Seminars: Fall 2014

 

Date      Speaker From Title
  Shlomo Shamai Israel Institute of Technology Information Theory: Old and New-A Personal View
Aug 13 Mischa Dohler Centre for Telecom Research 50 Billion M2M Devices in 5G?
Sep 11 Michael Rice Brigham Young University Space-Time Coding in Aeronautical Telemetry
Sep 11 Jie Xu University of California, Los Angeles Real-time knowledge discovery and decision making from Big Data
Sep 25 Yezekael Hayel University of Avignon, France Advanced Game Theoretical Models and Applications in Networks
Oct 3 Jun Wang The Chinese University of Hong Kong Neurodynamic Optimization Approaches to Robust Pole Assignment for Synthesizing Feedback Control Systems
Oct 16 Deep Medhi University of Missouri-Kansas City How beneficial is Multipath Routing?
Oct 23 Shlomo Shamai Israel Institute of Technology Information Theory: Old and New--A Personal View
Oct 27 Emrah Akyol University of Illinois at Urbana-Champaign Optimal Zero-Delay Communication and Jamming Strategies over Additive Noise Channels

 

50 Billion M2M Devices in 5G?

Speaker: Mischa Dohler, Centre for Telecom Research
Time: 11:00 am - 12:00 pm, Aug 13, 2014
Location: 2MTC, 10th floor, Room 10.099, Brooklyn, NY

A hyper-connected cyber-physical world, by some touted as the 4th Industrial Revolution with unprecedented economic and social opportunities, will heavily rely on the machine-to-machine (M2M) paradigm. Previous designs, which made us believe that low power radios or provisioning of horizontal platforms are driving factors, failed. This talk thus revisits the lessons we learned and how we apply them to invoke architectural and protocol changes to emerging 5G design efforts so that machine type communications (MTC) become a solid constituent of the future IoT connectivity landscape. The talk is based on first-hand experience gained from ETSI M2M, IETF ROLL and other standardization efforts; as well as the successful creation of the pioneering M2M company Worldsensing.

About the Speaker: Mischa Dohler is full Professor in Wireless Communications at King's College London, Head of the Centre for Telecommunications Research, co-founder and member of the Board of Directors of the smart city pioneer Worldsensing, Fellow and Distinguished Lecturer of the IEEE, and Editor-in-Chief of the Transactions on Emerging Telecommunications Technologies.

Dr. Hang holds 13 patents (Taiwan, US and Japan) and has published over 190 technical papers related to image compression, signal processing, and video codec architecture. He was an associate editor (AE) of the IEEE Transactions on Image Processing (1992-1994, 2008-2012) and the IEEE Transactions on Circuits and Systems for Video Technology (1997-1999). He is a co-editor and contributor of the Handbook of Visual Communications published by Academic Press in 1995. He is currently a Distinguished Lecturer and Board Member of the Asia-Pacific Signal and Information Processing Association (APSIPA) (2012- 2013, 2013-2014). He is a recipient of the IEEE Third Millennium Medal and is a Fellow of IEEE and IET and a member of Sigma Xi.

Space-Time Coding in Aeronautical Telemetry

Speaker: Michael Rice, Brigham Young University
Time: 11:00 am - 12:00 pm, Sep 11, 2014
Location: 2MTC, 10th floor, Room 10.099, Brooklyn, NY

In air-to-ground radio links, transmission can sometimes be blocked when the aircraft maneuvers place the fuselage between the airborne transmit antenna and the ground-based receive antenna. This problem is usually overcome by using two transmit antennas on the aircraft (say, on the top and bottom). But this creates a new problem: when the same signal is transmitted from both antennas, the communications link suffers from self-interference caused by destructive interference between the two copies of the transmitted radio signal. This talk describes a solution to this problem based on Alamouti space-time block code to overcome the problem. The organization of the talk is as follows: the basic principles of the space-time block code are presented, experimental hardware realizations of the transmitter and receiver are described, and the results of flight tests at Edwards AFB, California are summarized.

