Keynote speakers on ACIRS 2018


Yow Kin Choong, GIST College, Gwangju Institute of Science and Technology, Korea

Yow Kin Choong obtained his B.Eng (Elect) with 1st Class Honours from the National University of Singapore in 1993, and his Ph.D. from Cambridge University, UK in 1998. He joined the Gwangju Institute of Science and Technology (GIST) in March 2013, where he is presently an Associate Professor in the EECS Department. Prior to joining GIST, he was a Professor at the Shenzhen Institutes of Advanced Technology (SIAT), P.R. China (2012-2013), and Associate Professor at the Nanyang Technological University (NTU), Singapore (1998-2012). In 1999-2005, he served as the Sub-Dean of Computer Engineering in NTU, and in 2006-2008, he served as the Associate Dean of Admissions in NTU.

Yow Kin Choong's research interest is in Ambient Intelligence which includes passive remote sensing such as Computer Vision, wireless communications such as Ad hoc and Sensor Networks, and computational intelligence such as Fuzzy-Neuro Inference Systems. He has published over 80 top quality international journal and conference papers, and he has served as reviewer for a number of premier journals and conferences, including the IEEE Wireless Communications and the IEEE Transactions on Education. He has been invited to give presentations at various scientific meetings and workshops, such as the CNET Networks Event (2002) as well as the Microsoft Windows Server 2003 Launch (2003). He is also a member of the IEEE, ACM, and the Singapore Computer Society (SCS).

His pioneering work in Mobile and Interactive Learning won the HP Philanthropy grant in 2003 for applying Mobile Technologies in a Learning Environment. Only 7 awards were given to the 21 Asia Pacific Countries who were invited, and his project was the only one from Singapore to win it. Also, in 2003, he was one of the only 2 Singaporeans to be awarded participation to the ASEAN Technology Program on Multi Robot Cooperation Development held in KAIST, Korea.

He was the winner of the NTU Excellence in Teaching Award 2005, and he won the Most Popular SCE Year 1 lecturer for 4 consecutive years 2004-2007. He has led numerous student teams to National and International victories such as the IEEE Computer Society International Design Competition (CSIDC) (2001), the Microsoft Imagine Cup (2002, 2003 and 2005), and the Wireless Challenge (2003).

Speech Title: Binocular Cognitive Autonomous Vehicles
Binocular robots have always been of special interest due to its advantages of depth perception over monocular robots. In this talk, I will discuss how we can apply a cognitive perceptual framework to a robot equipped with binocular vision to allow it to consistently navigate through an environment to target locations without spatial reference coordinates. Our system combines the PowerBot platform with a pair of 2 DOF pan-tilt cameras on a rotating frame, with added saccade, vergence and attention capabilities. We demonstrate that using a cognitive perceptual framework and a reinforcement-based learning algorithm, the robot can learn and unlearn navigational information about its environment, finding new paths when obstacles are present, and reverting to the original path when obstacles are removed.



Prof. Bin Zi, Hefei University of Technology, China

 Bin Zi is currently a professor, the Dean of School of Mechanical Engineering, and the Director of Robotics Institute, Hefei University of Technology, China. He received the Ph.D. degree from Xidian University, China, in 2007. From 2011 to 2012 he worked as a visiting scholar with Chair of Mechanics and Robotics, University of Duisburg-Essen, Germany. He was a visiting professor at the Robotics and Automation Laboratory of the University of Ontario Institute of Technology, Canada in 2015. He has authored and coauthored 2 monographs and more than 100 journal and conference publications. His research interests include robotics and automation, mechatronics, and multirobot systems.


Prof. Huafeng Ding, China University of Geosciences, China

Prof. Dr. Huafeng Ding is a Full Professor and the dean of the School of Mechanical Engineering and Electronic Information, China University of Geosciences. He received his first Ph.D. in Robotics and Mechatronics from Yanshan University, China, in June 2007. He received his second Ph.D. in Mechanics and Robotics from University of Duisburg-Essen, Germany, in February 2015. He worked as an Alexander von Humboldt Fellow in Germany from 2010 to 2012. In 2014, he won the Natural Science fund for Outstanding Youth Scholars and the Fok Ying-Tong Education Foundation.

Dr. Ding's research interests include structural synthesis of mechanisms, conceptual design, control and applications of planar and spatial mechanisms. He published over 100 research papers, 1 book published by Springer. He has more than 60 patents for his inventions. He is Associate Editor for the International Journal of Mechanism and Machine Theory, International Journal of Mechanisms and Robotic Systems, the Journal of China Mechanical Engineering.


