Location: University Politehnica of Bucharest (UPB), AN building – Monday, August 27th

Morning tutorials:

9:00 – 10:30 first part of the tutorial
10:30 – 10:45 cofee break
10:45 – 12:15 second part of the tutorial
12:15 – 14:00 lunch on place (AN building)

Afternoon tutorials:

14:00 – 15:30 first part of the tutorial
15:30 – 15:45 cofee break
15:45 – 17:15 second part of the tutorial

Morning tutorials
Program for Monday, August 27th, 2012
9:00 – 12:15

Afternoon tutorials
Program for Monday, August 27th, 2012
14:00 – 17:15

Tutorial details

T1: Teaching Signal Processing with Geometry

Monday, August 27th, 9:00 – 12:15
Venue: UPB, room AN 010
Speakers: Martin Vetterli and Vivek Goyal

The theory and practice of signal processing benefit greatly from extending "real world" (Euclidean) geometric insights to abstract signals. However, typical electrical engineering curricula do not promote geometric thinking, especially at the undergraduate level. While the attendees of this tutorial may gain some geometric insights into signal processing, the purpose of the tutorial is to share the presenters' experience on teaching signal processing with an emphasis on Hilbert space geometry. With this approach, results in finite dimensions, discrete time, and continuous time are often unified, thus making it easier to focus on the few essential differences. Unifying results geometrically helps students generalize beyond Fourier domain insights, taking them farther, faster. For example, many important results are corollaries of the projection theorem or follow from recognizing certain operators as adjoint pairs.

The tutorial is particularly timely as more undergraduate EE curricula get restructured to start with integrative experiences, leaving less time in which to squeeze the conventional foundation courses. It is thus a key time in which to rethink how to teach signal processing to students who may be more mature overall and have less time to reach contemporary topics. The tutorial is not intended to teach signal processing, but rather to give highlights on how to emphasize geometric concepts in teaching signal processing. The basic sequence of topics will be: extending from the Euclidean world to Hilbert spaces; the projection theorem and its consequences; decompositions; bases; frames; sampling; and structured bases. Along with Jelena Kovačević, the presenters are coauthors of forthcoming textbooks on signal processing that have free online versions at www.FourierAndWavelets.org. Examples will be given using Mathematica code that generates the figures in the textbooks.

Martin Vetterli received the Dipl. El.-Ing. degree from ETH Zurich (ETHZ), Switzerland, in 1981, the MS degree from Stanford University in 1982, and the Doctorat ès Sciences degree from EPF Lausanne (EPFL), Switzerland, in 1986. In 1986 he joined Columbia University in New York, where he was last an Associate Professor of Electrical Engineering and co-director of the Image and Advanced Television Laboratory. In 1993 he joined the University of California at Berkeley, where he was a Professor in the Department of Electrical Engineering and Computer Sciences until 1997, and now holds an Adjunct Professor position. Since 1995 he is a Professor of Communication Systems at EPFL and heads the Audiovisual Communications Laboratory. At EPFL, he previously chaired the Communications Systems Division (1996-’97), directed the National Competence Center in Research on Mobile Information and Communication Systems (2001-’04), and served as Vice President for Institutional Affairs (2004-’11). Since March 2011, he is Dean of the School of Computer and Communications Systems. Starting in 2013, he will lead the Swiss National Science Foundation. He is a fellow of the IEEE, a fellow of ACM, a member of SIAM. He is on the editorial boards of Applied and Computational Harmonic Analysis, the Journal of Fourier Analysis and Applications, and IEEE Journal on Selected Topics in Signal Processing. He received the Best Paper Award of EURASIP in 1984 for his paper on multidimensional subband coding, the Research Prize of the Brown Bovery Corporation (Switzerland) in 1986 for his doctoral thesis, the IEEE Signal Processing Society's Senior Award in 1991, 1996, and 2007 (for papers with D. LeGall, K. Ramchandran, and P. Marziliano and T. Blu, respectively). He won the Swiss National Latsis Prize in 1996, the SPIE Presidential award in 1999, and the IEEE Signal Processing Technical Achievement Award in 2001. He was a member of the Swiss Council on Science and Technology until 2003. He was a plenary speaker at various conferences (e.g. IEEE ICIP, ICASSP, ISIT) and is the co-author of books with J. Kovačević (Wavelets and Subband Coding), P. Prandoni (Signal Processing for Communications), and J. Kovačević and V. K. Goyal (Signal Processing: Foundations and Signal Processing: Fourier and Wavelet Representations). He has published about 150 journal papers on a variety of topics in signal/image processing and communications holds a dozen patents and is an ISI highly cited researcher in engineering. His research interests include sampling, wavelets, multirate signal processing, computational complexity, signal processing for communications, digital image/video processing, joint source/channel coding and signal processing for sensor networks.

