Keynote Speakers | 主讲专家



Prof. Ting-Chung Poon
IEEE/IOP/OSA/SPIE Fellow
Virginia Polytechnic Institute and State University, USA
 

Ting-Chung Poon is a Professor of Electrical and Computer Engineering at Virginia Tech, Virginia, USA. His current research interests include 3-D image processing, and optical scanning holography (OSH). Dr. Poon is the author of the monograph Optical Scanning Holography with MATLAB (Springer, 2007), and is the co-author of, among other textbooks, Introduction to Modern Digital Holography with MATLAB (Cambridge University Press, 2014). He is also Editor of the book Digital Holography and Three-Dimensional Display (Springer, 2006). Dr. Poon served as Division Editor of Applied Optics from 2008 to 2014, and was Associate Editor-in-Chief of Chinese Optics Letters. Currently, Prof. Poon is Specialty Chief Editor of Frontiers in Photonics, and Editor of Applied Sciences. Dr. Poon is the founding Chair of the Optical Society (OSA) topical meeting Digital holography and 3-D imaging (2007). He was a Chair of the OSA Emmett N. Leith Medal Committee and a member of the OSA Joseph Fraunhofer Award/Robert Burley Prize Committee. Currently he is General Chair of 2021 Frontier in Optics + Laser Science (FiO LS).

Dr. Poon is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), the Institute of Physics (IOP), the Optical Society (OSA), and the International Society for Optics and Photonics (SPIE). He received the 2016 Dennis Gabor Award of the SPIE for "pioneering contributions to optical scanning holography (OSH), which has contributed significantly to the development of novel digital holography and 3-D imaging."

Speech Title: Holographic Approach to 3D Object Recognition
Abstract: In this talk, I will discuss two topics in modern information optics: holography and pattern recognition. In the first part of the talk, I will briefly review the basic principle of holography, utilizing the concept of Fresnel zone plates (FZPs). As it turns out, a FZP is the hologram of a point object. In the second part of the talk, optical pattern recognition, i.e., pattern recognition using optics, will be described using the concept of 2D correlation. The use of holography and correlation will then be discussed in the context of 3D object recognition. Finally, holography by 2D raster optical scanning known as optical scanning holography (OSH) is presented to show how holograms with high single-to-noise ratio (S/N) and 3D object recognition can be achieved.

     
     
     
     


Prof. Changsheng Xu
IEEE/IAPR Fellow

Institute of Automation, Chinese Academy of Sciences, China
 

Changsheng Xu is a professor of Institute of Automation, Chinese Academy of Sciences. His research interests include multimedia content analysis/indexing/retrieval, pattern recognition and computer vision. He has hold 50+ granted/pending patents and published over 400 refereed research papers including 100+ IEEE/ACM Trans. papers in these areas.

Prof. Xu serves as Editor-in-Chief of Multimedia Systems Journal and Associate Editor of ACM Trans. on Multimedia Computing, Communications and Applications. He received the Best Paper Awards of ACM Multimedia 2016, 2016 ACM Trans. on Multimedia Computing, Communications and Applications and 2017 IEEE Multimedia. He served as Associate Editor of IEEE Transactions on Multimedia and Program Chair of ACM Multimedia 2009. He has served as associate editor, guest editor, general chair, program chair, area/track chair and TPC member for over 20 IEEE and ACM prestigious multimedia journals, conferences and workshops. He is an ACM Distinguished Scientist, IEEE Fellow, and IAPR Fellow.

Speech Title: Connecting Isolated Social Multimedia Big Data
Abstract: The explosion of social media has led to various Online Social Networking (OSN) services. Today's typical netizens are using a multitude of OSN services. Exploring the user-contributed cross-OSN heterogeneous data is critical to connect between the separated data islands and facilitate value mining from big social multimedia. From the perspective of data fusion, understanding the association among cross-OSN data is fundamental to advanced social media analysis and applications. From the perspective of user modeling, exploiting the available user data on different OSNs contributes to an integrated online user profile and thus improved customized social media services. This talk will introduce a user-centric research paradigm for cross-OSN mining and applications and some pilot works along two basic tasks: (1) From users: cross-OSN association mining and (2) For users: cross-OSN user modeling.

     
     
     
     


Prof. Guang-Ren Duan
Academician of the Chinese Academy of Sciences
CAA/IEEE Fellow
Southern University of Science and Technology, China
 

Guang-Ren Duan received his Ph.D. degree in Control Systems Sciences from Harbin Institute of Technology, Harbin, P. R. China, in 1989. After a two-year post-doctoral experience at the same university, he became professor of control systems theory at that university in 1991. He is the founder and the Honorary Director of the Center for Control Theory and Guidance Technology at Harbin Institute of Technology, and recently he is also in charge of the Center for Control Science and Technology at the Southern University of Science and Technology. He visited the University of Hull, the University of Sheffield, and also the Queen's University of Belfast, UK, from December 1996 to October 2002, and has served as Member of the Science and Technology Committee of the Chinese Ministry of Education, Vice President of the Control Theory and Applications Committee, Chinese Association of Automation (CAA), and Associate Editors of a few international journals. He is currently an Academician of the Chinese Academy of Sciences, and Fellow of CAA, IEEE and IET. His main research interests include parametric control systems design, nonlinear systems, descriptor systems, spacecraft control and magnetic bearing control. He is the author and co-author of 5 books and over 380 SCI indexed publications.

