The dogma of signal processing maintains that a signal must be sampled at a rate at least twice its highest frequency in order to be represented without error. However, in practice, we often compress the data soon after sensing, trading off signal representation complexity (bits) for some error (consider JPEG image compression in digital cameras, for example). Clearly, this is wasteful of valuable sensing resources. Over the past few years, a new theory of "compressive sensing" has begun to emerge, in which the signal is sampled (and simultaneously compressed) at a greatly reduced rate.
As the compressive sensing research community continues to expand rapidly, it behooves usto heed Shannon's advice.
Compressive sensing is also referred to in the literature by the terms: compressed sensing, compressive sampling, and sketching/heavy-hitters.
Submitting a Resource To submit a new or corrected paper for this listing, please complete the form atdsp.rice.edu/cs/submit. To submit a resource that isn't a paper, please email
Tutorials and Reviews Emmanuel Candès, Compressive Sampling. ((Int. Congress of Mathematics, 3, pp. 1433-1452, Madrid, Spain, 2006)) Richard Baraniuk, Compressive sensing. (IEEE Signal Processing Magazine, 24(4), pp. 118-121, July 2007) Emmanuel Candès and Michael Wakin, An introduction to compressive sampling. (IEEE Signal Processing Magazine, 25(2), pp. 21 - 30, March 2008) [High-resolution version] Justin Romberg, Imaging via compressive sampling. (IEEE Signal Processing Magazine, 25(2), pp. 14 - 20, March 2008) Dana Mackenzie, Compressed Sensing Makes Every Pixel Count. (Mackenzie, Dana (2009), "Compressed sensing makes every pixel count", What's Happening in the Math. Sciences, AMS, 114-127) Richard Baraniuk, More Is less: Signal processing and the data deluge. (Science 331 (6018), pp. 717 - 719, February 2011) Massimo Fornasier and Holger Rauhut, Compressive sensing. (Chapter in Part 2 of the Handbook of Mathematical Methods in Imaging (O. Scherzer Ed.), Springer, 2011) Mark Davenport, Marco Duarte, Yonina Eldar, and Gitta Kutyniok, Introduction to compressed sensing, (Chapter in Compressed Sensing: Theory and Applications , Cambridge University Press, 2012) Marco Duarte and Yonina Eldar, Structured compressed sensing: Theory and applications. (To appear in IEEE Transactions on Signal Processing) Rebecca Willett, Roummel Marcia, and Jonathan Nichols, Compressed sensing for practical optical imaging systems: a tutorial. (Optical Engineering, vol. 50, no. 7, pp. 072601 1-13, 2011) L. Jacques and P. Vandergheynst, "Compressed Sensing: When sparsity meets sampling". ((see below, this box is too small)) Gitta Kutyniok, Compressed Sensing: Theory and Applications. (Preprint) See below for tutorial talks on compressive sensing. Compressive Sensing Emmanuel Candès, Justin Romberg, and Terence Tao, Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. (IEEE Trans. on Information Theory, 52(2) pp. 489 - 509, February 2006) Emmanuel Candès and Justin Romberg, Quantitative robust uncertainty principles and optimally sparse decompositions. (Foundations of Comput. Math., 6(2), pp. 227 - 254, April 2006) David Donoho, Compressed sensing. (IEEE Trans. on Information Theory, 52(4), pp. 1289 - 1306, April 2006) Emmanuel Candès and Terence Tao, Near optimal signal recovery from random projections: Universal encoding strategies? (IEEE Trans. on Information Theory, 52(12), pp. 5406 - 5425, December 2006) Emmanuel Candès and Justin Romberg, Practical signal recovery from random projections. (Preprint, Jan. 2005) David Donoho and Yaakov Tsaig, Extensions of compressed sensing. (Signal Processing, 86(3), pp. 533-548, March 2006) Emmanuel Candès, Justin Romberg, and Terence Tao, Stable signal recovery from incomplete and inaccurate measurements. (Communications on Pure and Applied Mathematics, 59(8), pp. 1207-1223, August 2006) Jarvis Haupt and Rob Nowak, Signal reconstruction from noisy random projections. (IEEE Trans. on Information Theory, 52(9), pp. 4036-4048, September 2006) Emmanuel Candès and Terence Tao, The Dantzig Selector: Statistical estimation when p is much larger than n (To appear in Annals of Statistics) Richard Baraniuk, Mark Davenport, Ronald DeVore, and Michael Wakin, A simple proof of the restricted isometry property for random matrices. (Constructive Approximation, 28(3), pp. 253-263, December 2008) [Formerly titled "The Johnson-Lindenstrauss lemma meets compressed sensing"] Albert Cohen, Wolfgang Dahmen, and Ronald DeVore, Compressed sensing and best k-term approximation. (Preprint, 2006) [Formerly titled "Remarks on compressed sensing"] Martin J. Wainwright, Sharp thresholds for high-dimensional and noisy recovery of sparsity (Proc. Allerton Conference on Communication, Control, and Computing, Monticello, IL, September 2006) Holger Rauhut, Karin Schnass, and Pierre Vandergheynst, Compressed sensing and redundant dictionaries. (IEEE Trans. on Information Theory, 54(5), pp. 2210 - 2219, May 2008) Emmanuel Candès and Justin Romberg, Sparsity and incoherence in compressive sampling. (Inverse Problems, 23(3) pp. 969-985, 2007) Ronald A. DeVore, Deterministic constructions of compressed sensing matrices. (J. of Complexity, 23, pp. 918 - 925, August 2007) Piotr Indyk, Explicit constructions for compressed sensing of sparse signals. (Symp. on Discrete Algorithms, 2008) Yin Zhang, A simple proof for recoverability of ell-1-minimization: go over or under? (Rice CAAM Department Technical Report TR05-09, 2005) Yin Zhang, A simple proof for recoverability of ell-1-minimization (II): the nonnegative case. (Rice CAAM Department Technical Report TR05-10, 2005) Yin Zhang, When is missing data recoverable? (Rice CAAM Department Technical Report TR05-15, 2005) Boris S. Kashin and Vladimir N. Temlyakov, A remark on compressed sensing. (Matem. Zametki, 82, pp. 821--830, 2007) Waheed Bajwa, Jarvis Haupt, Gil Raz, Stephen Wright, and Robert Nowak, Toeplitz-structured compressed sensing matrices. (IEEE Workshop on Statistical Signal Processing (SSP), Madison, Wisconsin, August 2007) Weiyu Xu and Babak Hassibi, Efficient compressive sensing with determinstic guarantees using expander graphs. (IEEE Information Theory Workshop, Lake Tahoe, September 2007) Yoav Sharon, John Wright, and Yi Ma, Computation and relaxation of conditions for equivalence between ell-1 and ell-0 minimization. (Preprint, 2007) Thong T. Do, Trac D. Tran, and Lu Gan, Fast compressive sampling with structurally random matrices. (Preprint, 2007) Radu Berinde and Piotr Indyk, Sparse recovery using sparse random matrices. (Preprint, 2008) P. Wojtaszczyk, Stability and instance optimality for Gaussian measurements in compressed sensing. (Preprint, 2008) Venkat Chandar, A negative result concerning explicit matrices with the restricted isometry property. (Preprint, 2008) Florian Sebert, Leslie Ying, and Yi Ming Zou, Toeplitz block matrices in compressed sensing. (Preprint, 2008) Alyson K. Fletcher, Sundeep Rangan, and Vivek K Goyal, Necessary and sufficient conditions on sparsity pattern recovery. (Submitted to IEEE Trans. Information Theory) R. Berinde, A. C. Gilbert, P. Indyk, H. Karloff, and M. J. Strauss, Combining geometry and combinatorics: A unified approach to sparse signal recovery. (Preprint, 2008) Dapo Omidiran and Martin J. Wainwright, High-dimensional subset recovery in noise: Sparsified measurements without loss of statistical efficiency. (Preprint, 2008) Sina Jafarpour, Weiyu Xu, Babak Hassibi, and Robert Calderbank, Efficient compressed sensing using high-quality expander graphs. (to appear in IEEE Trans Info Theory, 2009) Shamgar Gurevich, Ronny Hadani, and Nir Sochen, On some deterministic dictionaries supporting sparsity. (To appear in Journal of Fourier Analysis and Applications) Emmanuel Candès, The restricted isometry property and its implications for compressed sensing. (Compte Rendus de l'Academie des Sciences, Paris, Series I, 346, pp. 589-592, 2008) T. Tony Cai, Guangwu Xu, and Jun Zhang, On recovery of sparse signals via ell-1 minimization. (Preprint, 2008) Venkatesh Saligrama, Deterministic designs with deterministic guarantees: Toeplitz compressed sensing matrices, sequence design and system identification. (Preprint, 2008) Weiyu Xu and Babak Hassibi, Compressed sensing over the Grassmann manifold: A unified analytical framework. (Preprint, 2008) Justin Romberg, Compressive sensing by random convolution. (Preprint, 2008) Yin Zhang, On theory of compressive sensing via ell-1-minimization: Simple derivations and extensions. (Rice CAAM Department Technical Report TR08-11, 2008) Ronald DeVore, Guergana Petrova, and Przemyslaw Wojtaszczyk, Instance-optimality in probability with an ell-1 decoder. (Preprint, 2008) Shamgar Gurevich and Ronny Hadani, Incoherent dictionaries and the statistical restricted isometry property. (Preprint, 2008) Jarvis Haupt, Waheed U. Bajwa, Gil Raz, and Robert Nowak, Toeplitz compressed sensing matrices with applications to sparse channel estimation. (Preprint, 2008) Anatoli Juditsky and Arkadi Nemirovskim, On verifiable sufficient conditions for sparse signal recovery via ell-1 minimization. (Preprint, 2008) J.L. Nelson and V.N. Temlyakov, On the size of incoherent systems. (Preprint, 2008) Yaron Rachlin and Dror Baron, The secrecy of compressed sensing measurements. (Proc. Allerton Conference on Communication, Control, and Computing, Monticello, IL, September 2008) Robert Calderbank, Stepen Howard, and Sina Jafarpour, Construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property. (To appear in the Compressed Sensing Special Issue of IEEE Journal of Selected Topics in Signal Processing) Jeffrey Blanchard, Coralia Cartis, and Jared Tanner, Compressed Sensing: How Sharp is the Restricted Isometry Property?. [Extended Tech Report] (Under revision, 2009) Jeffrey Blanchard, Coralia Cartis, and Jared Tanner, Decay properties of restricted isometry constants. (IEEE Signal Processing Letters, 16(7), 572-575, 2009) Mark Iwen, Simple Deterministically Constructible RIP Matrices with Sublinear Fourier Sampling Requirements. (Preprint, CISS 2009, Baltimore, MD) M.Amin Khajehnejad, Alexandros G. Dimakis, Weiyu Xu, Babak Hassibi, Sparse Recovery of Positive Signals with Minimal Expansion . (Preprint, 2009) Matthew A. Herman and Thomas Strohmer, General Deviants: An Analysis of Perturbations in Compressed Sensing . (Preprint, 2009) Holger Rauhut, Circulant and Toeplitz matrices in compressed sensing. (In Proc. SPARS'09, Saint Malo, 2009) Gilles Gnacadja, Counting the Scaled +1/-1 Matrices that Satisfy the Restricted Isometry Property. (Preprint, 2009) P. Wojtaszczyk, Stability of l1 minimization in compressed sensing. (in Proc. SPARS'09 St. Malo 2009) Lu Gan, Cong Ling, Thong T. Do, and Trac D. Tran, Analysis of the statistical restricted isometry property for deterministic sensing matrices using Stein’s method. (Preprint, 2009) Paul Tune, Sibi Raj Bhaskaran, Stephen Hanly, Number of measurements in sparse signal recovery. (ISIT 2009, to appear) Jeffrey D. Blanchard, Coralia Cartis, Jared Tanner, Andrew Thompson, Phase transitions for greedy sparse approximation algorithms. (Preprint, August 2009) Jeffrey D. Blanchard, Andrew Thompson, On support sizes of restricted isometry constants. (Preprint, August 2009) Sundeep Rangan, Alyson K. Fletcher, Vivek K Goyal, Asymptotic analysis of MAP estimation via the replica method and applications to compressed sensing. (Submitted to IEEE Trans. Information Theory) Tomas Tuma, Paul Hurley, On the incoherence of noiselet and Haar bases. (SAMPTA 2009) Jason Laska, Mark Davenport, and Richard Baraniuk, Exact signal recovery from sparsely corrupted measurements through the pursuit of justice. (Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, California, November 2009) Mark Davenport, Jason Laska, Petros Boufounos, and Richard Baraniuk, A simple proof that random matrices are democratic. (Rice University ECE Department Technical Report TREE-0906, November 2009) T. Tony Cai, Le Wang, Guangwu Xu, New bounds for restricted isometry constants. (Preprint, Nov 2009) D. Guo, D. Baron, S. Shamai, A single-letter characterization of optimal noisy compressed