Vivek K. Goyal received the B.S. degree in mathematics and the B.S.E. degree in electrical engineering from the University of Iowa, where he received the John Briggs Memorial Award for the top undergraduate across all colleges. He received the M.S. and Ph.D. degrees in electrical engineering from the University of California, Berkeley, where he received the Eliahu Jury Award for outstanding achievement in systems, communications, control, or signal processing. He was a Member of Technical Staff in the Mathematics of Communications Research Department of Bell Laboratories, Lucent Technologies, 1998-2001; and a Senior Research Engineer for Digital Fountain, Inc., 2001-2003. He joined the Massachusetts Institute of Technology in 2004, where he is currently Associate Professor of Electrical Engineering and a member of the Research Laboratory of Electronics. His research interests include source coding theory, sampling, quantization, magnetic resonance imaging, and optical imaging. Dr. Goyal is a member of Phi Beta Kappa, Tau Beta Pi, Sigma Xi, Eta Kappa Nu, and SIAM. He is a Senior Member of the IEEE. He was awarded the 2002 IEEE Signal Processing Society Magazine Award and an NSF CAREER Award, and his students have been awarded several thesis and conference best paper awards. He served a six-year term on the IEEE Signal Processing Society's Image and Multiple Dimensional Signal Processing Technical Committee and was a plenary speaker at IEEE Data Compression Conference and IEEE Multimedia Signal Processing Workshop. He is a Technical Program Committee Co-chair of IEEE ICIP 2016 and a permanent Conference Co-chair of the SPIE Wavelets and Sparsity conference series. He is a co-author of forthcoming textbooks available for download at FourierAndWavelets.org.

T2: Speech Enhancement for Acoustic Communication using Multiple Microphones and Diffusion Maps

Monday, August 27th, 9:00 – 12:15
Venue: UPB, room AN 034
Speakers: Israel Cohen, Sharon Gannot, Emanuël Habets, and Ronen Talmon

A major challenge in modern acoustic communica¬tion systems is the acquisition of the sound of inter¬est. In the last decade, spatial signal processing has been shown to provide useful and viable solutions. Especially in adverse acoustic conditions, encoun¬tered in reverberant and noisy environments, the use of multiple microphones as well as harmonic analy¬sis and manifold learning methods have been shown to provide significant advantages. This tutorial will briefly review room acoustics in order to explain the properties of different sound fields. It will then out¬line current and emerging techniques for spatial sig¬nal processing. In particular, the problem of acquir¬ing an estimate of a desired sound will be addressed. This problem will be tackled from the perspective of linear spatial processing and parametric processing and will be supported by numerous audio examples. We will then present innovative ways to exploit the geometry of signals using applied harmonic analysis and manifold learning methods. The tutorial will close with an outlook, highlighting important open questions and promising research directions.

Israel Cohen is an Associate Professor in the De¬partment of Electrical Engineering at the Technion - Israel Institute of Technology. He received the B.Sc. (Summa Cum Laude), M.Sc. and Ph.D. degrees in electrical engineering from the Technion in 1990, 1993 and 1998, respectively. From 1990 to 1998, he was a Research Scientist with RAFAEL Research Laboratories, Haifa, Israel Min¬istry of Defense. From 1998 to 2001, he was a Post¬doctoral Research Associate with the Computer Sci¬ence Department, Yale University, New Haven, CT. In 2001 he joined the Electrical Engineering Depart¬ment of the Technion. His research interests are statistical signal process¬ing, analysis and modeling of acoustic signals, speech enhancement, noise estimation, microphone arrays, source localization, blind source separation, system identification and adaptive filtering. He is a coeditor of the Multichannel Speech Process¬ing section of the Springer Handbook of Speech Pro¬cessing (Springer, 2008), a coauthor of Noise Reduc¬tion in Speech Processing (Springer, 2009), a coeditor of Speech Processing in Modern Communication: Challenges and Perspectives (Springer, 2010), and a general co-chair of the 2010 International Workshop on Acoustic Echo and Noise Control (IWAENC). Dr. Cohen is a recipient of the Alexander Goldberg Prize for Excellence in Research, and the Muriel and David Jacknow award for Excellence in Teaching. He served as Associate Editor of the IEEE Transac¬tions on Audio, Speech, and Language Processing and IEEE Signal Processing Letters, and as Guest Editor of a special issue of the EURASIP Journal on Advances in Signal Processing on Advances in Multi-microphone Speech Processing and a special issue of the EURASIP Speech Communication Journal on Speech Enhancement.

