Multiple antennas at the transmitter and/or receiver can be
exploited in many different ways to improve the performance of
wireless systems, in terms of reliability, throughput and coverage.
This tutorial will cover various link and system-level issues of MIMO
systems, ranging from channel characterization to interference
Table of contents:
- MIMO Channel characterization and modeling. Channel rank,
stochastic geometric and non-geometric models.
- The capacity of a MIMO link
- Some common misconceptions
- Basic transmission options, with and without channel knowledge
at the transmitter.
- Exploiting partial channel information at the transmitter, using
covariance feedback or noisy, quantized or outdated channel
- Interference handling at the receiver and transmitter.
- Space Division Multiple Access. Downlink beamforming, scheduling
in time, space and frequency, opportunistic schemes.
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. He has
authored papers that received the Signal Processing Society Paper
Award in 1993 and 2001. In 1991 he was appointed Professor of Signal
Processing at the Royal Institute of Technology (KTH), Stockholm and
he is currently dean of the School of Electrical Engineering at KTH.
From 1992 to 2004 he was head of the department for Signals, Sensors,
and Systems at KTH. Dr. Ottersten is also a visiting professor at the
University of Luxembourg. Dr. Ottersten has served as Associate Editor
for the IEEE Transactions on Signal Processing and a member of the
editorial board of EURASIP Journal of Applied Signal Processing. He is
currently editor in chief of EURASIP Signal Processing Journal and a
member of the editorial board of IEEE Signal Processing Magazine. Dr.
Ottersten is a Fellow of the IEEE. His research interests include
wireless communications, stochastic signal processing, sensor array
processing, and time series analysis.
received the M.S. degree in computer science from Linköping
University in 1991 and the Tech. Lic. and Ph.D. degrees in electrical
engineering from the Royal Institute of Technology (KTH), Stockholm,
Sweden, in 1997 and 2000, respectively.
From 1991 to 1995, he was with Ericsson Telecom AB Karlstad. He
currently holds a position as Research Associate at the Royal
Institute of Technology. His research interests include statistical
signal processing and its applications to antenna array processing and
communications, radio resource management and propagation channel
Ultra Wideband Communications: From Concept
In February 2002, a law-and-order of the Federal
Communications Commission (FCC) gave the "green light'' (spectral mask
in therange 3.1-10.6 GHz) for commercial applications of Ultra
Wideband (UWB) systems. Since this recent FCC release, UWB has emerged
asan exciting technology whose "time has come'' for wireless
communications, and local area networking. Companies (such as
TimeDomain, AetherWire, and XtremeSpectrum) have recently produced
chipsets for UWB communication systems. UWB technology, referring to
bandwidth exceeding 2GHz or fractional bandwidth of more than25\, is
attracting increasing interest in academia, industry and government
Conveying information over Impulse-like Radio (IR) waveforms, UWB
technology comes with unique features: low-power carrier-free
transmissions, ample multipath diversity, low-complexity baseband
transceivers and a potential for increase in capacity. The scarcity of
bandwidth resources coupled with the capability of IR to overlay
existing systems, welcomes UWB connectivity in the workplace, and at
home for indoor and especially short range wireless links. Utilizing
ultra-short pulses, UWB also allows for very accurate delay estimates
providing position and location capabilities within a few centimeters.
However, to realize these attractive features, UWB research and
development has to cope with formidable challenges that include: high
sensitivity to timing the reception of ultra-short pulses, mitigation
of fading propagation effects with pronounced frequency-selectivity,
low-complexity constraints in decoding high-performance multiple
access protocols, and strict power limitations imposed by the desire
to minimize interference between UWB communicators, and co-existing RF
These challenges call for advanced signal processing expertise in UWB
communications - a view also shared by government agencies andindustry.
Testament to this growing trend towards signal processing topics for
UWB related applications is also provided by the number of UWB
sessions in conferences, and plenary talks devoted to this
subject.Responding to such an interest, this tutorial will provide a
comprehensive overview of the state-of-the-art in UWB communications
with emphasis on the unique features, challenges, and research
directions tailored to signal processing aspects.
