and trainable parameters are real numbers\, even in tasks involving complex-valued domains.\nComplex-valued neural networks (CVNN) have emerged as a powerful approach for addressing problems associated with the complex-valued nature of input data. However\, the application of complex-valued transformers remains largely unexplored within the field of wireless communication\, where most task inputs\, such as received symbols and channel coefficients\, are intrinsically complex-valued. \nThis seminar aims to unveil the potential of complex-valued transformers for complex-valued domain tasks in wireless communications\, specifically focusing on channel estimation in single-input-single-output system and device activity detection in grant-free massive access scenario. To this end\, we propose tailored complex-valued transformer designs that incorporate complex-valued attention mechanisms for both tasks. In particular\, the proposed frameworks exploit the relationship between the real and imaginary parts of signals as implicit constraints\, while capturing temporal and spatial correlations of complex-valued input features. Moreover\, we introduced a novel complex-to-real layer to convert the complex-valued feature into the probabilistic representation for the activity detection task. \nNumerical results demonstrate the superiority of the proposed complex-valued transformers framework compared with other deep-learning based methods and optimization approaches. \nSpeaker\nMiss Leng Yang\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nBiography of the speaker\nMiss Leng Yang received the B.Eng. degree in Electronic and Information Science and Technology from Fudan University in 2022. She is currently pursuing the MPhil degree with the Department of Electrical and Electronic Engineering\, The University of Hong Kong\, Hong Kong. \nOrganizer\nProf. Yik-Chung WU \nAll are welcome.
URL:https://ece.hku.hk/events/20240514-1/
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:20240508T103000
DTEND;TZID=Asia/Hong_Kong:20240508T113000
DTSTAMP:20260511T140113
CREATED:20240429T062517Z
LAST-MODIFIED:20250114T063611Z
UID:18468-1715164200-1715167800@ece.hku.hk
SUMMARY:RPG Seminar – Transformer-based Architectures for Automated Annotation in 3D Point Clouds
DESCRIPTION:Abstract\nManual annotation of 3D point clouds is notoriously labor-intensive\, prompting the need for automated solutions. Existing automated annotation methods\, however\, are typically complex and may neglect the crucial inter-object feature relationships that are informative for annotating challenging samples. In response\, we introduce two end-to-end Transformer-based models\, CAT and CAT++\, which are streamlined to serve as automated 3D-box labelers. These models leverage a minimal set of human annotations to produce precise 3D box annotations from 2D boxes. Our architecture employs a dual encoder strategy: a local intra-object encoder and a global inter-object encoder\, both utilizing self-attention mechanisms to process sequence and batch dimensions. The intra-object encoder captures point-level interactions within objects\, while the inter-object encoder discerns feature relationships across objects\, enhancing scene comprehension. The advanced CAT++ model incorporates a Hierarchical-interleaved encoding scheme and an implicit neural representation\, further refining the annotation process. Benchmarking experiments on the KITTI and nuScenes datasets demonstrate our models’ superior performance over current state-of-the-art methods\, particularly in annotating complex scenarios encompassing all hard samples. \nSpeaker\nMs. Xiaoyan QIAN\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nBiography of the speaker\nMs. Xiaoyan QIAN received the B.Eng. degree in Industrial Engineering from the Zhejiang University of Technology\, Zhejiang\, China. She is currently a Ph.D. candidate in the Department of Electrical and Electronic Engineering at the University of Hong Kong\, under the supervision of Dr. N Wong and Prof. SC Tan. Her current research interests mainly focus on 3D point clouds\, weakly supervised 3D object detection\, and auto-driving. \nOrganizer\nProf. N. WONG \nAll are welcome.
