Recently a lot of researchers have investigated Vital Sign Acquisition by Medical Doppler Radar. The vital sign can be acquired contactless manner so that we can easily acquire the vital signs rather than contactless system such as ECC or PPG. This paper demonstrates the signal processing of the vital sign signals on time domain and peak detection. The process is so light that low performance MCU can process them to acquire the vital signs at fast time.
Dr. Koichiro Ishibashi has been a professor of The University of Electro-Communications (UEC), Tokyo, Japan since 2011. He has been the Director of UEC ASEAN Research and Education Center (UAREC) since 2017. He has been serving a guest professor at Ho Chi Minh City University of Technology and Ho Chi Minh City University of Science since 2012.
After receiving PH. D degree from Tokyo Institute of Technology in 1985, he worked at Central Research Laboratory, Hitachi Ltd., at Semiconductor Technology Academic Research Center (STARC) and at Renesas Electronics, where he had investigated low power technologies for high density SRAMs and MCUs. Since 2005, he has been a Fellow of IEEE for the technical contributions to developments of low-power SRAMs and MCUs.
He has presented more than 200 academic papers at international conferences and journals including more than 20 invited presentations. He was awarded R&D 100 for the development of SH4 Series Microprocessor in 1999.
His current interests are IoT technologies including Ultra low power LSI design technology, Technologies for energy harvesting sensor networks and applications, and Biomedical electronics using contactless sensors and data processing by AI.
By “moving” computing resources closer to users and infrastructure, edge computing has become an enabler of new emerging services and smart infrastructures like intelligent transportations, industry automation, real-time gaming, virtual/augmented reality. This talk explores how recent advances in distributed learning (DL) can empower edge computing to turn existing infrastructure into smart one with augmented intelligence. Specifically, we first discuss hurdles like low-latency requirement, straggling effect, and users’ security-related risks of DL in wireless edge networks. We then present potential solutions by leveraging coded computing, network economics to address the straggling problem, protect users' privacy, and incentivize users to efficiently contribute to the learning process. We will also present an in-network computation framework to enable large-scale distributed learning systems in wireless edge networks.
Diep N. Nguyen (Senior Member, IEEE) received the M.E. degree in electrical and computer engineering from the University of California at San Diego (UCSD) and the Ph.D. degree in electrical and computer engineering from The University of Arizona (UA). He is currently a Faculty Member of the Faculty of Engineering and Information Technology, University of Technology Sydney (UTS).
Before joining the UTS, he was a DECRA Research Fellow at Macquarie University, and a member of Technical Staff at Broadcom, CA, USA, and ARCON Corporation, Boston, consulting the Federal Administration of Aviation on turning detection of UAVs and aircraft, U.S. Air Force Research Lab on antijamming.
His current research interests include computer networking, wireless communications, and machine learning applications, with an emphasis on systems performance and security/privacy. He has received several awards from LG Electronics, UCSD, UA, U.S. National Science Foundation, and Australian Research Council. He is an Editor, Associate Editor of the IEEE TRANSACTIONS ON MOBILE COMPUTING, IEEE ACCESS, IEEE SENSORS JOURNAL, and IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY (OJ-COMS).
Full-accepted papers will be published in a special issue of REV Journal on Electronics and Communications, published by The Radio and Electronics Association of Vietnam, ISSN: 1859-378X.