2018年7月6日学术报告

编辑:吴秦时间:2018-06-21点击数:

报告题目(Title): An introduction to signal processing on graphs

报告人姓名(Speaker): Antonio G. Marques

时间(Date&Time): 2018.7.6, 13:30 

地点(Location): B240, School of IoT

报告摘要(Abstract): Networks can be understood as complex systems formed by multiple nodes, where global network behaviour arises from local interactions between connected nodes. The simplicity of this definition drives the application of graphs and networks to a wide variety of disciplines such as biology, sociology, economics, engineering, or computer science. Often, networks have intrinsic value and are themselves the object of study. In other occasions, the network defines an underlying notion of proximity, but the object of interest is a signal defined on top of the graph, i.e., data associated with the nodes of the network. This is the matter addressed by graph signal processing (GSP), where the notions of, e.g., frequency and linear filtering are extended to signals supported on graphs. The goal of this talk is to introduce people with a general knowledge of signal processing and statistics to the fundamentals of GSP. During the first part of the talk, the concepts of graph signals, graph Fourier Transform and graph filters will be introduced. During the second part, we will apply those concepts to the problems of sampling, reconstruction and blind deconvolution of signals defined on graphs.

报告人简介(Biography): Antonio G. Marques received the telecommunications engineering degree and the Doctorate degree, both with highest honors, from the Carlos III University of Madrid, Madrid, Spain, in 2002 and 2007, respectively. In 2007, he became a faculty in the Department of Signal Theory and Communications, King Juan Carlos University, Madrid, Spain, where he currently develops his research and teaching activities as an Associate Professor. From 2005 to 2015, he held different visiting positions at the University of Minnesota, Minneapolis, MN, USA. In 2015 and 2016, he was a Visitor Scholar in the University of Pennsylvania, Philadelphia, PA, USA.

His research interests lie in the areas of signal processing, networking and communications. His current research focuses on stochastic optimization of wireless and power networks, signal processing for graphs, and nonlinear network optimization. He has served the IEEE in a number of posts, collaborating on the organization of more than 20 IEEE conferences and workshops. Currently, he is an Associate Editor of the SIGNAL PROCESSING LETTERS, a member of the IEEE Signal Processing Theory and Methods Technical Committee and a member of the IEEE Signal Processing for Big Data Special Interest Group. Dr. Marques’ work has been awarded in several conferences and workshops, with recent best paper awards including Asilomar 2015, IEEE SSP 2016 and IEEE SAM 2016.


报告题目(Title): Compressive Covariance Sensing

报告人姓名(Speaker): Geert Leus

时间(Date&Time): 2018.7.6, 13:30

地点(Location): B240

报告摘要(Abstract): Spectrum sensing is a crucial ingredient of various types of applications, such as frequency spectrum sensing for cognitive radio and angular spectrum sensing for direction of arrival estimation. A popular tool that has recently been introduced for spectrum sensing is compressive sampling. Adopting this technique, it is possible to sample the measured signal below the Nyquist rate without compromising the reconstruction error, under the condition that the measured signal is sparse in some domain (frequency, angular, etc.). Current compressive spectrum sensing techniques mainly focus on reconstructing the signal itself. However, for many applications, estimating the power on every frequency or angle is sufficient. In this talk, we therefore present a novel framework for reconstructing the power spectrum or covariance information from compressive measurements, coined as compressive covariance sensing. This allows for improved compression rates, and if designed properly, it even works without any sparsity constraints on the spectrum, i.e., it can also be used to reconstruct non-sparse spectra.

报告人简介(Biography): 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 an "Antoni van Leeuwenhoek" 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 a 2002 IEEE Signal Processing Society Young Author Best Paper Award and a 2005 IEEE Signal Processing Society 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, 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 Vice-Chair of the EURASIP Special Area Team on Signal Processing for Multisensor Systems, an Associate Editor of Foundations and Trends in Signal Processing, and the Editor in Chief of EURASIP Signal Processing.


