SICE Forum (Session 11, 2021)】Introduction to Graph Signal Processing

Author:| Time: 2021-11-15 13:25:42|

The "SICE Forum" organized by the School of Information and Communication Engineering (SICE) invites Prof. Geert Leus, Delft University of Technology, to deliver an academic webinar. Below are the details for the event, and interested students and faculty members are welcome to attend.

Topic:Introduction to Graph Signal Processing

Speaker: Geert Leus (Delft University of Technology,Fellow of the IEEE and EURASIP)

Time: 16:00 (Monday), November 15, 2021

Venue: Online platform: ZOOM Meeting

Conferencelinks:

https://tudelft.zoom.us/j/96738191592?pwd=NVUxZUc4bEE1MFg1TXdRa0hOUndmdz09

Introduction:

Although processing and analyzing audio, images and video is still of great importance in current society, more and more data is originating from networks with an irregular structure, e.g., social networks, brain networks, sensor networks, and communications networks to name a few. To handle such signals, graph signal processing has recently been coined as a proper tool set. In graph signal processing the irregular structure of the network is captured by means of a graph, and the data is viewed as a signal on top of this graph, i.e., a graph signal. Graph signal processing extends concepts and tools from classical signal processing to the field of graph signals, e.g., the Fourier transform, filtering, sampling, stationarity, etc. In this talk, we introduce the field of graph signal processing and mainly focus on the graph Fourier transform and graph filters. They find applications in image denoising, network data interpolation, signal and link prediction, learning of graph signals and building recommender systems. More recently, connections to distributed optimization as well as neural networks have been established. These last two applications rely heavily on core signal processing techniques such as iterative inversion algorithms and linear time-invariant filters. Graph filters extend these concepts to graphs, leading to key developments in distributed optimization and neural networks.

Speakers’profiles

Geert Leus received the M.Sc. and Ph.D. degree in Electrical Engineering from the KU Leuven, Belgium, in June 1996 and May 2000, respectively. Geert Leus is now a Full Professor at the Faculty of Electrical Engineering, Mathematics and Computer Science of the Delft University of Technology, The Netherlands. His research interests are in the broad area of signal processing, with a specific focus on wireless communications, array processing, sensor networks, and graph signal processing. Geert Leus received the 2021 EURASIP Individual Technical Achievement Award, a 2005 IEEE Signal Processing Society Best Paper Award, and a 2002 IEEE Signal Processing Society Young Author Best Paper Award. He is a Fellow of the IEEE and a Fellow of EURASIP. Geert Leus was a Member-at-Large of the Board of Governors of the IEEE Signal Processing Society, the Chair of the IEEE Signal Processing for Communications and Networking Technical Committee, a Member of the IEEE Sensor Array and Multichannel Technical Committee, a Member of the IEEE Big Data Special Interest Group, and the Editor in Chief of the EURASIP Journal on Advances in Signal Processing. He was also on the Editorial Boards of the IEEE Transactions on Signal Processing, the IEEE Transactions on Wireless Communications, the IEEE Signal Processing Letters, and the EURASIP Journal on Advances in Signal Processing. Currently, he is the Chair of the EURASIP Technical Area Committee on Signal Processing for Multisensor Systems, a Member of the IEEE Signal Processing Theory and Methods Technical Committee, an Associate Editor of Foundations and Trends in Signal Processing, and the Editor in Chief of EURASIP Signal Processing.

Qingshuihe Campus Address: Block B, Scientific Research Building,
University of Electronic Science and Technology of China, No. 2006,
Xiyuan Avenue, High-tech Zone (West), Chengdu

Postcode:611731    Email: xintong@uestc.edu.cn

Telephone:028-61830156  Fax:028-61831665

Share