URL:https://ece.hku.hk/events/20240508-2/
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:20240508T100000
DTEND;TZID=Asia/Hong_Kong:20240508T110000
DTSTAMP:20260511T140113
CREATED:20240429T062039Z
LAST-MODIFIED:20250114T063647Z
UID:18467-1715162400-1715166000@ece.hku.hk
SUMMARY:RPG Seminar – A New Adaptive Fading Instrumental Variable Pseudolinear Kalman Filter for 3D AOA Target Tracking
DESCRIPTION:Meeting ID: 990 0206 5927\nPassword: 585304 \nAbstract:\nThe instrumental variable pseudolinear Kalman filter (IV-PLKF) algorithm\, used for 3D angle-of-arrival (AOA) target tracking\, has been proven to be more robust to initialization errors\, with superior estimation performance and lower computational complexity compared to other state-of-the-art methods. However\, the IV-PLKF algorithm requires prior knowledge of the state and angle measurement noise information\, which is not available in practice. Improper selection of these values or mismatches due to time-varying changes can significantly impact the stability and estimation performance of the algorithm. To address this issue\, we propose a new adaptive fading (AF-) IV-PLKF algorithm that adaptively mitigates the possible scale mismatches in the state and measurement noise covariance matrices and the IV parameters. Simulation results demonstrate that the proposed algorithm outperforms the conventional IVPLKF under mismatched state and measurement noise covariance scenarios. Moreover\, the proposed method can even achieve comparable estimation performance to that of IV-PLKF with perfect knowledge of the noise information. \nSpeaker:\nMs. Mengxia HE\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nBiography of the speaker:\nMs. Mengxia HE received her B.Eng. degree from the University of Science and Technology Beijing in 2018 and her M.Eng. degree from the Beijing University of Posts and Telecommunications in 2021. She is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering\, The University of Hong Kong\, Hong Kong. \nOrganizer:\nProf. S. C. CHAN \nAll are welcome.
URL:https://ece.hku.hk/events/20240508-1/
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:20240507T100000
DTEND;TZID=Asia/Hong_Kong:20240507T110000
DTSTAMP:20260511T140113
CREATED:20240426T084308Z
LAST-MODIFIED:20250114T063723Z
UID:18465-1715076000-1715079600@ece.hku.hk
SUMMARY:RPG Seminar – Hybrid Module with Multiple Receptive Fields and Self-attention Layers for Medical Image Segmentation
DESCRIPTION:Meeting ID: 958 6149 4641\nPassword: 505358 \nAbstract:\nRecent advances in medical image segmentation models combine convolution with the attention mechanism which provides an effective approach to formulate long-term dependencies. However\, many works either replaced the convolutional layers with attention layers or embedded attention layers into convolutional neural network (CNN)-based models. To explore the potential of hybrid architecture\, we propose a simple cascade module that builds up multiple receptive fields using convolutional kernels with different sizes and learns global context via self-attention layers. Benefiting from the powerful representation ability of the proposed module\, multilayer perceptrons (MLPs) with shift operation are adopted to bridge the encoder and decoder to reduce the model size without losing accuracy. Experiments show that our model consistently outperforms the latest 2D and 3D models by large margins on three public tasks and is more resilient to shape\, size\, and boundary variations. \nSpeaker:\nMr. Wenbo QI\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the speaker:\nMr. Wenbo QI received the B.Eng. degree from the University of Science and Technology of China in 2019\, and the M.Eng. degree from The University of Hong Kong in 2020\, where he is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering. His research interests include computer vision\, medical image processing. \nOrganizer:\nProf. S. C. CHAN \nAll are welcome.
URL:https://ece.hku.hk/events/20240507-1/
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:20240506T160000
DTEND;TZID=Asia/Hong_Kong:20240506T170000
DTSTAMP:20260511T140113
CREATED:20240429T092539Z
LAST-MODIFIED:20250114T063806Z
UID:18469-1715011200-1715014800@ece.hku.hk
SUMMARY:RPG Seminar – Vertical Layering of Quantized Neural Networks for Heterogeneous Inference
DESCRIPTION:Abstract:\nAlthough considerable progress has been obtained in neural network quantization for efficient inference\, existing methods are not scalable to heterogeneous devices as one dedicated model needs to be trained\, transmitted\, and stored for one specific hardware setting\, incurring considerable costs in model training and maintenance. In this seminar\, we study a new vertical-layered representation of neural network weights for encapsulating all quantized models into a single one. It represents weights as a group of bits (i.e.\, vertical layers) organized from the most significant bit (also called the basic layer) to less significant bits (i.e.\, enhance layers). Hence\, a neural network with an arbitrary quantization precision can be obtained by adding corresponding enhance layers to the basic layer. However\, we empirically find that models obtained with existing quantization methods suffer severe performance degradation if they are adapted to vertical-layered weight representation. To this end\, we propose a simple once quantization-aware training (QAT) scheme for obtaining high-performance vertical-layered models. Our design incorporates a cascade downsampling mechanism with the multi-objective optimization employed to train the shared source model weights such that they can be updated simultaneously\, considering the performance of all networks. After the model is trained\, to construct a vertical-layered network\, the lowest bit-width quantized weights become the basic layer\, and every bit dropped along the downsampling process act as an enhance layer. Experiments show that the proposed vertical-layered representation and developed once QAT scheme are effective in embodying multiple quantized networks into a single one and allow one-time training\, and it delivers comparable performance as that of quantized models tailored to any specific bit-width. \nSpeaker:\nMr. Hai WU\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nBiography of the speaker:\nMr. Hai WU (Graduate Student Member\, IEEE) received the BEng degree from the Department of Electronic and Electrical Engineering\, Southern University of Science and Technology\, China\, in 2020. He is currently working toward the PhD degree with the Department of Electrical and Electronic Engineering\, The University of Hong Kong\, Hong Kong. \nOrganizer:\nProf. Kaibin HUANG \nAll are welcome.