About the Speaker: Michael Rice received his PhD from Georgia Tech in 1991. Dr. Rice was with Digital Transmission Systems, Inc. in Atlanta and joined the faculty at Brigham Young University in 1991 where he is currently the Jim Abrams Professor in the Department of Electrical & Computer Engineering. Professor Rice was a NASA/ASEE Summer Faculty Fellow at the Jet Propulsion Laboratory during 1994 and 1995 where he worked on land mobile satellite systems. During the 1999-2000 academic year, Professor Rice was a visiting scholar at the Communication Systems and Signal Processing Institute at San Diego State University.

He was the chair of the Communication Theory Technical Committee in the IEEE Communications Society from 2009 -- 2010. He is currently serving and as Technical Editor for Command, Control and Communication Systems for IEEE Transactions on Aerospace and Electronic Systems. In addition, he is an associate member of the Range Commanders Council, and is the chair of the Signal Processing and Communications Society Chapter in the Utah Section of IEEE.

Real-time knowledge discovery and decision making from Big Data

Speaker: Jie Xu, University of California, Los Angeles
Time: 1:30 pm - 2:30 pm, Sep 11, 2014
Location: 2MTC, 9th floor, Room 9.101, Brooklyn, NY

As the world becomes ever more connected and instrumented, decision-makers have ever more rapid access to ever changing and growing streams of data – but this makes the decision-maker’s problems ever more complex as well, because it is impossible to learn everything in the time frame in which decisions must be made. What the decision-maker must do, therefore, is to discover in real time what is relevant in the enormous stream of data and use the relevant information to make good decisions. This talk presents a systematic framework and associated algorithms that enable a decision-maker to do this, and shows how to use them in real-time traffic prediction as an application scenario. With the vast availability of traffic sensors from which traffic information can be derived in real-time, a lot of research effort has been devoted to developing traffic prediction techniques, which in turn improve route navigation, traffic regulation, urban area planning and etc. One key challenge in traffic prediction is how much to rely on prediction models that are constructed using historical data in real-time traffic situations. Our decision framework learns from the current traffic situation in real-time and predicts the future traffic by matching the current situation to the most effective prediction model. When the traffic prediction involves multiple distributed learners but only the feedback about the overall effect of their decisions is available, we also propose fast learning algorithms by exploiting the informativeness of the global feedback. The algorithms we propose yield strong performance guarantees for both the long run and the short run. The applications are numerous besides traffic prediction, including patient monitoring, surveillance, social networks etc.

About the Speaker: Jie Xu is a final year PhD student at University of California at Los Angeles. He is advised by Prof. Mihaela van der Schaar in the Department of Electrical Engineering. Prior to attending UCLA, he received his BS and MS degrees in Electrical Engineering from Tsinghua University in China and graduated with honor. His research spans the area of machine learning, data mining and game theory, with an emphasis on learning and incentive design in networks.

Advanced Game Theoretical Models and Applications in Networks

Speaker: Yezekael Hayel, University of Avignon
Time: 11:00 am - 12:00 pm, Sep 25, 2014
Location: 2MTC, 10th floor, Room 10.099, Brooklyn, NY

Abstract: New communication networks are more and more complex (heterogeneous, dynamic, etc) and mainly more decentralized. Performances of those networks are difficult to determine, even more when the network is composed of non-cooperative agents. Examples of such networks are: new wireless networks like « self-organized networks », social networks, transportation networks, etc. In the last ten years, Game Theory has become one of the more suitable mathematical tools to study and to optimize those decentralized complex networks. In this presentation, I will expose some works related to advanced game theoretical models and applications to decentralized resource allocation problems in networks. In fact, some particular networks assume strong properties about the interactions between the agents and on the network characteristics. Hierarchical games assume a multi-level structure between the decision makers. Important results can be obtained using this type of non-cooperative game for new wireless networks like Cognitive Radio Networks, based on opportunistic spectrum usage. Evolutionary games, inspired by Darwin's evolution principle, are able to propose models for complex networks with large number of players. Particularly, those models take into consideration robustness of the equilibrium against deviations of a fraction of individuals and also can serve as basement to build learning procedures that converge to equilibrium. Finally, routing games, initiated in transportation networks, propose facilities to model management problems in networks dealing with content distribution and to understand how pricing mechanisms can be used to control/to influence usage in different types of networks (social networks, transportation, content, etc).

