Dva predavanja u okviru DISTINGUISHED LECTURER programa IEEE SP SOCIETY

IEEE SCG SECTION, CAS-SP JOINT CHAIR, ELEKTROTEHNIČKI FAKULTET UNIVERZITETA U BEOGRADU, I INSTITUT MIHAJLO PUPIN

ORGANIZUJU

DVA PREDAVANJA U OKVIRU DISTINGUISHED LECTURER PROGRAMA IEEE SP SOCIETY

Elektrotehnički fakultet, petak, 07.11.2008, 15:30, sala 61

The Particle Filtering Methodology in Signal Processing

Abstract:

Particle filtering is a Monte Carlo-based methodology for sequential signal processing. It is designed for estimation of hidden processes that are dynamic and that can exhibit most severe nonlinearities. Also, it can be applied with equal ease to problems that involve any type of probability distributions. Therefore, it is not surprising that particle filtering has gained immense popularity. In this talk, first, the basics of particle filtering will be provided with description of its essential steps. Then some important topics of the theory will be addressed including Rao-Blackwellization, smoothing, and estimation of constant parameters.  Finally, a presentation of most recent advances in the theory will be given. The talk will contain signal processing examples which will aid in gaining valuable insights about the methodology.

The Model Selection Problem in Signal Processing

Abstract:

The model selection problem is one of the most important problems in science and engineering. Given a set of models and data that are generated by one of the considered models, a general objective is to choose the correct model. Model selection in signal processing is frequently encountered. Typical examples include selection of the number of sinusoids in communication signals or the number of present metabolites in data obtained in magnetic resonance spectroscopy or selecting the model order of an ARMA process. In this talk, first a brief review of various approaches for model selection will be given, and then more recent developments in the theory of model selection will be provided. The latter will include methodologies for selecting complex models where Monte Carlo sampling methods are employed.

Predavač

Prof. Dr. Petar M. Đurić (Fellow IEEE), Stony Brook University, Stony Brook, NY (USA)

About speaker:

Petar M. Djuric (F) received his Dipl. Ing. and M.S. degrees in Electrical Engineering from the University of Belgrade, in 1981 and 1986, respectively, and his Ph.D. degree in Electrical Engineering from the University of Rhode Island (1990). From 1981 to 1986, Prof. Djuric was a Research Associate with the Institute of Nuclear Sciences, Vinca, Belgrade. Since 1990, he has been with Stony Brook University, where he is Professor, Department of Electrical and Computer Engineering. His research interests are in the area of statistical signal processing, and his primary interests are in the theory of modeling, detection, estimation, and time series analysis and its application to a wide variety of disciplines including wireless communications and biomedicine.

Prof. Djuric has served on numerous technical committees for the IEEE and has been invited to lecture at universities in the United States and overseas. His SPS activities include: Vice President-Finance (2006-09); Area Editor of Special Issues, IEEE Signal Processing Magazine (2002-05); Associate Editor, IEEE Transactions on Signal Processing (1994-96 and 2003-05); Chair, SPS Signal Processing Theory and Methods Technical Committee (2005-06); and Treasurer, SPS Conference Board (2001-03). He is an Editorial Board Member, IEEE Journal on Special Topics in Signal Processing, Elsevier Digital Signal Processing, Elsevier Signal Processing, and the EURASIP Journal on Wireless Communications and Networking.

Prof. Djuric is an IEEE Fellow, as well as a Member of the American Statistical Association and the International Society for Bayesian Analysis.

Branimir Reljin (SM, IEEE), IEEE SCG CAS-SP Chair