Martin Vetterli - Short CV

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 es Sciences degree from EPF Lausanne (EPFL), Switzerland, in 1986.

He was a research assistant at Stanford and EPFL, and has worked for Siemens and AT&T Bell Laboratories. 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 EPF Lausanne, Switzerland, where he chaired the Communications Systems Division (1996/97), and heads the Audiovisual Communications Laboratory. From 2001 to 2004 he directed the National Competence Center in Research on mobile information and communication systems. He also was a Vice-President at EPFL from October 2004 to February 2011 in charge, among others, of international affairs and computing services. He has held visiting positions at ETHZ (1990) and Stanford (1998). From March 2011 on, he is Dean of the School of Computer and Communication Sciences of EPFL.

He is a fellow of IEEE, a fellow of ACM, a fellow of EURASIP, and a member of SIAM. He is on the editorial boards of Applied and Computational Harmonic Analysis, the Journal of Fourier Analysis and Application and the IEEE Journal on Selected Topics in Signal Processing.

He received the Best Paper Award of EURASIP in 1984, the Research Prize of the Brown Bovery Corporation (Switzerland) in 1986, the IEEE Signal Processing Society's Senior Paper Awards in 1991, in 1996 and in 2006 (for papers with D. LeGall, K. Ramchandran, and Marziliano and Blu, respectively). He won the Swiss National Latsis Prize in 1996, the SPIE Presidential award in 1999, the IEEE Signal Processing Technical Achievement Award in 2001, the IEEE Signal Processing Society Award in 2010 and is an ISI highly cited researcher in engineering. He was a member of the Swiss Council on Science and Technology from 2000 to 2003.

He was a plenary speaker at various conferences (e.g. IEEE ICIP, ICASSP, ISIT) and is the co-author of three books with J. Kovacevic, "Wavelets and Subband Coding", 1995, with P. Prandoni "Signal Processing for Communications", 2008 and with J. Kovacevic and V.K. Goyal, "Fourier and Wavelet Signal Processing'', 2010.

He has published about 145 journal papers on a variety of topics in signal/image processing and communications and holds a dozen patents.

His research interests include sampling, wavelets, multirate signal processing, computational complexity, signal processing for communications, digital image/video processing, joint source/channel coding, signal processing for sensor networks and inverse problems like acoustic tomography.

Martin Vetterli - Abstract

Sampling is a central topic not just in signal processing and communications, but in all fields where the world is analog, but computation is digital. This includes sensing, simulating, and rendering the real world. The question of sampling is very simple: when is there a one-to-one relationship between a continuous-time function and adequately acquired samples of this function?

Sampling has a rich history, dating back to Whittaker, Nyquist, Kotelnikov, Shannon and others, and is an active area of contemporary research with fascinating new results. The classic result of sampling is the one on bandlimited functions, where taking measurements at the Nyquist rate (or twice the maximum bandwidth) is sufficient for perfect reconstruction. These results were extended to shift-invariant subspaces and multiscale spaces during the development of wavelets, as well as in the context of splines. All these methods are based on subspace structures, and on linear approximation.

Recently, non-linear methods have appeared. Non-linear approximation in wavelet spaces has been shown to be a powerful approximation and compression method. This points to the idea that functions that are sparse in a basis (but not necessarily on a fixed subspace) can be represented efficiently.

The idea is even more general than sparsity in a basis, as pointed out in the framework of signals with finite rate of innovation. Such signals are non-bandlimited continuous-time signals, but with a parametric representation having a finite number of degrees of freedom per unit of time. This leads to sharp results on sampling and reconstruction of such sparse continuous-time signals, namely that 2K measurements are necessary and sufficient to perfectly reconstruct a K-sparse continuous-time signal. In accordance with the principle of parsimony, we call this sampling at Occam's rate. We indicate an order K^3 algorithm for reconstruction, and describe the solution when noise is present, or the model is only approximately true.

Next, we consider the connection to compressed sensing and compressive sampling, a recent approach involving random measurement matrices. This is a discrete time, finite dimensional set up, with strong results on possible recovery by relaxing the l_0 into l_1 optimization, or using greedy algorithms. These methods have the advantage of unstructured measurement matrices (actually, typically random ones) and therefore a certain universality, at the cost of some redundancy. We compare the two approaches, highlighting differences, similarities, and respective advantages.