URL:https://ece.hku.hk/events/20240506-1/
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:20240506T150000
DTEND;TZID=Asia/Hong_Kong:20240506T160000
DTSTAMP:20260511T140113
CREATED:20240429T093019Z
LAST-MODIFIED:20250114T063840Z
UID:18470-1715007600-1715011200@ece.hku.hk
SUMMARY:RPG Seminar – On-the-fly communication-and-computing for distributed data analytics and edge intelligence
DESCRIPTION:Abstract:\nEnormous amounts of data are generated by billions of edge devices in mobile networks. Distributed data analytics can support a broad range of mobile applications\, from edge AI to IoT sensing. Enabling such analytics while improving its effectiveness has triggered a paradigm shift from separated optimization between communication techniques and computation algorithms to a joint design. \nConventionally\, the wireless implementation of computation algorithms\, such as statistic data analytics and AI models\, has followed a one-shot approach. This approach first computes local results at devices using local data and then aggregates them to a server with communication-efficient techniques. However\, this implementation is confronted with issues such as limited on-device storage and computation capacities\, link interruption\, and coarse efficiency-accuracy trade-offs. \nIn this seminar\, I will introduce a novel framework of on-the-fly communication-and-computing (FlyCom2). FlyCom2 exploits streaming low-complexity computation and progressive transmission to realize demanding computation algorithms in a mobile network\, such as distributed data analytics and device-server fine-tuning of large language models (LLMs). I will elaborate on the distinct features and advantages of FlyCom2 as well as the possible challenges for materializing it. Furthermore\, I will introduce two use cases explored in my studies on FlyCom2. \nSpeaker:\nMr. Xu CHEN\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nBiography of the speaker:\nMr. Xu CHEN received the B.Eng. and M.Eng. from Harbin Institute of Technology (HIT)\, Harbin\, China in 2018 and 2020\, respectively. He is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering\, The University of Hong Kong\, Hong Kong. His research interests include MIMO communications\, distributed computing\, and integrated sensing and edge AI. \nOrganizer:\nProf. Kaibin HUANG \nAll are welcome.
URL:https://ece.hku.hk/events/20240506-2/
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:20240503T140000
DTEND;TZID=Asia/Hong_Kong:20240503T150000
DTSTAMP:20260511T140113
CREATED:20240417T011530Z
LAST-MODIFIED:20250114T063925Z
UID:18275-1714744800-1714748400@ece.hku.hk
SUMMARY:RPG Seminar – Unified Hierarchical Federated Learning: Bridging Autonomous Driving and Construction Inspection
DESCRIPTION:Meeting ID: 957 7820 8166\nPassword: 631839 \nSpeaker:\nMr. Weibin KOU\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAbstract:\nIn this seminar\, I will present the application of hierarchical federated learning (HFL) to address challenges in two distinct domains: autonomous driving and construction quality defect inspection. For autonomous driving\, we introduce an optimization-based framework\, Communication Resource Constrained Hierarchical Federated Learning (CRCHFL)\, which enhances HFL by incorporating optimization scheme to improve communication efficiency and model generalization under constrained communication resources. The effectiveness of this framework is validated through simulations\, showing significant improvements over traditional federated learning approaches. In the construction sector\, we propose a HFL framework tailored for privacy-preserving collaboration among robots performing quality defect inspections. This method utilizes a lightweight deep learning model suitable for resource-constrained robots\, focusing on image-based crack segmentation to ensure the safety and serviceability of infrastructures. Experimental results demonstrate that this federated approach outperforms the other baselines. Both implementations underline the versatility and efficiency of HFL in processing large datasets across distributed environments while adhering to privacy constraints\, offering substantial improvements in both operational efficiency and data security. \nBiography of the speaker:\nMr. Weibin KOU is currently working toward a Ph.D. degree with the Department of Electrical and Electronic Engineering\, The University of Hong Kong. His research interests include federated learning\, autonomous driving and robotic perception\, and Large Models (LMs). \nOrganizer:\nProf. Yik-Chung WU \nAll are welcome.
