Future Directions in Compressed Sensing and the Integration of Sensing and Processing What Can and Should We Know by 2030?
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Anna C. Gilbert
Anna C. Gilbert Dept. of Mathematics University of Michigan 530 Church Street Ann Arbor, MI 48109-1109 [email protected] 734-763-5728 (phone) http://www.math.lsa.umich.edu/~annacg 734-763-0937 (fax) Revision date: 30 October, 2017. Research • General interests: analysis, probability, signal processing, and algorithms. • Specialization: randomized algorithms with applications to massive datasets and signal processing. Education • Ph.D., Mathematics, Princeton University, June 1997, Multiresolution homogenization schemes for differential equations and applications, Professor Ingrid Daubechies, advisor. • S.B., Mathematics (with honors), University of Chicago, June 1993. Positions Held • Herman H. Goldstine Collegiate Professor. Department of Mathematics, University of Michigan, 2014{ present. • Full Professor (Courtesy appointment). Division of Electrical and Computer Engineering, University of Michigan, 2010{present. • Full Professor. Department of Mathematics, University of Michigan, 2010{present. • Associate Professor (Courtesy appointment). Division of Electrical and Computer Engineering, Uni- versity of Michigan, 2008{2010. • Associate Professor. Department of Mathematics, University of Michigan, 2007{2010. • Assistant Professor. Department of Mathematics, University of Michigan, 2004{2007. • Principal technical staff member. Internet and Network Systems Research Center, AT&T Labs-Research, 2002{2004. • Senior technical staff member. Information Sciences Research Center, AT&T Labs-Research, 1998{ 2002. • Visiting instructor. Department of Mathematics, Stanford University, Winter quarter 2000. • Postdoctoral research associate. Yale University and AT&T Labs-Research, 1997{1998. • Intern. Lucent Technologies Bell Laboratories, summer 1996. • Research assistant. Princeton University, 1994{1995. • Intern. AT&T Bell Laboratories, summers 1993{1995. Publications Refereed Journal Publications • F. J. Chung, A. C. Gilbert, J. G. Hoskins, and J. C. Schotland, \Optical tomography on graphs", Inverse Problems, Volume 33, Number 5, 2017. -
Download PDF Call for Papers
2020 IEEE International Symposium on Information Theory Los Angeles, CA, USA |June 21-26, 2020 The Westin Bonaventure Hotel & Suites, Los Angeles Call for Papers Interested authors are encouraged to submit previously unpublished contributions in any area related to information theory, including but not limited to the following topic areas: Topics ➢ Communication and Storage Coding ➢ Distributed Storage ➢ Network Information Theory ➢ Coding Theory ➢ Emerging Applications of IT ➢ Pattern Recognition and ML ➢ Coded and Distributed Computing ➢ Information Theory and Statistics ➢ Privacy in Information Processing ➢ Combinatorics and Information Theory ➢ Information Theory in Biology ➢ Quantum Information Theory ➢ Communication Theory ➢ Information Theory in CS ➢ Shannon Theory ➢ Compressed Sensing and Sparsity ➢ Information Theory in Data Science ➢ Signal Processing ➢ Cryptography and Security ➢ Learning Theory ➢ Source Coding and Data Compression ➢ Detection and Estimation ➢ Network Coding and Applications ➢ Wireless Communication ➢ Deep Learning for Networks ➢ Network Data Analysis Researchers working in emerging fields of information theory or on novel applications of information theory are especially encouraged to submit original findings. The submitted work and the published version are limited to 5 pages in the standard IEEE format, plus an optional extra page containing references only. Information about paper formatting and submission policies can be found on the conference web page, noted below. The paper submission deadline is Sunday, January 12, 2020, at 11:59 PM, Eastern Time (New York, USA). Acceptance notifications will be sent out by Friday, March 27, 2020. We look forward to your participation in ISIT 2020! General Chairs: Salman Avestimehr, Giuseppe Caire, and Babak Hassibi TPC Chairs: Young-Han Kim, Frederique Oggier, Greg Wornell, and Wei Yu https://2020.ieee-isit.org . -
Martin J. Strauss April 22, 2015
