数学与统计学院学术报告[2021] 062号

(高水平大学建设系列报告562号)

报告题目: Approaches to Separation of Non-stationary Multi-component Signals

报告人：  蒋庆堂  教授 （密苏里大学圣路易分校）

报告时间：2021年7月7日9:00—10:00

腾讯会议ID：175 643 196

报告内容：In nature, real-world phenomena in the form of signals (or with data acquired as time series) are often affected by a number of factors and appear as time-overlapping multi-component signals (or time series). To better understand such phenomena and to facilitate processing the signals, the unknown components of the multi-component signal of interest should be extracted from the blind-source data. In this talk, first, we will discuss two important decomposition methods for non-stationary signals: Empirical mode decomposition (EMD) and Iterative filtering decomposition (IFD). After that we consider two recent time-frequency approaches: Synchrosqueezing transform (SST) and Signal separation operation (SSO). Finally we will introduce our recent methods: Adaptive SST with a time-varying parameter, SSO based on linear chirp local approximation, time-frequency-chirp_rate transform and time-scale-chirp_rate transform for separation of signals with crossover instantaneous frequencies. Demonstrative examples will also be presented.

报告人简历：He received his Ph.D. in Mathematics from Peking University in Beijing, China, and his post-doctoral training, first as an NSTB fellow, then as a research fellow, both at the National University of Singapore, and finally as a visiting scholar at the University of Alberta, Canada. His most recent research interests include: image recovery, sparse signal representation, and component recovery of non-stationary signals.

欢迎感兴趣的师生参加！

　　　　　　　　　　　　　　　　　　　　　　　　　 数学与统计学院

　　　　　　　　　　　　　　　　　　　　　 2021年7月6日