The contents of the proposed tutorial will be structured as
II. History of UWB;
III. Motivating Applications;
IV. UWB Communications at the Physical Layer;
IV-A Transmitter Design;
IV-C Channel Modeling and Estimation;
IV-D Receiver Design;
IV-E Multiple Access and Interference Suppression;
V. UWB Communications at the Networking Layer;
VI. Implementation Issues;
VII. Conclusions and Open Problems;
Graduate students, researchers and engineers with
general interests in Communications, Signal Processing, Information
Theory, and specific interests in ultra-wideband diversity techniques,
fading countermeasures, space-time processing, mobile, multiple
access, as well as UWB implementation, and networking aspects. The
background needed is that of an M.Sc. Degree holder or commensurate
experience with random processes, linear algebra, statistical signal
processing, detection-estimation, and basic information theory and
GEORGIOS B. GIANNAKIS (Fellow'97)
G. B. Giannakis received his Diploma in Electrical Engineering from
the National Technical University of Athens, Greece, 1981.
September 1982 to July 1986 he was with the University of Southern
California (USC), where he received his MSc. in Electrical Engineering,
1983, MSc. in Mathematics, 1986, and Ph.D. in Electrical Engineering,
1986. After lecturing for one year at USC, he joined the University of
Virginia in 1987, where he became a professor of Electrical
Engineering in 1997. Since 1999 he has been a professor with the
Department of Electrical and Computer Engineering at the University of
Minnesota, where he now holds an ADC Chair in Wireless
His general interests span the areas of communications and signal
processing, estimation and detection theory, time-series analysis, and
system identification -- subjects on which he has published more than
250 journal papers, 400 conference papers and two edited books.
Current research focuses on diversity techniques for fading channels,
complex-field and space-time coding, multicarrier, ultra-wide band
wireless communication systems, cross-layer designs and sensor
G. B. Giannakis is the (co-) recipient of six paper awards from the
IEEE Signal Processing (SP) and Communications Societies (1992, 1998,
2000, 2001, 2003, 2004). He also received Technical Achievement Awards
from the SP Society in 2000 and from EURASIP in 2005.
He served as Editor in Chief for the IEEE SP Letters, as
Associate Editor for the IEEE Trans. on Signal Proc. and the
IEEE SP Letters, as secretary of the SP Conference Board, as
member of the SP Publications Board, as member and vice-chair of the
Statistical Signal and Array Processing Technical Committee, as chair
of the SP for Communications Technical Committee and as a member of
the IEEE Fellows Election Committee. He has also served as a member of
the IEEE-SP Society's Board of Governors, the Editorial Board for the
Proceedings of the IEEE and the steering committee of the
IEEE Trans. on Wireless Communications.
Iterative ('turbo') algorithms on factor
Theory and applications
Originally developed for decoding turbo codes,
normal factor graphs are a natural setting for the description of
iterative techniques for detecting coded signals transmitted on a
variety of channels.
In addition, they provide a unified framework allowing one to
understand the connections among seemingly different detection
problems. This tutorial describes the application of normal factor
graphs to a number of these problems, such as equalization of coded
signals, multiuser detection, decoding of multilevel coded modulation,
and reception of space--time coded signals.
1. Soft Decoding. Factor graphs and the
2. Decoding codes described by factor graphs. The turbo algorithm.
3. Low-density parity-check codes. Turbo codes.
4. Analysis of turbo algorithms: EXIT charts.
5. Turbo algorithms for multiuser detection.
6. Turbo algorithms for space--time decoding in MIMO systems.
7. Turbo algorithms for coded modulation.
EZIO BIGLIERI is presently a professor with
Universitat Pompeu Fabra, Barcelona, Spain. His previous positions
include professorships with Universita` di Napoli (1975--1977), with
UCLA (1987--1989), and with Politecnico di Torino (1977--1987 and
He has held visiting positions with the Department of System Science,
UCLA, the Mathematical Research Center, Bell Laboratories, Murray Hill,
NJ, the Bell Laboratories, Holmdel, NJ, the Department of Electrical
Engineering, UCLA, the Telecommunication Department of The Ecole
Nationale Superieure des Telecommunications, Paris, France, the
University of Sydney, Australia, the Yokohama National University,
Japan, the Electrical Engineering Department of Princeton University,
the University of South Australia, Adelaide, the University of
Melbourne, the Institute for Communications Engineering, Munich
Institute of Technology, Germany, and the Institute for Infocomm
Research, National University of Singapore.