Martin J. Strauss April 22, 2015 Work Address: Dept. of Mathematics University of Michigan Ann Arbor, MI 48109-1043 [email protected] http://www.eecs.umich.edu/~martinjs/ +1-734-717-9874 (cell) Research Interests • Sustainable Energy. • Fundamental algorithms, especially randomized and approximation algorithms. • Algorithms for massive data sets. • Signal processing and and computational harmonic analysis. • Computer security and cryptography. • Complexity theory. Pedagogical Interests • Manipulative Mathematics (teaching via pipe cleaners, paper folding and other mutilation, etc.) • Kinesthetic Mathematics (teaching through movement, including acting out algorithms, per- mutations, and other transformations). Education • Ph.D., Mathematics, Rutgers University, October 1995. Thesis title: Measure in Feasible Complexity Classes. Advisor: Prof. E. Allender, Dept. of Computer Science. • A.B. summa cum laude, Mathematics, Columbia University, May 1989. Minor in Computer Science. Employment History • Professor, Dept. of Mathematics and Dept. of Electrical Engineering and Computer Science (jointly appointed), University of Michigan, 2011{present. • Associate Professor, Dept. of Mathematics and Dept. of Electrical Engineering and Computer Science (jointly appointed), University of Michigan, 2008{2011. • Assistant Professor, Dept. of Mathematics and Dept. of Electrical Engineering and Computer Science (jointly appointed), University of Michigan, 2004{2008. • Visiting Associate Research Scholar, Program in Applied and Computational Mathematics, Princeton University, Sept. 2006{Feb. 2007. Page 1 of 17 • Principal investigator, AT&T Laboratories|Research, 1997{2004. Most recent position: Principal Technical Staff Member, Internet and Network Systems Research Center. • Consultant, Network Services Research Center, AT&T Laboratories, 1996. • Post-Doctoral Research Associate, Department of Computer Science, Iowa State University, September 1995{May 1996. • Intern. Speech recognition group, IBM Watson Research Center, summers, 1989{1990. -
Compressive Phase Retrieval Lei Tian
Compressive Phase Retrieval by Lei Tian B.S., Tsinghua University (2008) S.M., Massachusetts Institute of Technology (2010) Submitted to the Department of Mechanical Engineering in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY June 2013 c Massachusetts Institute of Technology 2013. All rights reserved. Author.............................................................. Department of Mechanical Engineering May 18, 2013 Certified by. George Barbastathis Professor Thesis Supervisor Accepted by . David E. Hardt Chairman, Department Committee on Graduate Students 2 Compressive Phase Retrieval by Lei Tian Submitted to the Department of Mechanical Engineering on May 18, 2013, in partial fulfillment of the requirements for the degree of Doctor of Philosophy Abstract Recovering a full description of a wave from limited intensity measurements remains a central problem in optics. Optical waves oscillate too fast for detectors to measure anything but time{averaged intensities. This is unfortunate since the phase can reveal important information about the object. When the light is partially coherent, a complete description of the phase requires knowledge about the statistical correlations for each pair of points in space. Recovery of the correlation function is a much more challenging problem since the number of pairs grows much more rapidly than the number of points. In this thesis, quantitative phase imaging techniques that works for partially co- herent illuminations are investigated. In order to recover the phase information with few measurements, the sparsity in each underly problem and efficient inversion meth- ods are explored under the framework of compressed sensing. In each phase retrieval technique under study, diffraction during spatial propagation is exploited as an ef- fective and convenient mechanism to uniformly distribute the information about the unknown signal into the measurement space. -
Sampling Signals on Graphs: from Theory to Applications
GRAPH SIGNAL PROCESSING: FOUNDATIONS AND EMERGING DIRECTIONS Yuichi Tanaka, Yonina C. Eldar, Antonio Ortega, and Gene Cheung Sampling Signals on Graphs From theory to applications he study of sampling signals on graphs, with the goal of building an analog of sampling for standard signals in the T time and spatial domains, has attracted considerable atten- tion recently. Beyond adding to the growing theory on graph signal processing (GSP), sampling on graphs has various promising applications. In this article, we review the current progress on sampling over graphs, focusing on theory and potential applications. Although most methodologies used in graph signal sam- pling are