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X-WR-CALNAME:Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
X-ORIGINAL-URL:https://ece.hku.hk
X-WR-CALDESC:Events for Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
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TZID:Asia/Hong_Kong
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TZOFFSETFROM:+0800
TZOFFSETTO:+0800
TZNAME:HKT
DTSTART:20220101T000000
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20230904
DTEND;VALUE=DATE:20230905
DTSTAMP:20260515T202108
CREATED:20230807T094456Z
LAST-MODIFIED:20250114T080719Z
UID:17567-1693785600-1693871999@ece.hku.hk
SUMMARY:Towards open-source MR software and hardware with Pulseq and CoilGen
DESCRIPTION:MRI is a mature non-invasive medical imaging technology\, which is why the human MRI machines from all major manufacturers are very similar. Nonetheless\, the development and dissemination of novel MR acquisition techniques is hampered by the notoriously difficult and time-consuming task of implementing new methods on a particular MR vendor’s platform since it must be done using that vendor’s low-level and proprietary programming environment. Furthermore\, distributing a new pulse sequence to another vendor’s platform is generally not possible since each vendor’s software ecosystem is different and tightly sealed. This discourages scientific and clinical collaboration by introducing artificial boundaries\, leading to fragmentation within the research community. Whereas for image reconstruction and image post-processing a great variety of open source software tools exist\, little can be found for the MR pulse sequence design and even less so for the MR hardware. With our recent tools Pulseq[1\,2] and CoilGen[3\,4] we are actively changing the established predominantly proprietary landscape by contributing towards the open source and open science culture[5]. \nReferences:\n[1] http://pulseq.github.io/\n[2] Layton\, MRM 2017\, doi:10.1002/mrm.26235;\n[3] https://github.com/Philipp-MR/CoilGen\n[4] Amrein\, MRM 2022\, doi: 10.1002/mrm.29294;\n[5] https://www.opensourceimaging.org/ \nBiography of the speaker: \nProf. Maxim Zaitsev graduated from the Belarussian State University in 1997 with a Diploma Degree in Physics\, major Biophysics (equivalent of today’s Master of Science) and after a short detour to software industry has joined a Ph.D. program at the University of Cologne\, Germany in Fall of 1999. After defending his Ph.D. thesis on method development for magnetic resonance imaging (MRI) in 2002 he moved to the University of Freiburg\, Germany\, where he pursued a career from a postdoc to a senior scientist and a leader of the group MR Technologies. In 2019 he accepted a University Professor position at the Medical University of Vienna\, Vienna\, Austria\, where he acted as a Co-Director of the High Field Imaging Center. In January 2022 Prof. Zaitsev returned to Freiburg\, Germany\, as a Head of the Medical Physics Division at the Department of Radiology\, University Medical Center Freiburg. Prof. Zaitsev is a co-author of over 130 scientific papers and named as inventor on over 20 patents. \nAll are welcome.
URL:https://ece.hku.hk/events/towards-open-source-mr-software-and-hardware-with-pulseq-and-coilgen/
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20230906
DTEND;VALUE=DATE:20230907
DTSTAMP:20260515T202108
CREATED:20230831T024217Z
LAST-MODIFIED:20250114T100115Z
UID:17604-1693958400-1694044799@ece.hku.hk
SUMMARY:Seminar - Functional soft materials to interface with biology: from non-invasive mechanochemical drug delivery to miniaturized soft robotic actuators
DESCRIPTION:Non-invasive\, localized delivery and activation of chemical reactions and chemical release deep inside body is still challenging. In this talk\, I will cover how soft materials with energy transduction capabilities can address these challenges. In the first part\, I will showcase our efforts in developing soft materials that facilitate mechanochemistry\, a chemical process initiated by mechanical stress\, with biocompatible focused ultrasound assisted by acoustically-active proteins. To address the challenges of targeted delivery within the body\, in the second part of the talk\, I will discuss the development of cell-mimicking miniaturized soft actuators based on molecularly anisotropic polymer networks made by liquid crystalline elastomers (LCEs) with the goal to achieve untethered microrobots to navigate inside body for localized payload delivery. \nBiography of the speaker: \nDr. Yuxing Yao is a Resnick postdoctoral scholar in Chemical Engineering at California Institute of Technology working with Prof. Mikhail G. Shapiro. Dr. Yao received his B.S. in Chemistry from Tsinghua University and his Ph.D. in Chemistry and Chemical Biology from Harvard University. His research focuses on developing soft functional materials to interface with Biology. Yuxing’s work has been recognized by Foresight Institute Distinguished Student Award (Previous awardees: Yi Cui (Stanford)\, Jing Kong (MIT)) and DSM Science & Technology Award Finalist (4 ppl. nationwide under the ACS Div. of Polymer Chemistry). \nAll are welcome.