Speech Title: Fully Actuated System Approach—Background, Developments and Advances
Abstract: Inspired by the practical mechanical fully actuated systems, the fully actuated system (FAS) approach, which is parallel to the well-known state-space one, has been recently proposed for general dynamical control system designs. The state-space models are convenient for obtaining the state vectors (state responses or estimates), but not the control vectors, while the FAS models are those from which the control vectors can be explicitly solved out, and thus can best perform the control. The FAS approach has found its great power in dealing with control of complicated nonlinear dynamical systems, including the time-varying nonlinear systems with time-varying delays. In this talk, the background and the development of the FAS approach are briefly outlined, and recent advances in the stabilization of a type of nonholonomic systems are also briefly presented. New point views and concepts are presented from the FAS approach angle.

     
     
     
     


Prof. Shaohua Zhou
AIMBE/IEEE Fellow
Suzhou Institute for Advanced Research, University of Science and Technology of China, China
 

Prof. S. Kevin Zhou obtained his PhD degree from University of Maryland, College Park. Currently he is a professor and executive dean of School of Biomedical Engineering, Suzhou Institute for Advanced Research, University of Science and Technology of China (USTC) and an adjunct professor at Institute of Computing Technology, Chinese Academy of Sciences and Chinese University of Hong Kong (CUHK), Shenzhen. Prior to this, he was a principal expert and a senior R&D director at Siemens Healthcare Research. Dr. Zhou has published 240+ book chapters and peer-reviewed journal and conference papers, registered 140+ granted patents, written two research monographs, and edited three books. The two recent books he led the edition are entitled "Deep Learning for Medical Image Analysis, SK Zhou, H Greenspan, DG Shen (Eds.)" and "Handbook of Medical Image Computing and Computer Assisted Intervention, SK Zhou, D Rueckert, G Fichtinger (Eds.)". He has won multiple awards including R&D 100 Award (Oscar of Invention), Siemens Inventor of the Year, and UMD ECE Distinguished Alumni Award. He has been a program co-chair for MICCAI2020, an editorial board member for IEEE Trans. Medical Imaging and Medical Image Analysis, and an area chair for AAAI, CVPR, ICCV, MICCAI, and NeurIPS. He has been elected as a treasurer and board member of the MICCAI Society, an advisory board member of MONAI (Medical Open Network for AI), and a fellow of AIMBE, IEEE, and NAI (National Academy of Inventors).

Speech Title: Neural Medical Image Recovery
Abstract: Medical imaging is widely used in clinical decision making. However, medical image acquisition or its acquired image still suffers from an array of challenges such as metal artifacts, slow acquisition time, anisotropic resolution, strong noise, etc. In this talk, we present several learning approaches that attempt to recover the original images under these adverse conditions:
(i) a dual domain network (DuDoNet) for reducing metal artifacts in CT via joint learning in both sinogram and image domains;
(ii) a dual domain recurrent network (DuDoRNet) for MRI image reconstruction from undersampled k-space data via joint and recurrent learning in both frequency and image domains;
(iii) a spatially adaptive interpolation network (SAINT) for synthesizing slices to mitigate the anisotropic resolution issue; and
(iv) an artifact disentanglement network (ADN) for removing artifacts or noises without paired data while preserving anatomical structures.
Our approaches, both supervised and unsupervised, leverage deep neural networks as cores, integrate specific domain knowledge, and yield high quality recovery for both simulated data and clinical images.

     
     
     
     


Prof. Makoto Iwasaki
IEEE Fellow

Nagoya Institute of Technology, Japan
 

Makoto Iwasaki received the B.S., M.S., and Dr. Eng. degrees in electrical and computer engineering from Nagoya Institute of Technology, Nagoya, Japan, in 1986, 1988, and 1991, respectively. He is currently a Professor at the Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology. As professional contributions of the IEEE, he has participated in various organizing services, such as, a Co-Editors-in-Chief for IEEE Transactions on Industrial Electronics since 2016, a Vice President for Planning and Development in term of 2018 to 2021, etc. He is IEEE fellow class 2015 for "contributions to fast and precise positioning in motion controller design".

He has received a number of awards and honors for his academic contributions, like the Best Paper and Technical Awards of IEE Japan, the Nagamori Award, the Ichimura Prize, and the Commendation for Science and Technology by the Japanese Minister of Education, respectively. He is also a fellow of IEE Japan, and a member of Science Council of Japan. His current research interests are the applications of control theories to linear/nonlinear modeling and precision positioning, through various collaborative research activities with industries.

Speech Title: GA-Based Optimization in Mechatronic Systems: System Identification and Controller Design
Abstract: Fast-response and high-precision motion control is one of indispensable techniques in a wide variety of high performance mechatronic systems including micro and/or nano scale motion, such as data storage devices, machine tools, manufacturing tools for electronics components, and industrial robots, from the standpoints of high productivity, high quality of products, and total cost reduction. In those applications, the required specifications in the motion performance, e.g. response/settling time, trajectory/settling accuracy, etc., should be sufficiently achieved. In addition, the robustness against disturbances and/or uncertainties, the mechanical vibration suppression, and the adaptation capability against variations in mechanisms should be essential properties to be provided in the performance.

The keynote speech presents practical optimization techniques based on a genetic algorithm (GA) for mechatronic systems, especially focusing on auto-tuning approaches in system identification and motion controller design. Comparing to conventional manual tuning techniques, the auto-tuning technique can save the time and cost of controller tuning by skilled engineers, can reduce performance deviation among products, and can achieve higher control performance. The technique consists of two main processes: one is an autonomous system identification process, involving in the use of actual motion profiles of system. The other is, on the other hand, an autonomous control gain tuning process in the frequency and time domains, involving in the use of GA, which satisfies the required tuning control specifications, e.g., control performance, execution time, stability, and practical applicability in industries. The proposed technique has been practically evaluated through experiments performed, by giving examples in industrial applications to a galvano scanner in laser drilling manufacturing and an actual six-axis industrial robot.

     
     
     

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