报告题目(Title): Topology Identification and Learning over Graphs: Accounting for Nonlinearities and Dynamics  

报告人姓名(Speaker): Georgios B. Giannakis

时间(Date&Time): 2018.7.6, 13:30

地点(Location): B240

报告摘要(Abstract): Learning the topology of graphs as well as processes evolving over graphs are tasks emerging in application domains as diverse as gene-regulatory, brain, power, and social networks, to name a few. Scalable approaches to deal with such high-dimensional settings aim to address the unique modeling and computational challenges associated with data-driven science in the modern era of big data analytics. Albeit simple and tractable, linear time-invariant models are limited as they are incapable of modeling changing topologies, as well as nonlinear and dynamic dependencies between nodal processes. To this end, novel approaches are presented to leverage nonlinear counterparts of partial correlation and partial Granger causality, as well as nonlinear structural equations and vector auto-regressions, along with attributes such as low rank, sparsity, and smoothness to capture even directional dependencies with abrupt change points, as well as dynamic processes over possibly time-evolving topologies. The unifying framework inherits the versatility and generality of kernel-based methods, and lends itself to batch and computationally affordable online learning algorithms, which include novel Kalman filters and smoothers over graphs. Real data experiments highlight the impact of the nonlinear and dynamic models on gene-regulatory and functional connectivity of brain networks, where connectivity patterns revealed exhibit discernible differences relative to existing approaches.

报告人简介(Biography): Professor Georgios B. Giannakis received his Diploma in Electrical Engr. from the Ntl. Tech. Univ. of Athens, Greece, 1981. From 1982 to 1986 he was with the Univ. of Southern California (USC), where he received his MSc. in Electrical Engineering, 1983, MSc. in Mathematics, 1986, and Ph.D. in Electrical Engr., 1986. He was with the U. of Virginia from 1987 to 1998, and since 1999 he has been a professor with the U. of Minnesota, where he holds a Chair in Wireless Communications, a University of Minnesota McKnight Presidential Chair in ECE, and serves as director of the Digital Technology Center.  His general interests span the areas of communications, networking and statistical signal processing – subjects on which he has published more than 400 journal papers, 700 conference papers, 25 book chapters, two edited books and two research monographs (h-index 131). Current research focuses on data science and network science with applications to social, brain, and power networks with renewables. He is the (co-) inventor of 32 patents issued, and the (co-) recipient of 9 best journal paper awards from the IEEE Signal Processing (SP) and Communications Societies. He also received Technical Achievement Awards from the SP Society (2000), from EURASIP (2005), and the inaugural IEEE Fourier Tech. Field Award (2015). He is a Fellow of EURASIP, and has served the IEEE in various posts including that of a Distinguished Lecturer.



报告题目(Title): Sparse Signal Processing for Communications

报告人姓名(Speaker): Zhi Tian

时间(Date&Time): 2018.7.6, 13:30

地点(Location): B240

报告摘要(Abstract): Sparse signal processing has demonstrated its usefulness in wireless communications over recent years. In the emerging era of data deluge, wireless systems such as 5G and Internet of Things (IoT) have to be able to sense and process an unprecedentedly large amount of data in real time, which render traditional communication and signal processing techniques inefficient or inapplicable. Meanwhile, there are exciting new developments on the theory and algorithms of sparse signal processing and compressive sensing, which offer powerful tools to effectively deal with high-dimensional signals, large-size problems, and big-volume data. This talk presents recent development on sparse signal processing principles and techniques as applied to various wireless applications where signal and information acquisition costs are high, such as wideband spectrum sensing in cognitive radios and sparse channel estimation using large-antenna arrays in both millimeter-wave communication systems and IoT applications.

报告人简介(Biography): Professor Zhi Tian has been a Professor in the Electrical and Computer Engineering Department of George Mason University since 2015. Previously she was on the faculty of Michigan Technological University. Her research interests lie in statistical signal processing, wireless communications, and decentralized network optimization. She is an IEEE Fellow. She is Chair of the IEEE Signal Processing Society Big Data Special Interest Group. She was General Co-Chair of the IEEE GlobalSIP Conference in 2016. She served as an IEEE Distinguished Lecturer, and Associate Editor for the IEEE Transactions on Wireless Communications and IEEE Transactions on Signal Processing.



邀请人 (Inviter): 李正权

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