Yuri I. Abramovich

Adaptive superdirectivity of 2D oversampled HF antenna arrays: Theory, computational aspects and experimental results

In this talk, we present results of theoretical and experimental signal-to-external noise ratio (SENR) performance assessment for optimal (adaptive) beamforming in uniform rectangular (oversampled) antenna arrays (URA’s) with inter-element spacing smaller than one half-wavelength. These arrays are considered as alternatives to a conventional one-dimensional uniform linear array (ULA) when in a quest for a significant enhancement of SENR the aperture of such a ULA becomes impractically long. In the case of uniform external noise distribution, the definitions of SENR gain with respect to an input (per element) SENR, and the antenna array directivity, coincide. Therefore, any SENR gains delivered by the optimum (vs. conventional) beamforming should be attributed to superdirective properties of these oversampled two-dimensional (2D) URA’s. In addition to this uniform external noise distribution, we introduce several “tapered” noise distributions associated with the propagating phenomenology of high frequency (HF) noise over ionospheric channels in surfacewave (SW) and skywave over-the-horizon radars (OTHR). This talk also explores the Cramér-Rao bound (CRB) for azimuth (Az) and elevation (El) direction-of-arrival (DOA) estimation and specifies the role of superdirectivity in DOA estimation accuracy enhancement. We demonstrate that for relatively small antenna arrays used in SWOTHR applications, the oversampled 2D URA can significantly outperform 1D ULA’s with the same number of elements and inter-element spacing. Oversampled 2D URA’s utilized for advanced skywave OTHR applications deliver SENR and DOA estimation accuracy that approaches the performance of a 1D ULA with the same large number of antenna elements, but with impractical large apertures. Other benefits of 2D antenna arrays, associated with the improved selectivity in elevation, are not considered in this analysis which is focused on radar performance in strong external noise environments, typical for “night-time” skywave OTHR operation.

Yuri I. Abramovich received the Dipl. Eng (Hons.) degree (1967) and the Cand. Sci degree (Ph.D. equivalent, 1971), both from Odessa Polytechnic University, Ukraine and got the D.Sc. degree in radar from the Leningrad Institute for Avionics in 1981. From 1968-1994, he was with Odessa Polytechnic University. In 1980, he got the Award from Scientific Council for Radio physics of the Soviet Academy of Science for his contribution to adaptive processing in various defense radars. His diagonally loaded sample matrix inversion technique, published in 1980, became the commonly used in adaptive beamforming. From 1994 -2010, he was with the Cooperative Research Centre and the Australian Defense Science and Technology Organization in Adelaide. During this period, Dr. Abramovich developed the new concept of surface-wave radar (the US Patent 20030142011). The new methodology of OTHR array calibration was awarded by the Oliver Lodge Premium of IEE in 2001. Dr. Abramovich became a co-author of the novel OTH radar MIMO technology that demonstrated significant radar performance enhancement. In 2010 Dr. Y. Abramovich received the Australian Chief Scientist Award for “exceptional contribution to radar signal processing.” In 2011, Dr. Abramovich joined the W R System , Fairfax, VA ,working on the US OTHR.

In 2008, he was elevated to the IEEE Fellow grade for “contributions to adaptive signal processing for detection and estimation in radar arrays.” In 2011, he was elected to the IEEE AESS Board of Governors. In 2011, he received the Technical Achievement Award for “fundamental contribution to adaptive array processing for radar” from the European Association for Signal Processing. In 2014 he was awarded by the IEEE Dennis J. Pickard Medal for Radar Technologies and Applications “For seminal contributions to adaptive radar signal processing algorithms and Over-the-Horizon Radar.”

Dr. Abramovich is an author of 110 journal articles, 140 Western conference papers and chapters in six books.

Richard G. Baraniuk

A Probabilistic Theory of Deep Learning

A grand challenge in machine learning is the development of computational algorithms that match or outperform humans in perceptual inference tasks that are complicated by nuisance variation. For instance, visual object recognition involves the unknown object position, orientation, and scale in object recognition while speech recognition involves the unknown voice pronunciation, pitch, and speed. Recently, a new breed of deep learning algorithms have emerged for high-nuisance inference tasks that routinely yield pattern recognition systems with near- or super-human capabilities. But a fundamental question remains: Why do they work? Intuitions abound, but a coherent framework for understanding, analyzing, and synthesizing deep learning architectures has remained elusive. We answer this question by developing a new probabilistic framework for deep learning based on the Deep Rendering Model: a generative probabilistic model that explicitly captures latent nuisance variation. By relaxing the generative model to a discriminative one, we can recover two of the current leading deep learning systems, deep convolutional neural networks and random decision forests, providing insights into their successes and shortcomings, a principled route to their improvement, and new avenues for exploration.

Richard G. Baraniuk is the Victor E. Cameron Professor of Electrical and Computer Engineering at Rice University. His research interests lie in new theory, algorithms, and hardware for sensing, signal processing, and machine learning. He is a Fellow of the IEEE and AAAS and has received national young investigator awards from the US NSF and ONR, the Rosenbaum Fellowship from the Isaac Newton Institute of Cambridge University, the ECE Young Alumni Achievement Award from the University of Illinois, the Wavelet Pioneer and Compressive Sampling Pioneer Awards from SPIE, and the IEEE Signal Processing Society Technical Achievement Award. His work on the Rice single-pixel compressive camera has been widely reported in the popular press and was selected by MIT Technology Review as a TR10 Top 10 Emerging Technology. For his teaching and education projects, including Connexions (cnx.org) and OpenStax College (openstaxcollege.org), he has received the C. Holmes MacDonald National Outstanding Teaching Award from Eta Kappa Nu, the Tech Museum of Innovation Laureate Award, the Internet Pioneer Award from the Berkman Center for Internet and Society at Harvard Law School, the World Technology Award for Education, the IEEE-SPS Education Award, the WISE Education Award, and the IEEE James H. Mulligan, Jr. Medal for Education.

