A pioneer in developing image, video, and multidimensional signal processing theory and methods for solving image processing, computer vision, and pattern recognition problems, Ramalingam Chellappa has profoundly affected the development of systems for face recognition and verification, image and video synthesis and analytics, real-time action detection, and active authentication. During the 1980s, Chellappa developed groundbreaking parameter estimation and neighborhood selection rules for Markov random field (MRF) models and developed algorithms for image restoration, texture analysis, and segmentation using MRFs. His linear discriminant analysis-based algorithm developed during the 1990s pioneered discriminative methods for training face recognition systems. Chellappa has also been active in creating tools for 3D recovery from one or more images using discrete and continuous methods. The Frankot-Chellappa algorithm was developed for extracting integrable 3D surfaces from a single image. He pioneered the area of video-based 3D modeling algorithms using batch and recursive estimation methods. He was also an early contributor to neural networks for image processing and computer vision applications. His recent work in this area includes deep-learning-based face, object, and activity recognition systems and methods that combat attacks against classification systems. Specifically, he has developed deep convolution neural network (DCNN)-based algorithms to help overcome the problems of aging, pose, and illumination, affecting accurate face recognition and providing results that rivaled human performance. His work on the HyperFace and UltraFace algorithms resulted in programs that perform not only face detection and classification but also gender recognition and age and pose estimation. The end-to-end system built by his group has been used for child exploitation/crime prevention and other U.S. homeland security applications.
An IEEE Life Fellow and recipient of the 2012 K.S. Fu Prize from the International Association for Pattern Recognition, Chellappa is a Distinguished University Professor with the Electrical and Computer Engineering Department at the University of Maryland, College Park, MD, USA.
Alan Willsky’s ability to foresee important connections between signal processing and disciplines such as automatic control, information theory, optimization, and statistical estimation has had a phenomenal technical, professional, and societal impact on many fields. His work and that of others inspired by it have demonstrated the benefits signal processing provide to applications ranging from medical imaging to control of chemical processes and from modeling groundwater hydrology to failure detection in avionics systems. He provided foundational research on model-based signal processing and was one of the leaders in advancing multiresolution statistical methods, which have profoundly impacted many fields with applications including in computer vision, biomedical image analysis, and oceanographic remote sensing. He has made fundamental contributions to the use of statistical methods for the extraction of geometric structure from multidimensional data with applications ranging from computer vision to biomedical image analysis and has contributed strongly to graphical models and machine learning. His focus on interactions between signal processing and system theory provided the foundation for a methodology for failure detection and identification. His contributions to the successful flight-test of a sensor failure detection system led to his fundamental research on robust dynamic parity checks. Failure and anomaly detection are increasingly important due to the broad need for system monitoring in the environment of “big data.”
Willsky cofounded Alphatech, Inc. where he made important contributions to the transition of advanced statistical methods to practical systems, primarily for defense. Areas in which Alphatech played a major role include multitarget tracking systems using advanced radar and other sensing modalities, multisensor data fusion systems, and automatic object recognition. A prolific author, Willsky has contributed more than 400 widely cited technical papers, a number of which received some of IEEE’s most prestigious awards. He has also received the leading awards made by the IEEE Signal Processing Society and is co-author of the widely used text Signals and Systems.
An IEEE Life Fellow and member of the U.S. National Academy of Engineering, Willsky is the Edwin Sibley Webster Professor of Electrical Engineering and Computer Science (retired) at the Massachusetts Institute of Technology, Cambridge, MA, USA.
