(Attendance fees for the tutorials are complementarily included in the cost of workshop registration, but anyone interested in attending one or both of them MUST explicitly register on EDAS.)
Robust Statistical Methods in Signal Processing: Recent AdvancesVisa Koivunen
Technische Universität Darmstadt,
In this tutorial, recent advances and emerging topics on robust statistical methods for signal processing are presented. A signal processing procedure is statistically robust if it is not sensitive to departures from the assumed signal and noise models. The goal of a signal processing practitioner is then to design robust procedures that give a close to optimal performance at the nominal models and a highly reliable performance even in the worst case scenarios. We will provide a brief overview of basic concepts and tools needed in developing and analyzing robust methods. These tools facilitate devising signal processing techniques that are provably robust, and establishing their statistical properties. Examples illustrating these basic concepts are given in multivariate settings encountered in sensor array and multichannel signal processing. A commonly used assumption in signal processing is that observations are independent and identically distributed. Recent advances in robust statistical techniques for dependent data are presented. Applications, for example, in estimating the state of the power grid and the processing of time series data are provided. Statistically robust methods for processing large scale data (Big Data) are described as well. The volume and dimensionality of the data may be so high that it cannot be processed or stored in a single computing node. We describe a scalable, statistically robust and computationally efficient bootstrap method that is compatible with distributed processing and storage systems. Finally, statistically robust methods for processing sparse tensor-valued data are described with practical signal processing examples.
Biographies of the Presenters
Visa Koivunen (IEEE Fellow) received his D.Sc. (EE) degree with honors from the University of Oulu, Dept. of Electrical Engineering. He received the primus doctor (best graduate) award among the doctoral graduates in years 1989-1994. He is a member of Eta Kappa Nu. From 1992 to 1995 he was a visiting researcher at the University of Pennsylvania, Philadelphia, USA. Years 1997 -1999 he was faculty at Tampere UT. Since 1999 he has been a full Professor of Signal Processing at Aalto University (formerly known as Helsinki Univ of Technology) , Finland. He received the Academy professor position (distinguished professor nominated by the Academy of Finland). He is one of the Principal Investigators in SMARAD Center of Excellence in Research nominated by the Academy of Finland. Years 2003-2006 he has been also adjunct full professor at the University of Pennsylvania, Philadelphia, USA. During his sabbatical term year 2007 he was a Visiting Fellow at Princeton University, NJ, USA. He has also been a part-time Visiting Fellow at Nokia Research Center (2006-2012). He has spent multiple mini-sabbaticals at Princeton University (summers 2010, 2011 and 2012). He is currently on sabbatical at Princeton University for the full academic year 2013-2014.
Dr. Koivunen's research interest include statistical, communications, sensor array and multichannel signal processing. He has published about 350 papers in international scientific conferences and journals. He co-authored the papers receiving the best paper award in IEEE PIMRC 2005, EUSIPCO'2006, EUCAP (European Conference on Antennas and Propagation) 2006 and COCORA 2012. He has been awarded the IEEE Signal Processing Society best paper award for the year 2007 (with J. Eriksson). He served as an associate editor for IEEE Signal Processing Letters, IEEE Transactions on Signal Processing, Signal Processing and Journal of Wireless Communication and Networking. He is co-editor for IEEE JSTSP special issue on Smart Grids. He is a member of editorial board for IEEE Signal Processing Magazine. He has been a member of the IEEE Signal Processing Society technical committees SPCOM-TC and SAMTC. He was the general chair of the IEEE SPAWC conference 2007 conference in Helsinki, Finland June 2007. He is the the Technical Program Chair for the IEEE SPAWC 2015 as well as Array Processing track chair for 2014 Asilomar conference.
Abdelhak M. Zoubir is a Fellow of the IEEE and an IEEE Distinguished Lecturer (Class 2010-2011). He received his Dr.-Ing. from Ruhr-Universität Bochum, Germany in 1992. He was with Queensland University of Technology, Australia from 1992-1998 where he was Associate Professor. In 1999, he joined Curtin University of Technology, Australia as a Professor of Telecommunications. In 2003, he moved to Technische Universität Darmstadt, Germany as Professor of Signal Processing and Head of the Signal Processing Group. His research interest lies in statistical methods for signal processing with emphasis on bootstrap techniques, robust detection and estimation, and array processing applied to telecommunications, radar, sonar, automotive monitoring and safety, and biomedicine. Most recently he has been more involved in the advancement of the theory on robust sequential detection, whose potential in applications in engineering practice is enormous. He has published over 300 journal and conference papers on the above areas.
