本期讲坛有幸邀请加拿大Ryerson大学教授张晓平做题为《Foundations in Graph Signal Processing》的线上报告。
Foundations in Graph Signal Processing
Defining a sound shift operator for graph signals, similar to the shift operator in classical signal processing, is a foundation in graph signal processing (GSP), since almost all operations, such as filtering, transformation, prediction, are directly related to the graph shift operator. In this talk, I first introduce the basics and motivations of GSP. Then we define a set of energy-preserving shift operators that satisfy many properties similar to their counterparts in classical signal processing, but are different from the shift operators defined in the literature. We decouple the graph structure represented by eigengraphs and the eigenvalues of the adjacency matrix or the Laplacian matrix. We show that the adjacency matrix of a graph is indeed a linear shift invariant (LSI) graph filter with respect to the defined shift operator. We further define autocorrelation and cross-correlation functions of signals on the graph, enabling us to obtain the solution to the optimal filtering on graphs, i.e., the corresponding Wiener filtering on graphs and the efficient spectra analysis and frequency domain filtering in parallel with those in classical signal processing. This new shift operator based GSP framework enables the signal analysis along a correlation structure defined by a graph shift manifold as opposed to classical signal processing operating on the assumption of the correlation structure with a linear time shift manifold. Several illustrative simulations are presented to validate the performance of the designed optimal LSI filters.
Xiao-Ping (Steven) Zhang received the B.S. and Ph.D. degrees from Tsinghua University, in 1992 and 1996, respectively, all in electronic engineering. He holds an MBA in Finance and Economics with Honors from the University of Chicago Booth School of Business. He is now Professor and Director of Communication and Signal Processing Applications Laboratory (CASPAL), with the Department of Electrical and Computer Engineering, Ryerson University. He has served as Program Director of Graduate Studies. He is cross-appointed to the Finance Department at the Ted Rogers School of Management at Ryerson University. He has been a Visiting Scientist at Research Laboratory of Electronics (RLE), Massachusetts Institute of Technology. He is a frequent consultant for biotech companies and investment firms. His research interests include statistical signal processing and big data analytics, multimedia analysis, sensor networks and IoT, machine learning/AI, and applications in finance, economics, and marketing.
Dr. Zhang is Fellow of the Canadian Academy of Engineering, Fellow of the Engineering Institute of Canada, Fellow of the IEEE, a registered Professional Engineer in Ontario, Canada, and a member of Beta Gamma Sigma Honor Society. He is the general Co-Chair for the IEEE International Conference on Acoustics, Speech, and Signal Processing, 2021. He is the general co-chair for 2017 GlobalSIP Symposium on Signal and Information Processing for Finance and Business, and the general co-chair for 2019 GlobalSIP Symposium on Signal, Information Processing and AI for Finance and Business. He was an elected Member of the ICME steering committee. He is the General Chair for the IEEE International Workshop on Multimedia Signal Processing, 2015. He is a Senior Area Editor for the IEEE TRANSACTIONS ON IMAGE PROCESSING. He was a Senior Area Editor the IEEE TRANSACTIONS ON SIGNAL PROCESSING and an Associate Editor for the IEEE TRANSACTIONS ON IMAGE PROCESSING, the IEEE TRANSACTIONS ON MULTIMEDIA, the IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, the IEEE TRANSACTIONS ON SIGNAL PROCESSING, and the IEEE SIGNAL PROCESSING LETTERS. He is incoming Editor-in-Chief for the IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING. He is elected Vice-Chair (Chair-elect) for Image, Video, and Multidimensional Signal Processing Technical Committee (IVMSP TC) of IEEE Signal Processing Society. He received 2020 Sarwan Sahota Ryerson Distinguished Scholar Award – the Ryerson University highest honor for scholarly, research and creative achievements. He is selected as an IEEE Signal Processing Society Distinguished Lecturer for the term from January 2020 to December 2021, and an IEEE Circuits and Systems Society Distinguished Lecturer for the term 2021 to 2022.
会议 ID：613 209 134