Sharon Gannot is an Associate Professor in the Fac¬ulty of Engineering at Bar-Ilan University, Israel. He received his B.Sc. degree (summa cum laude) from the Technion – Israel Institute of Technology, in 1986 and the M.Sc. (cum laude) and Ph.D. degrees from Tel-Aviv University, Israel in 1995 and 2000, respec¬tively, all in electrical engineering. In the year 2001 he held a post-doctoral position at the department of Electrical Engineering (ESAT) at K.U.Leuven, Belgium. From 2002 to 2003 he held a research and teaching position at the Faculty of Elec¬trical Engineering, Technion-Israel Institute of Tech¬nology, Haifa, Israel. Dr. Gannot is the recipient of Bar-Ilan University outstanding lecturer award for the year 2010. He is a coeditor of the Speech Enhancement section of the Springer Handbook of Speech Processing (Springer, 2008), and a coeditor of Speech Processing in Mod¬ern Communication: Challenges and Perspectives (Springer, 2010). Dr. Gannot serves as Associate Edi¬tor of the IEEE Transactions on Audio, Speech and Language Processing, and a member of the IEEE Au¬dio and Acoustic Signal Processing Technical Com¬mittee. He served as an Associate Editor of EURASIP Journal on Advances in Signal Processing in 2003-2011, an Editor of two special issues on Multi-micro¬phone Speech Processing of the same journal, and a guest editor of ELSEVIER Speech Communication journal. He has been a member of the Technical and Steering committee of the International Workshop on Acoustic Echo and Noise Control (IWAENC) since 2005 and was the general co-chair of IWAENC 2010 held in Tel-Aviv, Israel. He is a general co-chair of the International Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) to be held in Mohonk Mountain House, New Paltz, New York in 2013. His research interests include parameter estimation, statistical signal processing and speech processing using either single- or multi-microphone arrays and in particular, speaker extraction, noise reduction, dereverberation and speaker localization in adverse conditions.

Emanuël Habets is an Associate Professor in the International Audio Laboratories Erlangen (a joint institution of the Friedrich-Alexander University of Erlangen-Nuremberg and Fraunhofer IIS) and Chief Scientist Spatial Audio Processing at Fraunhofer IIS, Germany. He received the B.Sc degree in electrical engineering from the Hogeschool Limburg, The Netherlands, in 1999, and the M.Sc and Ph.D. degrees in electrical engineering from the Technische Universiteit Eindhoven (TU/e), The Netherlands, in 2002 and 2007, respectively. From March 2007 until February 2009, he was a post¬doctoral fellow at the Technion - Israel Institute of Technology and at the Bar-Ilan University in Ramat-Gan, Israel. In 2009, he was awarded a Marie Curie Intra-European Fellowship for Career Development. From February 2009 until November 2010, he was a Member of the Research Staff in the Communica¬tion and Signal Processing Group at Imperial College London, United Kingdom. Dr. Habets is a co-author of the SpringerBriefs “Speech Enhancement in the STFT Domain”. He was a member of the organization committee of the 2005 International Workshop on Acoustic Echo and Noise Control (IWAENC) and is a member of the IEEE Sig¬nal Processing Society Technical Committee on Au¬dio and Acoustic Signal Processing. He is a general co-chair of the International Workshop on Applica¬tions of Signal Processing to Audio and Acoustics (WASPAA) to be held in Mohonk Mountain House, New Paltz, New York in 2013. His research interests are in the areas of speech and audio signal processing, and he has worked in par¬ticular on speech dereverberation, microphone ar¬ray processing, echo cancellation and suppression, acoustic system identification and equalization, and localization and tracking of stationary and moving acoustic sources.

Ronen Talmon received the B.A. degree (cum laude) in mathematics and computer science from the Open University, Ra’anana, Israel, in 2005, and the Ph.D. degree in electrical engineering from the Technion—Israel Institute of Technology, Haifa, in 2011. From 2000 to 2005, he was a software developer and researcher at a technological unit of the Israeli De-fense Forces. From 2005 to 2011, he was a Teaching Assistant and a Project Supervisor with the Signal and Image Processing Lab (SIPL), Electrical Engineering Department, Technion. He is currently a Gibbs As¬sistant Professor in the Mathematics Department at Yale University. His research interests are statistical signal processing, analysis and modeling of signals, speech enhancement, applied harmonic analysis, and diffusion geometry. Dr. Talmon is the recipient of the Irwin and Joan Jacobs Fellowship for 2011, the Viterbi Fellowship for 2011-2012, the Excellent Project Supervisor Award for 2010, and the Excellence in Teaching Award for outstanding teaching assistants for 2008 and 2011.