URL:https://ece.hku.hk/events/20240503-1/
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:20240430T160000
DTEND;TZID=Asia/Hong_Kong:20240430T170000
DTSTAMP:20260511T140113
CREATED:20240422T075807Z
LAST-MODIFIED:20250114T064040Z
UID:18359-1714492800-1714496400@ece.hku.hk
SUMMARY:Towards Human-enabled Intelligent Robots: Perception\, Imitation and Morphology
DESCRIPTION:Meeting ID: 972 6774 1607 \nAbstract:\nThe robotics industry has manufactured multiple successful robots that are deployed in various domains and have been playing a significant role in the modern economy. How to efficiently build\, train and deploy different robots with improved cost and operational safety in diverse tasks in a scalable way? I argue that efficiently using human intelligence embedded in human’s daily activities is the key to help achieve so\, and in this talk\, I will introduce my research works towards achieving this goal. \nI will first introduce my research on extracting useful state information about humans and objects via visual perception\, and focus on efficient training data collection and annotation that can best utilize human capability. I will then introduce my research on human-to-robot imitation\, specifically a new type of methodology that leverages continuous transformation of robot embodiments to co-develop robot hardware and skills\, allowing continuous transformation of a human agent to a robot agent and transferring the human skills along the way. As an application\, I show how this methodology can be used to efficiently control\, design and optimize robots with new morphology and use human experience in this process. I conclude the talk with discussions on my future research plan on improving various aspects of human-enabled safe and low-cost robot systems\, as well as their broader impacts on science\, engineering and society. \nSpeaker:\nDr. Xingyu LIU\nPostdoctoral Associate\,\nCarnegie Mellon University (CMU) \nBiography of the Speaker:\nDr. Xingyu LIU is currently a Postdoctoral Associate at Carnegie Mellon University (CMU) where he works with Professor Ding Zhao in CMU SafeAI Lab. He received his Ph.D. degree from Stanford University where he was advised by Professor Jeannette Bohg. During his Ph.D.\, he spent time in research labs including Google Brain Robotics and Adobe Research. Prior to Ph.D.\, he received M.S. degree from Stanford University and B.Eng. degree from Tsinghua University. His research interest is at the intersection of robotics\, machine learning and computer vision and he reviews regularly for conferences such as RSS\, NeurIPS and CVPR. His research works have been recognized with a Best Paper Award Finalist at CVPR 2022 conference\, a Best Demo Award Finalist at RoboSoft 2024 conference\, multiple (Long) Oral Presentation honors at top AI conferences and are covered by media outlets including Scientific American magazine\, ACM Tech News and O’Reilly. \nOrganizer:\nProf. Kaibin HUANG \nAll are welcome! We look forward to seeing you!
URL:https://ece.hku.hk/events/20240430-3/
LOCATION:Online via Zoom
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/04/1280.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Hong_Kong:20240430T140000
DTEND;TZID=Asia/Hong_Kong:20240430T150000
DTSTAMP:20260511T140113
CREATED:20240416T065435Z
LAST-MODIFIED:20250114T064217Z
UID:18274-1714485600-1714489200@ece.hku.hk
SUMMARY:RPG Seminar – Towards Parameter-free Ultrasound Localization Microscopy by Vision Transformer
DESCRIPTION:Speaker:\nMr. Wang Renxian\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAbstract:\nUltrasound localization microscopy (ULM) has emerged as an unprecedented noninvasive microvascular imaging technique that breaks the acoustic diffraction limit. However\, current ULM workflow relies significantly on prior knowledge\, including the impulse response and empirical parameters (e.g.\, the number of microbubbles (MBs) per frame M)\, or training-test dataset consistency in deep learning (DL)-based studies. In this seminar\, a general ULM pipeline that is free from priors will be presented. Specifically\, a channel attention vision transformer model (ViT) was trained using a progressive learning strategy to distill microbubble signals and reduce speckles simultaneously from a single frame without estimation of the impulse response and M. This approach enables the model to learn global information through training on patch sizes that increase progressively. Ample synthetic ultrasound data were generated using the k-Wave toolbox to provide various MB patterns\, thus overcoming the deficiency of labeled data. The ViT output was further processed by a standard radial symmetry method for sub-pixel localization. Our method performed well on model-unseen public datasets: one in silico flow dataset with ground truth and four in vivo datasets of mouse tumor\, rat brain\, rat brain bolus\, and rat kidney\, in terms of precision and accuracy for in silico dataset\, the number of vessels for diverse in vivo datasets while preserving comparable resolutions. The proposed ViT-based model\, seamlessly integrated with state-of-the-art downstream ULM steps\, improved the overall ULM performance with no priors. \nBiography of the speaker:\nMr. Renxian WANG received the B.S. degree in Material Physics from Northwestern Polytechnical University in 2019 and the MPhil degree in Department of Physics from The Chinese University of Hong Kong in 2021\, 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. \nAll are welcome.