sensing. (Asilomar Conf. on Signals, Systems, and Computers, Monterey, California, November 2009) G. Reeves, M. Gastpar, A note on optimal support recovery in compressed sensing. (Asilomar Conf. on Signals, Systems, and Computers, Monterey, CA, November 2009) Vladimir N. Temlyakov and Pavel Zheltov, On performance of greedy algorithms. (Submitted to Journal of Approximation Theory, Jan 2010) Gongguo Tang, Arye Nehorai, Performance analysis for sparse support recovery. (Preprint, Nov 2009) Bubacarr Bah, Jared Tanner, Improved bounds on restricted isometry constants for Gaussian matrices. (Preprint, March 2010) Robert Calderbank and Sina Jafarpour, Reed Muller Sensing Matrices and the Lasso (Preprint, April 2010) Waheed U. Bajwa, Robert Calderbank, and Sina Jafarpour, Model selection: Two fundamental measures of coherence and their algorithmic significance (Proc. IEEE Int. Symp. Information Theory, June 2010) Waheed U. Bajwa, Robert Calderbank, and Sina Jafarpour, Why Gabor Frames? Two Fundamental Measures of Coherence and Their Role in Model Selection. (Submitted for Publication) Charles Dossal, Gabriel Peyré, Jalal Fadili, A Numerical Exploration of Compressed Sampling Recovery. (Linear Algebra and its Applications, Vol. 432(7), p.1663-1679, 2010) Maxim Raginsky, Rebecca Willett, Zachary Harmany, and Roummel Marcia., Compressed sensing performance bounds under Poisson noise. (IEEE Transactions on Signal Processing, vol. 58, no. 8, pp. 3990-4002, 2010) Maxim Raginsky, Sina Jafarpour, Zachary Harmany, Roummel Marcia, Rebecca Willett, and Robert Calderbank,Performance bounds for expander-based compressed sensing in Poisson noise. (Submitted to IEEE Transactions on Signal Processing, 2010) Jae Young Park, Han Lun Yap, Christopher J. Rozell, Michael B. Wakin, Concentration of Measure for Block Diagonal Matrices with Applications to Compressive Sensing. (preprint) Zhiqiang Xu, Deterministic Sampling of Sparse Trigonometric Polynomials. (arXiv:1006.2221) Kezhi Li, Lu Gan, and Cong Ling, Orthogonal Symmetric Toeplitz Matrices for Compressed Sensing: Statistical Isometry Property. (submitted for publication, Dec. 2010.) Laurent Gosse, Compressed sensing with preconditioning for sparse recovery with subsampled matrices of Slepian prolate functions. (Preprint (2010)) Arian Maleki, Laura Anitori, Zai Yang, and Richard Baraniuk, Asymptotic Analysis of Complex LASSO via Complex Approximate Message Passing (CAMP). (submitted to IEEE Trans. on Information Theory) Dornoosh Zonoobi, Ashraf A. Kassim, Yedatore V. Venkatesh, Gini Index as Sparsity Measure for Signal Reconstruction from Compressive Samples. (IEEE journal of selected topics in signal processing) Justin Ziniel, Lee C. Potter, and Philip Schniter, Tracking and Smoothing of Time-Varying Sparse Signals via Approximate Belief Propagation. (Asilomar Conf. on Signals, Systems, and Computers (SS&C), (Pacific Grove, CA), Nov. 2010) Kanke Gao, Stella N. Batalama, Dimitris A. Pados, and Bruce W. Suter, Compressive Sampling with Generalized Polygons. (IEEE Trans. on Signal Processing, to appear October 2011) Emmanuel Candès and Mark Davenport, How well can we estimate a sparse vector? (Preprint, April 2011) Gongguo Tang and Arye Nehorai, Verifiable and computable performance analysis of sparsity recovery. (submitted for publication, arXiv:1102.4868) Gongguo Tang and Arye Nehorai, Fixed point theory and semidefinite programming for computable performance analysis of block-sparsity recovery. (Submitted for publication, arXiv: 1110.1078) Amin Khajehnejad, Weiyu Xu, Alex Dimakis and Babak Hassibi, Sparse Recovery of Nonnegative Signals with Minimal Expansion. (IEEE Transactions on Signal Processing, 2010, Vol. 59 (1), pp. 196-208) Jian Wang and Byonghyo Shim, On the Recovery Limit of Sparse Signals using Orthogonal Matching Pursuit. (To appear in IEEE Trans. on Signal Process. ) Yoav Sharon, John Wright, and Yi Ma, Minimum sum of distances estimator: Robustness and stability. (Proc. 2009 American Control Conference (ACC '09), pp. 524-530, June 2009) Extensions of Compressive Sensing Gabriel Peyré, Best basis compressed sensing. (IEEE Transactions on Signal Processing, Vol. 58(5), p.2613-2622 , 2010) [See also related conference publication:NeuroComp 2006] Yue Lu and Minh Do, A theory for sampling signals from a union of subspaces. (IEEE Trans. on Signal Processing, 56(6), pp. 2334 - 2345, June 2008) Lawrence Carin, Dehong Liu, and Ya Xue, In Situ Compressive Sensing. (Inverse Problems, 24(1), Feb. 2008) [See also related conference publication:SSP 2007] Remi Gribonval and Morten Nielsen, Beyond sparsity : recovering structured representations by ell-1-minimization and greedy algorithms - Application to the analysis of sparse underdetermined ICA. (To appear in Advances in Computational Mathematics) Cynthia Dwork, Frank McSherry, and Kunal Talwar, The price of privacy and the limits of LP decoding. (Symp. on Theory of Computing (STOC), San Diego, California, June, 2007) Akram Aldroubi, Carlos Cabrelli, and Ursula Molter, Optimal non-linear models for sparsity and sampling. (Preprint, 2007) Lawrence Carin, Dehong Liu, Wenbin Lin, and Bin Guo, Compressive sensing for multi-static scattering analysis. (Preprint, 2007) Benjamin Rect, Maryam Fazel, and Pablo A. Parrilo, Guaranteed minimum-rank solution of linear matrix equations via nuclear norm minimization. (Preprint, 2007) Mona Sheikh and Richard Baraniuk, Blind error-free detection of transform-domain watermarks. (IEEE Int. Conf. on Image Processing (ICIP), San Antonio, Texas, September 2007) Gotz Pfander, Holger Rauhut, and Jared Tanner, Identification of matrices having a sparse representation. (Preprint, 2007) [See also relatednote] Gotz Pfander and Holger Rauhut, Sparsity in time-frequency representations. (Preprint, 2007) Alfred M. Bruckstein, Michael Elad, and Michael Zibulevsky, A non-negative and sparse enough solution of an underdetermined linear system of equations is unique. (Preprint, 2007) Thomas Blumensath and Mike E. Davies, Sampling theorems for signals from the union of linear subspaces. (Preprint, 2007) Rick Chartrand and Valentina Staneva, Restricted isometry properties and nonconvex compressive sensing. (Inverse Problems, vol. 24, no. 035020, pp. 1--14, 2008) Emmanuel Candès and Yaniv Plan, Near-ideal model selection by ell-1 minimization. (Preprint, 2007) Basarab Matei and Yves Meyer, A variant on the compressed sensing of Emmanuel Candès. (Preprint, 2008) Rachel Ward, Compressed sensing with cross validation. (Preprint, 2008) [Formerly titled "Cross validation in compressed sensing via the Johnson Lindenstrauss Lemma"] Vivek K Goyal, Alyson K. Fletcher, and Sundeep Rangan, Compressive sampling and lossy compression. (IEEE Signal Processing Magazine, 25(2), pp. 48-56, March 2008) Petros Boufounos and Richard G. Baraniuk, Reconstructing sparse signals from their zero crossings. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, Nevada, April 2008) Namrata Vaswani, Kalman filtered compressed sensing. (IEEE Int. Conf. on Image Processing (ICIP), San Diego, California, October 2008) Lawrence Carin, Dehong Liu, and Bin Guo, In situ compressive sensing for multi-static scattering: Imaging and the restricted isometry property. (Preprint, 2008) Emmanuel Candès and Benjamin Recht, Exact matrix completion via convex optimization. (Preprint, 2008) Rayan Saab, Rick Chartrand, and Özgür Yilmaz, Stable sparse approximation via nonconvex optimization. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, Nevada, April 2008) Mike E. Davies and Rémi Gribonval, Restricted isometry constants where ell-p sparse recovery can fail for 0 < p <= 1. (Preprint, 2008) Simon Foucart and Ming-Jun Lai, Sparsest solutions of underdetermined linear systems via ell-q minimization for 0 < q <= 1. (Preprint, 2008) Rayan Saab and Özgür Yilmaz, Sparse recovery by non-convex optimization - instance optimality. (Preprint, 2008) Yonina Eldar, Uncertainty relations for analog signals. (Preprint, 2008) Giuseppe Valenzise, Giorgio Prandi, Mario Tagliasacchi, and Augusto Sarti, Identification of sparse audio tampering using distributed source coding and compressive sensing techniques. (Preprint, 2008) [See also related conference publication:DAFX 2008] Marco Tagliasacchi, Giuseppe Valenzise, and Stefano Tubaro, Hash-based identification of sparse image tampering. (Preprint, 2008) [See also related conference publication:ICIP 2008] Jian-Feng Cai, Emmanuel Candès , and Zuowei Shen, A singular value thresholding algorithm for matrix completion. (Preprint, 2008) Dmitry Malioutov, Sujay Sanghavi, and Alan Willsky, Compressed sensing with sequential observations. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, Nevada, April 2008) Justin P. Haldar, Diego Hernando, Rank-Constrained Solutions to Linear Matrix Equations using PowerFactorization. (IEEE Signal Processing Letters, 16:584-587, 2009) Massimo Fornasier and Rachel Ward, Iterative thresholding meets free discontinuity problems. (Preprint, January 2009) Magali Anastasio and Carlos Cabrelli, Sampling in a union of frame generated subspaces. (Preprint, 2009) D. Angelosante, E. Grossi, G. B. Giannakis, Compressed Sensing of time-varying signals. (DSP 2009, Santorini, Greece) Joshua Trzasko, Armando Manduca, Relaxed Conditions for Sparse Signal Recovery with General Concave Priors. (to appear in IEEE Trans. Signal Processing, 2009) G. Gasso, A. Rakotomamonjy, S. Canu, Recovering sparse signals with non-convex penalties and DC programming. (IEEE Trans. Signal Processing, to appear, 2009) Thomas Blumensath, Sampling and reconstructing signals from a union of linear subspaces. (Preprint, November 2009) Petros T. Boufounos, Gitta Kutyniok and Holger Rauhut, Sparse Recovery from Combined Fusion Frame Measurements. (Accepted to IEEE Trans Information Theory) Namrata Vaswani, LS-CS-residual (LS-CS): Compressive Sensing on the Least Squares Residual. (Accepted to IEEE Trans. Signal Processing) Namrata Vaswani, Wei Lu, Modified-CS: Modifying Compressive Sensing for Problems with Partially Known Support. (Revised and resubmitted to IEEE Trans. Signal Processing) Laurent Jacques, A Short Note on Compressed Sensing with Partially Known Signal Support. (Signal Processing, (in press), doi:10.1016/j.sigpro.2010.05.025) [http://arxiv.org/abs/0908.0660] Sadegh Jokar, Volker Mehrmann, Marc Pfetsch, Harry Yserentant , Sparse Approximate Solution of Partial Differential Equations. (Applied Numerical Mathematics 60, No. 4 (2010), 452-472) Sadegh Jokara, Volker Mehrmann, Sparse solutions to underdetermined Kronecker product systems. (Linear Algebra and its Applications, 431(12), pp. 2437-2447, December 2009) Volkan Cevher, Learning with Compressible Priors. (NIPS 2009) Qiyu Sun, Recovery of sparsest signals via Massimo Fornasier, Karin Schnass, and Jan Vybiral, Learning Functions of Few Arbitrary Linear Parameters in High Dimensions. (preprint, arXiv:1008.3043v1 [math.NA]) Massimo Fornasier, Holger Rauhut, and Rachel Ward, Low-rank matrix recovery via iteratively re-weighted least squares minimization. (preprint, October 2010) Omid Taheri and Sergiy A. Vorobyov, Segmented compressed sampling for analog-to-information conversion: Method and performance analysis. (accepted for publication in IEEE Trans. Signal Processing ) Irina Rish and Genady Grabarnik, Sparse Signal Recovery with Exponential-Family Noise. (Proc. Allerton Conference on Communication, Control, and Computing, Monticello, IL, September 2009) Yangyang Xu, Wotao Yin, Zaiwen Wen, Yin Zhang, An Alternating Direction Algorithm for Matrix Completion with Nonnegative Factors. (Rice CAAM Technical Report TR11-03) Chenlu Qiu, Namrata Vaswani, Real-tine Robust Principal Components' Pursuit. (Allerton Conference on Communication, Control, and Computing, Monticello, IL, Oct 2010) David L. Donoho and Gitta Kutyniok, . Microlocal Analysis of the Geometric Separation Problem (Preprint, 2010) Yue Hu, Sajan Goud Lingala, Mathews Jacob, A fast majorize-minimize algorithm for the recovery of sparse and low rank matrices. (IEEE Trans. on Image Processing) Gongguo Tang and Arye Nehorai, Constrained Cramérâ��Rao bound for robust principal component