
Prof. Ting-Chung Poon
IEEE/IOP/OSA/SPIE Fellow
Virginia Polytechnic Institute and State University, USA |
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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.
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Prof. Changsheng Xu
IEEE/IAPR Fellow
Institute of Automation, Chinese Academy of Sciences, China |
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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. |
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Prof. Guang-Ren Duan
Academician of the Chinese Academy of Sciences
CAA/IEEE Fellow
Southern University of Science and
Technology, China |
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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.
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Prof. Shaohua Zhou
AIMBE/IEEE Fellow
Suzhou Institute for Advanced
Research, University of Science and Technology of China, China |
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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.
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Prof. Makoto Iwasaki
IEEE Fellow
Nagoya Institute of Technology, Japan |
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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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