Bede Liu’s pioneering work on signal processing focused primarily on lowering implementation complexity and reducing power consumption, which have been central to the creation of cost-effective, high performance, low-power signal processing, important in the development of mobile and multimedia systems. To battle the cost of implementing digital signal processing algorithms, Liu developed novel methods that require much less computation by replacing computation with memory. His revolutionary approach of incorporating simple shift-and-adds for multiplier-free filters provides a 3-to-1 savings in computation over the traditional methods, thus enabling highly efficient implementation of digital filters and the fast Fourier transform algorithm. Multiplier-less processing has been widely used and commercialized in chips for control and imaging applications, dynamic signal analyzers, and a discrete cosine transform chip that was the fastest at the time. Liu’s proposal to use 1-bit coefficients on over-sampled data achieved significant savings in chip area and power. This approach is now widely used to allow for tradeoffs among clock rate, area, and power consumption in implementations for applications including mobile information devices. In video coding, Liu’s novel way to determine motion vectors that reduces computation by a factor of 4 was incorporated in software packages and also led to a number of other proposed efficient approaches. In video analysis, his proposal to use reduced resolution processing to extract video content cuts computation by two orders of magnitude. He also developed novel ways of transcoding the conversion of an encoded video bit stream to another with a smaller bit rate in order to adapt to network conditions.
An IEEE Life Fellow and member of the U.S. National Academy of Engineering, an academician of Academia Sinica (Taipei), and a foreign member of the Chinese Academy of Sciences (Beijing), Liu is a Professor Emeritus with the Department of Electrical Engineering at Princeton University, Princeton, NJ, USA.
One of the founding fathers of filter bank and wavelet theory, the signal processing algorithms and architectures developed by Martin Vetterli have helped advance the audio coding, image compression, and wireless technologies critical to our multimedia communications world. Vetterli’s Ph.D. thesis established a theory of filter banks and efficient implementations that made filter banks an essential signal processing tool and launched a new wave of applications. In this research he proposed multidimensional subband coding, which is now an integral component of the JPEG2000 image compression standard. His concept of perfect transmultiplexing now allows designers to perfectly modulate signals onto a single channel, enabling orthogonal frequency division multiplexing in WiFi systems. And his concept of nonseparable multidimensional filter banks are used in today’s high-dimensional image and video processing methods. Vetterli then went on to propose solutions for joint source-channel coding based on multiresolution concepts. This work has been critical to enabling video over the Internet. He also introduced multiresolution for broadcast television, which allows graceful degradation and backward compatibility between HDTV and standard television. His contributions to joint source-channel coding for video multicasting over the Internet accommodates multiple users with varying channels or access rates. Vetterli has also made groundbreaking contributions to sampling and quantization techniques for bridging the analog and digital worlds. He derived a new sampling theory for signals of finite rate innovation FRI related to compressed sensing, which provides improvements to oversampling methods for analog-to-digital conversion (ADC). This permits high bandwidth signals with low-dimensional representation to be sampled at radically lower bandwidth signal with a low-dimensional representation to be sampled at radically lower rates. Vetterli’s work on FRI has made compressed sensing one of the hottest topics in signal processing and has important implications for the design of new ADCs, cameras, radars, and medical imaging devices.
An IEEE Fellow, Vetterli is the president of and a professor in the School of Computer and Communications Sciences at Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Pioneering and introducing the use of statistical invariance for the design of detectors and estimators, Louis Scharf has profoundly impacted the way statistics are used in modern signal processing to provide solutions for a wide range of engineering problems. Scharf is most known for his work on modal analysis, invariance theories for subspace signal processing, dimension reduction in subspaces for managing performance metrics, and for his recent work on coherence statistics for space-time signal processing. His work on modal analysis is being applied to mode tracking in power systems to identify and track low-frequency modes of oscillation that reveal vulnerabilities to system instabilities. He introduced invariance as an important principle for designing optimal detectors, which has resulted in matched and adaptive subspace detectors for radar, sonar, and hyper-spectral imaging. These detectors adaptively find tell-tale signatures in broadband multisensor time series while maintaining invariance to unknown channel variations that cannot be modeled or estimated. Scharf’s work on coherence is bringing attention to the commonality of a variety of seemingly disparate problems in detection and estimation theory. He also pioneered the geometric approach to interpreting signal processing problems and their solutions, leading to the application of problem-solving tools such as subspace projections (orthogonal and oblique), canonical coordinates, and principal angles between subspaces. His early work on the geometrical point of view provided a pathway for future researchers resulting in new insights and useful ways of approaching and solving problems. Scharf’s Statistical Signal Processing: Detection, Estimation, and Time Series Analysis (Addison Wesley, 1991) is considered a definitive text on the subject.