Dr. Zoubir served as General Chair and Technical Chair of numerous international IEEE conferences and workshops; most recently he was the Technical Co-Chair of ICASSP-14 held in Florence, Italy. He also served on publication boards of various journals, notably as Editor-In-Chief of the IEEE Signal Processing Magazine (2012-2014). Dr. Zoubir was the Chair (2010-2011) of the IEEE Signal Processing Society (SPS) Technical Committee "Signal Processing Theory and Methods". He serves on the Board of Directors of the European Association of Signal Processing (EURASIP) since 2008 and on the Board of Governors of the IEEE SPS since 2015.
Decentralized Estimation and Tracking in Wireless Sensor NetworksMark J. Coates
CanadaMichael G. Rabbat
During the past 15 years there has been a tremendous amount of work on communication- and/or energy-efficient methods for distributed signal processing. The aim of this tutorial is to provide and introduction and overview to state-of-the-art methods for decentralized estimation and tracking using gossip algorithms. We will begin with a review gossip algorithms for distributed averaging, focusing in particular on the push-sum and broadcast gossip algorithms which are especially attractive for use in wireless networks. Then we will discuss distributed particle filtering methods for estimation and tracking. We will highlight methods that have been developed during the past five years for reducing the communication overhead in the context of distributed particle filtering. A major challenge in the distributed setting is to fuse information contained in the measurements gathered at each sensor in a communication-efficient manner (i.e., without simply flooding all measurements over the network). We will discuss a variety of methods for approximating either the joint log likelihood function of the observations from all sensors, or for approximating the posterior distribution of the target state given all of the sensors’ observations. Finally, we will present recent results on error bounds for distributed particle filters. Throughout, as running examples, we will consider the problems of tracking an underwater target target using a network of bearings-only sensors and tracking targets using signal strength measurements. We will conclude with a discussion of open problems and potential directions for future work.
Biographies of the Presenters
Mark J. Coates received the B.E. degree in computer systems engineering from the University of Adelaide, Australia, in 1995, and a Ph.D. degree in information engineering from the University of Cambridge, U.K., in 1999. He joined McGill University (Montreal, Canada) in 2002, where he is currently an Associate Professor in the Department of Electrical and Computer Engineering. He was a research associate and lecturer at Rice University, Texas, from 1999-2001. In 2012-2013, he worked as a Senior Scientist at Winton Capital Management, Oxford, UK. He was an Associate Editor of IEEE Transactions on Signal Processing from 2007-2011 and is currently a Senior Area Editor for IEEE Signal Processing Letters. In 2006, his research team received the NSERC Synergy Award in recognition of their successful collaboration with Canadian industry, which has resulted in the licensing of software for anomaly detection and Video-on-Demand network optimization. Coates research interests include communication and sensor networks, statistical signal processing, and Bayesian and Monte Carlo inference. His most influential and widely cited contributions have been on the topics of network tomography and distributed particle filtering. His contributions on the latter topic received awards at the International Conference on Information Fusion in 2008 and 2010.
Michael G. Rabbat received the B.Sc. degree from the University of Illinois at Urbana-Champaign in 2001, the M.Sc. degree from Rice University, Houston, TX, in 2003, and the Ph.D. from the University of Wisconsin-Madison in 2006, all in electrical engineering. He joined McGill University, Montreal, Canada, in 2007 and currently holds the rank of associate professor. He was a Visiting Researcher at Applied Signal Technology, Inc., during summer 2003. He conducts research at the intersection of statistical signal processing, networking, and machine learning. His current research focuses on distributed signal processing, network inference, and signal processing of data supported on graphs, with applications to sensor networks and network monitoring.
Dr. Rabbat co-authored the paper which received the Best Paper Award (Signal Processing and Information Theory Track) at the 2010 International Conference on Distributed Computing in Sensor Systems (DCOSS). He received Honourable Mention for Outstanding Student Paper Award at the 2006 Conference on Neural Information Processing Systems (NIPS) and a Best Student Paper Award at the 2004 ACM/IEEE International Symposium on Information Processing in Sensor Networks (IPSN). He is currently an Associate Editor for IEEE Signal Processing Letters.