URL:https://ece.hku.hk/events/seminar-functional-soft-materials-to-interface-with-biology-from-non-invasive-mechanochemical-drug-delivery-to-miniaturized-soft-robotic-actuators/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/02/Seminar-s-banner.jpg
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20230908
DTEND;VALUE=DATE:20230909
DTSTAMP:20260515T202108
CREATED:20230829T081126Z
LAST-MODIFIED:20250114T100048Z
UID:17600-1694131200-1694217599@ece.hku.hk
SUMMARY:Mapping Charge Carrier Dynamics in Solar Cell Materials
DESCRIPTION:The separation and collection of photo-generated charge carriers in light-harvesting devices are limited by the losses and ambiguous dynamical events at the surfaces and interfaces of the absorber layers.1-3 These events occur in ultrafast time scales and can only be visualized selectively in space and time by scanning ultrafast electron microscopy (the sole technique capable of surface-selective visualization of light-triggered carrier dynamics at nanometer and femtosecond scales). In this method\, the surface of the photoactive materials is excited by a clocking optical pulse and the photo-induced changes will be directly imaged using a pulsed electron beam that generate secondary electrons with a couple of electron volts energy\, which are emitted from the very top surface of the material in a manner that is extremely sensitive to the localization of the electron and hole on the photoactive material surfaces. This powerful technique along with ultrafast laser spectroscopy allow us to directly and precisely investigate and decipher the trajectory of charge carriers on materials surfaces and interfaces in real space and real time. Through this work\, we have optimized the properties of photoactive materials for applications in light-harvesting devices that led to the world-record solar cell devices based on perovskite crystals. Moreover\, we have clearly demonstrated in space and time how the surface orientations\, surface oxidation and passivation can significantly impact the overall dynamical processes of photo-generated charge carriers in optoelectronic materials.4-5 Finally\, I will talk about our recent ground-breaking work in X-ray imaging technology that include cutting-edge materials discovery\, heavy-atom engineering\, state-of-the-art characterization and efficient (nearly 100%) interfacial energy transfer between sensitizers and scintillators that has led to the development of novel X-ray imaging screens with outstanding sensitivity\, ultralow detection limit\, unprecedented spatial image resolution and low-cost fabrication\, with potential applications in medical imaging\, industrial monitoring and security screenings. 6-9  \nReferences  \n1- O. M. Bakr\, O. F. Mohammed.\, Science 355\, 1260 (2017). \n2- R. Begum\, M. R. Parida\, A. L. Abdelhady\, B. Murali\, N. Alyami\, G. H. Ahmed\, M. N. Hedhili\, O. M. Bakr\, and O. F. Mohammed.\, J. Am. Chem. Soc. 139\, 731 (2017). \n3- O. F. Mohammed\, D.-S. Yang\, S. Pal\, A. H. Zewail\, J. Am. Chem. Soc. 133\, 7708 (2011). \n4- R. Bose\, A. Bera\, M. R. Parida\, A. Adhikari\, B. S. Shaheen\, E. Alarousu\, J. Sun\, T. Wu\, O. M. Bakr\, O. F. Mohammed\, Nano Lett. 16\, 4417 (2016). \n5- A. M. El-Zohry\, B. S. Shaheen\, V. M. Burlakov\, J. Yin\, M. N. Hedhili\, S. Shikin\, B. S. Ooi\, O. M. Bakr\, O. F. Mohammed\, Chem\, 5\, 706-718 (2019). \n6- P. Maity\, N. A. Merdad\, J. Yin\, K. J. Lee\, L. Sinatra\, O. M. Bakr\, O. F. Mohammed\, ACS Energy Lett.\, 6\, 2602 (2021). \n7- Y. Zhang\, R. Sun\, X. Ou\, K. Fu\, Q. Chen\, Y. Ding\, L-J Xu\, L. Liu\, Y. Han\, A. V. Malko\, X. Liu\, H. Yang\, O. M. Bakr\, H. Liu\, O. F. Mohammed\, ACS Nano\, 13\, 2520 (2019). \n8- J.-X. Wang\, L. Gutie´rrez-Arzaluz\, X. Wang\, M. Almalki\, J. Yin\, J. Czaban-Jóźwiak\, O. Shekhah\, Y. Zhang\, O. B. Bakr\, M. Eddaoudi\, O. F. Mohammed\, Matter\, 5\, 253-265 (2022). \n9- J-X. Wang\, Chen\, L. Gutiérrez-Arzaluz\, X. Wang\, T. He\, Y. Zhang\, M. Eddaoudi\, O. M. Bakr\, O. F. Mohammed\, Nature Photonics\, 16\, 869-875 (2022). \nBiography of the speaker: \nDr. Mohammed is Professor of Chemistry and Materials Science & Engineering; and the principal investigator of ultrafast laser spectroscopy and four-dimensional (4D) electron imaging laboratory at KAUST. He earned a Ph.D in Physical and Theoretical Chemistry from Humboldt University of Berlin\, Germany. Prior to joining KAUST\, Dr. Mohammed was a senior research associate at Caltech\, where he worked with Professor Zewail\, a Nobel laureate\, on developing innovative laser spectroscopic and time-resolved electron imaging techniques. During his time at Caltech\, Dr. Mohammed made