An IEEE Life Fellow and recipient of an IEEE Third Millennium Medal (2000) as well as a Technical Achievement Award (1995) and Society Award (2004) from the IEEE Signal Processing Society, Scharf is Research Professor of Mathematics and Emeritus Professor of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, USA.
An internationally recognized expert in radar, sonar, communications, and signal processing, Harry L. Van Trees is considered one of the founders of detection and estimation theory, which has had important implications in engineering. After graduating from West Point, serving in the U.S. Army, and receiving his Sc.D. from M.I.T., he joined the faculty of the Electrical Engineering Department at M.I.T. First published in 1968-1971, Dr. Van Trees’ three-volume series of textbooks on detection, estimation, and modulation theory provided a unified approach to communications, radar, and sonar. Part I, the classic in the field, is used in graduate schools around the world and has educated several generations of engineers. Many of the current military radar, sonar, and missile defense systems rely on the concepts in Dr. Van Trees’ textbooks and were designed by engineers educated with these books. Dr. Van Trees used his expertise to oversee the implementation of the theory in actual systems through a series of DoD positions, Chief Scientist of both the Defense Communications Agency and the U.S. Air Force, Principle Deputy Assistant Secretary of Defense (C3I), and Acting ASD (C3I). The fourth volume, Optimum Array Processing, published in 2002, provides a comprehensive development of optimum array processing for students and practicing engineers. In 2013, he published the second edition of Part I (in collaboration with Drs. Bell and Tian), which has been revised and expanded so that it is the most extensive and up-to-date text in the field. Dr. Van Trees was the originator of the family of Bayesian bounds. The first bound, published in 1964, was the Bayesian version of the classic Cramer-Rao bound, which provided the foundation for the family of Bayesian bounds. In 2007, he collaborated with Dr. Bell to publish Bayesian Bounds for Parameter Estimation and Nonlinear Filter/Tracking.
An IEEE Life Fellow and a recipient of the Presidential Award for Meritorious Executive (1980), Dr. Van Trees was elected to the National Academy of Engineering in 2015. He is a University Professor Emeritus with George Mason University, Fairfax, VA, USA.
Thomas Pinkney Barnwell, III is considered one of the historically most important contributors to the field of signal processing over the last 50 years for the breadth of his digital signal processing innovations that have advanced the very foundations of the field. Credited with some of the most definitive work in speech processing, Dr. Barnwell developed what is known as the “Barnwell Windowing” technique. This made possible the G.728 international standard for 16 kbit/second speech coding, which is used for Internet telephony. He also introduced the mixed-excitation linear prediction (MELP) speech coding standard, which is considered the most influential speech coder of the past 20 years. It became a standard for government and military secure communications and has been incorporated in commercial products such as digital answering machines and speech synthesis chips. His pioneering work on filter banks and wavelets has been integral to image compression, digital audio, and wireless communication applications. Dr. Barnwell founded the digital signal processing research group (now known as the Center for Signal and Information Processing) at the Georgia Institute of Technology, GA, USA, and helped grow it into a world-class research center. As cofounder of Atlanta Signal Processors, Inc. (ASPI, now a division of Polycom, Inc.), Dr. Barnwell was instrumental in providing important DSP hardware to industry such as the first DSP chip for a PC, the first DSP speech coder on a DSP microprocessor, and other high-speed DSP microprocessors. A champion of e-learning initiatives, Dr. Barnwell’s contributions to technology-enhanced education include one of the first computer-based textbooks for teaching with hands-on engagement.
An IEEE Life Fellow and recipient of two IEEE Signal Processing Society Paper Awards and an IEEE Signal Processing Society Technical Achievement Award, Dr. Barnwell is a Professor Emeritus with the Georgia Institute of Technology, Atlanta, GA, USA.