He was elected three times to the Board of Governors of the IEEE
Information Theory Society, and served as its President in 1999.
He was the general co-chairman of the "IEEE 2000 International
Symposium on Information Theory," Sorrento, Italy, June 2000, and the
general co-chairman of ISITA 2004, Parma, Italy. He is a Fellow of the
IEEE, and a Distinguished Lecturer for the IEEE Information Theory
Society and the IEEE Communications Society.
He was an Editor of the IEEE Transactions on Communications, the IEEE
Transactions on Information Theory, the IEEE Communications Letters,
the Journal on Communications and Networks, and the Editor in Chief of
the European Transactions on Telecommunications. Since 2004 he has
been the Editor-in-Chief of the IEEE Communications Letters.
He has published 6 books and in excess of 120 journal papers on
digital communications. Among other honors, he received the IEEE
Third-Millennium Medal, the IEEE Donald G. Fink Prize Paper Award, and
the IEEE Communications Society Edwin Howard Armstrong Achievement
Energy Conservation in Adaptive Filtering
Adaptive filters are systems that respond to
variations in their environment by adapting their internal structure
in order to meet desired performance specifications. Adaptive filters
are endowed with both learning and tracking abilities that make them
extremely useful in a variety of applications ranging from signal
processing, to communications, biomedical engineering, consumer
products, and military electronics.
This tutorial discusses three emerging trends in the study of adaptive
filters in view of the increased degrees of mobility and complexity in
modern applications. One trend relates to a shift in emphasis from the
design of adaptive filters to the design of interactive adaptive
blocks within complex systems, especially within distributed networks.
A second trend relates to the need to endow adaptive filters with more
complex learning mechanisms that can extract and exploit information
about the surrounding environment. And a third trend relates to the
need to characterize more fully the limits of performance of existing
schemes and to develop variants that can meet more stringent
The performance of an adaptive filter is traditionally evaluated in
terms of its transient behavior and its steady-state behavior. The
former provides information about how fast a filter learns, while the
latter provides information about how well a filter learns. Such
performance analyses tend to be challenging since adaptive filters
are, by design, time-variant, nonlinear, and stochastic systems. For
this reason, it has been common in the literature to study different
adaptive schemes separately and under different assumptions due to the
variations that exist in their update equations. This tutorial
provides an overview of an energy conservation approach to the
performance analysis of adaptive filters. The framework is based on
studying the energy flow through successive iterations of an adaptive
filter and on establishing a fundamental energy conservation relation;
the relation bears resemblance with Snell’s Law in optics and has far
reaching consequences on the study of adaptive schemes. In this way,
many new and old results can be pursued uniformly across different
classes of algorithms. The analysis will also highlight some
interesting phenomena regarding the learning ability of adaptive
filters. It will be seen that adaptive filters generally learn at a
rate that is better than that predicted by least-mean-squares theory;
that is, they are "smarter" than originally thought! It will also be
seen that adaptive filters actually have two distinct rates of
convergence; they learn at a slower rate initially and at a faster
rate later; perhaps in a manner that mimics the human learning process.
The tutorial will also provide an overview of recent work on adaptive
distributed systems that are able to exploit the temporal and spatial
dimensions of the data collected at spatially distributed nodes. Such
distributed networks will form the backbone of future data
communication and control networks. Applications will range from
sensor networks to precision agriculture, environment monitoring,
disaster relief management, smart spaces, and medical applications. In
all these cases, the distribution of the nodes in the field yields
spatial diversity, which should be exploited by the adaptive filters
alongside the temporal dimension in order to enhance the robustness of
the processing tasks and improve the probability of signal and event
detection. Adaptation is needed not only because the environmental
conditions vary with time and space, but also because the network
topology may vary. Adaptive distributed structures will be described
and their performance will be examined by means of energy conservation
Ali H. Sayed received his PhD in Electrical
Engineering from Stanford University in 1992. He is Professor and
Chairman of Electrical Engineering at UCLA where he also directs the
Systems Laboratory. He has published widely in the areas of
adaptive filtering, estimation theory, and signal processing for
communications with over 250 articles and 4 books, including the
textbook Fundamentals of Adaptive Filtering (Wiley, NY, 2003). He is a
Fellow of IEEE and has served as Editor-in-Chief of the IEEE
Transactions on Signal Processing (2003-2005). He serves as
Editor-in-Chief of the EURASIP Journal on Applied Signal Processing.
