Venue & Accommodation
June 28th(Mon) – 30th(Wed) / Grand Hyatt Jeju, Republic of Korea
Submission of Paper
March 27, 2021April 3, 2021
Notification of Acceptance
April 24, 2021
Submission of Final Paper
May 19, 2021
TODAY 2022. 01. 25
Simulation and Implement of 5G System for Academic Research
- King Mongkut's University of Technology North Bangkok
This tutorial is about how to understand the benefits of 5G technology by firstly simulating the 5G concept on the MATLAB software and then using the Software Defined Radio (SDR) to do the real experiment of the 5G system such as LDPC coding/ Polar coding/ MIMO technology/ Optical core network.
Vitawat Sittakul was born in Bangkok, Thailand, in March 1979. He received the B.Eng. degree in telecommunication from Chulalongkorn University, Bangkok, Thailand, in 2000 and the M.Sc. degree (with distinction) in optical and communication systems from Northumbria University, Newcastle, U.K., in 2003 where he worked on the thesis of Investigation of Microstrip Antennas using U-slot. He awarded the Ph.D. degree funding from Thai government and he received the Ph.D. degree from Department of Electrical and Electronic Engineering, University of Bristol, Bristol, U.K., in 2006. He worked in the industry and government for more than 15 years. From 2003 to 2004, he was a Measurement Engineer with Fabinet, where he was a global engineering- and manufacturing services provider. From 2004 to 2006, he was a Senior RF Engineer with Advance Information Service Company, Ltd. (AIS), which is the biggest mobile service provider in Bangkok. From 2006 to 2009, he was a research assistant at the University of Bristol, Bristol, U.K where he advised the students and designs the laboratory experiment. From 2009 to 2015, he worked as an RF Metrologist in the National Institute of Metrology (Thailand) where he developed and calibrated industrial electrical equipment in the primary standard levels. He had been a consultant to many companies such as FPRI advisor company and Telco-Economics company in telecommunication area. He is currently an associate professor in King Monkut’s University of Technology North Bangkok. He had been the chief keynote speaker for more than 5 international conferences and he has published 13 journals and 35 conference proceedings on RF devices & Antenna measurement, optical communication, wireless communication, RF-over-fiber, active integrated antennas, antenna design, IoT.
Sampling Signals on Networks: Graph Sampling Theory and Its Applications
- Tokyo University of Agriculture and Technology/ PRESTO
The study of sampling signals on graphs, with the goal of building an analog of sampling for standard signals in the time and spatial domains, has attracted considerable attention recently.
Beyond adding to the growing theory on graph signal processing (GSP), sampling on graphs has various promising applications. In this talk, current progress on sampling over graphs is introduced, both for theory and potential applications. Most methodologies used in graph signal sampling are designed to parallel those used in sampling for standard signals, however, sampling theory for graph signals significantly differs from that for Shannon-Nyquist and shift invariant signals. This is due in part to the fact that the definitions of several important properties, such as shift invariance and bandlimitedness, are different in GSP systems. This talk presents similarities and differences between standard and graph sampling and highlight open problems and challenges.
Yuichi Tanaka received the B.E., M.E. and Ph.D. degrees in electrical engineering from Keio University, Yokohama, Japan, in 2003, 2005, and 2007, respectively. He was a Postdoctoral Scholar at Keio University, Yokohama, Japan, from 2007 to 2008, and supported by the Japan Society for the Promotion of Science (JSPS). From 2006 to 2008, he was also a visiting scholar at the University of California, San Diego. From 2008 to 2012, he was an Assistant Professor in the Department of Information Science, Utsunomiya University, Tochigi, Japan. Since 2012, he has been an Associate Professor in the Department of Electrical Engineering and Computer Science, Tokyo University of Agriculture and Technology, Tokyo, Japan. Currently he has a cross appointment as a PRESTO Researcher, Japan Science and Technology Agency. His current research interests are in the field of high-dimensional signal processing and machine learning which includes: graph signal processing, geometric deep learning, sensor networks, image/video processing in extreme situations, biomedical signal processing, and remote sensing.
Dr. Tanaka served as an associate editor for the IEEE Transactions on Signal Processing from 2016 to 2020 and the IEICE Transactions on Fundamentals from 2013 to 2017. Currently he is an elected member of the APSIPA SIPTM (Signal and Information Processing Theory and Methods) and IVM (Image, Video and Multimedia) Technical Committees. He was a recipient of the Yasujiro Niwa Outstanding Paper Award in 2010, the TELECOM System Technology Award in 2011, and Ando Incentive Prize for the Study of Electronics in 2015. He also received IEEE Signal Processing Society Japan Best Paper Award in 2016 and Best Paper Awards in APSIPA ASC 2014 and 2015.
Vision-based Human Pose Estimation: Body, Hand and Gaze
Hyung Jin Chang
- University of Birmingham
In this tutorial, we are going to present current topics on vision-based human body pose, human hand pose, and eye gaze estimation methods. In the introduction video, I will give a brief overview of the three vision-based research topics: body, hand, and gaze estimation research, using the state-of-the-art computer vision research outputs. In the following, we are going to see more detailed issues, challenges, methodologies and related datasets of each of the topics.
Hyung Jin Chang is an Assistant Professor of the school of computer science at the University of Birmingham, United Kingdom. before joining the University of Birmingham, he was a post-doctoral researcher at Imperial College London and received his PhD degree from Seoul National University. His research combines multiple areas of artificial intelligence including computer vision, machine learning, robotics, and human-computer interaction. His research career started with a focus on theoretical aspects of machine learning, and it has converged on applying these aspects to more practical problems in visual surveillance and robotics. Recently, his research has focused on exploiting and making advances in robot vision, and learning techniques to move toward intelligent human-robot interaction based on visual data. Computer vision and machine learning including deep learning are his expertise research area.