designed to parallel those used in sampling for standard signals, sampling theory for graph signals signifi- cantly differs from the theory of Shannon–Nyquist and shift- invariant (SI) sampling. This is due, in part, to the fact that the definitions of several important properties, such as shift invariance and bandlimitedness, are different in GSP systems. Throughout this review, we discuss similarities and differenc- es between standard and graph signal sampling and highlight open problems and challenges. Introduction Sampling is one of the fundamental tenets of digital signal pro- cessing (see [1] and the references therein). As such, it has been studied extensively for decades and continues to draw consid- erable research efforts. Standard sampling theory relies on concepts of frequency domain analysis, SI signals, and band- ©ISTOCKPHOTO.COM/ALISEFOX limitedness [1]. The sampling of time and spatial domain sig- nals in SI spaces is one of the most important building blocks of digital signal processing systems. However, in the big data era, the signals we need to process often have other types of connections and structure, such as network signals described by graphs. -
Compressed Digital Holography: from Micro Towards Macro
Compressed digital holography: from micro towards macro Colas Schretter, Stijn Bettens, David Blinder, Beatrice Pesquet-Popescu, Marco Cagnazzo, Frederic Dufaux, Peter Schelkens To cite this version: Colas Schretter, Stijn Bettens, David Blinder, Beatrice Pesquet-Popescu, Marco Cagnazzo, et al.. Compressed digital holography: from micro towards macro. Applications of Digital Image Processing XXXIX, SPIE, Aug 2016, San Diego, United States. hal-01436102 HAL Id: hal-01436102 https://hal.archives-ouvertes.fr/hal-01436102 Submitted on 10 Jan 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Compressed digital holography: from micro towards macro Colas Schrettera,c, Stijn Bettensa,c, David Blindera,c, Béatrice Pesquet-Popescub, Marco Cagnazzob, Frédéric Dufauxb, and Peter Schelkensa,c aDept. of Electronics and Informatics (ETRO), Vrije Universiteit Brussel, Brussels, Belgium bLTCI, CNRS, Télécom ParisTech, Université Paris-Saclay, Paris, France ciMinds, Technologiepark 19, Zwijnaarde, Belgium ABSTRACT The age of computational imaging is merging the physical hardware-driven approach of photonics with advanced signal processing methods from software-driven computer engineering and applied mathematics. The compressed sensing theory in particular established a practical framework for reconstructing the scene content using few linear combinations of complex measurements and a sparse prior for regularizing the solution. -
Mathematics of Data Science
SIAM JOURNAL ON Mathematics of Data Science Volume 2 • 2020 Editor-in-Chief Tamara G. Kolda, Sandia National Laboratories Section Editors Mark Girolami, University of Cambridge, UK Alfred Hero, University of Michigan, USA Robert D. Nowak, University of Wisconsin, Madison, USA Joel A. Tropp, California Institute of Technology, USA Associate Editors Maria-Florina Balcan, Carnegie Mellon University, USA Vianney Perchet, ENSAE, CRITEO, France Rina Foygel Barber, University of Chicago, USA Jonas Peters, University of Copenhagen, Denmark Mikhail Belkin, University of California, San Diego, USA Natesh Pillai, Harvard University, USA Robert Calderbank, Duke University, USA Ali Pinar, Sandia National Laboratories, USA Coralia Cartis, University of Oxford, UK Mason Porter, University of Califrornia, Los Angeles, USA Venkat Chandrasekaran, California Institute of Technology, Maxim Raginsky, University of Illinois, USA Urbana-Champaign, USA Patrick L. Combettes, North Carolina State University, USA Bala Rajaratnam, University of California, Davis, USA Alexandre d’Aspremont, CRNS, Ecole Normale Superieure, Philippe Rigollet, MIT, USA France Justin Romberg, Georgia Tech, USA Ioana Dumitriu, University of California, San Diego, USA C. Seshadhri, University of California, Santa Cruz, USA Maryam Fazel, University of Washington, USA Amit Singer, Princeton University, USA David F. Gleich, Purdue University, USA Marc Teboulle, Tel Aviv University, Israel Wouter Koolen, CWI, the Netherlands Caroline Uhler, MIT, USA Gitta Kutyniok, University of Munich, Germany -
Compressive Sensing Off the Grid