About the Speaker: Yezekael Hayel is an Assistant Professor with tenure position at University of Avignon in France. He received one M.Sc. in Computer Science and one in Applied Mathematics from the University of Rennes 1, in 2001 and 2002 respectively. He had a Ph.D. in Computer Science from University of Rennes 1 and INRIA in 2005 on Network pricing. His research interests include performance evaluation of networks based on game theoretic and queueing models. He looks at applications in communication and transportation networks like: wireless flexible networks, bio-inspired and self-organizing networks, economics models of the internet and yield management. Recently, his application domains enlarge to transportation networks, social networks and complex networks. Since he has joined the networking group of the LIA/CERI, Yezekael Hayel participates in several national (ANR) and international projects (European, cefipra, etc) with industrial companies like Orange Labs, Alcatel-Lucent, IBM and academic partners like Supelec, CNRS, UCLA. For the academic year 2014-2015, he is a visiting professor at NYU School of Enginerring.

Neurodynamic Optimization Approaches to Robust Pole Assignment for Synthesizing Feedback Control Systems

Speaker: Jun Wang, The Chinese University of Hong Kong
Time: 10:30 am - 11:30 pm, Oct 3, 2014
Location: LC433, 5 MetroTech Center, Brooklyn, NY

Pole assignment (placement) is a basic approach for linear control system design. It is concerned with the assignment of the poles (eigenvalues) and their associated eigenvectors via feedback control laws, which can meet the various closed-loop design specifications in control systems. Given a linear system and the desired closed-loop spectrum, the robust pole assignment problem is to find the feedback gains such that the robustness of the eigensystem is optimized. The robust pole assignment problem was first formulated by means of minimizing the spectral condition number of the eigenvector matrix, as the closed-loop poles move at a rate no greater than the condition number per unit change in the norm of the variation of the closed-loop system matrix. As the spectral condition number is nonconvex, it is difficult to reach its global minima. Although several alternative robustness measures were developed, they are still nonconvex with limited successes by means of conventional or gradient-flow approaches. In this talk, novel neurodynamic optimization approaches to robust pole assignment will be presented for synthesizing linear control systems via state and output feedback. The problem is formulated as a pseudoconvex optimization problem with the spectral condition number as the objective function (robustness measure) and linear matrix equality constraints for exact pole assignment. Two coupled recurrent neural networks are applied for solving the formulated problem in real time. In contrast to existing approaches, the exponential convergence of proposed neurodynamcs to global optimal solutions can be guaranteed even with lower model complexity in terms of the number of variables. Simulation results of the proposed neurodynamic approaches for eleven benchmark problems will be reported to demonstrate their superiority. In addition, the application of the proposed approach to piecewise linear systems will be delineated. The extensions of the present results based on convex reformulations will be also discussed.

About the Speaker: Jun Wang is a Professor and the Director of the Computational Intelligence Laboratory in the Department of Mechanical and Automation Engineering at the Chinese University of Hong Kong. Prior to this position, he held various academic positions at Dalian University of Technology, Case Western Reserve University, and University of North Dakota. He also held various short-term visiting positions at USAF Armstrong Laboratory (1995), RIKEN Brain Science Institute (2001), Universite Catholique de Louvain (2001), Chinese Academy of Sciences (2002), Huazhong University of Science and Technology (2006–2007), and Shanghai Jiao Tong University (2008-2011) as a Changjiang Chair Professor. Since 2011, he is a National Thousand-Talent Chair Professor at Dalian University of Technology on a part-time basis. He received a B.S. degree in electrical engineering and an M.S. degree in systems engineering from Dalian University of Technology, Dalian, China. He received his Ph.D. degree in systems engineering from Case Western Reserve University, Cleveland, Ohio, USA. His current research interests include neural networks and their applications. He published 160 journal papers, 13 book chapters, 10 edited books, and numerous conference papers in these areas. He has been an Associate Editor of the IEEE Transactions on Cybernetics and its predecessor for ten years and a member of the editorial board or editorial advisory board of Neural Networks and International Journal of Neural Systems. He also served as an Associate Editor of the IEEE Transactions on Neural Networks (1999-2009) and IEEE Transactions on Systems, Man, and Cybernetics – Part C (2002–2005), as a guest editor of special issues of European Journal of Operational Research (1996), International Journal of Neural Systems (2007), Neurocomputing (2008), and International Journal of Fuzzy Systems (2010, 2011). He was an organizer of several international conferences such as the General Chair of the 13th International Conference on Neural Information Processing (2006) and the 2008 IEEE World Congress on Computational Intelligence. He was an IEEE Computational Intelligence Society Distinguished Lecturer (2010-2012) and served in many standing/technical committees such as the President of Asia Pacific Neural Network Assembly (APNNA), IEEE Fellow Committee and IEEE Computational Intelligence Society Fellow and Awards Committees, and currently is on the Board of Governors of the IEEE Systems, Man and Cybernetics Society. He is an IEEE Fellow, IAPR Fellow, and a recipient of an IEEE Transactions on Neural Networks Outstanding Paper Award, APNNA Outstanding Achievement Award in 2011, and IEEE Neural Networks Pioneer Award in 2014, among other distinctions.