Bishnu S. Atal’s pioneering research on speech coding for providing natural-sounding speech over digital devices has resulted in standards that lie at the heart of practically every mobile phone in use today. Speech coding employs signal-processing techniques to accurately model voice signals for producing high-quality speech while compressing the signals for bandwidth-efficient transmission. Dr. Atal pioneered linear predictive methods for speech coding, now known as “digital cellular,” which is considered one of the most important advancements in communications technology. His work has enabled wireless networks to use less spectrum space and fewer towers to aid in the mass deployment of digital cellular systems. This has played an important role in providing a wireless path to the information superhighway for countries lacking well-established fiber-optic infrastructures. Dr. Atal first developed linear predictive coding (LPC) and then code-excited linear prediction (CELP) techniques for efficient transmission of speech over digital networks. He demonstrated during the 1960s that LPC could represent the varying characteristics of human voice and encode the speech signal at a fraction of conventional methods for more efficient transmission. LPC quickly became the basis for military communication standards. Dr. Atal introduced the CELP method in 1985, and it is now used in practically all digital cellular speech standards as well as standards for digital voice communication over the Internet.
An IEEE Life Fellow and member of the U.S. National Academy of Sciences, Dr. Atal’s many honors include the Thomas Edison Patent Award (1994) and the New Jersey Hall of Fame Inventor of the Year Award (2000). Dr. Atal is an affiliate professor with the Electrical Engineering Department at the University of Washington, Seattle, WA, USA.
G. Clifford Carter’s pioneering contributions to determining and using coherence and time-delay estimation have had lasting impact on the field of signal processing including sonar detection, classification, and localization. Dr. Carter’s algorithms are used today in applications ranging from underwater acoustics for the U.S. Navy’s submarine fleet to healthcare. Dr. Carter’s research on generalized correlation methods for time-delay estimation resulted in what is considered a landmark paper in the signal processing field. Published in 1976, he presented powerful methods of statistically optimum processing for estimating the relative delay of a random signal received by two sensors in the presence of noise. The generalized correlation algorithm became one of signal processing’s most fundamental algorithms and is still used in numerous signal processing systems. Dr. Carter’s pioneering work on defining and statistically characterizing the coherence function and providing an algorithm was significant to the development of high-definition sonar. He determined the limitations on length and resolution for sonar arrays necessary for handling partially coherent oceanic noise fields. Dr. Carter provided an understanding of how to measure signal coherence in these noise fields and determine confidence limits. His automatic coherent processing algorithms are used in the U.S. Navy’s acoustic surveillance processing systems. His more recent patented work includes a close-range sonar system that provides sufficient warning to allow maneuvering to avoid collisions, overcoming the signal processing problems presented by ship’s own noise.
An IEEE Life Fellow, Dr. Carter retired from the Naval Undersea Warfare Center Division Newport, Newport, R.I., in 2009 as a senior technologist for acoustic signal processing.
Considered one of the best known female mathematicians, Ingrid Daubechies’ creation of practical wavelet transforms revolutionized signal processing and has impacted audio, image, and video devices and communications systems. Her development of orthogonal bases of compact support in 1998 was a watershed moment for the field of signal processing. Her work opened up new pathways of theory and applications for wavelets and filter banks and showed that their practical use in applications was indeed possible. Used for signal coding and data compression, the “Daubechies Wavelets” are now an indispensable tool for signal processors.
In her groundbreaking work, Dr. Daubechies demonstrated how to design well-behaved orthogonal wavelets using well-known filter banks and was able to provide a complete analysis. Dr. Daubechies then extended her wavelet techniques to expand their range of applications. In 1992, with A. Cohen and J.C. Feauveau, she developed a family of symmetrical biorthogonal wavelet bases to better handle image and video encoding problems. The eventual JPEG 2000 image coding standard, which enables applications such as next-generation entertainment systems and medical systems for telediagnosis, would be based on these wavelets and filter banks. Dr. Daubechies also worked with Wim Sweldens to apply Sweldens’ lifting algorithm to general wavelet transforms. The resulting algorithms offered state-of-art performance in speed and memory. She also applied lifting to a integer-to-integer wavelet transform that eliminated the noise present in standard transform coding algorithms. The lifted and integer-to-integer wavelets are key components of the JPEG 2000 standard.