Compressive sensing off the grid Abstract— We consider the problem of estimating the fre- Indeed, one significant drawback of the discretization quency components of a mixture of s complex sinusoids approach is the performance degradation when the true signal from a random subset of n regularly spaced samples. Unlike is not exactly supported on the grid points, the so called basis previous work in compressive sensing, the frequencies are not assumed to lie on a grid, but can assume any values in the mismatch problem [13], [15], [16]. When basis mismatch normalized frequency domain [0; 1]. We propose an atomic occurs, the true signal cannot be sparsely represented by the norm minimization approach to exactly recover the unobserved assumed dictionary determined by the grid points. One might samples, which is then followed by any linear prediction method attempt to remedy this issue by using a finer discretization. to identify the frequency components. We reformulate the However, increasing the discretization level will also increase atomic norm minimization as an exact semidefinite program. By constructing a dual certificate polynomial using random the coherence of the dictionary. Common wisdom in com- kernels, we show that roughly s log s log n random samples pressive sensing suggests that high coherence would also are sufficient to guarantee the exact frequency estimation with degrade the performance. It remains unclear whether over- high probability, provided the frequencies are well separated. discretization is beneficial to solving the problems. Finer Extensive numerical experiments are performed to illustrate gridding also results in higher computational complexity and the effectiveness of the proposed method. -
Digital Communication Systems 2.2 Optimal Source Coding
Digital Communication Systems EES 452 Asst. Prof. Dr. Prapun Suksompong [email protected] 2. Source Coding 2.2 Optimal Source Coding: Huffman Coding: Origin, Recipe, MATLAB Implementation 1 Examples of Prefix Codes Nonsingular Fixed-Length Code Shannon–Fano code Huffman Code 2 Prof. Robert Fano (1917-2016) Shannon Award (1976 ) Shannon–Fano Code Proposed in Shannon’s “A Mathematical Theory of Communication” in 1948 The method was attributed to Fano, who later published it as a technical report. Fano, R.M. (1949). “The transmission of information”. Technical Report No. 65. Cambridge (Mass.), USA: Research Laboratory of Electronics at MIT. Should not be confused with Shannon coding, the coding method used to prove Shannon's noiseless coding theorem, or with Shannon–Fano–Elias coding (also known as Elias coding), the precursor to arithmetic coding. 3 Claude E. Shannon Award Claude E. Shannon (1972) Elwyn R. Berlekamp (1993) Sergio Verdu (2007) David S. Slepian (1974) Aaron D. Wyner (1994) Robert M. Gray (2008) Robert M. Fano (1976) G. David Forney, Jr. (1995) Jorma Rissanen (2009) Peter Elias (1977) Imre Csiszár (1996) Te Sun Han (2010) Mark S. Pinsker (1978) Jacob Ziv (1997) Shlomo Shamai (Shitz) (2011) Jacob Wolfowitz (1979) Neil J. A. Sloane (1998) Abbas El Gamal (2012) W. Wesley Peterson (1981) Tadao Kasami (1999) Katalin Marton (2013) Irving S. Reed (1982) Thomas Kailath (2000) János Körner (2014) Robert G. Gallager (1983) Jack KeilWolf (2001) Arthur Robert Calderbank (2015) Solomon W. Golomb (1985) Toby Berger (2002) Alexander S. Holevo (2016) William L. Root (1986) Lloyd R. Welch (2003) David Tse (2017) James L. -
Principles of Communications ECS 332
Principles of Communications ECS 332 Asst. Prof. Dr. Prapun Suksompong (ผศ.ดร.ประพันธ ์ สขสมปองุ ) [email protected] 1. Intro to Communication Systems Office Hours: Check Google Calendar on the course website. Dr.Prapun’s Office: 6th floor of Sirindhralai building, 1 BKD 2 Remark 1 If the downloaded file crashed your device/browser, try another one posted on the course website: 3 Remark 2 There is also three more sections from the Appendices of the lecture notes: 4 Shannon's insight 5 “The fundamental problem of communication is that of reproducing at one point either exactly or approximately a message selected at another point.” Shannon, Claude. A Mathematical Theory Of Communication. (1948) 6 Shannon: Father of the Info. Age Documentary Co-produced by the Jacobs School, UCSD- TV, and the California Institute for Telecommunic ations and Information Technology 7 [http://www.uctv.tv/shows/Claude-Shannon-Father-of-the-Information-Age-6090] [http://www.youtube.com/watch?v=z2Whj_nL-x8] C. E. Shannon (1916-2001) Hello. I'm Claude Shannon a mathematician here at the Bell Telephone laboratories He didn't create the compact disc, the fax machine, digital wireless telephones Or mp3 files, but in 1948 Claude Shannon paved the way for all of them with the Basic theory underlying digital communications and storage he called it 8 information theory. C. E. Shannon (1916-2001) 9 https://www.youtube.com/watch?v=47ag2sXRDeU C. E. Shannon (1916-2001) One of the most influential minds of the 20th century yet when he died on February 24, 2001, Shannon was virtually unknown to the public at large 10 C. -