Prof. Kenji Suzuki, Tokyo Institute of Technology, Japan

Kenji Suzuki, Ph.D. (by Published Work; Nagoya University) worked at Hitachi Medical Corporation, Japan, Aichi Prefectural University, Japan, as a faculty member, and in Department of Radiology, University of Chicago, as Assistant Professor. In 2014, he joined Department of Electric and Computer Engineering and Medical Imaging Research Center, Illinois Institute of Technology, as Associate Professor. In 2017, he was jointly appointed in World Research Hub Initiative at Tokyo Institute of Technology as Full Professor (Specially Appointed). He published more than 320 papers (including 110 peer-reviewed papers in leading journals). His papers were cited more than 8,500 times by other researchers. He has an h-index of 42. He is inventor on 30 patents (including 13 granted patents), which were licensed to several companies and commercialized. He published 10 books and 22 book chapters, and edited 13 journal special issues. He was awarded/co-awarded more than 25 grants as PI including NIH R01 and ACS. He served as the Editor of a number of leading international journals, including Pattern Recognition and Medical Physics. He served as a referee for 80 international journals, an organizer of 30 international conferences, and a program committee member of 150 international conferences. He gave 95 invited talks and keynote speeches at international conferences and universities. His research was covered in 42 articles in international newspapers, magazines and journals by press and media. He received 25 awards, including 3 RSNA Certificate of Merit Awards, IEEE Outstanding Member Award, Cancer Research Foundation Young Investigator Award, Kurt Rossmann Award for Excellence in Teaching from Univ of Chicago, IEICE 2014 Best Journal Paper Award, Springer-Nature EANM Most Cited Journal Paper Award 2016, and Marquis Who's Who 2017 Albert Nelson Marquis Lifetime Achievement Award.
Speech Title: Deep Learning and Its Advanced Applications in Medical Image Processing, Analysis, and Diagnosis
It is said that artificial intelligence driven by deep learning would make the 4th Industrial Evolution. Deep leaning becomes one of the most active areas of research in computer vision, pattern recognition, and imaging fields, because "learning from examples or data" is crucial to handling a large amount of data ("big data") coming from informatics and imaging systems. Deep learning is a versatile, powerful framework that can acquire image-processing and analysis functions through training with image examples; and it is an end-to-end machine-learning model that enables a direct mapping from raw input data to desired outputs, eliminating the need for handcrafted features in conventional feature-based machine learning. I invented ones of the earliest deep-learning models for image processing, semantic segmentation, object enhancement, and removal of specific patterns in medical imaging. I have been actively studying on deep learning in medical imaging in the past 20 years or so. In this talk, deep learning in computer vision and imaging is overviewed to make clear a) what has changed in machine learning after the introduction of deep learning and b) differences and advantages over conventional feature-based machine learning. Advanced deep-learning applications to medical image processing, analysis, and diagnosis are described, including 1) separation of bones from soft tissue in chest radiographs, 2) computer-aided diagnosis for lung nodule detection in chest radiography and thoracic CT, 3) distinction between benign and malignant nodules in CT, 4) polyp detection and classification in CT colonography, and 5) radiation dose reduction in CT and mammography.


Assoc. Prof. Dr. Matsumoto Mitsuharu, University of electro-communications, Japan

Mitsuharu Matsumoto is currently an associate professor in the University of Electro-Communications. He received a B.E. in Applied Physics, and M.E. and Dr. Eng. in Pure and Applied Physics from Waseda University, Tokyo, Japan, in 2001, 2003, and 2006, respectively. His research interests include acoustical signal processing, image processing, pattern recognition, self-assembly, human-robot interaction and robotics. He received Ericsson Young Scientist Award from Nippon Ericsson K.K, Japan and FOST Kumada Award, in 2009 and 2011, respectively. He published around a hundred of journal and international conference papers. He is a member of the Institute of Electrical and Electronic Engineers (IEEE).
Speech Title: Nonlinear filters and its application to image and audio processing
In this speech, I focus on a nonlinear filter called epsilon-filter, and introduce its application to image and audio processing. Epsilon-filter is a simple nonlinear filter developed about 30 years ago. Original filter is developed for noise reduction from the image, but several improved versions are developed for many applications. They are also applicable not only to image signal but also to audio signal as some features used in epsilon-filter are common in image and audio signals. Therefore, in this speech, I also introduce some examples of the application of epsilon-filter to audio signal and discuss their common points

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