analysis. (IEEE Trans. Signal Processing, vol. 59, no. 10, pp. 5070-5076, Oct. 2011.) Gongguo Tang and Arye Nehorai, Lower bounds on mean-squared error for low-rank matrix reconstruction. (IEEE Trans. Signal Processing, vol. 59, no. 10, pp. 4559-4571, Oct. 2011.) Yue Hu and Mathews Jacob, Higher Degree total variation (HDTV) regularization for image recovery. ( IEEE Trans. on Image Processing, accepted) Li-Wei Kang, Chao-Yung Hsu, Hung-Wei Chen, Chun-Shien Lu, Chih-Yang Lin, and Soo-Chang Pei,Feature-based Sparse Representation for Image Similarity Assessmen. (IEEE Trans. on Multimedia, Vol. 13, No. 5, pp. 1019-1030, 2011.) Li-Wei Kang and Chun-Shien Lu, Compressive Sensing-based Image Hashing. (Proc. IEEE Int. Conf. on Image Processing, pp. 1285-1289, November 7-11, 2009.) Yair Rivenson and Adrian Stern, Compressed imaging with separable sensing operator. (IEEE Signal Processing Letters, 16(6), 449-452 ) Mahdi S. Hosseini and Oleg Michailovich, Derivative compressive sampling with application to phase unwrapping. (in Proc. of the 17th European Signal Processing Conference (EUSIPCO), August 24-28, Glasgow, UK, 2009.) Mahdi S. Hosseini, Derivative Compressive Sampling and its Application to Inverse Problems and Imaging. (M.A.Sc. Thesis, ECE Dep., University of Waterloo, August 2010.) Multi-Sensor and Distributed Compressive Sensing Dror Baron, Marco F. Duarte, Michael B. Wakin, Shriram Sarvotham, and Richard G. Baraniuk,Distributed compressive sensing. (Preprint, 2005) [See also relatedtechnical report and conference publications:Allerton 2005, Asilomar 2005, NIPS 2005,IPSN 2006] Waheed Bajwa, Jarvis Haupt, Akbar Sayeed, and Rob Nowak, Compressive wireless sensing. (Int. Conf. on Information Processing in Sensor Networks (IPSN), Nashville, Tennessee, April 2006) Michael Rabbat, Jarvis Haupt, Aarti Singh, and Rob Nowak, Decentralized compression and predistribution via randomized gossiping. (Int. Conf. on Information Processing in Sensor Networks (IPSN), Nashville, Tennessee, April 2006) Massimo Fornasier and Holger Rauhut, Recovery algorithms for vector valued data with joint sparsity constraints. (SIAM Journal on Numerical Analysis, 46(2) pp. 577-613, 2008) Rémi Gribonval, Holger Rauhut, Karin Schnass, and Pierre Vandergheynst, Atoms of all channels, unite! Average case analysis of multi-channel sparse recovery using greedy algorithms. (Preprint, 2007) [See also related conference publication:ICASSP 2007] Wei Wang, Minos Garofalakis, and Kannan Ramchandran, Distributed sparse random projections for refinable approximation. (Int. Conf. on Information Processing in Sensor Networks (IPSN), Cambridge, Massachusetts, April 2007) W. Bajwa, J. Haupt, A. Sayeed and R. Nowak, Joint source-channel communication for distributed estimation in sensor networks. (IEEE Trans. on Information Theory, 53(10) pp. 3629-3653, October 2007) Shuchin Aeron, Manqi Zhao, and Venkatesh Saligrama, Sensing capacity of sensor networks: Fundamental tradeoffs of SNR, sparsity, and sensing diversity. (Information Theory and Applications Workshop, January 2007) Shuchin Aeron, Manqi Zhao, and Venkatesh Saligrama, On sensing capacity of sensor networks for the class of linear observation, fixed SNR models. (Preprint, 2007) Shoulie Xie, Susanto Rahardja, Zhengguo Li, Wyner-Ziv Image Coding from Random Projections. (IEEE Intl Conf Multimedia & Expo (ICME’07), Beijing, China, 2007) Moshe Mishali and Yonina C. Eldar, Reduce and boost: Recovering arbitrary sets of jointly sparse vectors. (IEEE Trans. on Signal Processing, 56(10), pp. 4692-4702, October 2008) Volkan Cevher, Marco Duarte, and Richard Baraniuk, Distributed target localization via spatial sparsity. (European Signal Processing Conf. (EUSIPCO), Lausanne, Switzerland, August 2008) Jong Chul Ye and Su Yeon Lee, Non-iterative exact inverse scattering using simultanous orthogonal matching pursuit (S-OMP). (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, Nevada, April 2008) Yang Xiao, Underwater acoustic sensor networks. (Excerpt, Auerbach Publications, 2008) Marco Duarte, Shriram Sarvotham, Dror Baron, Michael Wakin, and Richard Baraniuk,Performance limits for jointly sparse signals via graphical models. (Sensor, Signal and Info. Proc. Workshop (SenSIP), Sedona, Arizona, May 2008) [See also relatedtechnical report] Volkan Cevher, Ali Gurbuz, James McClellan, and Rama Chellappa, Compressive wireless arrays for bearing estimation of sparse sources in angle domain. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, Nevada, April 2008) Ali Gurbuz, James McClellan, and Volkan Cevher, A compressive beamforming method. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, Nevada, April 2008) Tao Wan, Nishan Canagarajah, and Alin Achim, Compressive image fusion. (IEEE ICIP 2008, San Diego, CA, Oct., 2008) Li-Wei Kang and Chun-Shien Lu, Distributed compressive video sensing (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Taipei, Taiwan, April 2009) Yonina C. Eldar, Holger Rauhut, Average Case Analysis of Multichannel Sparse Recovery Using Convex Relaxation. (Preprint, 2009) G. Quer, R. Masiero, D. Munaretto, M. Rossi, J. Widmer and M. Zorzi, On the Interplay Between Routing and Signal Representation for Compressive Sensing in Wireless Sensor Networks. (Information Theory and Applications Workshop (ITA 2009), San Diego, CA) Jia Meng, Husheng Li, and Zhu Han, Sparse Event Detection in Wireless Sensor Networks using Compressive Sensing. (CISS 2009, Baltimore, MD) Ewout van den Berg, Michael P. Friedlander, Joint-sparse recovery from multiple measurements. (Preprint, 2009) Yasamin Mostofi, Pradeep Sen, Compressive Cooperative Sensing and Mapping in Mobile Networks. (Proceedings of American Control Conference (ACC), Page(s):3397 - 3404, June 2009) Riccardo Masiero, Giorgio Quer, Michele Rossi, Michele Zorzi, A Bayesian Analysis of Compressive Sensing Data Recovery in Wireless Sensor Networks. (The International Workshop on Scalable Ad Hoc and Sensor Networks (SASN'09), Saint Petersburg, Russia, Oct. 2009) Marco F. Duarte, Richard G. Baraniuk, Kronecker Compressive Sensing. (Preprint, 2009) Benjamin Miller, Joel Goodman, Keith Forsythe, John Sun, Vivek Goyal, A multi-sensor compressed sensing receiver: Performance bounds and simulated results. (Forty-Third Asilomar Conference on Signals and Systems, pp. 1571-1575, Nov. 2009) Riccardo Masiero, Giorgio Quer, Daniele Munaretto, Michele Rossi, Joerg Widmer, Michele Zorzi,Data Acquisition through joint Compressive Sensing and Principal Component Analysis. (IEEE Globecom, Nov.-Dec. 2009) Mohammadreza Mahmudimanesh, Abdelmajid Khelil, Nasser Yazdani, Map-Based Compressive Sensing Model for Wireless Sensor Network Architecture, A Starting Point. (First International Workshop on Wireless Sensor Networks Architectures, Simulation and Programming (WASP), pp. 75-84, 2009) Mohammadreza Mahmudimanesh, Abdelmajid Khelil and Neeraj Suri, Reordering for Better Compressibility: Efficient Spatial Sampling in Wireless Sensor Networks. (The Third IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC), 2010.) Jia Meng, Wotao Yin, Husheng Li, Ekram Hossain, and Zhu Han, Collaborative Spectrum Sensing from Sparse Observations in Cognitive Radio Networks. (To appear in IEEE JSAC Special Issue on Cognitive Radio Networking and Communications) Rossano Gaeta, Marco Grangetto, Matteo Sereno, Local Access to Sparse and Large Global Information in P2P Networks: a Case for Compressive Sensing. (IEEE Int. Conf. on peer-to-peer computing, August 2010) Scott Pudlewski, Tommaso Melodia, Arvind Prasanna, C-DMRC: Compressive Distortion-Minimizing Rate Control for Wireless Multimedia Sensor Networks. (in Proc. of IEEE Intl. Conf. on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), Boston, MA, June 2010) Lin, Y.G.; Zhang, B.C.; Hong, W.; Wu, Y.R.; , Along-track interferometric sar imaging based on distributed compressed sensing. (IET Electronics Letters, 46(12), pp. 858 - 860, June 2010 ) Chong Luo, Feng Wu, Jun Sun, Chang Wen Chen, Compressive Data Gathering for Large-Scale Wireless Sensor Networks. (MobiCom '09 ) Yasamin Mostofi and Alejandro Gonzalez-Ruiz, Compressive Cooperative Obstacle Mapping in Mobile Networks. (Proceedings of the 29th Military Communications Conference (Milcom), pp. 947-953, November 2010) [ACC 2009] J. M. Kim, O. K. Lee and J. C. Ye, Compressive MUSIC: A Missing Link between Compressive Sensing and Array Signal Processing. (IEEE Trans. on Information Theory, 2011 (in press)) G. Oliveri and A. Massa, Bayesian Compressive Sampling for Pattern Synthesis With Maximally Sparse Non-Uniform Linear Arrays. (IEEE Transactions on Antennas and Propagation, vol. 59, no. 2, pp. 467-481, Feb. 2011) Mu Lin, Chong Luo, Feng Liu and Feng Wu, Compressive Data Persistence in Large-Scale Wireless Sensor Networks. (IEEE Globecom, 2010) J. Oliver and Heung-No Lee, A Realistic Distributed Compressive Sensing Framework for Multiple Wireless Sensor Networks. (Signal Processing with Adaptive Sparse Structured Representation, Edinburgh, Scotland, June 27-30, 2011) Giulio Coluccia, Enrico Magli, Aline Roumy, Velotiaray Toto-Zarasoa, Lossy Compression of Distributed Sparse Sources: a Practical Scheme. (The 2011 European Signal Processing Conference (EUSIPCOâ��2011), 29/08/2011, Barcellona (Spain)) Justin Ziniel and Philip Schniter, Efficient High-Dimensional Inference in the Multiple Measurement Vector Problem. (Preprint, Nov. 2011) [Related conference publication:Asilomar 2011] Model-based Compressive Sensing Marco Duarte, Fast reconstruction from random incoherent projections. (Rice ECE Department Technical Report TREE 0507, May 2005) Marco Duarte, Michael Wakin, and Richard Baraniuk, Fast reconstruction of piecewise smooth signals from random projections. (SPARS Workshop, November 2005) Chinh La and Minh Do, Signal reconstruction using sparse tree representations. (SPIE Wavelets XI, San Diego, California, September 2005) Marco Duarte, Michael Wakin, and Richard Baraniuk, Wavelet-domain compressive signal reconstruction using a hidden Markov tree model. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, Nevada, April 2008) Yonina C. 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Duarte and Volkan Cevher, Compressive Sensing Recovery of Spike Trains Using a Structured Sparsity Model. (Workshop on Signal Processing with Adaptive Sparse Structured Representations (SPARS), April 2009.) Y.C. Eldar, P. Kuppinger, H. Bolcskei, Compressed Sensing of Block-Sparse Signals: Uncertainty Relations and Efficient Recovery. (Submitted to IEEE Transactions on Signal Processing, June 2009) Marco F. Duarte, Volkan Cevher and Richard G. Baraniuk, Model-Based Compressive Sensing for Signal Ensembles. (Allerton Conference on Communication, Control, and Computing, October 2009.) Chinmay Hegde and Richard G. Baraniuk, Compressive Sensing of Streams of Pulses. (Allerton Conference on Communication, Control, and Computing, October 2009.) Marco F. Duarte, Richard G. Baraniuk, Spectral Compressive Sensing. (Preprint, 2010) Chinmay Hegde and Richard G. Baraniuk, Compressive Sensing of a Superposition of Pulses. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Dallas, Texas, March 2010) Chinmay Hegde and Richard G. Baraniuk, Sampling and Recovery of Pulse Streams. (Preprint, 2010) Phil Schniter, Turbo Reconstruction of Structured Sparse Signals. (Proc. CISS 2010 (Princeton, NJ)) Jiao Wu, Fang Liu, L.C. Jiao, Xiaodong Wang and Biao Hou, Multivariate Compressive Sensing for Image Reconstruction in the Wavelet Domain: Using Scale Mixture Models. (IEEE Trans. on Image Processing (in press), 2011. (doi: 10.1109/TIP.2011.2150231)) Joel Goodman, Keith Forysthe, Benjamin Miller, Efficient reconstruction of block-sparse signals . (IEEE Statistical Signal Processing Workshop (SSP), pp 629-632, June 2011) Amin Khajehnejad, Weiyu Xu, Salman Avestimehr, Babak Hassibi, Analyzing Weighted L1 Minimization for Sparse Recovery With Nonuniform Sparse Models. (IEEE Transactions on Signal Processing, Vol. 59(5), pp. 1985-2001, 2011) Compressive Sensing and Quantization Wei Dai, Hoa Vinh Pham, and Olgica Milenkovic, Distortion-Rate functions for quantized compressive sensing. (Preprint, 2009) Petros Boufounos and Richard Baraniuk, Quantization of sparse representations. (Rice ECE Department Technical Report TREE 0701 - Summary appears in Data Compression Conference (DCC), Snowbird, Utah, March 2007) Petros Boufounos and Richard G. Baraniuk, Sigma delta quantization for compressive sensing. (Preprint, 2007) Jason Laska, Petros Boufounos, and Richard Baraniuk, Finite range scalar quantization for compressive sensing. (International Conference on Sampling Theory and Applications (SampTA) May 2009) Jason Laska, Petros Boufounos, Mark Davenport, and Richard Baraniuk, Democracy in action: Quantization, saturation, and compressive sensing. (Applied and Computational Harmonic Analysis, 31(3), pp. 429 - 443, November 2011) Sinan Gunturk, Alex Powell, Rayan Saab, Ozgur Yilmaz, Sobolev duals for random frames and sigma-delta quantization of compressed sensing measurements. (Preprint, 2010) L. Jacques, D. K. Hammond, and M. J. Fadili, Dequantizing compressed sensing with non-gaussian constraints. (Preprint, 2009) L. Jacques, D. K. Hammond, and M. J. Fadili, Dequantizing compressed sensing: When oversampling and non-gaussian constraints combine. (Technical Report, 2009)1-Bit Compressive Sensing Petros Boufounos and Richard G. Baraniuk, 1-Bit compressive sensing. (Conf. on Info. Sciences and Systems (CISS), Princeton, New Jersey, March 2008) Jason N. Laska, Zaiwen Wen, Wotao Yin, and Richard G. Baraniuk, Trust, but Verify: Fast and Accurate Signal Recovery from 1-bit Compressive Measurements. (Preprint.) P. Boufounos, Reconstruction of sparse signals from distorted randomized measurements, in Proc. Intl. 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(IEEE Journal of Selected Topics in Signal Processing: Special Issue on Convex Optimization Methods for Signal Processing, 1(4), pp. 586-598, 2007) Seung-Jean Kim, Kwangmoo Koh, Michael Lustig, Stephen Boyd, and Dimitry Gorinevsky,A method for large-scale ell-1-regularized least squares problems with applications in signal processing and statistics. (Preprint, 2007) David L. Donoho, Yaakov Tsaig, Iddo Drori, and Jean-Luc Starck, Sparse solution of underdetermined linear equations by stagewise orthogonal matching pursuit. (Preprint, 2007) Thomas Blumensath and Mike E. Davies, Iterative thresholding for sparse approximations. (Preprint, 2007) Thomas Blumensath and Mike E. Davies, Gradient pursuits. (IEEE Trans. on Signal Processing, 56(6), pp. 2370 - 2382, June 2008) Karen Egiazarian, Alessandro Foi, and Vladimir Katkovnik, Compressed sensing image reconstruction via recursive spatially adaptive filtering. (Preprint, 2007) Ingrid Daubechies, Massimo Fornasier, and Ignace Loris, Accelerated projected gradient method for linear inverse problems with sparsity constraints. (Preprint, 2007) Massimo Fornasier, Domain decomposition methods for linear inverse problems with sparsity constraints. (Inverse Problems, 23(6), pp. 2505 - 2526, Dec. 2007) Ewout van den Berg and Michael Friedlander, In pursuit of a root. (Preprint, 2007) Deanna Needell and Roman Vershynin, Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit. (Preprint, 2007) Kristan Bredies and Dirk A. Lorenz, Iterated hard shrinkage for minimization problems with sparsity constraints. (Preprint, 2007) José Bioucas-Dias and Mário Figueiredo, A new TwIST: two-step iterative shrinkage/thresholding algorithms for image restoration. 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(Magnetic Resonance in Medicine, 57(6), pp. 1086-1098, 2007) Jianwei Ma, Compressed sensing by inverse scale space and curvelet thresholding. (Applied Mathematics and Computation, 206, pp. 980-988, 2008) Ingrid Daubechies, Ronald DeVore, Massimo Fornasier, C. Sinan Güntürk, Iteratively re-weighted least squares minimization for sparse recovery. (Preprint, 2008) S. Wright, R. Nowak, M. Figueiredo, Sparse reconstruction by separable approximation. (Preprint, 2008) Venkatesh Saligrama, Manqi Zhao, Thresholded basis pursuit: Quantizing linear programming solutions for optimal support recovery and approximation in compressed sensing. (Preprint, 2008) Hossein Mohimani, Massoud Babaie-Zadeh, Christian Jutten, A fast approach for overcomplete sparse decomposition based on smoothed ell-0 norm. (Preprint, 2008) [See also related conference publications:ICA 2007 ICASSP 2008] R. Berinde, P. Indyk, M. Ružić, Practical near-optimal sparse recovery in the ell-1 norm. (Proc. 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(IEEE Trans. on Information Theory, 56(9), pp. 4395 - 4401, September 2010) Yilun Wang, Wotao Yin, Compressed sensing via iterative support detection. (Rice CAAM TR09-30, September 2009) Zachary Harmany, Roummel Marcia, Rebecca Willett, Sparse Poisson intensity reconstruction algorithms. (Proc. IEEE Workshop on Statistical Signal Processing, 2009) Ming Gu, Lek-Heng Lim, Cinna Julie Wu, PARNES: A rapidly convergent algorithm for accurate recovery of sparse and approximately sparse signals. (Preprint, 2009) W. Yin, S. P. Morgan, J. Yang, Y. Zhang, Practical compressive sensing with Toeplitz and circulant matrices. (Rice University CAAM Technical Report TR10-01, Submitted to VCIP 2010)[additional info] Entao Liu, V.N. Temlyakov Orthogonal super greedy algorithm and applications in compressed sensing. (Preprint, Jan 2010) Wei Lu, Namrata Vaswani, Modified basis pursuit denoising (Modified-BPDN) for noisy compressive sensing with partially known support. (IEEE ICASSP 2010) Wei Lu, Namrata Vaswani, Regularized Modified-BPDN for compressive sensing with partially known support. (Preprint, Feb 2010) G. Mileounis, B. Babadi, N. Kalouptsidis, V. Tarokh, An adaptive greedy algorithm with application to sparse NARMA identification. (IEEE ICASSP 2010) Gerasimos Mileounis, Behtash Babadi, Nicholas Kalouptsidis, Vahid Tarokh, An adaptive greedy algorithm with application to nonlinear communications. (To appear in the IEEE Trans. on Signal Processing, 2010) D. Angelosante, J.-A. Bazerque, G. B. Giannakis, Online Adaptive Estimation of Sparse Signals: where RLS meets the L1-norm. (Accepted to IEEE Trans. on Sign. Proc., 2010) K. Qiu, A. Dogandžić, Double overrelaxation thresholding methods for sparse signal reconstruction. (Proc. 44th Annu. Conf. Inform. Sci. Syst., Princeton, NJ, Mar. 2010) Eugene Livshitz, On the optimality of Orthogonal Greedy Algorithm for M-coherent dictionaries. (Preprint, March 2010) Atul Divekar, Okan Ersoy, Probabilistic Matching Pursuit for Compressive sensing. (Purdue ECE Technical Report TR-ECE-10-03, 2010) Behtash Babadi, Nicholas Kalouptsidis, Vahid Tarokh, SPARLS: The Sparse RLS Algorithm. (To appear in the IEEE Trans. on Signal Processing, 2010) Eugene Livshitz, On efficiency of Orthogonal Matching Pursuit . (Preprint, 2010) Zachary T. Harmany, Roummel F. Marcia, and Rebecca M. Willett, This is SPIRAL-TAP: Sparse Poisson Intensity Reconstruction ALgorithms -- Theory and Practice. (Submitted to IEEE Transactions on Image Processing) Rebecca M. Willett, Zachary T. Harmany, and Roummel F. Marcia, Poisson Image Reconstruction with Total Variation Regularization. (IEEE International Conference on Image Processing, September 2010) Zachary T. Harmany, Daniel O. Thompson, Rebecca M. Willett, and Roummel F. Marcia,Gradient Projection for Linearly Constrained Convex Optimization in Sparse Signal Recovery. 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(IEEE Trans. on Image Processing (in press), doi: 10.1109/TIP.2010.2104159 ) Zhilin Zhang, Bhaskar D. Rao, Iterative Reweighted Algorithms for Sparse Signal Recovery with Temporally Correlated Source Vectors. (ICASSP 2011) Zhilin Zhang, Bhaskar D. Rao, Exploiting Correlation in Sparse Signal Recovery Problems: Multiple Measurement Vectors, Block Sparsity, and Time-Varying Sparsi. (ICML 2011 Workshop on Structured Sparsity: Learning and Inference) Nazim Burak Karahanoglu and Hakan Erdogan, A* Orthogonal Matching Pursuit: Best-First Search for Compressed Sensing Signal Recovery. (Preprint, 2010) [See also related conference publication:ICASSP 2011] Amin Khajehnejad, Juhwan Yoo, Animashree Anandkumar ana Babak Hassibi, Summary Based Structures with Improved Sublinear Recovery for Compressed Sensing. (International Symposium on Information Theory, 2011) Yuzhe Jin, Bhaskar D. Rao, MultiPass Lasso Algorithms for Sparse Signal Recovery. (ISIT 2011, St. Petersburg, Russia.) Adam S. Charles, Pierre Garrigues, Christopher J. Rozell, Analog Sparse Approximation with Applications to Compressed Sensing. (arXiv:1111.4118v1 [math.OC]) Zai Yang, Cishen Zhang, Jun Deng, and Wenmiao Lu, Orthonormal expansion l1-minimization algorithms for compressed sensing. (IEEE Trans. on Signal Processing, vol. 59, no. 12, pp. 6285--6290, 2011) [Matlab codes] Prateek Jain, Ambuj Tewari, Inderjit S. Dhillon, Orthogonal Matching Pursuit with Replacement. (Advances in Neural Information Processing Systems 24 (NIPS 2011), pp. 1215-1223, 2011) Gerlind Plonka, Jianwei Ma, Curvelet-wavelet regularized split Bregman iteration for compressed sensing. (International Journal of Wavelets, Multiresolution and Information Processing, 2011, 9(1), 79-110) Jaewook Kang, Heung-No Lee, and Kiseon Kim, On Detection-Directed Estimation approach for Noisy Compressive Sensing. (Submitted to IEEE Trans. Signal processing Jan., 2012) J. Wang, S. Kwon, and B. Shim, Near optimal bound of orthogonal matching pursuit using restricted isometric constant. (To appear in Eurasip journal on advances in signal processing) Yunbin Zhao and Duan Li, Reweighted l1-Minimization for Sparse Solutions to Underdetermined Linear Systems. (Submitted to SIAM J. Optimization) Foundations and Connections Coding and Information Theory Emmanuel Candès and Terence Tao, Decoding by linear programming. (IEEE Trans. on Information Theory, 51(12), pp. 4203 - 4215, December 2005) Emmanuel Candès and Terence Tao, Error correction via linear programming. (Preprint, 2005) Mark Rudelson and Roman Vershynin, Geometric approach to error correcting codes and reconstruction of signals. (Int. Mathematical Research Notices, 64, pp. 4019 - 4041, 2005) Emmanuel Candès and Justin Romberg, Encoding the ell-p ball from limited measurements. (IEEE Data Compression Conference (DCC), Snowbird, UT, 2006) Shriram Sarvotham, Dror Baron, and Richard Baraniuk, Measurements vs. bits: Compressed sensing meets information theory. (Allerton Conference on Communication, Control, and Computing, Monticello, IL, September 2006) Emmanuel Candès and Paige Randall, Highly robust error correction by convex programming. (Preprint, 2006) Martin Wainwright, Information-theoretic bounds on sparsity recovery in the high-dimensional and noisy setting. (IEEE Int. Symposium on Information Theory (ISIT), Nice, France, June 2007) Mehmet Akcakaya and Vahid Tarokh, A frame construction and a universal distortion bound for sparse representations. IEEE Transactions on Signal Processing, vol. 56, pp. 2443-2450, June 2008. Rick Chartrand, Nonconvex compressed sensing and error correction. