Title: “Internet of Gas Sensing Things”
Assoc. Prof. Dr. Chatchawal Wongchoosuk
Assistant President for Research and Creation of Kasetsart University, Bangkok, Thailand
Assoc. Prof. Dr. Chatchawal Wongchoosuk is Assistant President for Research and Creation of Kasetsart University, Bangkok, Thailand. He is a specialist in developments of smart sensors and intelligent systems for food, agricultural and environmental applications. He has received over 30 research awards such as TRF–OHEC–SCOPUS Young Researcher Award in physical science, Invention Award from National Research Council of Thailand, Highest Citation Award for the young researcher, etc. He has served as reviewer for more than 80 ISI journals. He acts as the guest editor and associate editor for several scientific journals such as Frontiers in Sensors, Crystals, Frontiers in Chemistry, etc. He is the World’s Top 2% Scientist in the field of “Applied Physics” in 2022 and “Electrical & Electronic Engineering” in 2021 and 2020 ranked by Stanford University. He has published several dozens of articles in reputed journals, proceedings, book chapters, patents and copyrights. His research interests cover the topics of innovative technologies ranging from theoretical modeling of nanomaterials to fabrication of intelligent devices for several applications such as DFTB, hybrid gas sensors, electronic nose, chemical sensors, electrochemical sensors, printed sensors, flexible electronics, IoT system, intelligent food and agricultural sensors and smart devices.
Abstract Gas pollutants have been increasing released since the industrial area has been expanded every years. Based on the World Health Organization (WHO) report, 9 out of 10 people breathe air containing high levels of pollutants and around 7 million people die every year from both outdoor and household air pollution. Therefore, the real-time detection of emitted toxic gases is still important for protection of short- and long-term effects on human health. Although, several toxic gas detector devices are available in markets yet, they are very expensive, big, offline, and no wearable device. Therefore, most of people cannot use them in daily life. In this work, I will present new smart gas sensor technologies combined with machine learning and Internet of Things (IoT). The talk will be carried out all aspects of internet of gas sensing things ranging from design of sensing materials to targeted gas molecules based on self-consistent charge density functional tight-binding (SCC-DFTB), synthesis of sensing nanocomposites, fabrication of gas sensors to integration with machine learning and IoT system into the smart devices to use in real-world applications.
Title : “Digital Transformation in Thailand”
Nipon Theera-Umpon, Ph.D., SMIEEE, Director, Biomedical Engineering Institute, Chiang Mai University, Chiang Mai, Thailand
Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand
Associate Prof. PhD. (E-mail:email@example.com), Nipon Theera-Umpon received his B.Eng. (Hons.) degree from Chiang Mai University, Thailand, M.S. degree from University of Southern California, U.S.A., and Ph.D. degree from the University of Missouri-Columbia, U.S.A., all in electrical engineering. Since 1993, he has been working with the Department of Electrical Engineering, Chiang Mai University. He is currently serving as the director of Biomedical Engineering Institute, Chiang Mai University. He has served as editor, reviewer, general chair, technical chair and committee member for several journals and conferences. He has been bestowed several royal decorations and won several awards. He was associate dean of Engineering, chairman for graduate study in electrical engineering, and chairman for graduate study in biomedical engineering. He is a member of Thai Robotics Society, Biomedical Engineering Society of Thailand, Council of Engineers in Thailand, and Engineering Institute of Thailand. He has served as Vice President of the Thailand Health Technology Association and the Thai Engineering in Medicine and Biology Society. Dr. Theera-Umpon is a senior member of the IEEE and is a member of IEEE-IES Technical Committee on Human Factors. He has published more than 200 full research papers in international refereed publications and a handful of them in national publications. His textbooks in Thai language include Digital Signal and Image Processing: Theories and Applications, Advanced Digital Signal Processing, Digital Signal Processing in Telecommunications, etc. Whereas the textbook “Digital Signal and Image Processing: Theories and Applications” received The Outstanding Engineering Textbook Award 2022 from The Fund Management Committee for Education and Research in Engineering Under The Royal Patronage of His Royal Highness Crown Prince Maha Vajiralongkorn, Engineering Institute of Thailand Under H.M. The King’s Patronage. His research interests include Pattern Recognition, Digital Image Processing, Artificial Intelligence, Neural Networks, Fuzzy Sets and Systems, Machine Learning, Big Data Analysis, Data Mining, Medical Signal and Image Processing.
Abstract In this talk, some aspects of Digital Transformation, particularly for Thailand, are presented. The talk begins with a brief introduction of digital technology. To give the current status, Thailand’s statistics in 2022 regarding digital technologies are shown. Then we cover the global/Thailand megatrends, for example, The fourth industrial revolution, The growth of smart city and smart ecosystems, The rise of E-commerce and convenience stores, The increasing connectedness and decreasing privacy, The aging society, etc. We then show how these megatrends associate with digital technologies. Thailand 4.0 Initiative is also mentioned. Finally, a short list of selected digital technologies for Thailand, i.e., Internet of Things (IoT), Artificial Intelligence (AI), Data Analytics, Next Generation Telecommunication, Distributed Ledger Technology (DLT), Quantum Computing, and Automation, are shown. Each chosen technology is elaborated in more details and its trend in Thailand is also presented.