significant contributions to the profound understanding of the dynamics of photo-generated charge carriers in photoactive materials\, and pioneered the development of advanced characterization techniques for studying surface and interfacial dynamics on nanometer and femtosecond scales. The current research activities of Dr. Mohammed are focused on the development of highly efficient solar cells\, light-emitting diodes and X-ray imaging scintillators with the aid of ultrafast laser spectroscopy\, 4D electron imaging and computational materials. \nDr. Mohammed has published over 310 articles in international peer-reviewed journals including Science\, Nature\, Nature Materials\, Nature Energy and Nature Photonics\, large number of these papers are currently highly cited ( 39 papers). Dr. Mohammed has more than 32\,000 citations and 84 h-index. In 2019 and 2020\, 2021\, 2022 Dr. Mohammed was identified as a Highly Cited Researcher by Web of Science. In January 2020\, he joined the Editorial Advisory Board of the Journal of Physical Chemistry Letters. In February\, 2021\, he was named a Fellow of the Royal Society of Chemistry (FRSC). In March\, 1\, 2021\, Dr. Mohammed was appointed an Associate Editor of ACS Applied Materials & Interfaces. In January 2023\, he joined the Editorial Advisory Board of ACS Materials Letters and the Journal of Physical Chemistry A & B & C (American Chemical Society) – some of the leading journals of the field of Physical Chemistry and Materials Science. In July 2023\, he was named a Fellow of the Institute of Physics (IOP). Finally\, Dr. Mohammed is the recipient of several prestigious awards\, including the Distinguished Scholar Award from Arab Fund for Economic and Social Development\, Kuwait; Long-term Fellowship\, Germany\, the Japan Society for the Promotion of Science (JSPS) fellowship\, Japan\, the State Prize in Basic Sciences\, Egypt\, Shoman Prize in Photochemistry\, Shoman Foundation\, Jordan\, and Kuwait Prize in Physics\, Kuwait Foundation\, Kuwait. \nAll are welcome.
URL:https://ece.hku.hk/events/mapping-charge-carrier-dynamics-in-solar-cell-materials/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230918
DTEND;VALUE=DATE:20230919
DTSTAMP:20260515T202108
CREATED:20230808T085759Z
LAST-MODIFIED:20250114T095818Z
UID:17569-1694995200-1695081599@ece.hku.hk
SUMMARY:From basic concepts to clinical translation of deep learning in MRI reconstruction
DESCRIPTION:In 2016\, deep learning techniques have been introduced to solve the inverse problem of MR image reconstruction from undersampled data from accelerated acquisitions (1\,2\,3). Since then\, the field has grown substantially\, and a wide range of machine learning methods have been developed and applied to a wide range of imaging applications. In this talk\, I will give a short overview of the background of a deep learning reconstruction that is based on iterative reconstruction methods used in compressed sensing. I will discuss advantages as well as ongoing challenges that need to be met when translating these approaches into daily clinical practice (4). This will include a discussion of the lessons learnt from the recent fastMRI image reconstruction challenges (5\,6). \nReferences:\n1. Learning a variational model for compressed sensing MRI reconstruction. Hammernik\, et al. Proc. ISMRM p33 (2016).\n2. Accelerating magnetic resonance imaging via deep learning. Wang et al. IEEE ISBI 514-517 (2016).\n3. Learning a Variational Network for Reconstruction of Accelerated MRI Data. Hammernik et al. MRM\, 79:3055-3071 (2018).\n4. Deep Learning Reconstruction Enables Prospectively Accelerated Clinical Knee MRI. Johnson et al.\, Radiology 307:e220425 (2023).\n5. Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge. Knoll et al. MRM 84 (6)\, 3054-3070 (2020).\n6. Results of the 2020 fastmri challenge for machine learning MR image reconstruction. Muckley et al. IEEE TMI 40 (9)\, 2306-2317(2021). \nBiography of the speaker: \nFlorian Knoll received his PhD in electrical engineering in 2011 from Graz University of Technology. From 2015 to 2021\, he was Assistant Professor for Radiology at the Center for Biomedical Imaging at NYU Grossman School of Medicine. Since 2021\, he is Professor and head of the Computational Imaging Lab at the Department Artificial Intelligence in Biomedical Engineering at Friedrich-Alexander University Erlangen Nuremberg. In currently holds two grants from the German research fund (DFG) as well as an R01 and a P41 TR&D project award from NIH. His research interests include iterative MR image reconstruction\, parallel MR imaging\, Compressed Sensing and Machine Learning. \nAll are welcome.
