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Adaptive Synchrosqueezing Transform for Instantaneous Frequency Estimation and Signal Separation

Time: Jun 10, 2019

地址 Ⅲ-201 in the Building of North Campus 事件时间: 2019-06-11 09:00:00

https://meeting.xidian.edu.cn/uploads/images/201906/1559812730.png

Title:

Adaptive   Synchrosqueezing Transform for Instantaneous Frequency Estimation and Signal   Separation

Lecturer:

Qingtang Jiang

Time:

2019-06-11   09:00:00

Venue:

-201 in the Building of North Campus

Lecturer    Profile

Qingtang   Jiang received the B.S. and M.S. degrees from Hangzhou University (now   is Zhejiang University), Hangzhou, China, in 1986 and 1989, respectively, and   the Ph.D. degree from Peking University, Beijing, China, in 1992, all in   mathematics.

He was with Peking University from 1992 to 1995. He was an   NSTB postdoctoral fellow and then a research fellow at the National   University of Singapore from 1995 to 1999. Before he joined the University of   Missouri-St. Louis, in 2002, he held visiting positions at University of   Alberta, Canada, and West Virginia University, USA. He is now a Professor in   the Department of Math and Computer Sci., University of Missouri-St. Louis.   His current research interests include signal classification, image   processing, surface subdivision and signal sparse representation. He is the   editorial board member of the journal Applied and Computational Harmonic   Analysis from 2005 and the awardee of The Air Force 2011, 2015 Visiting   Faculty Research Program.

Lecture    Abstract

Recently the synchrosqueezing transform (SST) has been   developed for signal separation and a sharp time-frequency representation of   a non-stationary signal by assigning the scale variable of the signal's   continuous wavelet transform to the frequency variable by a phase   transformation. In this talk we will discuss the adaptive SST with a   time-varying parameter for instantaneous frequency estimation and signal   separation. We will address the separation condition for a multicomponent   non-stationary signal with the adaptive SST and discuss the selection of the   time-varying parameter. In this talk we will discuss the analysis of the   adaptive SST. More specifically, we will provide the error of instantaneous   frequency estimation and the error of component recovery of a multicomponent   non-stationary signal with the adaptive SST.

 

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