演講題目：Learning Dynamics by Smart Systems

演  講  者：Prof. Hsiao-Chun Wu, Electrical and Computer Engineering, Louisiana State University, USA

時　　間：2022/12/19, Monday 10:00~12:00

地　　點：Delta Building R304, NTHU

ABSTRACT

How to construct a dynamic system has been appealing to researchers in physics, applied mathematics, computer science, and engineering since it can facilitate system identification, trajectory tracking, and time series forecast. In recent years, advanced smart systems capable of identifying dynamics inherent in the observed data are in high demand as they can enable the machine intelligence for predicting and detecting human behaviors and environmental evolution. According to our recent studies, the time series can be transformed into critical homological structures and/or features such that robust machine learning can be established using the homological analysis, especially for classifying, detecting, and predicting object motions, human behaviors, and fundamental characteristics in time varying (dynamic) signals. The conventional statistical signal processing and machine learning approaches are often under the unrealistic assumption of i.i.d. (statistically independently and identically distributed) data and thus the dynamics across data samples are not allowed. Based on the aforementioned new machine learning paradigm, it is possible to convert any non i.i.d. time series resulting from a Markov process to a neat geometric structure so the detection. prediction, and classification can be accurately fulfilled. In this talk, the mathematical framework and algorithms of the advanced homological analysis to learn dynamics by smart systems will be introduced and then the corresponding effectiveness on several practical applications will be demonstrated.

BIOGRAPHY

Hsiao-Chun Wu graduated from University of Florida in 1999 with a Ph. D. degree in Electrical and Computer Engineering, where he started to dedicate research on Signal Processing under the guidance of Dr. Jose C. Principe. Since March of 1999, he had joined Motorola Personal Communications Sector research labs and gotten involved with the ongoing research for Motorola VR Lite speech recognition software. His research in Motorola included novel robust speech detection and enhancement algorithms in a wide variety of background noise. In January of 2001, he joined the faculty at the Department of Electrical and Computer Engineering, Louisiana State University as a tenure-track assistant professor; he became a tenured associate professor in 2007. In July to August 2007, Dr. Wu was a visiting assistant professor at Television and Networks Transmission Group, Communications Research Centre, Ottawa, Canada. From August to December 2008, he was a visiting associate professor at Department of Electrical Engineering, Stanford University, California, USA. His current interests are in graph-based algorithms, topological analysis, finite-field transforms, audio/speech signal processing, image processing, neural-networks/artificial-intelligence, pattern recognition and machine learning, wireless systems, wireless communications, intelligent systems, robotic technologies, indoor and outdoor localization/ranging/navigation mechanisms, non-destructive evaluation for material, civil, and mechanical structures, and biometric instrumentation. Dr. Wu has published more than 300 refereed journal and conference papers in signal processing, broadcasting, wireless communications, computer, electronics, sensor networks and ultrasonics areas (more than 260 of them are published by IEEE or ACM). He has ever served on twenty journal editorial boards in the area of electrical and computer engineering including IEEE Transactions on Signal Processing, IEEE Transactions on Communications, IEEE Transactions on Mobile Computing, IEEE Transactions on Wireless Communications, IEEE Transactions on Broadcasting, IEEE Transactions on Vehicular Technology, IEEE Communications Magazine, IEEE Communications Letters, IEEE Signal Processing Letters, etc. From 2009 to 2011, he has been serving on IEEE Multimedia Technical Committee. Dr. Wu is currently an IEEE Distinguished Lecturer and an IEEE Fellow of Class 2015.