An IEEE Fellow, Dr. Daubechies is currently a professor in the Mathematics Department at Duke University, Durham, N.C.
A contributor of some of the earliest research concerning digital signal processing (DSP), Ronald W. Schafer helped shape the field and has continued to impact it throughout his career. Dr. Schafer’s contributions began in the late 1960s when he helped develop the “complex cepstrum” for speech processing. The methodology of “cepstral analysis” led to the one of most important representations of speech for automatic speech recognition applications. It is also an important tool for processing seismic data, biomedical signals and audio. Dr. Schafer played a role in developing many of the classical concepts associated with signal processing algorithms and digital filters, such as multirate interpolation, short-time Fourier analysis and synthesis and the chirp z-transform. He also explored nonlinear processing based on mathematical morphological operators, which became an important part of image processing applications, and led efforts concerning digital image restoration. In 1974, Dr. Schafer joined the Georgia Institute of Technology, Atlanta, where he helped create the Center for Signal and Image Processing (CSIP), one of the most prestigious academic signal processing laboratories in the world. Under his leadership the faculty grew from two professors to more than a dozen of the world’s leading minds in DSP. Over a 30-year academic career, he introduced thousands of students to DSP; co-authored six widely used textbooks, and supervised graduate research in speech, image, biomedical and communication signal processing.
An IEEE Life Fellow and a member of the National Academy of Engineering, Dr. Schafer is currently an HP Fellow in the Multimedia Communication and Networking Laboratory at Hewlett-Packard Laboratories, Palo Alto, Calif.
Recognizing the promise of digital signal processing (DSP) long before most others, Charles Sidney Burrus helped move DSP from an obscure discipline to a key component of the information age by making important contributions to digital filtering and Fast Fourier Transforms (FFTs). Known for the ability to extend and explain difficult concepts in simple language, Dr. Burrus generalized the Cooley-Tukey algorithm (one of the most common FFTs) as well as prime factor algorithms (the preferred method for implementing FFTs of lengths other than powers of two).
His original work on methods to automatically generate code have been the basis for subsequent developments in Fourier transform computation, and recently his work on using FFTs to factor very high-order polynomials has been recognized as an alternative for phase unwrapping and determining the complex cepstrum of long data sequences. Dr. Burrus’ contributions to theory and design of digital filters include introducing the first direct design method for infinite impulse response digital filters, which is still in use today, and improving upon the Parks-McClellan algorithm for finite impulse filter design by developing a filter able to handle the very long lengths required by modern applications.
Dr. Burrus helped build Rice University’s reputation as a leading institution in the field, receiving Rice’s highest teaching award five times, and plays a key role in the “Connexions” project, a Web-based open-source textbook initiative for sharing signal processing educational materials. An IEEE Life Fellow, Dr. Burrus is currently the Maxfield and Oshman Professor Emeritus of Engineering at Rice University, Houston, Texas, where he has worked since 1965.
Robert M. Gray’s collective contributions in speech processing over the last 35 years have led to major breakthroughs in speech recognition, cellular telephony and medical imaging. He led the development of structured vector quantizers at greatly reduced complexities without sacrificing performance. Specifically, he pioneered tree-structured, pruned tree-structured, finite-state, hierarchical and entropy-coded vector quantization, as well as Lagrangian methods for quantizer optimization.
The Linde-Buzo-Gray (LBG) algorithm, which he developed with two of his students, is still the benchmark with which other design algorithms are compared. His work includes an early precursor to code excited linear predictive speech coding that is widely used to design many other types of data compression methods and Lagrangian methods, which have become the standard for rate control in video coding. He pioneered methods for combined compression and classification and is considered by many to be a leader in bringing practical and effective compression methods to medical imaging including mammography.