T3: Population Monte Carlo Sampling in Signal Processing

Monday, August 27th, 9:00 – 12:15
Venue: UPB, room AN 015
Speaker: Petar M. Djurić

Statistical signal processing as a discipline has grown to a point where it can take on addressing very challenging problems. They are usually related to complex nonlinear models with large numbers of unknowns and where the underlying probability distributions may be non-Gaussian and multimodal. A methodology that has the potential of addressing these problems is the Population Monte Carlo (PMC) sampling. PMC sampling can be considered as an alternative to Markov chain Monte Carlo (MCMC) sampling. With MCMC sampling the objective is to use the generated particles (samples) to approximate a target distribution. The approximation is a random measure composed of the generated particles with equal weights assigned to all of them. With PMC sampling, the objective is the same except that the weights of the generated samples are all different. An important concept at the heart of the PMC sampling methodology is importance sampling. The latter is a procedure for generation of particles from a distribution other than the target distribution, this difference being compensated with weights assigned to the particles. A distinguishing feature of PMC sampling is that all of the generated particles are accepted for statistical inference. MCMC sampling has been used in many signal processing applications, but PMC sampling has been much less employed. The purpose of the tutorial is to provide an introduction and thorough review of the PMC sampling methodology and its properties as an inference tool for solving difficult signal processing problems. The tutorial is planned to start with a general introduction to Monte Carlo sampling methods, followed by examination of the concepts of importance sampling and adaptive importance sampling and then dedicating the remaining time to the PMC sampling method. The tutorial will contain in-depth presentation of the strengths and weaknesses of PMC sampling and how it compares to MCMC sampling. Since the main objective of PMC sampling is to make inference about unknown parameters and models, they will be addressed both from theoretical and practical points of view.

Petar M. Djurić received his B.S. and M.S. degrees in electrical engineering from the University of Belgrade, and his Ph.D. degree in electrical engineering from the University of Rhode Island. Since then he has been with Stony Brook University, where he is Professor in the Department of Electrical and Computer Engineering. He works in the area of statistical signal processing, and his primary interests are in the theory of signal modeling, detection, and estimation and application of the theory to a wide range of disciplines. Prof. Djurić has been invited to lecture at universities in the United States and overseas and has served on numerous committees for the IEEE. During 2008-2009 he was Distinguished Lecturer of the IEEE Signal Processing Society and in 2008 he was elected Chair of Excellence of Universidad Carlos III de Madrid-Banco de Santander. He has been on the Editorial Boards of many professional journals. In 2007, he received the IEEE Signal Processing Magazine Best Paper Award and in 2012, the EURASIP Technical Achievement Award. Prof. Djurić is a Fellow of IEEE.

T4: Information Theoretic Methods in Genomic Signal Processing

Monday, August 27th, 9:00 – 12:15
Venue: UPB, room AN 017
Speaker: Ioan Tabus

The tutorial will introduce advanced modeling methods based on information theoretic principles intended for applications in genomic signal processing.

The field coined genomic signal processing has a history of more than one decade, during which it has attracted a lot of interest due to the natural match between signal processing methodology and the need of modeling and interpreting a huge amount of data arising in molecular biology measurements. The typical data encountered is either in the form of continuous valued data, e.g., data obtained in microarray or in protein arrays experiments, or in the form of discrete sequences, e.g., DNA or protein sequences. One of the earliest signal processing methodology adopted in the new filed was the analysis of biological sequences based on hidden Markov models; a next wave of high impact achievements were obtained by using image processing techniques for the analysis of microarray image data. The complex problems arising in feature selection, estimation, and modeling of biological data are handleled with a mixture of techniques, combining signal processing with pattern recognition and information theoretic methods.

This tutorial covers a number of applications of information theoretic methods for solving analysis tasks, e.g., for estimating concentrations in lysate array data, for modeling, compression, and for analyzing similarity in biological sequences. Examples are shown where familiar methods from signal processing are used with biological data, and then, in a reversed flow, where the signal processing methodologies used with biological data are imported back into other mainstream signal processing applications.