URL:https://ece.hku.hk/events/from-basic-concepts-to-clinical-translation-of-deep-learning-in-mri-reconstruction/
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20230925
DTEND;VALUE=DATE:20230926
DTSTAMP:20260515T202108
CREATED:20230913T094624Z
LAST-MODIFIED:20250114T100203Z
UID:17640-1695600000-1695686399@ece.hku.hk
SUMMARY:Control of renewable-energy-dominated power systems
DESCRIPTION:Renewable energy is the key to achieving carbon neutrality. However\, the large-scale integration of renewable energy sources poses tremendous challenges to the stable operation of modern power systems\, as the systems become much more complex and are under lots of uncertainties. In particular\, the grid interface of renewable generators (i.e.\, power electronics converters) generally have distinct dynamics compared with traditional fossil-fuel-based synchronous generators\, resulting in new stability problems in practice. Moreover\, it has been widely recognized that conventional PLL-based control methods of power converters cannot support a stable renewable-energy-dominated power system because they have no grid-forming capability. Hence\, it is essential to develop advanced control strategies for power converters. This talk will introduce the challenges in controlling a renewable-energy-dominated power system and discuss how we can possibly understand the dynamics of such a system. I will present my research works in designing stabilizing\, robust\, and optimal controllers for renewable generators to handle uncertainties in the system and enable their grid-forming capabilities. I will demonstrate how data-driven control can equip renewable generators with adaptability and ensure robust and optimal performance under variable grid conditions. Moreover\, I will discuss other open problems in renewable-energy-dominated power systems and envision future research directions.\n \nBiography of the speaker: \nLinbin Huang is a postdoctoral researcher at ETH Zurich since September 2020\, working in the Automatic Control Laboratory. He received his Ph.D. degree in College of Electrical Engineering at Zhejiang University in 2020 and a B. Eng. degree from the same institution in 2015. From 2018 to 2019\, he was a visiting scientist at ETH Zurich. His research interests include power system stability\, optimal control of power electronics\, and data-driven control. \nAll are welcome.
URL:https://ece.hku.hk/events/control-of-renewable-energy-dominated-power-systems/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230926
DTEND;VALUE=DATE:20230927
DTSTAMP:20260515T202108
CREATED:20230921T090122Z
LAST-MODIFIED:20250114T100315Z
UID:17666-1695686400-1695772799@ece.hku.hk
SUMMARY:Geometric Robot Learning for Generalizable Skills Acquisition
DESCRIPTION:Robot learning has witnessed significant progress in terms of generalization in the past few years. At the heart of such a generalization\, the advancement of representation learning\, such as image and text foundation models plays an important role. While these achievements are encouraging\, most tasks conducted are relatively simple. In this talk\, I will talk about our recent efforts on learning generalizable skills focusing on tasks with complex physical contacts and geometric reasoning. Specifically\, I will discuss our research on: (i) the use of a large number of low-cost\, binary force sensors to enable Sim2Real manipulation; (ii) unifying 3D and semantic representation learning to generalize policy learning across diverse objects and scenes. I will showcase the real-world applications of our research\, including dexterous manipulation\, language-driven manipulation\, and legged locomotion control. \nZoom Link:\nhttps://hku.zoom.us/j/95792387978?pwd=YmJGU1ZxMC80alNWZ1gwS2lyNWFydz09 \nBiography of the speaker:\nXiaolong Wang is an Assistant Professor in the ECE department at the University of California\, San Diego\, affiliated with the TILOS NSF AI Institute. He received his Ph.D. in Robotics at Carnegie Mellon University. His postdoctoral training was at the University of California\, Berkeley. His research focuses on the intersection between computer vision and robotics. His specific interest lies in learning 3D and dynamics representations from videos and physical robotic interaction data. These comprehensive representations are utilized to facilitate the learning of robot skills\, with the goal of generalizing the robot to interact effectively with a wide range of objects and environments in the real physical world. He is the recipient of the NSF CAREER Award\, Intel Rising Star Faculty Award\, and Research Awards from Sony\, Amazon\, Adobe\, and Cisco. \nAll are welcome.
URL:https://ece.hku.hk/events/geometric-robot-learning-for-generalizable-skills-acquisition/
CATEGORIES:Seminar
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