Finally, we move to applications of these results, which cover wideband communications, noise removal, distributed sampling, and super-resolution imaging, to name a few. In particular, we describe a recent result on multichannel sampling with unknown shifts, which leads to an efficient super-resolution imaging method.

Inald Lagendijk - Short CV

Inald Lagendijk is full professor in the field of multimedia signal processing at TU Delft, and holds the chair of Information and Communication Theory. The fundamental question that he is interested in, is how multimedia information (images, video, audio) can be represented such that it is not only efficient in communication bandwidth or storage capacity, but that it is also easily identified when stored in large volumes (video libraries, internet) or transmitted over networks (e.g. P2P networks), that it is robust against errors when transmitted, that it can be protected against unauthorized usage, and that it has a good (audio-visual) quality. Research projects he is currently involved in cover subjects such as multimedia content security (fingerprinting, watermarking, secure signal processing), multimedia information retrieval, and (wireless) multi-media communications. In the past he was involved in research on image sequence restoration and enhancement, and video compression. Professor Lagendijk is member of the Royal Netherlands Academy of Arts and Sciences (KNAW), and he is a Fellow of the IEEE (for Contributions to Image Processing).

Inald Lagendijk - Abstract

I will discuss the problems, principles and examples of protecting the privacy of users in multimedia applications. Some multimedia applications pose serious privacy threats for their users as they rely on privacy-sensitive information that can be misused. To protect the privacy of users, an emerging paradigm shows that it is attractive and feasible to combine signal processing and cryptography. The focus is on those applications that are executed remotely or “in the cloud”, such as on-line recommendation services (amazon.com, Google.com) but also face-recognition systems. The new and exciting research area of privacy-preserving signal processing aims at making privacy-sensitive data of the user of such multimedia applications inaccessible by means of encryption. Although it is then impossible for the service provider to access directly the content of the encrypted data without the decryption key, the service provider can still process the data under encryption to perform the required task. The protocols to process the encrypted data are designed by using cryptographic primitives like homomorphic cryptosystems and secure multiparty computation techniques. I describe a number of examples that show solutions for privacy-preserving signal processing, including privacy-preserving face recognition and secure clustering.

Björn Ottersten - Short CV

Björn Ottersten was born in Stockholm, Sweden, 1961. He received the M.S. degree in electrical engineering and applied physics from Linköping University, Linköping, Sweden, in 1986. In 1989 he received the Ph.D. degree in electrical engineering from Stanford University, Stanford, CA. Dr. Ottersten has held research positions at the Department of Electrical Engineering, Linköping University, the Information Systems Laboratory, Stanford University, and the Katholieke Universiteit Leuven, Leuven. During 96/97 Dr. Ottersten was Director of Research at ArrayComm Inc, San Jose, California, a start-up company based on Ottersten's patented technology. He has co-authored papers that received an IEEE Signal Processing Society Best Paper Award in 1993, 2001, 2006, and 2007. Since 1991 he is Professor of Signal Processing at the Royal Institute of Technology (KTH), Stockholm. From 2004 to 2008 he was dean of the School of Electrical Engineering at KTH and from 1992 to 2004 he was head of the department for Signals, Sensors, and Systems at KTH. Currently, Dr. Ottersten is Director for the Interdisciplinary Centre for Security, Reliability and Trust at the University of Luxembourg, Luxembourg. Dr. Ottersten has served as Associate Editor for the IEEE Transactions on Signal Processing and on the editorial board of IEEE Signal Processing Magazine. He is currently editor in chief of EURASIP Signal Processing Journal and a member of the editorial board of EURASIP Journal of Advances Signal Processing. Dr. Ottersten is a Fellow of the IEEE and EURASIP. He is a first recipient of the European Research Council advanced research grant.