Dr. Gray, Lucent Technologies professor of engineering at Stanford University, Calif., has written seminal papers and books in this field, among them the well-known book, “Vector Quantization and Signal Compression” that he co-authored with Allen Gersho. An IEEE Fellow, Dr. Gray is the recipient of a number of prestigious awards as well as other honors, including a Presidential Award for Excellence in Science, Mathematics, and Engineering Mentoring. Dr. Gray holds the bachelor’s and master’s degrees from the Massachusetts Institute of Technology, Cambridge and a doctorate from the University of Southern California, Los Angeles, all in electrical engineering.
Alan V. Oppenheim is considered one of the early pioneers of digital signal processing (DSP) as well as an innovator and teacher. He is recognized in the field for his contributions to DSP and their impact on a wide variety of areas including speech compression and recognition, seismic signal processing, artificial intelligence, and communications systems.
His research has impacted virtually every area of DSP and his early work on homomorphic systems played a key role in many of the digital signal advancements that were to follow. He was a key originator of the complex cepstrum and its mathematical formulation, which found widespread use in speech and seismic processing and remains, to this day, a foundation of speech coding systems. The textbook “Discrete-Time Signal Processing,” which he co-authored with colleague Ronald W. Schafer, has been a widely used teaching and reference tool.
Dr. Oppenheim joined the faculty of the Massachusetts Institute of Technology (MIT) in 1964. He currently holds the position of Ford Professor of Engineering and is a MacVicar Faculty Fellow. He is also affiliated with MIT Lincoln Laboratory and the Woods Hole Oceanographic Institution.
An IEEE Life Fellow, Dr. Oppenheim is also a member of the National Academy of Engineering and has been a Guggenheim Fellow. He has also been a Sackler Fellow at Tel Aviv University. Dr. Oppenheim received his bachelor’s, master’s and doctorate degrees in electrical engineering from MIT. He has received a number of awards for outstanding research and teaching, including the IEEE Education Medal, the IEEE Centennial Award, the IEEE Third Millennium Medal, as well as the Education Award, Society Award, Technical Achievement Award and Senior Award of the IEEE Society on Acoustics, Speech and Signal Processing.
The Hitachi America Professor of Engineering, Emeritus, at Stanford University, Thomas Kailath is a respected leader in digital signal processing and system theory. A prolific researcher, his work has influenced two generations of electrical engineers and applied mathematicians, several of whom he has personally trained and mentored.
More than 40 years ago, Dr. Kailath recognized that engineering theory would play a critical role in meeting technological challenges in the disciplines of communication, computation, control and signal processing. Since then, his theoretical work has led to fundamental breakthroughs in communications, information theory, signal detection and estimation, sensor array signal processing, VLSI architectures for signal processing, and semiconductor manufacturing. He has written widely acclaimed books and papers in those fields as well as in probability and statistics, linear algebra, and matrix and operator theory. Specific signal processing contributions include algorithms for feedback communications, universal estimator-correlator detector structures for random signals in noise, and the concept of displacement structure leading to fast algorithms in many fields. Much of his early work outpaced what could be implemented at the time; as technology advanced, Dr. Kailath and his students were able to successfully address industrial issues in areas such as optical lithography and multiple antenna wireless communications.
An IEEE Life Fellow, he is a past president of the IEEE Information Theory Society and a recipient of its Shannon Award. Other honors include an IEEE Education Medal, Guggenheim and Churchill fellowships, and election to the National Academy of Engineering, the American Academy of Arts and Sciences, the National Academy of Sciences, the Indian National Academy of Engineering and the Silicon Valley Engineering Hall of Fame.