MIMO Wireless Communications Ezio Biglieri, Robert Calderbank, Anthony Constantinides, Andrea Goldsmith, Arogyaswami Paulraj and H
Cambridge University Press 978-0-521-13709-6 - MIMO Wireless Communications Ezio Biglieri, Robert Calderbank, Anthony Constantinides, Andrea Goldsmith, Arogyaswami paulraj and H. Vincent Poor Frontmatter More information MIMO Wireless Communications Multiple-input multiple-output (MIMO) technology constitutes a breakthrough in the design of wireless communication systems, and is already at the core of several wireless standards. Exploiting multi-path scattering, MIMO techniques deliver significant performance enhancements in terms of data transmission rate and interference reduction. This book is a detailed introduction to the analysis and design of MIMO wireless systems. Beginning with an overview of MIMO technology, the authors then examine the fundamental capacity limits of MIMO systems. Transmitter design, including precoding and space–time coding, is then treated in depth, and the book closes with two chapters devoted to receiver design. Written by a team of leading experts, the book blends theoretical analysis with physical insights, and highlights a range of key design challenges. It can be used as a textbook for advanced courses on wireless communications, and will also appeal to researchers and practitioners working on MIMO wireless systems. Ezio Biglieri is a professor in the Department of Technology at the Universitat Pompeu Fabra, Barcelona. Robert Calderbank is a professor in the Departments of Electrical Engineering and Mathematics at Princeton University, New Jersey. Anthony Constantinides is a professor in the Department of Electrical and Electronic Engineering at Imperial College of Science, Technology and Medicine, London. Andrea Goldsmith is a professor in the Department of Electrical Engineering at Stanford University, California. Arogyaswami Paulraj is a professor in the Department of Electrical Engineering at Stanford University, California. -
Arxiv:2010.10473V2 [Cs.LG] 1 Feb 2021
Proceedings of Machine Learning Research vol 134:1{22, 2021 Regret-optimal control in dynamic environments Gautam Goel [email protected] California Insititute of Technology Babak Hassibi [email protected] California Insititute of Technology Abstract We consider control in linear time-varying dynamical systems from the perspective of regret minimization. Unlike most prior work in this area, we focus on the problem of designing an online controller which minimizes regret against the best dynamic sequence of control actions selected in hindsight (dynamic regret), instead of the best fixed controller in some specific class of controllers (policy regret). This formulation is attractive when the environ- ment changes over time and no single controller achieves good performance over the entire time horizon. We derive the state-space structure of the regret-optimal controller via a novel reduction to H1 control and present a tight data-dependent bound on its regret in terms of the energy of the disturbance. Our results easily extend to the model-predictive setting where the controller can anticipate future disturbances and to settings where the controller only affects the system dynamics after a fixed delay. We present numerical experiments which show that our regret-optimal controller interpolates between the performance of the H2-optimal and H1-optimal controllers across stochastic and adversarial environments. Keywords: dynamic regret, robust control 1. Introduction The central question in control theory is how to regulate the behavior of an evolving system with state x that is perturbed by a disturbance w by dynamically adjusting a control action u. Traditionally, this question has been studied in two distinct settings: in the H2 setting, we assume that the disturbance w is generated by a stochastic process and seek to select the control u so as to minimize the expected control cost, whereas in the H1 setting we assume the noise is selected adversarially and instead seek to minimize the worst-case control cost.