Patrick Flandrin

Graphs as Signals

Graphs are ubiquitous for representing interactions in networks, be they physical, biological or social. Whereas numerous studies are intended to develop methods for analyzing signals over graphs, it will here be shown how the analysis of graph structures themselves can be performed by using tools borrowed from signal processing. The core of the approach is to build a distance map from the adjacency matrix of a graph, from which a collection of signals can be obtained thanks to a multidimensional scaling technique. Spectral features of the so-obtained signals can then be derived, with distinctive features for graph structures of different natures (regular, Erdös-Rényi, communities, scale-free, etc.). Various issues related to this perspective will be discussed, including efficient ways of inverting the transformation on the basis of a few components only, thus paving the way for « graph filtering ». An extension to dynamic graphs will also be considered, in which the time evolution of spectral features defines a matrix that can be factorized non-negatively. (Based on joint work with R. Hamon, P. Borgnat and C. Robardet.)

Patrick Flandrin received the engineer degree from ICPI Lyon, France, in 1978, and the Doct.-Ing. and Docteur d’État degrees from INP Grenoble, France, in 1982 and 1987, respectively. He joined CNRS in 1982, where he is currently Research Director. Since 1991, he has been with the Signals, Systems and Physics Group, within the Physics Department at ENS de Lyon, France. He is currently President of GRETSI, the French Association for Signal and Image Processing. His research interests include mainly nonstationary signal processing (with emphasis on time-frequency and time-scale methods), scaling stochastic processes and complex systems. He published over 250 research papers and authored one monograph in those areas. Dr. Flandrin was awarded the Philip Morris Scientific Prize in Mathematics (1991), the SPIE Wavelet Pioneer Award (2001), the Prix Michel Monpetit from the French Academy of Sciences (2001) and the Silver Medal from CNRS (2010). Past Distinguished Lecturer of the IEEE Signal Processing Society (2010-2011), he is a Fellow of the IEEE (2002) and of EURASIP (2009), and he has been elected member of the French Academy of sciences in 2010.

José M. F. Moura

Network Processes

Traditionally, in engineering, dynamic systems are lumped systems described by an ordinary or partial differential or difference equation. In many recent applications of interest, for example, in large scale networked infrastructures, in social networks, in populations, systems are networks of possibly simple components or agents, and the system (network) state evolves through local interactions among its components. We explore methods to study the dynamics of these network processes and how to derive the system global behaviors that arise from the local interactions among the system components. (Work with June Zhang.)

José M. F. Moura is the Philip L. and Marsha Dowd University Professor at CMU, with interests in data science. He is 2016 IEEE VP for Technical Activities, was IEEE Board Director, President of the IEEE Signal Processing Society (SPS), and Editor in Chief for the Transactions on SP. Moura received the IEEE SPS Technical Achievement Award and Society Award. He is Fellow of the IEEE and of AAAS, corresponding member of the Academy of Sciences of Portugal, Fellow of the US National Academy of Innovation, and member of the US National Academy of Engineering.

Pramod K. Varshney

Distributed Inference in the Presence of Byzantines

In this talk, we discuss the problem of Byzantines in the context of Distributed Inference Networks. Distributed inference networks have many applications including military surveillance, cognitive radio networks and smart grid. A distributed inference network typically consists of local sensors sending information to a central processing unit (known as the Fusion Center) that is responsible for inference. The network may contain malicious sensors that may engage in data falsification which can result in a wrong inference at the Fusion Center. Drawing parallel to the "Byzantine Generals Problem", the local sensors are the generals who try to make a decision in the presence of traitors called "Byzantines". We present an overview of recent research on this problem. Discussion includes the susceptibility of distributed inference networks to Byzantines, and then the possible protection of these networks through mitigation of Byzantines. A game theoretic formulation of the problem is also discussed. Several applications are considered and some avenues for further research are provided.

Pramod K. Varshney received the B.S. degree in electrical engineering and computer science (with highest honors), and the M.S. and Ph.D. degrees in electrical engineering from the University of Illinois at Urbana-Champaign in 1972, 1974, and 1976 respectively. Since 1976 he has been with Syracuse University, Syracuse, NY where he is currently a Distinguished Professor of Electrical Engineering and Computer Science and the Director of CASE: Center for Advanced Systems and Engineering. He is also an Adjunct Professor of Radiology at Upstate Medical University in Syracuse, NY. His current research interests are in distributed sensor networks and data fusion, detection and estimation theory, wireless communications, and security. He has published extensively.

While at the University of Illinois, Dr. Varshney was a James Scholar, a Bronze Tablet Senior, and a Fellow. He is a member of Tau Beta Pi and is the recipient of the 1981 ASEE Dow Outstanding Young Faculty Award. He was elected to the grade of Fellow of the IEEE in 1997 for his contributions in the area of distributed detection and data fusion. In 2000, he received the Third Millennium Medal from the IEEE and Chancellor's Citation for exceptional academic achievement at Syracuse University. He is the recipient of the IEEE 2012 Judith A. Resnik Award and an honorary doctorate from Drexel University in June 2014. He recently served as a distinguished lecturer for the AES society of the IEEE. He is on the editorial boards of Journal on Advances in Information Fusion and IEEE Signal Processing Magazine. He was the President of International Society of Information Fusion during 2001.