A member of the science and technology faculty at Meijo University in Nagoya, Japan, Dr. Fumitada Itakura is a foremost pioneer in statistical signal processing and its application to speech communications. His early work on speech spectral and format estimation, which used the maximum likelihood method, laid the foundation for much of the early progress in speech signal processing. These contributions ranged from vocoder designs for efficient speech transmission to distance measures for speech recognition.
During his tenure in the Speech and Acoustics Research Section of NTT Laboratories in Tokyo, Japan, Dr. Itakura developed linear predictive coding for speech analysis and synthesis. His ground breaking research and development activities go well beyond speech coding to include areas as diverse as speaker recognition and verification, text-to-speech synthesis, and various forms of speech analysis.
He created an insolated word recognizer based on the minimum prediction residual principle, which provided a solid framework for integrating speech analysis, representation and pattern matching into a complete engineering system. His work on autoregressive modeling of speech is used in nearly every low-to-medium, bit-rate speech transmission system.In addition, the line spectral pair representation he developed in 1975 is now found in nearly all cellular telephone systems.
An IEEE Fellow, Dr. Itakura is the recipient of the IEEE Signal Processing Society?s Senior and Society Awards, the IEEE Third Millennium Medal, the IEEE Morris N. Liebmann Memorial Award and the Purple Ribbon Medal of Japan.
Hans Wilhelm Schessler has made substantial and long-lasting contributions to the theory, design and implementation of digital filters. He has shaped the research community immeasurably, particularly in Europe, where his vision has helped to expand signal processing research.
Among the first to use computers to simulate digital filters in the 1950s, Professor Schessler made connections between signal processing and computing that helped lead signal processing from the analog to the digital domain. In the decades since, his research and teaching have seen breakthroughs in finite and infinite impulse response filters, digital filter implementation, sampling rates, quantization effects and more.
A professor at the University of Erlangen-Nuremberg, Germany since 1966, he has supervised nearly 50 Ph.D. candidates and taught courses at Cornell University, the Massachusetts Institute of Technology, Cambridge University, the University of Aachen, Karlsruhe University and Rice University. His research has focused on the design of pulseforming analog networks, digital filter synthesis and implementation, mobile communications and more. His work has been published in 90 technical papers and four books, including the first digital signal processing textbook published in German. He chaired Germany's first signal processing conference in 1973.
A recipient of the IEEE Acoustics, Speech and Signal Processing Society Award and an IEEE Centennial Medal, Professor Schessler has been elected a member of the Bavarian Academy of Sciences and the Senate of the Deutsche Forschungsgemeinschaft. He has received numerous other honors including the Karl-Kupfmuller Award of the German Informationstechnische
In 1965, Drs. James W. Cooley and John W. Tukey (IEEE 1982 Medal of Honor recipient) published a paper describing the Fast Fourier Transform (FFT) algorithm, which led to an explosion in digital signal processing. Their landmark research offered enormous improvements in processing speeds and played an essential role in the digital revolution. Today, digital signal processing is an integral part of communications, information processing, consumer electronics, control systems, radar and sonar, medical diagnosis, seismology, scientific instrumentation and more.
Following the paper?s publication, Dr. Cooley determined to help others understand the algorithm and its use. While at the IBM Watson Research Center in Yorktown Heights, N.Y, he made countless contributions to the promotion of the FFT, including serving for many years on the Digital Signal Processing Committee of the IEEE Acoustics, Speech, and Signal Processing Society (now the IEEE Signal Processing Society). His IEEE Arden House Workshops also laid the groundwork for the IEEE Signal Processing Society.
After his retirement from IBM in 1991, he joined the Electrical Engineering Department at the University of Rhode Island. Today he serves URI as an adjunct and continues to participate in research projects in signal detection.
An IEEE Fellow and a member of the National Academy of Engineering, Dr. Cooley received several IEEE Society awards and an IEEE Centennial medal.
Dr. Thomas S. Huang has made fundamental contributions to imaging and image processing throughout his illustrious career, including helping to pioneer important image-sequencing processes with wide-ranging applications.