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Honolulu, Hawaii, April 2007) Galen Reeves, Sparse signal sampling using noisy linear projections. (Master's Thesis, December 2007) Galen Reeves and Michael Gastpar, Sampling bounds for sparse support recovery in the presence of noise. (Preprint, January 2008) Mehmet Akcakaya and Vahid Tarokh, Shannon theoretic limits on noisy compressed sensing. (Preprint, November 2007) Alyson K. Fletcher, Sundeep Rangan, Vivek K Goyal, and Kannan Ramchandran, Denoising by sparse approximation: Error bounds based on rate-distortion theory. (EURASIP J. Applied Signal Processing, 2006, Article ID 26318.) Alyson K. Fletcher, Sundeep Rangan, and Vivek K Goyal, On the Rate-Distortion Performance of Compressed Sensing. (IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), Honolulu, Hawaii, April 2007) Alyson K. Fletcher, Sundeep Rangan, and Vivek K Goyal, Rate-distortion bounds for sparse approximation. (IEEE Statistical Signal Processing Workshop (SSP), Madison, Wisconsin, August 2007) Wei Dai and Olgica Milenkovic, Weighted superimposed codes and constrained integer compressed sensing. (Preprint, 2008) [See also related conference publications:CISS 2008,ITW 2008] John Wright and Yi Ma, Dense error correction via ell-1 minimization (Preprint, 2008) Shuchin Aeron, Manqi Zhao, and Venkatesh Saligrama, Fundamental limits on sensing capacity for sensor networks and compressed sensing. (Preprint, 2008) Yuzhe Jin and Bhaskar D. Rao, Insights into the stable recovery of sparse solutions in overcomplete representations using network information theory (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, Nevada, April 2008) Yuzhe Jin and Bhaskar D. Rao, Performance limits of matching pursuit algorithms (IEEE Int. Symposium on Information Theory (ISIT), Toronto, Canada, June 2008) Mehmet Akçakaya, Jinsoo Park, Vahid Tarokh, Compressive Sensing Using Low Density Frames. (Preprint, 2009) Behtash Babadi, Nicholas Kalouptsidis, and Vahid Tarokh, Asymptotic Achievability of the Cramér–Rao Bound for Noisy Compressive Sampling. (IEEE Trans. Signal Processing, 57(3), pp. 1233-1236, March 2009) M. Salman Asif, William Mantzel and Justin Romberg, Channel Protection: Random Coding Meets Sparse Channels. (Information Theory Workshop, October 2009.) M. Salman Asif, William Mantzel and Justin Romberg, Random Channel Coding and Blind Deconvolution. (Allerton Conference on Communication, Control, and Computing, October 2009.) Alexandros G. Dimakis, Pascal O. Vontobel, LP Decoding meets LP Decoding: A Connection between Channel Coding and Compressed Sensing. (Allerton 2009) Arun Pachai Kannu and Philip Schniter, On Communication over Unknown Sparse Frequency-Selective Block-Fading Channels. (submitted to IEEE Trans. on Information Theory, June 2010) Yihong Wu and Sergio Verdú, Rényi Information Dimension: Fundamental Limits of Almost Lossless Analog Compression. (IEEE Trans. on Information Theory, 56(8), August 2010) [ISIT 09 version] Alexandros G. Dimakis and Roxana Smarandache and Pascal O. Vontobel, LDPC Codes for Compressed Sensing . ((submitted for publication)) [ LP meets LP publications] Amin Khajehnejad, Arash Saber Tehrani, Alex . Dimakis and Babak Hassibi, Explicit Matrices for Sparse Approximation. (International Symposium on Information Theory, 2011) Samet Oymak, Amin Khajehnejad and Babak Hassibi, Subspace Expanders and Matrix Rank Minimization. (International Symposium on Information Theory, 2011) High-Dimensional Geometry David Donoho, High-dimensional centrally-symmetric polytopes with neighborliness proportional to dimension. (Disc. Comput. Geometry, 35(4) pp. 617-652, 2006) David Donoho, Neighborly polytopes and sparse solutions of undetermined linear equations. (Preprint, 2005) David Donoho and Jared Tanner, Neighborliness of randomly-projected simplices in high dimensions. (Proc. National Academy of Sciences, 102(27), pp. 9452-9457, 2005) David Donoho and Jared Tanner, Counting faces of randomly-projected polytopes when the projection radically lowers dimension. (Journal of the AMS, 22(1), pp. 1-53, January 2009) Richard Baraniuk and Michael Wakin, Random projections of smooth manifolds. (To appear in Foundations of Computational Mathematics) [See also related conference publication:ICASSP 2006] Venkatesan Guruswami, James R. Lee, and Alexander Razborov, Almost Euclidean subspaces of ell-1-N via expander codes. (Electronic Colloquium on Computational Complexity, Report TR07-089, September, 2007) J. Haupt and R. Nowak, A generalized restricted isometry property. (University of Wisconsin Madison Technical Report ECE-07-1, May 2007) David Donoho and Jared Tanner, Counting the faces of radomly-projected hypercubes and orthants, with applications. (Preprint, 2008) Michael Wakin, Manifold-based signal recovery and parameter estimation from compressive measurements. (Preprint, 2008) Mark Davenport, Chinmay Hegde, Marco Duarte, and Richard Baraniuk, A theoretical analysis of joint manifolds. (Rice University ECE Department Technical Report TREE-0901, January 2009) Mark Davenport and Richard Baraniuk, Sparse geodesic paths. (AAAI Fall 2009 Symposium on Manifold Learning, Arlington, Virginia, November 2009) Mark Davenport, Chinmay Hegde, Marco Duarte, and Richard Baraniuk, Joint manifolds for data fusion. (IEEE Trans. on Image Processing, 19(10) pp. 2580-2594, October 2010) Mark Davenport, Chinmay Hegde, Marco. Duarte, and Richard Baraniuk, High-dimensional data fusion via joint manifold learning. (AAAI Fall 2010 Symposium on Manifold Learning, Arlington, Virginia, November 2010) Gitta Kutyniok, Data separation by sparse representations. (Chapter in Compressed Sensing: Theory and Applications , Cambridge University Press, 2012) Ell-1 Norm Minimization David Donoho, For most large underdetermined systems of linear equations, the minimal ell-1 norm solution is also the sparsest solution. (Communications on Pure and Applied Mathematics, 59(6), pp. 797-829, June 2006) David Donoho, For most large underdetermined systems of linear equations, the minimal ell-1 norm near-solution approximates the sparsest near-solution. (Communications on Pure and Applied Mathematics, 59(7), pp. 907-934, July 2006) David Donoho and Jared Tanner, Sparse nonnegative solutions of underdetermined linear equations by linear programming. (Proc. National Academy of Sciences, 102(27), pp.9446-9451, 2005) David Donoho and Jared Tanner, Thresholds for the recovery of sparse solutions via ell-1 minimization. (Conf. on Information Sciences and Systems, March 2006) Rémi Gribonval and Morten Nielsen, Highly sparse representations from dictionaries are unique and independent of the sparseness measure. (Applied and Computational Harmonic Analysis, 22(3), pp. 335-355, May 2007) [See also related conference publication:ICA 2004] Rémi Gribonval, Rosa Maria Figueras I Ventura, and Pierre Vandergheynst, A simple test to check the optimality of a sparse signal approximation. (EURASIP Signal Processing, special issue on Sparse Approximations in Signal and Image Processing, 86(3), pp. 496-510, March 2006) [See also related conference publication:ICASSP 2005] Statistical Signal Processing Marco Duarte, Mark Davenport, Michael Wakin, and Richard Baraniuk, Sparse signal detection from incoherent projections. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Toulouse, France, May 2006) Mark Davenport, Michael Wakin, and Richard Baraniuk, Detection and estimation with compressive measurements. (Rice ECE Department Technical Report TREE 0610, November 2006) Jarvis Haupt, Rui Castro, Robert Nowak, Gerald Fudge, and Alex Yeh, Compressive sampling for signal classification. (Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, California, October 2006) Mark Davenport, Richard Baraniuk, and Michael Wakin, Scalable inference and recovery from compressive measurements. (NIPS Workshop on Novel Applications of Dimensionality Reduction, Whistler, Canada, December 2006) Mark Davenport, Marco Duarte, Michael Wakin, Jason Laska, Dharmpal Takhar, Kevin Kelly, and Richard Baraniuk,The smashed filter for compressive classification and target recognition. (Computational Imaging V at SPIE Electronic Imaging, San Jose, California, January 2007) Jarvis Haupt and Robert Nowak, Compressive sampling for signal detection. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Honolulu, Hawaii, April 2007) Frank Boyle, Jarvis Haupt, Gerald Fudge, and Robert Nowak, Detecting signal structure from randomly-sampled data. (IEEE Workshop on Statistical Signal Processing (SSP), Madison, Wisconsin, August 2007) Marco Duarte, Mark Davenport, Michael Wakin, Jason Laska, Dharmpal Takhar, Kevin Kelly, Richard Baraniuk,Multiscale random projections for compressive classification. (IEEE Conf. on Image Processing (ICIP), San Antonio, Texas, September 2007) Mark Davenport, Chinmay Hegde, Michael Wakin, and Richard Baraniuk, Manifold-based approaches for improved classification. (NIPS Workshop on Topology Learning, Whistler, Canada, December 2007) Chinmay Hegde, Mark Davenport, Michael Wakin, and Richard Baraniuk, Efficient machine learning using random projections. (NIPS Workshop on Efficient Machine Learning, Whistler, Canada, December 2007) V. Cevher, P. Boufounos, R. G. Baraniuk, A. C. Gilbert, M. J. Strauss, Near-optimal bayesian localization via incoherence and sparsity. (Int. Conf. on Information Processing in Sensor Networks (IPSN), San Francisco, California, April 2009) Z. Ben-Haim and Y. C. Eldar, The Cramer-Rao bound for estimating a sparse parameter vector. (IEEE Trans. Signal Processing, 58(6), pp. 3384-3389, June 2010) [A more detailed version of this paper is available as atechnical report] N. Kalouptsidis, G. Mileounis, B. Babadi, V. Tarokh, Adaptive algorithms for sparse nonlinear channel estimation. (Proc. IEEE Workshop on Statistical Signal Processing (SSP'09), Sept. 2009,Cardiff, Wales, UK) Mark Davenport, Petros Boufounos, Michael Wakin, and Richard Baraniuk, Signal processing with compressive measurements. (IEEE J. of Selected Topics in Signal Processing, 4(2), pp. 445-460, April 2010) A. Jung, Z. Ben-Haim, F. Hlawatsch and Y. C. Eldar, Unbiased estimation of a sparse vector in white Gaussian noise. (submitted to IEEE Trans. Information Theory, May 2010.) Alexander Jung, Georg Tauböck, and Franz Hlawatsch, Compressive spectral estimation for nonstationary random processes. (in Proc. IEEE ICASSP-09, Taipei, Taiwan, R.O.C., April 2009, pp. 3029-3032) Alexander Jung, Georg Tauböck, and Franz Hlawatsch, Compressive nonstationary spectral estimation using parsimonious random sampling of the ambiguity function. (in Proc. IEEE SSP-09, Cardiff, Wales, UK, Aug.-Sept. 2009, pp. 642-645) Alexander Jung, Zvika Ben-Haim, Franz Hlawatsch, and Yonina C. Eldar, On unbiased estimation of sparse vectors corrupted by Gaussian noise. (in Proc. IEEE ICASSP-10, Dallas, TX, Mar. 2010, pp. 3990-3993) Armin Eftekhari, Justin Romberg, and Michael B. Wakin, Matched filtering from limited frequency samples. (Preprint, 2011) Michael A Lexa, Mike E Davies, John S Thompson, and Janosch Nikolic, Compressive power spectral density estimation. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2011) Yuzhe Jin, Bhaskar D. Rao, Algorithms for robust linear regression by exploiting the connection to sparse signal recovery. (IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2010) Machine Learning Michael Elad, Optimized projections for compressed sensing. (IEEE Trans. on Signal Processing, 55(12), pp. 5695-5702, December 2007) Julien Mairal, Guillermo Sapiro, and Michael Elad, Multiscale sparse image representation with learned dictionaries. (Preprint, 2007) John Wright, Allen Yang, Arvind Ganesh, Shankar Shastry, and Yi Ma, Robust face recognition via sparse representation. (To appear in IEEE Trans. on Pattern Analysis and Machine Intelligence) Allen Yang, John Wright, Yi Ma, and Shankar Sastry, Feature selection in face recognition: A sparse representation perspective. (Preprint, 2007) Chinmay Hegde, Michael Wakin, and Richard Baraniuk, Random projections for manifold learning. (Neural Information Processing Systems (NIPS), Vancouver, Canada, December 2007) [See also relatedtechnical report] D.P. Wipf and B.D. Rao, Sparse bayesian learning for basis selection. (IEEE Trans. on Signal Processing, Special Issue on Machine Learning Methods in Signal Processing, 52, pp. 2153 - 2164, August 2004) Julio Martin Duarte-Carvajalino and Guillermo Sapiro, Learning to sense sparse signals: Simultaneous sensing matrix and sparsifying dictionary optimization. (Preprint, 2008) J. F. Gemmeke and B. Cranen, Noise reduction through compressed sensing. (Interspeech 2008, Brisbane, Australia, September 2008) J. F. Gemmeke and B. Cranen, Using sparse representations for missing data imputation in noise robust speech recognition. (European Signal Processing Conf. (EUSIPCO), Lausanne, Switzerland, August 2008) J. F. Gemmeke and B. Cranen, Noise robust digit recognition using sparse representations. (ISCA Tutorial and Research Workshop (ITRW) on Speech Analysis and Processing for Knowledge Discovery, Aalborg, Denamrk, June 2008) Julien Mairal, Fracis Bach, Jean Ponce, Guillermo Sapiro, and Andrew Zisserman,Discriminative learned dictionaries for local image analysis. (IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Anchorage, Alaska, June 2008) Fernando Rodriguez and Guillermo