Ioan Tabus received the M.S. degree in electrical engineering in 1982, the Ph.D. degree from the "Politehnica" University of Bucharest, Romania, in 1993, and the Ph.D. degree (with honors) from Tampere University of Technology (TUT), Finland, in 1995. He was a Teaching Assistant from 1984 to 1990, Lecturer from 1990 to 1993, and Associate Professor from 1994 to 1995 with the Department of Control and Computers, "Politehnica" University of Bucharest. From 1996 to 1999, he was a Senior Researcher at TUT. Since January 2000, he has been a Professor with the Department of Signal Processing at TUT.

His research interests include genomic signal processing, speech, audio, image and data compression, joint source and channel coding, nonlinear signal processing, image processing. He is coauthor of two books and more than 210 publications in the fields of signal compression, image processing, bioinformatics, and system identification. He is a Senior Member of IEEE and was an Associate Editor for IEEE Transactions on Signal Processing between 2002-2005. He was a chair of IEEE SP/CAS Chapter of Finland Section and he was a member of the Bio Image and Signal Processing Technical Committee of IEEE Signal Processing Society. He currently is the Editor-in-Chief of EURASIP Journal on Bioinformatics and Systems Biology. He was Technical co-chair for Gensips 2006 and Gensips 2007. He co-organized four editions of the Workshop on Information Theoretic Methods in Science and Engineering (WITMSE) in Tampere and Helsinki, Finland, during 2008-2011.

T5: Inference and Learning for Image Processing and Computer Vision

Monday, August 27th, 14:00 – 17:15
Venue: UPB, room AN 034
Speakers: Nikos Komodakis, M. Pawan Kumar, and Nikos Paragios

Several problems in computer vision, image analysis and signal processing can be formulated using the discrete graphical models framework. The two main issues faced by researchers when using graphical models are: (i) Learning: How to estimate the parameters of the model? and (ii) Inference: How to find the best assignment for the variables of the model? In this tutorial we will discuss these two issues, starting from the basics and building up to the state of the art. A tentative outline of the tutorial follows.


  • Inference in graphical models and its challenges
  • Learning in graphical models and its challenges
  • Motivating applications


  • Belief propagation
  • Message-passing methods
  • Graph-cut based inference
  • Dual decomposition
  • Convex relaxations


  • Introduction to learning of graphical models
  • Maximum-likelihood learning
  • Max-margin learning
  • Subgradient methods and constraint generation methods for max-margin learning
  • Efficient max-margin training of high-order models via dual decomposition
  • Learning of high-order latent CRFs

Target Audience: The tutorial is aimed at researchers who wish to understand and use discrete graphical models in any domain of computer vision, image analysis and signal processing. No prior knowledge of graphical models will be assumed.

Nikos Komodakis obtained his Ph.D. in Computer Science with highest honours from the University of Crete in 2006. He is currently an adjunct professor at the Computer Science Department of the University of Crete as well as an affiliated researcher with INRIA (Saclay Ile-de-France). Prior to that, he was a postdoctoral research fellow and an adjunct professor at Ecole Centrale de Paris (Fellowship of the Agence Nationale de la Recherche). His research interests include computer vision, image processing, computer graphics and medical image analysis. He is the author of more than 45 publications in international conferences, journals and book chapters. His work has appeared multiple times in top rank international conferences and journals including ICCV, CVPR, ECCV, NIPS, IPMI, MICCAI, PAMI etc. Together with his collaborators, he won best paper awards at IPMI 2007 and ISBI 2010. He serves as a program committee member for a number of top international computer vision and pattern recognition conferences such as ICCV, ECCV and CVPR.

M. Pawan Kumar is an assistant professor at Ecole Centrale de Paris and a member of the GALEN team at INRIA Saclay, Ile-de-France. Previously, he was a post-doctoral researcher at Stanford University. He received a PhD from Oxford Brookes University in 2008. His research interests include machine learning and computer vision, especially efficient optimization for large-scale models. His work has appeared in several reputed conferences and journals such as ICCV, CVPR, NIPS, ICML, IJCV, PAMI and JMLR. Together with his collaborators, he won best paper awards at ICVGIP 2004, Rank Symposium 2007 and NIPS 2007. His PhD thesis was awarded the Sullivan Prize in 2008.