His research has received several recognitions including the 1996 IEEE
Donald G. Fink Prize, 2002 Best Paper Award from the IEEE Signal
Processing Society, 2003 Kuwait Prize, 2005 Frederick E. Terman Award,
and two Best Student Paper Awards at international meetings
(1999,2001). He has served as a 2005 Distinguished Lecturer of the
IEEE Signal Processing Society and as a member of the Publications
(2003-2005) and Award (2005) Boards of the same society. He is a
member of the Signal Processing Theory and Methods (SPTM) and Signal
Processing for Communications (SPCOM) technical committees of the IEEE
Signal Processing Society. He is also serving as General Chairman of
ICASSP 2008 to be held in Las Vegas.
Brain Source Modeling and Estimation Using
Electroencephalography (EEG) and
magnetoencephalography (MEG) are non-invasive techniques for detecting
and localizing electrical activities of the central nervous system.
They are important for studying the brain functions and in clinical
practice. One of their applications is the localization of epilepsy
seizure sources. Modern EEG/MEG techniques employ physical forward
models and statistical array processing approaches to solve the
In this seminar, I will present source modeling and estimation methods
using EEG/MEG arrays. I will first give a brief overview of the
background and current developments in this area. I will then present
the work in my lab over the last years. We employ both a spherical
head approximation and realistic head model combined with the boundary
element method (BEM) to set up the forward model.
I will first discuss source estimation methods using a dipole source
model, where it is assumed that the source is small compared with its
distances to the sensors. We consider spatiotemporally correlated
noise models and derive the maximum likelihood estimation (MLE) of the
source locations and signals using the Generalized Multivariate
ANalysis of VAriance (GMANOVA) method.
We then propose several line- and surface-source models to model
realistic spatially spread sources that appear for example in the N20
response and epilepsy cases. In these models, the source extent is
directly parameterized and estimated, providing more complete
information than the dipole model. We use basis functions to exploit
the prior information on the source distribution and reduce the number
of unknown parameters, then apply MLE for the inverse solution.
For all the above models, I will present our estimation results using
both simulated experiments and real clinical data. I will also show
results of Cram\'er-Rao bounds (CRB) that evaluate the estimation
performance. The CRB is a lower bound on the variance of any unbiased
estimator and provides the optimal estimation accuracy that can be
expected from a certain model. We use the CRB to analyze the effects
of source positions and sizes on the estimation accuracy.
In the last part, I will discuss the effects of the geometric head
model perturbations (inaccuracies) on the EEG source estimation
This topic is important since there are usually errors in the MRI or
CT images, which cause uncertainties in the head shape model and thus
affects the performance. We propose a meshless method to set up the
forward model and use a perturbation model for the inverse analysis.
The results will be presented in terms of the CRBs.
- Background of EEG/MEG
- Overview of the current modeling and estimation algorithms
II. Source estimation and detection
- Dipole source estimation in spatially correlated noise
- Estimating sources under low-rank interference using MEG beamforming
- Extended-source modeling and estimation
- Elliptic head modeling and estimation
- Performance bounds for a realistic head model
- Effects of geometric head model perturbation on EEG
III. Other EEG/MEG research topics
- Estimating brain conductivities using EEG
- Applications to fetal MEG (fMEG)
- Monitoring fetal heart rate on-line using fMEG
- Selecting models
- Vector sensors
Arye Nehorai received the B.Sc. and M.Sc. degrees
in electrical engineering from the Technion, Israel, and the Ph.D.
degree in electrical engineering from Stanford University, California.
From 1985 to 1995 he was a faculty member with the Department of
Electrical Engineering at Yale University. In 1995 he joined as Full
Professor the Department of Electrical Engineering and Computer
Science at The University of Illinois at Chicago (UIC). From 2000 to
2001 he was Chair of the department's Electrical and Computer
Engineering (ECE) Division, which then became a new department. In
2001 he was named University Scholar of the University of Illinois. In
2006 he assumed the Chairman position of the Department of Electrical
and Systems Engineering at Washington University in St.
Louis, where he is also the inaugural holder of the Eugene and Martha
Lohman Professorship of Electrical Engineering.