Sapiro, Sparse representations for image classification: Learning discriminative and reconstructive non-parametric dictionaries. (Preprint, 2008) Robert Calderbank, Sina Jafarpour, and Robert Schapire, Compressed learning: Universal sparse dimensionality reduction and learning in the measurement domain (Preprint, 2009) Odalric Maillard, Remi Munos, Compressed Least Squares Regression. (Neural Information Processing Systems (NIPS), Vancouver, Canada, December 2009) Bengt J. Borgstrom and Abeer Alwan, Utilizing Compressibility in Reconstructing Spectrographic Data, with Applications to Noise Robust ASR. (IEEE Signal Processing Letters, Vol. 16, Issue 5, pp. 398-401, 2009.) [www.ee.ucla.edu/~spapl/paper/borgstrom_DSP_09.pdf] Katya Scheinberg and Irina Rish, Learning Sparse Gaussian Markov Networks using a Greedy Coordinate Ascent Approach. (Proceedings of European Conference on Machine Learning (ECML 2010), Barcelona, Spain, September 2010) Zoltan Szabo, Barnabas Poczos, and Andras Lorincz, Online Group-Structured Dictionary Learning. (IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, USA, June 2011) M. H. Mahoor, M. Zhou, K. Veon, S. M. Mavadati and J. Cohn, Facial Action Unit Recognition with Sparse Representation. (2011 IEEE International Conference on Automatic Face & Gesture Recognition (FG 2011), pp.336-342, March 2011) li>Zoltan Szabo, Barnabas Poczos, and Andras Lorincz,Collaborative Filtering via Group-Structured Dictionary Learning. (Latent Variable Analysis and Signal Separation (LVA/ICA), volume 7191 of LNCS, pp. 247-254, Tel-Aviv, Israel, 12-15 March 2012) [extended TR,DOI] Bayesian Methods Mauricio Sacchi, Tadeusz Ulrych, and Colin Walker, Interpolation and extrapolation using a high-resolution discrete Fourier transform. (IEEE Trans. on Signal Processing, 46(1) pp. 31 - 38, January 1998) Shriram Sarvotham, Dror Baron, and Richard Baraniuk, Compressed sensing reconstruction via belief propagation. (Rice ECE Department Technical Report TREE 0601, 2006) Shihao Ji, Ya Xue, and Lawrence Carin, Bayesian compressive sensing. (IEEE Trans. on Signal Processing, 56(6) pp. 2346 - 2356, June 2008) [See also related conference publication:ICML 2007] David Wipf, Jason Palmer, Bhaskar Rao, and Kenneth Kreutz-Delgado, Performance evaluation of latent variable models with sparse priors. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Honolulu, Hawaii, May 2007) Shihao Ji, David Dunson, and Lawrence Carin, Multi-task compressive sensing. (Preprint, 2007) D.P. Wipf, J.A. Palmer, and B.D. Rao, Perspectives on Sparse Bayesian Learning. (Neural Information Processing Systems (NIPS), Vancouver, Canada, December 2004) D. Wipf and B. D. Rao, An empirical bayesian strategy for solving the simultaneous sparse approximation problem. (IEEE Trans. on Signal Processing, 55(7), pp. 3704 - 3716, July 2007) R.M. Castro, J. Haupt, R. Nowak, and G.M. Raz, Finding needles in noisy haystacks. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, Nevada, April 2008) Yuting Qi, Dehong Liu, David Dunson, and Lawrence Carin, Bayesian multi-task compressive sensing with dirichlet process priors. (Preprint, 2008) Matthias W. Seeger and Hannes Nickish, Compressed sensing and bayesian experimental design. (Int. Conf. on Machine Learning (ICML), Helsinki, Finland, July 2008) Phil Schniter, Lee Potter and Justin Ziniel, Fast Bayesian matching pursuit: Model uncertainty and parameter estimation for sparse linear models. (Preprint 2008) [See also related conference publication:ITA 2008 Lihan He and Lawrence Carin, Exploiting structure in wavelet-based bayesian compressed sensing. (Accepted for publication in IEEE Transactions on Signal Processing) S.D. Babacan, R. Molina, and A.K. Katsaggelos, Bayesian Compressive Sensing using Laplace Priors. (IEEE Transactions on Image Processing, Vol. 19, issue 1, 53-64, January 2010) Lachlan Blackhall, Michael Rotkowitz, Recursive Sparse Estimation using a Gaussian Sum Filter. (Proceedings of the IFAC World Congress, July 2008) Nicolas Dobigeon, Alfred O. Hero, Jean-Yves Tourneret, Hierarchical Bayesian sparse image reconstruction with application to MRFM. (IEEE Trans. Image Processing, vol. 18, no. 9, pp. 2059-2070, Sept. 2009) Nicolas Dobigeon, Jean-Yves Tourneret, Bayesian orthogonal component analysis for sparse representation. (Preprint, August 2009) Zhilin Zhang, Bhaskar D. Rao, Sparse Signal Recovery with Temporally Correlated Source Vectors Using Sparse Bayesian Learning. (IEEE Journal of Selected Topics in Signal Processing, vol.5, no. 5, pp. 912-926, 2011) Zhilin Zhang, Bhaskar D. Rao, Sparse Signal Recovery in the Presence of Correlated Multiple Measurement Vectors. (ICASSP 2010) Finite Rate of Innovation Martin Vetterli, Pina Marziliano, and Thierry Blu, Sampling signals with finite rate of innovation. (IEEE Trans. on Signal Processing, 50(6), pp. 1417-1428, June 2002) Irena Maravic and Martin Vetterli, Sampling and reconstruction of signals with finite rate of innovation in the presence of noise. (IEEE Trans. on Signal Processing, 53(8), pp. 2788-2805, August 2005) Yue Lu and Minh Do, A geometrical approach to sampling signals with finite rate of innovation. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Montreal, Canada, May 2004) Ivana Jovanovic and Baltasar Beferull-Lozano, Oversampled A/D conversion and error-rate dependence of nonbandlimited signals with finite rate of innovation. (IEEE Trans. on Signal Processing, 54(6), pp. 2140-2154 , June 2006) Pier Luigi Dragotti, Martin Vetterli, and Thierry Blu, Sampling moments and reconstructing signals of finite rate of innovation: Shannon meets Strang-Fix. (IEEE Trans. on Signal Processing, 55(7), pp. 1741-1757, May 2007) P. Shukla and P. L. Dragotti, Sampling schemes for multidimensional signals with finite rate of innovation. (IEEE Trans. on Signal Processing, 55(7), pp. 3670-3686, July 2007) Vincent Y. F. Tan and Vivek K Goyal, Estimating signals with finite rate of innovation from noisy samples: A stochastic algorithm. IEEE Trans. on Signal Processing, 56(10), pp. 5135-5146, October 2008 Julius Kusuma and Vivek K Goyal, Multichannel sampling of parametric signals with a successive approximation property. (IEEE Int. Conf. on Image Processing (ICIP), Atlanta, Georgia, October 2006) L. Baboulaz and P.L. Dragotti, Exact feature extraction using finite rate of innovation principles with an application to image super-resolution. (IEEE Trans. on Image Processing, 18(2), pp. 281 - 298, February 2009) R. Tur, Y. C. Eldar, Z. Friedman Low Rate Sampling of Pulse Streams with Application to Ultrasound Imaging. (Submitted to IEEE Transactions on Signal Processing, Mar. 2010) Kfir Gedalyahu, Ronen Tur and Yonina C. Eldar, Multichannel Sampling of Pulse Streams at the Rate of Innovation. (submitted to IEEE Trans. on Signal Processing, Apr. 2010.) Jesse Berent and Pier Luigi Dragotti and Thierry Blu, Sampling Piecewise Sinusoidal Signals with Finite Rate of Innovation Methods. (IEEE Trans. on Signal Processing, Vol. 58(2),pp. 613-625, February 2010.) H. Akhondi Asl and P.L. Dragotti and L. Baboulaz , Multichannel Sampling of Signals with Finite Rate of Innovation,. (IEEE Signal Processing Letter, Vo. 17(8), pp. 762-765, August 2010.) Tomer Michaeli and Yonina C. Eldar, Xampling at the rate of innovation. (to appear in IEEE Transactions on Signal Processing) Adaptive Sampling Methods for Sparse Recovery J. Haupt, R. Castro, and R. Nowak, Distilled sensing: selective sampling for sparse signal recovery. (to appear in Proc. 12th Conference on Artificial Intelligence and Statistics, Clearwater Beach, FL, April 2009) A. Aldroubi, H. Wanf and K. Zarringhalam, Sequential Adaptive compressed sampling via Huffman codes. (Preprint 2009) M. A. Iwen & A. H. Tewfik, Adaptive Group Testing Strategies for Target Detection and Localization in Noisy Environments. (Preprint, 2010) Data Stream Algorithms Heavy-Hitters Graham Cormode and S. Muthukrishnan, Towards an algorithmic theory of compressed sensing. (Technical Peport DIMACS TR 2005-25, 2005) Graham Cormode and S. Muthukrishnan, Combinatorial algorithms for compressed sensing. (Technical Report DIMACS TR 2005-40, 2005) S. Muthukrishnan, Some algorithmic problems and results in compressed sensing. (Preprint, 2006) Anna Gilbert, Martin Strauss, Joel Tropp, and Roman Vershynin, One sketch for all: Fast algorithms for compressed sensing. (Symp. on Theory of Computing (STOC), San Diego, California, June, 2007) T Bu, J Cao, A Chen, PPC Lee, A fast and compact method for unveiling significant patterns in high speed networks. (Proc. of IEEE INFOCOM, 2006) Random Sampling Anna Gilbert, Sudipto Guha, Piotr Indyk, S. Muthukrishnan, and Martin Strauss,Near-optimal sparse Fourier representations via sampling. (ACM Symposium on Theory of Computing (STOC), 2002) Anna Gilbert, S. Muthukrishnan, and M. Strauss, Improved time bounds for near-optimal sparse Fourier representation via sampling. (SPIE Wavelets XI, San Diego, California, September 2005) Holger Rauhut, Random sampling of sparse trigonometric polynomials. (Applied and Computational Harmonic Analysis, 22(1), pp. 16-42, Jan. 2007) Stefan Kunis and Holger Rauhut, Random sampling of sparse trigonometric polynomials II - Orthogonal matching pursuit versus basis pursuit. (Preprint, 2006) Holger Rauhut, Stability results for random sampling of sparse trigonometric polynomials. (Preprint, 2006) Histogram Maintenance Nitin Thaper, Sudipto Guha, Piotr Indyk, and Nick Koudas, Dynamic multidimensional histograms. (SIGMOD 2002, Madison, Wisconson, June 2002) Anna Gilbert, Sudipto Guha, Piotr Indyk, Yannis Kotidis, S. Muthukrishnan, and Martin J. Strauss,Fast small-space algorithms for approximate histogram maintenance. (Symp. on Theory of Computing (STOC), Montréal, Canada, May 2002) Dimension Reduction and Embeddings Anna Gilbert, Martin Strauss, Joel Tropp, and Roman Vershynin, Sublinear, Small-space approximation of compressible signals and uniform algorithmic embeddings. (Preprint, 2005) [See Vershynin's discussion of this paperhere] Anna Gilbert, Martin Strauss, Joel Tropp, and Roman Vershynin, Algorithmic linear dimension reduction in the ell-1 norm for sparse vectors. (Preprint, 2006) [See also related conference publication:Allerton 2006] Applications of Compressive Sensing Compressive Imaging Aswin C. Sankaranarayanan, Pavan K. Turaga, Richard G. Baraniuk and Rama Chellappa,Compressive Acquisition of Dynamic Scenes. (European Conference on Computer Vision, Crete, Greece, September 2010) Marco Duarte, Mark Davenport, Dharmpal Takhar, Jason Laska, Ting Sun, Kevin Kelly, and Richard Baraniuk,Single-pixel imaging via compressive sampling. (IEEE Signal Processing Magazine, 25(2), pp. 83 - 91, March 2008) Michael Wakin, Jason Laska, Marco Duarte, Dror Baron, Shriram Sarvotham, Dharmpal Takhar, Kevin Kelly, and Richard Baraniuk,An architecture for compressive imaging. (Int. Conf. on Image Processing (ICIP), Atlanta, Georgia, October 2006) Michael Wakin, Jason Laska, Marco Duarte, Dror Baron, Shriram Sarvotham, Dharmpal Takhar, Kevin Kelly, and Richard Baraniuk,Compressive imaging for video representation and coding. (Proc. Picture Coding Symposium (PCS), Beijing, China, April 2006) Dharmpal Takhar, Jason Laska, Michael Wakin, Marco Duarte, Dror Baron, Shriram Sarvotham, Kevin Kelly, and Richard Baraniuk,A new compressive imaging camera architecture using optical-domain compression. (Computational Imaging IV at SPIE Electronic Imaging, San Jose, California, January 2006) J. Haupt and R. Nowak, Compressive sampling vs conventional imaging. (Int. Conf. on Image Processing (ICIP), Atlanta, Georgia, October 2006) Lu Gan, Block compressed sensing of natural images. (Conf. on Digital Signal Processing (DSP), Cardiff, UK, July 2007) Ray Maleh and Anna Gilbert, Multichannel image estimation via simultaneous orthogonal matching pursuit. (IEEE Workshop on Statistical Signal Processing (SSP), Madison, Wisconsin, August 2007) Ray Maleh, Anna Gilbert, and Martin Strauss, Sparse gradient image reconstruction done faster. (IEEE Conf. on Image Processing (ICIP), San Antonio, Texas, September 2007) Karen Egiazarian, Alessandro Foi, and Vladimir Katkovnik, Compressed