Title : “Semiconductors Innovations for AI”Ph.D. Junjin Kong (Master, Samsung Electronics)
(Present) Samsung Electronics Co., Ltd., Semiconductor Business Division
(2005) Ph.D. @ University of Minnesota, USA, Electrical Engineering
(1989) Samsung Advanced Institute of Technology
Jun Jin Kong (Member, IEEE) received the B.S. and M.S. degrees in electronics engineering from Hanyang University, Seoul, South Korea, in 1986 and 1988, respectively, and the Ph.D. degree in electrical and computer engineering from the University of Minnesota, Minneapolis, MN, USA, in 2005. He joined Samsung Electronics Co., Ltd., Hwaseong, South Korea, in July 1989, where he is currently a Professor in Samsung Institute of Technology and a Master in Samsung. He has over 30 years of industrial experiences in the field of application specific integrated circuit (ASIC) development for telecommunication systems and memory/storage systems as a Technical Team Leader, a Project Leader, a Design Engineer, and a Research and Development Engineer. His research interests are on finding an effective decoding algorithm and its corresponding VLSI architectures of channel codes for communication and memory/storage systems. Dr. Kong was an Organizing Committee Member of The Korean Conference on Semiconductors in 2013, 2014, and 2016, a General Co-Chair of the International SoC Design Conference from 2014 to 2021, a Tutorial Chair of the Asia Pacific Conference on Circuits and Systems (APCCAS 2016), an Industrial Coordinator of International Symposium on Circuits and Systems (ISCAS 2012), and Honorary Chair of ICCE-Asia 2022. He is currently a Technical Committee Member of the IEEE Circuits and Systems Society (CASS) VLSI Systems and Applications (VSA). He was the President at The Institute of Electronics and Information Engineers (IEIE), South Korea, in 2021, and a Steering Committee Member of “the Coding and Information Society” in Korean Institute of Communication Science (KICS). He was awarded the Order of Industrial Service Merit (Silver Tower, 2014) from Korea Government.
Abstract. AI can be defined as H/W and S/W technology with data processing that imitates the human brain. Advanced AI (learning and reasoning) requires massive data storage and transmission. Conversely, it requires low data transmission energy for low power consumption and low cooling costs in a viewpoint of TCO (Total Cost Ownership). (Cooling cost example: Google Dallas data center accounts for 1/4 of Dallas city water use, https://zdnet.co.kr/view/?no=20221220081329) The memory semiconductors in AI era can be developed on two track keywords. The first is integration, which is the combination of heterogeneous chips for learning and reasoning functions. For example, CXL-based next-generation memory and computational chip combined HBM. Second is interconnection. The interconnection in chip and between chips will be used for very fast and low-energy data transmission (example: Through Silicon Via, Through Hole Via, Cu-to-Cu Bonding etc.). Through this presentation, we would like to introduce innovatoin of future memory for various application: servers, datacenters, self-driving cars, and individual devices.
Title : “AI for Science and Systems for AI”
Ph.D. Rajkumar Kettimuthu
Argonne National Laboratory
The University of Chicago
United States of America
The University of Chicago
United States of America
Dr. Rajkumar Kettimuthu is a Computer Scientist and Group Leader at Argonne National Laboratory, a Senior Scientist at The University of Chicago and a Senior Fellow at Northwestern University. His research interests include AI for science, advanced wired and wireless communications for science, and Quantum networks. Data transfer protocol and tools developed by him and his colleagues at Argonne have become the de facto standard for file transfers in many science environments. With 60K+ installations in six continents, these tools perform 50M+ file transfers & move 5 Petabytes+ of data every day. AI for science tools developed by his team at Argonne are being used in many science environments. These tools have been highlighted by top scientific journals and have won multiple awards at prestigious venues. He has co-authored 150+ peer-reviewed articles most of which appeared in premier journals and top IEEE/ACM conferences, and several of which won best paper award. His work has featured in 20+ news articles. He is a recipient of the prestigious R&D 100 award. He is a distinguished member of ACM and a senior member of IEEE.
Abstract Deep learning techniques use multi-layer (“deep”) neural networks (DNNs) to learn representations of data with multiple levels of abstraction. These techniques can discover intricate structure in a dataset by using a back-propagation algorithm to set the internal parameters that are used to transform data as they flow between network layers. We have applied deep learning methods to accelerate various science applications including ones from light sources and climate science. In this talk, I will provide an overview of this work. We have also developed systems such as FairDMS (Findable, Accessible, Interoperable and Reusable Data and Model Service) and FreeTrain (A Framework to Utilize Unused Supercomputer Nodes for Training Neural Networks) to accelerate deep learning training. I will discuss these systems as well in my talk.