URL:https://ece.hku.hk/events/20240430-1/
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:20240430T140000
DTEND;TZID=Asia/Hong_Kong:20240430T150000
DTSTAMP:20260511T140113
CREATED:20240411T072951Z
LAST-MODIFIED:20250114T064121Z
UID:18266-1714485600-1714489200@ece.hku.hk
SUMMARY:RPG Seminar – Communication-Efficient Joint Signal Compression and Activity Detection in Cell-Free Massive MIMO
DESCRIPTION:Speaker:\nMr. Lin Qingfeng\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAbstract:\nA great amount of endeavour has recently been devoted to device activity detection in massive machine-type communications. This seminar targets at a practical issue: communication-efficient joint signal compression and activity detection in cell-free massive MIMO with capacity-limited fronthauls. To this end\, we propose a novel deep learning framework which jointly optimizes the compression modules\, quantization modules at the access points\, and the decompression module and detection module at the central processing unit. Specifically\, deep unfolding is leveraged for designing the detection module in order to inherit the domain knowledge derived from the optimization algorithm\, and the other modules are constructed by generic layers for increasing the learning capability. A joint training strategy is proposed to optimize all the modules in an end-to-end manner. Numerical results demonstrate the superiority of the proposed end-to-end learning framework compared with classical optimization methods. \nBiography of the speaker:\nMr. Qingfeng LIN received the B.Eng. degree in communication engineering and the M.Eng. degree in information and communication engineering from the Harbin Institute of Technology in 2018 and 2020\, respectively. He is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering\, The University of Hong Kong\, Hong Kong. \nOrganizer:   Prof. Yik-Chung Wu \nAll are welcome.
URL:https://ece.hku.hk/events/20240430-2/
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:20240429T140000
DTEND;TZID=Asia/Hong_Kong:20240429T150000
DTSTAMP:20260511T140113
CREATED:20240411T013043Z
LAST-MODIFIED:20250114T064304Z
UID:18251-1714399200-1714402800@ece.hku.hk
SUMMARY:RPG Seminar – Learning A Low-Rank Feature Representation: Achieving Better Trade-Off between Stability and Plasticity in Continual Learning
DESCRIPTION:Speaker:\nMr. Zhenrong LIU\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nAbstract:\nDeep neural networks require the ability to continually learn and adapt to real-world conditions. This ability\, called “continual learning\,” is essential for AI systems to evolve. Among various representative continual learning methods\, null-space-projection-based algorithms have attracted considerable research interest due to their faster training speed and lower memory requirements. These algorithms optimize network parameters in the null space of past tasks’ feature representation matrices\, ensuring stability. However\, quantitatively analyzing the balance between stability and plasticity in null-space-projection-based methods poses significant challenges\, complicating efforts to refine and improve such approaches. \nIn this seminar\, we comprehensively examine null-space-projection-based continual learning methods and uncover two essential insights. Firstly\, to maintain stability\, the rank of the feature covariance increases with the number of continual learning tasks\, leading to a reduction in the dimension of the feature covariance’s null space. Secondly\, the dimension of the feature covariance’s null space significantly influences the plasticity of continual learning. Building on these insights\, we quantitatively demonstrate the stability-plasticity relationship inherent in null-space-projection-based continual learning methods. Then\, based on the stability-plasticity relationship\, we introduce a novel training algorithm named LRFR (Low-Rank Feature Representation) to enhance plasticity without compromising stability. Specifically\, we judiciously select only a subset of neurons in each layer of the network while training individual tasks to learn the past tasks’ feature representation matrix in low-rank. This increases the null space dimension when designing network parameters for subsequent tasks\, thereby enhancing the plasticity. Using CIFAR-100 and TinyImageNet as benchmark datasets for continual learning\, the proposed approach consistently outperforms state-of-the-art methods. \nBiography of the speaker:\nMr. Zhenrong LIU received the B.E. degree in automation from Northeastern University in 2018 and the M.E. degree in information and communication engineering from the Southern University of Science and Technology in 2020. He is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering\, The University of Hong Kong\, Hong Kong. \nOrganizer: Prof. Yik-Chung WU \nAll are welcome!