sensing image reconstruction via recursive spatially adaptive filtering. (IEEE Conf. on Image Processing (ICIP), San Antonio, Texas, September 2007) Lu Gan, Thong Do, Trac D. Tran, Fast compressive imaging using scrambled block Hadamard ensemble. (Preprint, 2008) V. Stankovic, L. Stankovic, and S. Cheng, Compressive video sampling. (European Signal Processing Conf. (EUSIPCO), Lausanne, Switzerland, August 2008) Roummel Marcia and Rebecca Willett, Compressive coded aperture video reconstruction. (European Signal Processing Conf. (EUSIPCO), Lausanne, Switzerland, August 2008) [See also related conference publication:ICASSP 2008] S. Dekel, Adaptive compressed image sensing based on wavelet-trees. (Preprint, 2008) Volkan Cevher, Aswin Sankaranarayanan, Marco Duarte, Dikpal Reddy, Richard Baraniuk, and Rama Chellappa,Compressive sensing for background subtraction. (European Conf. on Computer Vision (ECCV), Marseille, France, October 2008) L. Jacques, P. Vandergheynst, A. Bibet, V. Majidzadeh, A. Schmid, and Y. Leblebici,CMOS compressed imaging by random convolution. (Preprint, 2008) Pradeep Nagesh and Baoxin Li, Compressive Imaging of Color Images. (Preprint: IEEE Intl. Conf. on Acoustic, Speech & Signal Processing (ICASSP), Taipei, Taiwan, 2009). Roummel Marcia, Zachary Harmany, Rebecca Willett, Compressive Coded Aperture Imaging. (SPIE Electronic Imaging, 2009). W. L. Chan, K. Charan, D. Takhar, K. F. Kelly, R. G. Baraniuk, and D. M. Mittleman,A single-pixel terahertz imaging system based on compressive sensing. (Applied Physics Letters, 93, 121105, 2008) W. L. Chan, M. Moravec, R. Baraniuk, and D. Mittleman, Terahertz imaging with compressed sensing and phase retrieval. (Optics Letters, 33, pp. 974 - 976, 2008) M.B. Wakin, A Manifold Lifting Algorithm for Multi-View Compressive Imaging. (Picture Coding Symposium (PCS), Chicago, Illinois, May 2009) J.Y. Park and M.B. Wakin, A Multiscale Framework for Compressive Sensing of Video. (Picture Coding Symposium (PCS), Chicago, Illinois, May 2009) Albert C. Fannjiang, Compressive inverse scattering I. high-frequency SIMO/MISO and MIMO measurements . (Inverse Problems 26 (2010) 035008 (29pp)) Albert C. Fannjiang, Compressive Imaging of Subwavelength Structures. (Preprint, July, 2009) Albert Fannjiang, Compressive inverse scattering II. SISO measurements with Born scatterers. (Preprint, August 2009) W. Guo, W. Yin, EdgeCS: an edge guided compressive sensing reconstruction. (Rice University CAAM Technical Report TR10-02) S. Mun, J. E. Fowler, Block Compressed Sensing of Images Using Directional Transforms. (Proceedings of Int. Conf. on Image Processing, November 2009) Abdorreza Heidari, D. Saeedkia, A 2D Camera Design with a Single-pixel Detector. (Int. Conf. on Infrared, Millimeter and Terahertz Waves, Busan, South Korea, September 2009) A. Fannjiang, The MUSIC algorithm for sparse objects: an compressed sensing analysis. (Preprint, 2010) [arXiv: 1006.1678] Roummel F. Marcia, Rebecca M. Willett, and Zachary T. Harmany, Compressive Optical Imaging: Architectures and Algorithms. (Optical and Digital Image Processing: Fundamentals and Applications. Edited by G. Cristobal, P. Schelkens, and H. Thienpont.) Albert Fannjiang, Exact localization and superresolution with noisy data and random illumination. (arXiv:1008.3146) Amir Averbuch, Shai Dekel and Shay Deutsch, Adaptive Compressed Image Sensing Using Dictionaries . (preprint) Xianbiao Shu, Narendra Ahuja, Hybrid Compressive Sampling via a New Total Variation TVL1. (European Conference on Computer Vision, Crete, Greece, September 2010) Ahmet F. Coskun, Ikbal Sencan, Ting-Wei Su, and Aydogan Ozcan, Lensless wide-field fluorescent imaging on a chip using compressive decoding of sparse objects. (Opt. Express 18, 10510-10523 (2010)) Ahmet F. Coskun, Ting-wei Su, Ikbal Sencan, and Aydogan Ozcan, Lensfree Fluorescent On-Chip Imaging Using Compressive Sampling. (Optics & Photonics News 21(12), 27-27 (2010) ) Jie Xu, Jianwei Ma, Dongming Zhang, etc., Compressive video sensing based on user attention model. (28th Picture Coding Symposium, PCS 2010, Dec. 8-10, 2010, Nagoya, Japan.) Simon Hawe, Martin Kleinsteuber, and Klaus Diepold, Dense Disparity Maps from Sparse Disparity Measurements. (IEEE International Conference on Computer Vision (ICCV) , Barcelona, November 2011) Xianbiao Shu, Narendra Ahuja, Imaging via Three-dimensional Compressive Sampling (3DCS). (Proc. of ICCV 2011) S. Pudlewski, T. Melodia, A. Prasanna, Compressed-Sensing-Enabled Video Streaming for Wireless Multimedia Sensor Networks. (to appear in IEEE Transactions on Mobile Computing 2011) Jianwe Ma, Gerlind Plonka, M. Y. Hussaini, Compressive Video Sampling with Approximate Message Passing Decoding. (IEEE Trans. on Circuits and Systems for Video Technology, to appear) Yusuke Oike and Abbas El Gamal, A 256x256 CMOS Image Sensor with Î�Σ-Based Single-Shot Compressed Sensing. (IEEE International Solid-State Circuits Conference (ISSCC) Dig. of Tech. Papers, pp.386 -- 387, Feb. 2012.) Yusuke Oike and Abbas El Gamal, A 256x256 CMOS Image Sensor with Delta-Sigma-Based Single-Shot Compressed Sensing. (IEEE International Solid-State Circuits Conference (ISSCC) Dig. of Tech. Papers, pp.386 -- 387, Feb. 2012.) Medical Imaging Michael Lustig, David Donoho, and John M. Pauly, Sparse MRI: The application of compressed sensing for rapid MR imaging. (Magnetic Resonance in Medicine, 58(6) pp. 1182 - 1195, December 2007) [See also related conference publication:ISMRM 2006, SPARS 2005, ISMRM 2005] M. Lustig, J. M. Santos, D. L. Donoho, and J. M. Pauly, k-t SPARSE: High frame rate dynamic MRI exploiting spatio-temporal sparsity. (ISMRM, Seattle, Washington, May 2006) Hong Jung, Jong Chul Ye, and Eung Yeop Kim, Improved k-t BLASK and k-t SENSE using FOCUSS. (Phys. Med. Biol., 52 pp. 3201 - 3226, 2007) Jong Chul Ye, Compressed sensing shape estimation of star-shaped objects in Fourier imaging. (Preprint, 2007) Joshua Trzasko, Armando Manduca, and Eric Borisch, Highly undersampled magnetic resonance image reconstruction via homotopic ell-0-minimization. (IEEE Trans. Medical Imaging 28(1): 106-121, 2009) [See also related conference publication:SSP 2007] I.F. Gorodnitsky, J. George and B.D. Rao, Neuromagnetic source imaging with FOCUSS: A recursive weighted minimum norm algorithm. (Electrocephalography and Clinical Neurophysiology, 95, pp. 231 - 251, 1995) Simon Hu, Michael Lustig, Albert P. Chen, Jason Crane, Adam Kerr, Douglas A.C. Kelley, Ralph Hurd, John Kurhanewicz, Sarah J. Nelsona, John M. Pauly and Daniel B. Vigneron,Compressed sensing for resolution enhancement of hyperpolarized 13C flyback 3D-MRSI. (Journal of Magnetic Resonance, 192(2), pp. 258 - 264, June 2008) T. Cukur, M. Lustig, and D.G. Nishimura, Improving non-contrast-enhanced steady-state free precession angiography with compressed sensing. (Preprint, 2008) Hong Jung, Kyunghyun Sung, Krishna S. Nayak, Eung Yeop Kim, and Jong Chul Ye,k-t FOCUSS: A general compressed sensing framework for high resolution dynamic MRI. (Magnetic Resonance in Medicine, 61:103–116, 2009) Chenlu Qiu, Wei Lu and Namrata Vaswani, Real-time Dynamic MR Image Reconstruction using Kalman Filtered Compressed Sensing. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Taipei, Taiwan, April 2009) Hengyong Yu and Ge Wang, Compressed sensing based interior tomography. (Physics in Medicine and Biology, 54 (2009) 2791–2805) Yoon-Chul Kim, Shrikanth S. Narayanan, Krishna S. Nayak, Accelerated Three-Dimensional Upper Airway MRI Using Compressed Sensing. (Magnetic Resonance in Medicine, 61:1434–1440, 2009) Joshua Trzasko, Clifton Haider, Armando Manduca, Practical Nonconvex Compressive Sensing Reconstruction of Highly-Accelerated 3D Paralllel MR Angiograms. (Proc. of the IEEE International Symposium on Biomedical Imaging, p.1349, June 2009) Yujie Lu, Xiaoqun Zhang, Ali Douraghy, David Stout, Jie Tian, Tony F. Chan, Arion F. Chatziioannou,Source Reconstruction for Spectrally-resolved Bioluminescence Tomography with Sparse A priori Information. (Optics Express 17, 8062-8080, 2009) Rick Chartrand, Fast algorithms for nonconvex compressive sensing: MRI reconstruction from very few data. (IEEE International Symposium on Biomedical Imaging (ISBI), June 2009) H. Jung, J. S. Park, J. H. Yoo, J. C. Ye, Radial k-t FOCUSS for High-Resolution Cardiac Cine Magnetic Resonance Imaging. (In press, Magn. Reson. Med., 2009) J. Y. Choi, M. W. Kim, W. Seong, J. C. Ye, Compressed sensing metal artifact removal in dental CT. (Proc. IEEE International Symposium on Biomedical Imaging (ISBI), pp. 334-337, June 28–July 1, 2009, Boston, USA) H. Jung, J. C. Ye, Performance evalution of accelerated functional MRI acquisition using compressed sensing. (Proc. IEEE International Symposium on Biomedical Imaging (ISBI), pp. 702-705, June 28-July 1, 2009, Boston, USA) J. Provost, F. Lesage, The application of compressed sensing for photo-acoustic tomography. (IEEE Trans Med Imaging, 28(4):585-94, April 2009) Wei Lu, Namrata Vaswani, Modified Compressive Sensing for Real-time Dynamic MR Imaging. (IEEE international conference on Image Processing 2009) Hong Jung, Jong Chul Ye, Motion Estimated and Compensated Compressed sensing dynamic MRI: what we can learn from video compression techniques. (Inter. Jour. Imaging Systems and Technology, 20, pp.81-98, May, 2010) Xiaobo Qu, Weiru Zhang, Di Guo, Congbo Cai, Shuhui Cai, Zhong Chen, Iterative thresholding compressed sensing MRI based on contourlet transform. (Inverse Problems in Science and Engineering, to appear, May 2010) Alin Achim, Benjamin Buxton, George Tzagkarakis, and Panagiotis Tsakalides, Compressive Sensing for Ultrasound RF Echoes Using \alpha Mehmet Süzen and Turgut Durduran et al , Sparse Image Reconstruction in Diffuse Optical Tomography: An Application of Compressed Sensing. (Biomedical Optics (BIOMED) Miami, Florida April 11, 2010) Zachary T. Harmany, Roummel F. Marcia, and Rebecca M. Willett, Sparsity-regularized Photon-limited Imaging. (International Symposium on Biomedical Imaging (ISBI), April 2010) Ricardo Otazo, Daniel Kim, Leon Axel, Daniel Sodickson, Combination of compressed sensing and parallel imaging for highly accelerated first-pass cardiac perfusion MRI. (Magn Reson Med. 2010 Sep;64(3):767-76.) Mehmet Suzen, Alexia Giannoula, and Turgut Durduran,, Compressed sensing in diffuse optical tomograph. (Opt. Express 18, 23676-23690 (2010)) Sajan Goud, Yue Hu, Edward Dibella and Mathews Jacob, Accelerated dynamic MRI exploiting sparsity and low-rank structure: k-t SLR. (IEEE Transactions on Medical Imaging, (in press)) Kangjoo Lee, Sungho Tak, Jong Chul Ye, A data-driven sparse GLM for fMRI analysis using sparse dictionary learning with MDL criterion. (IEEE Trans. on Medical Imaging (to appear)) Oleg Michailovich and Yogesh Rathi, Fast and accurate reconstruction of HARDI data using Compressed Sensing. (Lecture Notes in Computer Science, 2010, Volume 6361/2010, 607-614) [http://www.ece.uwaterloo.ca/~olegm/publications.php] Justin P. Haldar and Zhi-Pei Liang, Spatiotemporal Imaging With Partially Separable Functions: A Matrix Recovery Approach. (IEEE Int. Symp. Biomed. Imag., pp. 716-719, April 2010) Bo Zhao, Justin P. Haldar, Cornelius Brinegar, and Zhi-Pei Liang, Low Rank Matrix Recovery for Real-Time Cardiac MRI. (IEEE Int. Symp. Biomed. Imag., pp. 996-999, April 2010) Bo Zhao, Justin P. Haldar, and Zhi-Pei Liang, PSF Model-Based Reconstruction with Sparsity Constraint: Algorithm and Application to Real-Time Cardiac MRI. (Conf. Proc. IEEE Eng. Med. Bio. Soc., pp. 3390-3393, August 2010) Justin P. Haldar, Diego Hernando, and Zhi-Pei Liang, Compressed-Sensing MRI with Random Encoding. (IEEE Trans. on Medical Imaging, 2010. In press ) M.K. Carroll, G.A.Cecchi, I. Rish, R. Garg, A.R. Rao, Prediction and Interpretation of Distributed Neural Activity with Sparse Models. (Neuroimage 44(1):112-22, 2009) I. Rish, G. Cecchi, M.N. Baliki, V. Apkarian, Sparse Regression Models of Pain Perception. (Proceedings of Brain Informatics (BI-10) conference, Toronto, Canada, August 2010) O. Lee, J.M. Kim, Y. Bresler, and J. C. Ye, Compressive Diffuse Optical Tomography: Non-Iterative Exact Reconstruction using Joint