In a career dedicated to working with images, Dr. Huang has made key contributions to multidimensional digital filtering, digital holography, compression of documents, transform coding of images, 3D motion analysis, multimodal human-computer interface, image/video databases, and more. Dr. Huang’s work includes collaborations with colleagues around the world, including work with Dr. Arun Netravali of Lucent Technologies’ Bell Labs. Together they have produced a number of papers which have inspired significant work in both industry and academia. Among the first to tackle digital image-processing research, they have solved many of the key problems of video processing.
Born in Shanghai, China, on 26 June 1936, Thomas S. Huang received his B.S. in Electrical Communication from National Taiwan University in 1956, and S.M. and Sc.D. degrees in Electrical Engineering from the Massachusetts Institute of Technology in 1960 and 1963, respectively.
Dr. Huang was on the faculty of MIT from 1963 to 1973. Subsequently, he became Professor of Electrical Engineering and Director of the Information Processing Lab at Purdue University. In 1980, he joined the University of Illinois at Urbana-Champaign, where he holds numerous titles, including the William L. Everitt Distinguished Professor of Electrical and Computer Engineering.
During sabbatical leaves, he has worked at MIT’s Lincoln Lab, the University of Hannover (Germany), the Swiss Institutes of Technology in Zurich and Lausanne, the University of Tokyo, the University of Quebec, IBM Zurich Research Lab, and Bell Northern Research Canada. He has served as a consultant to numerous industrial firms and government agencies both in the U.S. and abroad, including Lucent Technologies’ Bell Labs, Kodak, and Canon.
Dr. Huang is a Fellow of the IEEE, and has chaired a number of conferences and committees. He is also a Fellow of the Optical Society of America, SPIE, and the International Association of Pattern Recognition. His numerous honors include the IEEE Signal Processing Society’s Technical Achievement and Society Awards, the IEEE Third Millennium Medal, and the Honda Lifetime Achievement Award for contributions to Motion Analysis.
Dr. Arun N. Netravali is President of Bell Laboratories, responsible for research and development across all of Lucent Technologies.
A leader and pioneer of many areas of digital technology, Dr. Netravali has led important research and development in high-definition television, switching and networking, image processing, computer graphics, facsimile and graphics communications, digital compression, signal processing, human interfaces to computers, and more. Dr. Netravali’s contributions to image-sequence processing include repeated collaborations with Dr. Thomas S. Huang. Their seminal research has inspired significant developments in both industry and academia.
Born on 26 May 1946, in Bombay, India, Dr. Netravali received his undergraduate degree from the Indian Institute of Technology, Bombay, India, and an M.S. and a Ph.D. from Rice University in Houston, Texas. He holds an honorary doctorate from the Ecole Polytechnique Federale, in Lausanne, Switzerland.
Prior to joining Bell Labs in 1972, Dr. Netravali worked on problems relating to filtering, guidance, and control for the space shuttle for NASA. Dr. Netravali has been an adjunct professor at the Massachusetts Institute of Technology, and has taught graduate courses at City College (N.Y.), ColumbiaUniversity, and RutgersUniversity. He has served on the editorial board of the IEEE, and is currently an editor of several journals. He serves on a number of boards and committees, including the New Jersey Governor’s Committee on Schools program.
Dr. Netravali has authored more than 170 technical papers and co-authored three books: Digital Picture Representation and Compression, Visual Communications Systems, and Digital Video: An Introduction to MPEG-2. He holds more than 70 patents in the areas of computer networks, human interfaces to machines, picture processing, and digital television.
A Fellow of the IEEE and the AAAS, Dr. Netravali is also a member of the National Academy of Engineering, Tau Beta Phi, and Sigma Xi. He is an advisor to the Ecole Polytechnique Federale, in Lausanne, Switzerland, and to the Beckman Institute of the University of Illinois. For his scientific achievements, Dr. Netravali has received numerous awards, including the Alexander Graham Bell Medal, the L.G. Abraham Award, the Donald Fink Award, the Japanese C&C Prize, and the Thomas A. Edison Patent Award.