URL:https://ece.hku.hk/events/20240429-1/
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:20240429T110000
DTEND;TZID=Asia/Hong_Kong:20240429T120000
DTSTAMP:20260511T140113
CREATED:20240418T011701Z
LAST-MODIFIED:20250114T064341Z
UID:18277-1714388400-1714392000@ece.hku.hk
SUMMARY:RPG Seminar – Enhancing Performances of InGaN-MQW Thin-film Microdisk Laser with Hybrid ODRs
DESCRIPTION:Abstract:\nThe conventional GaN microdisk laser provides poor overlap between the whispering gallery modes (WGM) and the multi-quantum well (MQW) gain region. The thin-film microdisk structure was proposed to overcome this shortcoming\, but the absorptive nature of the metallic bonding layer cum mirror compromises optical confinement. In this work\, a dielectric distributed Bragg reflector (DBR) is integrated with the metallic mirror to form an omni-directional reflector (ODR) that provides high optical reflectance across a wide range of incidence angle to promote optical confinement in the microdisk. Optical-pumped lasing with average lasing threshold power density of 46.5 W/cm2 and Q factors of 18200 (near threshold) is achieved\, representing a major advancement in GaN microdisk laser technology. \nSpeaker:\nMiss Zhongqi WANG\nDepartment of Electrical and Electronic Engineering\nThe University of Hong Kong \nBiography of the speaker:\nMiss Zhongqi WANG received her B.Eng. degree in Materials Science and Engineering at Tsinghua University and now pursuing the Ph.D. degree in the Department of Electrical and Electronic Engineering at the University of Hong Kong. She is now working on GaN based laser fabrication GaN-based microdisk fabrication and analysis of lasing characteristics. \nOrganizer:\nProf. A.H.W. CHOI
URL:https://ece.hku.hk/events/20240429-2/
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:20240429T100000
DTEND;TZID=Asia/Hong_Kong:20240429T110000
DTSTAMP:20260511T140113
CREATED:20240419T082617Z
LAST-MODIFIED:20250114T064421Z
UID:18341-1714384800-1714388400@ece.hku.hk
SUMMARY:RPG Seminar – Uncertainty Quantification
DESCRIPTION:Abstract:\nUncertainty quantification plays a crucial role in electromagnetic compatibility and inference (EMC/EMI) in the field. Traditionally\, methods such as the Monte Carlo method\, stochastic Galerkin method\, stochastic collocation\, and linear regression have been developed to tackle the challenges of uncertainty quantification (UQ) problems. However\, these methods often face the issue of curse of dimensionality. In this study\, we propose two different approaches to quantify the uncertainty in EMC/EMI for partial equivalent element circuits. The first method utilizes interval analysis to establish the bounds of the quantities of interest. To further capture the stochastic parameters\, we employ the physical-informed neural network to construct the polynomial chaos expansion. Consequently\, the coefficients of the polynomial bases can be obtained. To construct a network without relying on computational models\, we employ the Wasserstein generative adversarial network with a gradient penalty to estimate the stochastic characteristics. This approach allows us to effectively estimate the uncertain properties without explicitly relying on a computational model. \nSpeaker:\nMs. Yuan Ping\nDepartment of Electrical and Electronic Engineering\,\nThe University of Hong Kong \nBiography of the speaker:\nYuan Ping received the B.E. degree and M. S. degree from Xidian University in 2016 and 2019. She is currently pursuing the Ph.D. degree with the Department of Electrical and Electronic Engineering\, The University of Hong Kong\, Hong Kong. Her research interests Uncertainty Quantification\, Phase retrieval and computational electromagnetic. \nOrganizer:   Prof. Lawrence K. YEUNG \nAll are welcome.
URL:https://ece.hku.hk/events/20240429-3/
LOCATION:Online via Zoom
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/11/rpg-seminar.jpg
END:VEVENT
END:VCALENDAR