Dr. Nehorai was Editor-in-Chief of the IEEE
Transactions on Signal Processing during the years 2000 to 2002. In
the years 2003 to 2005 he was Vice President (Publications) of the
IEEE Signal Processing Society, Chair of the Publications Board,
member of the Board of Governors, and member of the Executive
Committee of this Society. He is the founding editor of the special
columns on Leadership Reflections in the IEEE Signal Processing
Dr. Nehorai was co-recipient of the IEEE SPS 1989
Senior Award for Best Paper with P. Stoica, as well as co-author of
the 2003 Young Author Best Paper Award and of the 2004 Magazine Paper
Award with A. Dogandzic. He was elected Distinguished Lecturer of the
IEEE SPS for the term 2004 to 2005. He is the Principal Investigator
of the new multidisciplinary university research initiative (MURI)
project entitled Adaptive Waveform Diversity for Full Spectral
Dominance. He has been a Fellow of the IEEE since 1994 and of the
Royal Statistical Society since 1996.
Numerical Optimization Methods for Robust
In recent years there has been a surge of research
in robust optimization and interior point methods within the
mathematical programming community, leading to new powerful
optimization techniques as well as efficient software packages. These
advances have begun impacting various applied fields, where
computational efficiency and the ability to deal with uncertainties is
of paramount importance. The goal of this tutorial is to introduce new
methods for robust parameter estimation by applying these modern
optimization tools to solving core problems in statistical estimation
The first part of the tutorial will focus on the
problem of estimating an unknown deterministic parameter vector in a
linear model. Instead of utilizing data error methods, which can often
lead to a large estimation error, we discuss a mean-squared error (MSE)
framework for designing robust estimators with good MSE behavior. As
we demonstrate, this framework leads to new, powerful estimation
methods that can significantly outperform existing estimators such as
least-squares and Tikhonov regularization. We also treat the problem
of evaluating the performance of different estimators based on the
concepts of admissible and dominating estimators. We then show how
these ideas can be extended to more general, nonlinear models by
developing bounds on the MSE that are smaller than the traditional
Cramer-Rao lower bound for all values of the unknowns. The method of
development we present inherently leads to explicit constructions of
estimators that achieve these bounds in cases where an efficient
estimator exists, by performing a simple linear transformation on the
standard maximum likelihood (ML) estimator. This leads to estimators
that result in a smaller MSE than the ML method for all possible
values of the unknowns.
In the second part of the tutorial we focus on
Bayesian estimation of random parameters in the presence of model
uncertainties. We describe two approaches for estimation in this
context: minimax MSE and minimax regret. The latter is a competitive
approach, which seeks the estimator whose performance in terms of MSE
is as close as possible to that of the optimal estimator for the case
in which the model is known exactly. The advantage of this strategy is
that it is often less conservative than the conventional minimax MSE
approach, and can account for more general uncertainty regions.
• Estimation Models
• Background on convex analysis
Deterministic Parameter Estimation:
• Linear estimation in linear models
• Data error approaches
• MSE-based methods
• Minimax and blind minimax MSE estimation
• Minimax regret estimation
• Admissible and dominating estimation methods
• Maximum set estimators
• Estimation in nonlinear problems
• Uniform CRLB
• Improving the CRLB and maximum-likelihood estimation
Random Parameter Estimation:
• Finite-dimensional model with model uncertainties
• Minimax regret filtering
Yonina C. Eldar received the B.Sc. degree in
Physics in 1995 and the B.Sc. degree in Electrical Engineering in 1996
both from Tel-Aviv University (TAU), Tel-Aviv, Israel, and the Ph.D.
degree in Electrical Engineering and Computer Science in 2001 from the
Massachusetts Institute of Technology (MIT), Cambridge.
From January 2002 to July 2002 she was a Postdoctoral Fellow at the
Digital Signal Processing Group at MIT. She is currently an Associate
Professor in the Department of Electrical Engineering at the Technion
- Israel Institute of Technology, Haifa, Israel. She is also a
Research Affiliate with the Research Laboratory of Electronics at MIT.
From 1992 through 1996 she was in the program for outstanding
students in TAU. In 1998 she held the Rosenblith Fellowship for study
in Electrical Engineering at MIT, and in 2000 she held an IBM Research
Fellowship. She is currently a Horev Fellow in the Leaders in Science
and Technology program at the Technion, and an Alon Fellow. In 2004,
she was awarded the Wolf Foundation Krill Prize for Excellence in
Scientific Research, and in 2005 the Andre and Bella Meyer Lectureship.