How beneficial is Multipath Routing?

Speaker: Deep Medhi, University of Missouri-Kansas City
Time: 11:00 am - 12:00 pm, Oct 16, 2014
Location: 2MTC, 10th floor, Room 10.099, Brooklyn, NY

It's often believed that multipath routing is always beneficial. Taking a traffic engineering perspective, we consider commonly used goals such as congestion minimization, minimum average delay, and minimum cost routing. Using a well-known result from linear programming, we show that the number of paths taken by all demands at optimality is limited by the total number of demands and links in a network. When all node pairs (demands) in a network have traffic, multipath routing essentially becomes single-path routing, especially as the network becomes large when the number of links are in the order of nodes. Under certain traffic and capacity conditions, single-path routing is found to be optimal. We will also present results on a number of traffic scenarios and load conditions using topologies used by ISPs and in data center networks. These observations are counter-intuitive due to our commonly held belief about multipath routing.

About the Speaker: Deep Medhi is Curators' Professor in the Department of Computer Science and Electrical Engineering at the University of Missouri- Kansas City, USA. He received B.Sc. in Mathematics from Cotton College, Gauhati University, India, M.Sc. in Mathematics from the University of Delhi, India, and his Ph.D. in Computer Sciences from the University of Wisconsin-Madison, USA. Prior to joining UMKC in 1989, he was a member of the technical staff at AT&T Bell Laboratories. He was an invited visiting professor at the Technical University of Denmark, a visiting research fellow at Lund Institute of Technology, Sweden, a research visitor at University of Campinas, Brazil under the Brazilian Science Mobility Program and served as a Fulbright Senior Specialist. He is the Editor-in-Chief of Springer’s Journal of Network and Systems Management, and is on the editorial board of IEEE/ACM Transactions on Networking, IEEE Transactions on Network and Service Management, and IEEE Communications Surveys & Tutorials. He is co-author of the books, Routing, Flow, and Capacity Design in Communication and Computer Networks (2004) and Network Routing: Algorithms, Protocols, and Architectures (2007), both published by Morgan Kauffman/Elsevier.

Information Theory: Old and New--A Personal View

Speaker: Shlomo Shamai, Israel Institute of Technology
Time: 11:00 am - 12:00 pm, Oct 23, 2014
Location: Pfizer Auditorium, Bern Dibner Library of Science & Technology, 5 MetroTech Center, Brooklyn, NY

Part of Jack Keil Wolf Lecture Series

The presentation starts by demonstrating in a descriptive way the origin of information theory in Shannon’s 1948 monumental work, and pointing some interdisciplinary aspects within general areas of electrical engineering and beyond. We discuss a change of paradigms in information theory from being a pure mathematical theory of communications to a theory with widescope direct practical implications and applications. To demonstrate the rich aspects of the problems considered and their implications as well as some inter disciplinary connections, we focus on a simple matrix based linear additive Gaussian model. We elaborate on the information-estimation intimate connection, mentioning its impact on non-linear filtering and on recent views of efficient coding in single and multi-terminal channels. Possible extensions to general channels and a short outlook conclude the presentation.

