主题:Real-time Big Data-Signal Processing Perspectives
主讲人:Professor Xiaodong Wang
地点:松江校区二号学院楼226室
时间:2016-05-10 10:30:00
组织单位: 信息科学与技术学院、数字化纺织服装技术教育部工程研究中心
主讲人简介:
Xiaodong Wang received the Ph.D degree in Electrical Engineering from Princeton University.He is a Professor of Electrical Engineering at Columbia University in New York. Dr. Wang's research interests fall in the general areas of signal processing and communications, and has published extensively in these areas. Among his publications is a book entitled “Wireless Communication Systems:Advanced Techniques for Signal Reception”, published by Prentice Hall in 2003. His current research interests include wireless communications, statistical signal processing,and genomic signal processing.Dr. Wang received the 1999 NSF CAREER Award,the 2001 IEEE CommunicationsSociety and Information Theory Society Joint Paper Award,and the 2011 IEEECommunication Society Award for Outstanding Paper on New Communication Topics.He has served as an Associate Editor for the IEEE Transactions on Communications,the IEEE Transactions on Wireless Communications,the IEEETransactions on Signal Processing,and the IEEE Transactions on InformationTheory.He is a Fellow of the IEEE and listed as an ISI Highly-cited Author.
内容摘要:
In many applications that involve big data,real-time or online dataprocessing and information extraction is necessary.In this talk,I will discuss a number of signal processing aspects of real-time processing of big data through examples.The first issue is efficient data acquisition(i.e.,where to sample)through active learning,which is illustrated by a fine-grained indoor localization technique with adaptively sampled RF fingerprint. The second issue is low-rate sampling (i.e.,how to sample) for distributed information extraction, illustrated by a cooperative spectrum sensing system.Then I will discuss statistical inference for big data based on sequential Monte Carlo through an example of gene binding site discovery.Finally,I will discuss the application of quickest change detection to detecting cyber-attack and faults in smart grids.