Nikos Paragios obtained his B.Sc. and M.Sc. with the highest honors in Computer Science from the University of Crete (Greece) at 1994 and 1996 respectively, and his Ph.D. (highest honors) and D.Sc. (Habilitation a Diriger de Recherches) in electrical and computer engineering from the University of Nice/Sophia Antipolis (France) at 2000 and 2005. Currently, he is professor at the department of applied mathematics at the Ecole Centrale de Paris leading the Medical Imaging and Computer Vision Group. He is also affiliated with INRIA Saclay Ile-de-France, heading the GALEN research group. Prior to that he was professor/research scientist (2004-2005) at the Ecole Nationale de Ponts et Chaussees, affiliated with Siemens Corporate Research (Princeton, NJ, 1999-2004) as a project manager, senior research scientist and research scientist. In 2002 he was an adjunct professor at Rutgers University and in 2004 at New York University. N. Paragios was a visiting professor at Yale (2007) and at University of Houston (2009). He has co-edited four books, published more than hundred-fifty papers in the most prestigious journals and conferences of computer vision and medical imaging, has sixteen US issued patents and more than twenty pending. Professor Paragios is a Fellow of IEEE, associate editor for the IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), area editor for the Computer Vision and Image Understanding Journal (CVIU) and member of the Editorial Board of the International Journal of Computer Vision (IJCV), the Medical Image Analysis Journal (MedIA), the Journal of Mathematical Imaging and Vision (JMIV) and the Imaging and Vision Computing Journal (IVC). Professor Paragios was one of the program chairs of the 11th European Conference in Computer Vision (ECCV'10, Heraklion, Crete). In 2008 N. Paragios was the laureate of one of Greece's highest honor for young academics and scientists of nationality or descent (world-wide), the Bodossaki Foundation Prize in the field of applied sciences. In 2006, he was named one of the top 35 innovators in science and technology under the age of 35 from the MIT's Technology Review magazine. His research interests include image processing, computer vision, medical image analysis and human computer interaction.

T6: Geometric Space-Time Audio Processing

Monday, August 27th, 14:00 – 17:15
Venue: UPB, room AN 015
Speakers: Augusto Sarti and Fabio Antonacci

Geometrical acoustics offers a perspective on soundfield analysis, modeling and rendering that greatly differs from that of traditional wave theory. Working with acoustic “rays” is commonplace in architectural acoustics, where the aim is to roughly infer the room impulse response through the tracing of acoustic paths. In order to be effective, however, the acoustic wavelengths of interest must be small compared with the size of the acoustic reflectors (walls and objects). Because of this bandwidth limitation, issues on computational cost and problems of spatial aliasing, there is a tendency to believe that geometry is a tool of limited effectiveness for acoustic research.As a matter of fact, research in space-time audio processing relies on geometric tools more and more often. A wide range of solutions for localizing acoustic sources, for example, are based on the intersection of conics or quadrics. More recently, numerous papers have appeared in the literature, which use geometric methodologies for extracting information on the acoustic environment (shape and/or reflectivity). New methods have recently appeared for accurately modeling the acoustic wavefield in a fully geometric fashion, which account for propagation phenomena such as diffraction and diffusion. Finally even acoustic rendering solutions have begun benefiting from geometric tools. The aim of this tutorial is to show how geometric representations and tools can be successfully employed for applications of self-calibration of acoustic systems, environment inference, source characterization, as well as environment-aware analysis and rendering. We will try to offer an organic and unified perspective on geometric space-time audio processing and discuss new perspectives in this field.

Topics covered:

  • A Brief Overview of Geometrical Acoustics
  • Acoustic Building Blocks
  • Geometric Space-Time Audio Analysis
  • Geometric Acoustic Modeling
  • Rendering
  • Conclusions and Perspectives

Augusto Sarti received both his M.Sc. and his Ph.D. in electrical and electronic engineering from the University of Padua, Italy, in 1988 and 1993, respectively. His graduate studies included a joint graduate program with the University of California at Berkeley, USA. In 1993, he joined the Politecnico di Milano, Italy, as a Professor. His research interests are in the area of digital signal processing, with particular focus on space-time audio processing, sound analysis, synthesis and processing, image analysis, and 3D vision. He coauthored about 200 publications in International journal articles and conferences, and 16 patents in the area of signal processing. He promoted and coordinated numerous international (EC‐funded) research Projects in the area of multimedia signal processing. In particular, he recently promoted and coordinated the SCENIC project (Self-Configuring ENvironment-Aware Intelligent ACoustic sensing), an EC-funded Future and Emerging Technology project in which geometric space-time audio processing played a major role.He was co‐chairman of the 2005 Edition of the IEEE International Conference on Advanced Video and Signal based Surveillance (AVSS). He was also Chairman of Digital Audio Effects conference, DAFx 2009 and Area Chair of Audio and Electroacoustics for the European Signal Processing Conference, EUSIPCO 2006. He is currently in the Audio and Acoustics Signal Processing Technical Committee of the IEEE, and he is an Associate Editor of IEEE Signal Processing Letters.