About the Speaker: Shlomo Shamai received the B.Sc., M.Sc., and Ph.D. degrees in electrical engineering from the Technion—Israel Institute of Technology, in 1975, 1981 and 1986 respectively. Since 1986 he is with the Department of Electrical Engineering, Technion-Israel Institute of Technology, where he is now a Technion Distinguished Professor, and holds the William Fondiller Professor of Telecommunications chair. His research interests encompass a wide spectrum of topics in information theory and statistical communications.

Dr. Shamai is an IEEE Fellow and a Member of the Israeli Academy of Sciences and Humanities and a Foreign Member of the US National Academy of Engineering. He is the recepient of the 2014 Rothschild Prize in Mathematics/Computer Sciences and Engineering and the 2011 Claude E. Shannon Award. He has been awarded the 1999 van der Pol Gold Medal of the Union Radio Scientifique Internationale (URSI), and is a co-recipient of the 2000 IEEE Donald G. Fink Prize Paper Award, the 2003, and the 2004 joint IT/COM societies paper award, the 2007 IEEE Information Theory Society Paper Award, the 2009 European Commission FP7, Network of Excellence in Wireless COMmunications (NEWCOM++) Best Paper Award, and the 2014 EURASIP Best Paper Award for the EURASIP Journal on Wireless Communications and Networking. He is the recipient of the 2010 Thomson Reuters Award for International Excellence in Scientific Research and is listed in the 2014 Thomson Reuters "The World's Most Influential Scientific Minds". He is also the recipient of the 2000 Technion Henry Taub Prize for Excellence in Research. He has served as Associate Editor for the Shannon Theory of the IEEE Transactions on Information Theory, and has also served on the Board of Governors of the Information Theory Society. He has served on the Executive Editorial Board of the IEEE Transactions on Information Theory'.

Optimal Zero-Delay Communication and Jamming Strategies over Additive Noise Channels

Speaker: Emrah Akyol, University of Illinois at Urbana-Champaign
Time: 11:00 am - 12:00 pm, Oct 27, 2014
Location: LC433, 5 MetroTech Center, Brooklyn, NY

This talk is concerned with optimal zero-delay communication and jamming strategies over additive noise channels. First, we present the recently discovered characterization of conditions for linearity of optimal estimation, and its extension to communication settings in the context of zero-delay source-channel coding. Next, we consider the problem of optimal zero-delay jamming over an additive noise channel. Early work had solved this problem for a scalar Gaussian source and a scalar Gaussian channel. Building on a sequence of recent results on conditions for linearity of optimal estimation, and of optimal mappings in source-channel coding, the saddle-point solution to the jamming problem is derived for general sources and channels, without recourse to Gaussianity assumptions. The linearity conditions are shown to play a pivotal role in jamming, in the sense that the optimal jamming strategy is to effectively force both the transmitter and the receiver to default to linear mappings, i.e., the jammer ensures, whenever possible, that the transmitter and the receiver cannot benefit from non-linear strategies. The conditions and general settings where such unbeatable strategy can indeed be achieved by the jammer are analyzed. The analysis is also extended to vector spaces which involves a new aspect of optimization: the allocation of available transmit and jamming power over source and channel components. The optimal power allocation strategies for the jammer and the transmitter have an intuitive interpretation as the jammer allocates power according to water-filling over the channel eigenvalues, while the transmitter performs water-pouring (reverse water-filling) over the source eigenvalues. We finally present our numerical approaches-based on deterministic annealing- to such zero-delay communication and jamming problems which, as a byproduct, provide the best numerical solutions to the benchmark problems in decentralized control such as the well known Witsenhausen’s counterexample.

About the Speaker: Emrah Akyol received the Ph.D. degree in 2011 from the University of California at Santa Barbara. From 2006 to 2007, he held positions at Hewlett-Packard Laboratories and NTT Docomo Laboratories, both in Palo Alto, where he worked on topics in video compression. From 2013 to 2014, Dr. Akyol was a postdoctoral researcher in the Electrical Engineering Department, University of Southern California.

Currently, Dr. Akyol is a postdoctoral research associate in the Coordinated Science Laboratory, University of Illinois at Urbana-Champaign. His current research is on the interplay of networked information theory, game theory, communications, sensing and control. Dr. Akyol received the 2010 UCSB Dissertation Fellowship and the 2014 USC Postdoctoral Training Award.