Björn Ottersten - Abstract

To meet the competition from terrestrial communication networks, innovative and cost efficient applications and services provided by satellite systems must evolve. Satellite broadcast services provide an unprecedented coverage at low cost. A wide range of satellite communication applications can be envisioned included multimedia delivery, traffic information, fleet management, software downloads, and public safety communications etc. The commercial success of such services requires reliable and secure delivery to a wide range of users. This in turn poses technical challenges requiring diversity techniques introducing redundancy in time, polarization and space.

We discuss some challenges in designing novel systems which combine information from multiple cooperating satellites and/or terrestrial transmitters attempting to minimize latency and transceiver complexity. Advanced transmission and reception schemes based on interference rejection and multi-user detection allow increased spectral efficiency, higher throughput, more reliable communication, small dish antennas etc. Cooperative transmission and reception techniques based on Multiple-Input Multiple-Output (MIMO) communications allow reliable provisioning of two-way broadband, interactive and mobile applications in satellite systems. Ground based beamforming techniques can be applied to adapt and tailor transmission based on traffic and user requirements.

Jens Blauert - Short CV

Jens Blauert was born in 1938. He studied communication engineering at Aachen, where he received a Doctor-of-Engineering degree in 1969. In 1973, he delivered an inaugural dissertation to the Technical University of Berlin (habilitation), and in 1994 he was awarded an honorary degree (Dr. Tech.) by the University of Aalborg (DK). Since 1974 he held a chair in electrical engineering and acoustics at the Ruhr-Universitat Bochum, where he founded the Institute of Communication Acoustics (IKA) and headed it until 2003. Subsequently, he was assigned emeritus professor.

The author/coauthor of more than 150 papers and monographies, and supervisor of 52 successful PhD projects, has been awarded several patents. His major scientific fields of interest are spatial hearing, binaural technology, aural architecture, speech technology, virtual environments, telepresence and quality of experience, QoE.

He has provided services to the science community in positions such as chairman of the ITG committee on electroacoustics, dean of the Faculty of Electrical Engineering & Information Technologies at Bochum, senator of the Ruhr-Universitat, chairman of the board (and cofounder) of the European Acoustics Association, EAA, president and vice president of the German Acoustical Society, DEGA, associate board member of the International Commission for Acoustics, ICA, member of the Environmental-Protection Council of the State of North-Rhine Westphalia, board member (and cofounder) of the European Speech-Communication Association, now ISCA, and board member (and cofounder) of the section on noise and vibration, NALS, of the German Standard Association, DIN.

Prof. Blauert has been visiting professor in various countries worldwide. Currently, he is a distinguished visiting professor of Rensselaer Polytechnic Institute, Troy NY – adjunct to its program on architectural acoustics. He is a professional acoustical consultant, chartered in the state of North-Rhine Westphalia, Germany.

Jens Blauert - Abstract

The human binaural system, although using input from only two sensors, spaced about 14 cm apart, has a number of astonishing capabilities, such as precise localization of sound sources, analysis of auditory scenes and segregation of auditory streams, suppression of reverberance, noise and coloration, enhancement of desired talkers against undesired ones, providing spatial impression and the sense of immersion. To mimic these capabilities technologically, models of the binaural system using digital signal processing have been built and are constantly being improved. Modern models have a bottom-up, signal- driven part, complemented by a hypothesis-driven, top-down part.

The bottom-up part typically contains the following modules: external ears, middle ears and cochleae – further, modules for monaural pre-processing and subsequent binaural processing. The output of the bottom-up part is usually conceptualized as a binaural activity map, which may physiologically be situated at midbrain level. While the bottom-up part evaluates sound fields regarding the positions and the perceptual attributes of sounds, it takes further processing steps to assign meaning to the binaural activities. To this end a transition from signal processing to symbol processing, following a process of object building, has to be accomplished. Finally, the sets of symbols have to be interpreted by cognitive processes.

In this presentation, the current state of the art of models of binaural hearing will be reviewed and their potential regarding practical application will be discussed. Generic application areas are, for example, aural virtual environments, hearing aids, assessment of product-sound quality, room acoustics, speech technology, audio technology, robotic ears and tool for research into auditory physiology and perception. The talk will relate to activities of AABBA, an open international circle of researchers with a special interest in the application of binaural models.