She is a member of the IEEE Signal Processing Theory and Methods
technical committee and an Associate Editor for the IEEE Transactions
on Signal Processing.
The Factor Graph Approach to Signal
Factor graphs (or similar graphical models) allow a unified approach
to a number of topics in coding, signal processing, and machine
learning. Many of the best algorithms in these fields may be viewed as
message passing in a factor graph, and new efficient algorithms for
detection/estimation problems in complex system models may be obtained
by putting together tabulated message computation rules for the
building blocks of the system model.
The tutorial begins with an elementary introduction to factor graphs
and the sumproduct and max-product algorithms for discrete variables.
However, the focus of the tutorial is on algorithms for models with
continuous variables. Special attention is given to multivariable
Gaussian message passing on linear models, which includes Kalman
filtering and smoothing, linear minimum mean squared error (LMMSE)
estimation, recursive least squares (RLS), linear predictive coding (LPC)
analysis, and related topics. Beyond the Gaussian case, the tutorial
develops also the message passing approach to gradient
methods, particle filters (sequential Monte Carlo methods), and
With its emphasis on “local” computations, the factor graph approach
encourages, and helps, to mix and match all these techniques.
Hans-Andrea Loeliger received a diploma in electrical engineering in
1985 and a Ph.D. in 1992, both from ETH Zurich, Switzerland. From 1992
to 1995 he was with Link¨oping University, Sweden. From 1995 to 2000,
he was with Endora Tech AG, Basel, Switzerland, of which he is a
cofounder. Since 2000, he has been a Professor at ETH Zurich. His
research interests lie in the broad areas of signal processing,
information theory, communications, and electronics. He is a fellow of
The colored revolution of bio-imaging: new
opportunities for signal processing
During the past decade, biological imaging has undergone a
revolution thanks to the development of highly specific fluorescent
probes in conjunction with new high-resolution microscopes.
Fluorescence microscopy is becoming widespread and is having a
profound impact on the way research is being conducted in the life
sciences. Biologists can now visualize sub-cellular components and
processes in vivo, both structurally and functionally. Observations
can be made in two or three dimensions, at different wavelengths (spectroscopy),
possibly with time-lapse imaging to investigate cellular dynamics.
Signal processing is at the heart of these developments and is
expected to play an ever-increasing role in the field.
The goal of this tutorial is to introduce engineers to modern
fluorescence microscopy while making them aware of corresponding
research opportunities. The first part will cover the principles of
fluorescence imaging, while the second will concentrate on signal
processing aspects and challenges.
Part 1: Basics of Fluorescence Imaging
- Fluorescence labeling: the green/colored revolution
- Image formation: widefield and confocal microscopy; PSF
- Detectors and limiting factors
Part 2: Signal Processing Challenges
- Image preparation: calibration, feature detection
- Restoration: denoising, 3D deconvolution, inverse problems
- Registration and segmentation
- Quantitative image analysis: motion; particle tracking; model
Michael Unser received the M.S. (summa cum laude) and Ph.D. degrees
in Electrical Engineering in 1981 and 1984, respectively, from the
Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland. From
1985 to 1997, he worked as a scientist with the National Institutes of
Health, Bethesda USA. He is now professor and Director of the
Biomedical Imaging Group at the EPFL. His main research area is
biomedical image processing. He has a strong interest in sampling
theories, multiresolution algorithms, wavelets, the use of splines for
image processing and is the author of over 120 published journal
papers in these areas.
Dr. Unser is the associate Editor-in-Chief of the IEEE Transactions
on Medical Imaging and the Editor-in-Chief of the Wavelet Digest, the
electronic newsletter of the wavelet community. He was general chair
for the first IEEE International Symposium on Biomedical Imaging (ISBI'2002),
which was held in Washington, DC, July 7-10, 2002. He also chairs the
newly created technical committee of the IEEE-SP Society on Bio
Imaging and Signal Processing (BISP).
Dr. Unser is a fellow of the IEEE. He received the 1995 and 2003
Best Paper Awards and the 2000 Magazine Award from the IEEE Signal