Fabio Antonacci received his M.Sc. in 2004 and his Ph.D. in 2008 in Electrical Engineering and Computer Science. He joined as a researcher the Image and Sound Processing Lab of the Politecnico di Milano, Italy, in 2008, where he currently teaches a course on Computer Music.His research interests are on geometric acoustic modeling, acoustic scene analysis and rendering through microphone and loudspeaker arrays, with particular focus on source localization/tracking, reflector localization, estimation of acoustic parameters, rendering of the acoustics of virtual environments using wavefield synthesis techniques. He co-authored numerous publications in International journals and conferences, as well as several patents in the area of acoustic signal processing. He contributed to several EC-funded projects in the area of multimedia signal processing, with special emphasis on sound processing, particularly to the SCENIC project, an EC-funded project on environment-aware space-time audio processing.

T7: Network Signal Processing

Monday, August 27th, 14:00 – 17:15
Venue: UPB, room AN 010
Speakers: Hamid Krim and Anna Scaglione

The tutorial will be divided in two parts, the first focused on the exploration of network and the second on its exploitation for processing sensor data. In the the first part, the tutorial will lay down the basic graph theoretic concepts that will be used throughout the lecture, and will show how they can be used for modeling sensor networks. Algorithms to determine topological properties of graphs will be mapped onto decentralized protocols that can be used to capture the network state. The second part of the tutorial will discuss the so called topic of ``in network signal processing''. It will start form a simple primitive called average consensus gossiping, and continue on to generalize the local interaction rules among sensors to implement more complex optimization and statistical signal processing tasks. The specific list of topics covered is given in the following list.

PART I: H. Krim
1. Graphs and their simplicial generalizations (adjacency, incidence....)
2. Laplacians on graphs, graph intrinsic properties, and connection to continuous Laplacian
3. Laplace-Beltrami operators on graphs, and their intrinsice properties
4. Topology of graphs/simplicial complexes, and its algebraic formulation
5. Basic operators and their implied Homology and co-homology interpretations
6. Graph construction from a deployed sensor network
7. Algebraic topology view of common sensor problems
8. Distributed detection of sensor failures
9. Distributed localization of sensor failures
10. Mitigation of sensor failures
11. Dynamic failures and persistent tracking of sensor network state

PART II: A. Scaglione
1. Features of decentralized versus centralized processing in sensor applications
2. Computation of averages by diffusion: synchronous and asynchronous algorithms
3. Convergence properties of the algorithms and fast consensus
4. Signal processing via diffusion algorithm: detection and estimation primitives
5. Decentralized optimization and clustering
6. Adaptive diffusion methods
7. Extensions: models for social learning
8. Future challenges

Hamid Krim received his BSc. and MSc. in EE from University of Washington and a Ph.D. degree in ECE from Northeastern University. He was a Member of Technical Staff at AT&T Bell Labs, where he has conducted research and development in the areas of telephony and digital communication systems/subsystems. Following an NSF postdoctoral fellowship at Foreign Centers of Excellence, LSS/University of Orsay, Paris, France, he joined the Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA as a Research Scientist and where he was performing and supervising research. He is presently Professor of Electrical Engineering in the ECE Department, North Carolina State University, Raleigh, leading the Vision, Information and Statistical Signal Theories and Applications group. His research interests are in statistical signal and image analysis and mathematical modeling with a keen emphasis on applied problems in classification and recognition using geometric and topological tools. He has extensively collaborated with several international Laboratories including French Laboratories and Institutes (CNRS, and INRIA), Chalmers University, Institute for Information and Transmission Problems, Moscow (Russia) and others. He has authored/co-authored more than 120 Journal and Conference papers. He has co-edited (with A. Yezzi) a book on “Analysis and Statistics of Shapes” published by Birkhauser to appear in July 2005. He was a recipient of a Fellow award by the Japanese Society for Advancement of Science and Engineering and was a resident in Dept. of Informatics at University of Tokyo (Japan). He was a recipient of the NSF Career Award (2000). He was the co-founder, lead organizer and Technical Chair of the first Genomics, Signal Processing and Statistics (GENSIPS) Workshop in Oct. 2002, which has now become a regular feature for the community. He has served the SP society in several positions, and was a member of the SPTM, is a regular member of the Technical committee for several conferences including SSP and ICIP. Dr. Krim also served as an Associate Editor of the IEEE Transactions on Signal Processing and also of the Journal of the Franklin Institute. He is a Fellow member of IEEE and member of SIAM.

Anna Scaglione (M.Sc. '95, Ph.D. '99) is currently Professor in Electrical and Computer Engineering at University of California at Davis. She joined UC Davis in 2008, after leaving Cornell University, Ithaca, NY, where she started as Assistant Professor in 2001 and became Associate Professor in 2006; prior to joining Cornell she was Assistant Professor in the year 2000-2001, at the University of New Mexico. She is a Fellow of the IEEE since 2011. She is the Editor in Chief of the IEEE Signal Processing Letters, and served as Associate Editor for the IEEE Transactions on Wireless Communications from 2002 to 2005, and from 2008 to 2011 in the Editorial Board of the IEEE Transactions on Signal Processing, where she was Area Editor in 2010-11. She has been in the Signal Processing for Communication Committee from 2004 to 2009 and is in the steering committee for the conference Smartgridcomm since 2010. She was general chair of the workshop SPAWC 2005. Dr. Scaglione is the first author of the paper that received the 2000 IEEE Signal Processing Transactions Best Paper Award; she has also received the NSF Career Award in 2002 and she is co-recipient of the Ellersick Best Paper Award (MILCOM 2005). Her expertise is in the broad area of signal processing for communication systems and networks. Her current research focuses on signal processing algorithms for networks and for sensors systems, and on Smart Grid demand side management and reliable energy delivery.

T8: Advances in Power Line Communications and Application to the Smart Grid

Monday, August 27th, 14:00 – 17:15
Venue: UPB, room AN 017
Speaker: Andrea M. Tonello

This tutorial covers recent advances in Power line communication (PLC) which is among the most interesting and important communication technology candidates for application in the Smart Grid since the grid is not only the information source but it also offers the infrastructure for the information delivery.

An overview of the various application scenarios of PLC (such as in-home, in-vehicle and smart grids) and a summary about the evolution of PLC technology will be provided. The role of PLC for the Smart Grid and its application in HV, MV and LV networks will be discussed. We will then discuss the important topics of channel and noise modeling and report up-to-date results about statistical channel modeling, MIMO channel modeling, and noise/disturbances modeling. The main challenges of physical layer design for both narrow-band (NB-PLC) and broad-band PLC (BB-PLC) to encompass the presence of channel attenuation and frequency selectivity, interference, and various noise sources, will be addressed. In particular, we will describe existing and emerging modulation approaches, filter bank modulation approaches (as OFDM, DWMT, FMT), and ultra wide band techniques. We will show that advanced modulation techniques, combined with signal processing algorithms, coding and smart resource allocation algorithms are capable of granting robust performance and coexistence with other technologies. Finally, an overview of the main standards will be offered covering both NB-PLC and broad-band BB-PLC.

Andrea M. Tonello received his Doctor of Engineering degree in Electronics (1996, summa cum laude), and his Doctor of Research degree in Electronics and Telecommunications (2003), both from the University of Padova, Italy. In 1997, he became a Member of Technical Staff at Lucent Technologies’ Bell Laboratories, where he worked on the development of baseband algorithms for cellular handsets. He worked at Bell Laboratories’ Advanced Wireless Technology Laboratory until 2002, during which time he was appointed Managing Director of Bell Labs, Italy. Herein, he conducted research on wireless systems and he was involved in the standardization of the evolution of 2G and 3G cellular technology.

In 2003, Dr. Tonello joined the Dipartimento di Ingegneria Elettrica, Gestionale e Meccanica (DIEGM) of the University of Udine, Italy, where he is an Aggregate Professor and founder of the Wireless and Power Line Communication Lab. He is also the founder and CEO of WiTiKee, a spin-off company of the lab. His research focuses on next generation wireless systems, vehicular networks, and power line communications for smart grids. He has been involved in several European coordinated actions through FP5-FP7 EU funded projects. Dr. Tonello has received several awards, including the Lucent Bell Labs Recognition of Excellence award in 2003, the Distinguished Visiting Fellowship from the Royal Academy of Engineering, UK, in 2010, and the Distinguished Lecturer Award from the IEEE VTS in 2011. He also received the 2007 EURASIP Journal on Advances in Signal Processing Best Paper Award. He co-authored the papers that received the best student paper award at the IEEE International Symposium on Power Line Communications (ISPLC) in 2010 and in 2011, and the best paper award at IEEE Vehicular Technology Conference 2011 Spring. Dr. Tonello serves as an Associate Editor for the IEEE Transactions on Vehicular Technology and for the IEEE Transactions on Communications. He was the General Chair of IEEE ISPLC 2011 in Udine, Italy. He is the Vice-Chair of the IEEE Communications Society Technical Committee on Power Line Communications.