Sparsity. (IEEE Trans. on Medical Imaging, 2011 (in press)) Angshul Majumdar, Rabab K. Ward, An algorithm for sparse MRI reconstruction by Schatten p-norm minimization. (Magnetic Resonance Imaging, Volume 29, Issue 3, April 2011, Pages 408-417) Angshul Majumdar, Rabab K. Ward, Joint reconstruction of multiecho MR images using correlated sparsity. (Magnetic Resonance Imaging, In Press, Corrected Proof, Available online 14 May 2011) Angshul Majumdar, Rabab K. Ward, Accelerating multi-echo T2 weighted MR imaging: Analysis prior group-sparse optimization. (Journal of Magnetic Resonance, Volume 210, Issue 1, May 2011, Pages 90-97) Martin F. Schiffner, Georg Schmitz, Rapid Measurement of Ultrasound Transducer Fields in Water Employing Compressive Sensing. (Proceedings of the IEEE International Ultrasonics Symposium (IUS), San Diego, CA, USA, October 2010, pp. 1849-1852) JD Trzasko, CR Haider, EA Borisch, NG Campeau, JF Glockner, SJ Riederer, Sparse-CAPR: Highly accelerated 4D CE-MRA with parallel imaging and nonconvex compressive sensing. (Magnetic Resonance in Medicine, in press) JD Trzasko, Z Bao, A Manduca, KP McGee, and MA Bernstein, Sparsity and low-contrast object detectability. (Magnetic Resonance in Medicine, in press) D. Friboulet, H. Liebgott, R. Prost, Compressive sensing for raw RF signals reconstruction in ultrasound. (IEEE International Ultrasonics Symposium, San Diego, California, USA, 2010, pp. 367-370.) Martin F. Schiffner and Georg Schmitz, Fast Pulse-Echo Ultrasound Imaging Employing Compressive Sensing. (Proceedings of the IEEE International Ultrasonics Symposium (IUS), Orlando, FL, USA, October 2011, in press) Zheng Liu, postdoc. (Proc. SPIE 7961, 79613Z, Feb. 2011) Martin F. Schiffner and Georg Schmitz, Fast Pulse-Echo Ultrasound Imaging Employing Compressive Sensing. (Proceedings of the IEEE International Ultrasonics Symposium (IUS), Orlando, FL, USA, October 2011, in press) Florian Knoll, Christian Clason, Kristian Bredies, Martin Uecker, and Rudolf Stollberger,Parallel Imaging with Nonlinear Reconstruction using Variational Penalties. (Magnetic Resonance in Medicine, 2011, DOI:10.1002/mrm.22964) [See also related conference publication:ISMRM 2008] Zheng Liu; Brian Nutter; Jingqi Ao; Sunanda Mitra, Wavelet encoded MR image reconstruction with compressed sensing. (Proc. SPIE 7961, 79613Z (2011); http://dx.doi.org/10.1117/12.878707) Leyuan Fang, Shutao Li, Qing Nie, Joseph A. Izatt, Cynthia A. Toth, and Sina Farsiu,Sparsity based denoising of spectral domain optical coherence tomography images. (Biomedical Optics Express, Vol. 3, Issue 5, pp. 927-942, May 2012) Analog-to-Information Conversion Joel Tropp, Michael Wakin, Marco Duarte, Dror Baron, and Richard Baraniuk, Random filters for compressive sampling and reconstruction. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Toulouse, France, May 2006) Sami Kirolos, Jason Laska, Michael Wakin, Marco Duarte, Dror Baron, Tamer Ragheb, Yehia Massoud, and Richard Baraniuk,Analog-to-information conversion via random demodulation. (IEEE Dallas Circuits and Systems Workshop (DCAS), Dallas, Texas, 2006) Jason Laska, Sami Kirolos, Yehia Massoud, Richard Baraniuk, Anna Gilbert, Mark Iwen, and Martin Strauss,Random sampling for analog-to-information conversion of wideband signals. (IEEE Dallas Circuits and Systems Workshop (DCAS), Dallas, Texas, 2006) Jason Laska, Sami Kirolos, Marco Duarte, Tamer Ragheb, Richard Baraniuk, and Yehia Massoud,Theory and implementation of an analog-to-information converter using random demodulation. (IEEE Int. Symp. on Circuits and Systems (ISCAS), New Orleans, Louisiana, 2007) Tamer Ragheb, Sami Kirolos, Jason Laska, Anna Gilbert, Martin Strauss, Richard Baraniuk, and Yehia Massoud,Implementation models for analog-to-information conversion via random sampling. (Midwest Symposium on Circuits and Systems (MWSCAS), 2007) Yonina Eldar, Compressed sensing of analog signals. (Preprint, 2008) Moshe Mishali and Yonina Eldar, Blind multi-band signal reconstruction: compressed sensing for analog signals. (IEEE Trans. on Signal Processing, 57(30), pp. 993-1009, March 2009) Farid M. Naini, Rémi Gribonval, Laurent Jacques, and Pierre Vandergheynst, Compressive sampling of pulse trains: Spread the spectrum! (Preprint, 2008) Moshe Mishali, Yonina Eldar, and Joel Tropp, Efficient sampling of sparse wideband analog signals. (Conv. IEEE in Israel (IEEEI), Eilat, Israel, December 2008) Joel Tropp, Jason Laska, Marco Duarte, Justin Romberg, and Richard Baraniuk, Beyond Nyquist: Efficient sampling of sparse bandlimited signals. (Preprint, 2009) Moshe Mishali and Yonina Eldar, From theory to practice: Sub-Nyquist sampling of sparse wideband analog signals. (IEEE Journal of Selected Topics on Signal Processing, 4(2), pp. 375-391, April 2010) Mark Davenport, Petros Boufounos, and Richard Baraniuk, Compressive domain interference cancellation. (Workshop on Signal Processing with Adaptive Sparse Structured Representations (SPARS), Saint-Malo, France, April 2009) Kfir Gedalyahu and Yonina Eldar, Time delay estimation from low rate samples: A union of subspaces approach. (To appear in IEEE Transactions on Signal Processing) John Treichler, Mark Davenport, and Richard Baraniuk, Application of compressive sensing to the design of wideband signal acquisition receivers. (6th U.S. / Australia Joint Workshop on Defense Applications of Signal Processing (DASP), Lihue, Hawaii, September 2009) Moshe Mishali, Yonina Eldar, and Asaf Elron, Xampling: Signal acquisition and processing in union of subspaces. (CCIT Report #747 Oct-09, EE Pub No. 1704, EE Dept., Technion; [Online] arXiv 0911.0519, Oct. 2009) Moshe Mishali and Yonina Eldar, Expected RIP: Conditioning of the modulated wideband converter (ITW, October 2009) Moshe Mishali, Yonina Eldar, Oleg Dounaevsky and Eli Shoshan, Xampling: Analog to digital at sub-nyquist rates. (To appear in IET, Circuits, Devices & Systems; CCIT Report #751 Dec-09, EE Pub No. 1708, EE Dept., Technion, Dec. 2009) Mark Davenport, Stephen Schnelle, J.P. Slavinsky, Richard Baraniuk, Michael Wakin, and Petros Boufounos,A wideband compressive radio receiver. (Military Communications Conference (MILCOM), San Jose, California, October 2010) Moshe Mishali and Yonina Eldar, Xampling: Compressed sensing of analog signals. (Compressed Sensing: Theory and Applications (Chapter inCompressed Sensing: Theory and Applications , Cambridge University Press, 2012) Michael Lexa, Mike Davies, and John Thompson, Reconciling compressive sampling systems for spectrally-sparse continuous-time signals. (Preprint, 2011) Mark Davenport, Jason Laska, John Treichler, and Richard Baraniuk. The pros and cons of compressive sensing for wideband signal acquisition: Noise folding vs. dynamic range. (Preprint, April 2011) J.P. Slavinsky, Jason Laska, Mark Davenport, and Richard Baraniuk, The compressive multiplexer for multi-channel compressive sensing (IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Prague, Czech Republic, May 2011) Michael Lexa, Mike Davies and John Thompson, Reconciling compressive sampling systems for spectrally-sparse continuous-time signals. (Revised preprint, May 2011) Mark Davenport and Michael Wakin, Reconstruction and cancellation of sampled multiband signals using discrete prolate spheroidal sequences. (Workshop on Signal Processing with Adaptive Sparse Structured Representations (SPARS), Edinburgh, Scotland, June 2011) John Treichler, Mark Davenport, Jason Laska, and Richard Baraniuk, Dynamic range and compressive sensing acquisition receivers. (7th U.S. / Australia Joint Workshop on Defense Applications of Signal Processing (DASP), Coolum, Australia, July 2011) Mark Davenport and Michael Wakin, Compressive sensing of analog signals using discrete prolate spheroidal sequences. (Preprint, September 2011) Stephen Schnelle, J.P. Slavinsky, Petros Boufounos, Mark Davenport, and Richard Baraniuk.A compressive phase-locked loop. (Preprint, September 2011) Rogers, D.J., Elkis, R., Sang Chin, Wayne, M.A. ; , Compressive RF sensing using a physical source of entropy. (IEEE Statistical Signal Processing Workshop, pp. 609 - 612, June 2011) Ewa Matusiak and Yonina C. Eldar, Sub-Nyquist Sampling of Short Pulses. (IEEE Trans. on Signal Processing) Computational Biology Mona Sheikh, Olgica Milenkovic, and Richard Baraniuk, Compressed sensing DNA microarrays. (Rice ECE Department Technical Report TREE 0706, May 2007) Mona Sheikh, Shriram Sarvotham, Olgica Milenkovic, and Richard Baraniuk, DNA array decoding from nonlinear measurements by beleif propagation. (IEEE Workshop on Statistical Signal Processing (SSP), Madison, Wisconsin, August 2007) Mona Sheikh, Olgica Milenkovic, and Richard Baraniuk, Designing compressive sensing DNA microarrays. (IEEE Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), St. Thomas, U.S. Virgin Islands, December 2007) Wei Dai, Mona Sheikh, Olgica Milenkovic, and Richard Baraniuk, Compressive sensing DNA microarrays. (Preprint, 2008) Noam Shental, Amnon Amir, Or Zuk, Rare-Allele Detection Using Compressed Se(que)nsing. (Preprint, September 2009) Mojdeh Mohtashemi, Haley Smith, Felicia Sutton, David Walburger, James Diggans,Sparse Sensing DNA Microarray-Based Biosensor: Is It Feasible?. (2010 IEEE Sensors and Applications) Amnon Amir and Or Zuk, Bacterial Community Reconstruction Using A Single Sequencing Reaction. (arxiv preprint) Geophysical Data Analysis Tim Lin, Felix. J. Herrmann, Compressed wavefield extrapolation. (To appear in Geophysics, 2007) [See also related conference publication:SEG 2007] Felix J. Herrmann, Deli Wang, Gilles Hennenfent, Peyman Moghaddam, Curvelet-based seismic data processing: a multiscale and nonlinear approach. (To appear in Geophysics, 2007) Felix J. Herrmann, Gilles Hennenfent, Non-parametric seismic data recovery with curvelet frames. (UBC Earth & Ocean Sciences Department Technical Report TR-2007-1, 2007) Gilles Hennenfent and Felix J. Herrmann, Curvelet reconstruction with sparsity-promoting inversion: successes and challenges. (EAGE 2007) Gilles Hennenfent, Felix J. Herrmann, Irregular sampling: from aliasing to noise. (EAGE 2007) Felix J. Herrmann, Deli Wang, Gilles Hennenfent, Multiple prediction from incomplete data with the focused curvelet transform. (SEG 2007) Challa Sastry, Gilles Hennenfent, Felix J. Herrmann, Signal reconstruction from incomplete and misplaced measurements. (EAGE 2007) Felix J. Herrmann, Surface related multiple prediction from incomplete data. (EAGE 2007) Gilles Hennenfent and Felix J. Herrmann, Simply denoise: wavefield reconstruction via jittered undersampling. (Geophysics, 2008) R. Neelamani, C. Krohn, J. Krebs, M. Deffenbaugh, J. Romberg, Efficient Seismic Forward Modeling using Simultaneous Random Sources and Sparsity. (Society of Exploration Geophysicists (SEG) Annual Meeting, November 2008) Wen Tang, Jianwei Ma, Felix J. Herrmann, Optimized compressed sensing for curvelet-based seismic data reconstruction (Preprint, 2009) Felix J. Herrmann, Yogi A. Erlangga, Tim T. Y. Lin, Compressive simultaneous full-waveform simulation. (Submitted to Geophysics 74, A35, 2009) Felix J. Herrmann, Compressive imaging by wavefield inversion with group sparsity. (SEG 2009, Houston, TX, Technical Report TR-2009-01) Felix J. Herrmann, Yogi A. Erlangga, Tim T. Y. Lin, Compressive-wavefield simulations. (SAMPTA 2009, Marseille, France) Felix J. Herrmann, Sub-Nyquist sampling and sparsity: getting more information from fewer samples. (SEG 2009, Houston, TX) Gang Tang, Reza Shahidi, Felix J. Herrmann, Jianwei Ma, Higher dimensional blue-noise sampling schemes for curvelet-based seismic data recovery. (SEG 2009, Houston, TX) Tim T.Y. Lin, Felix J. Herrmann, Unified compressive sensing framework for simultaneous acquisition with primary estimation. (SEG 2009, Houston, TX, Technical Report TR-2009-02) Jafarpour B., Goyal V.K., Freeman W.T, McLaughlin D.B., Compressed History Matching: Exploiting Transform-Domain Sparsity for Regularization of Nonlinear Dynamic Data Integration Problems. (Mathematical Geosciences. Geophysics, 74, R69) Jafarpour B., Goyal V.K., Freeman W.T, McLaughlin D.B., Transform-domain Sparsity Regularization for Inverse Problems in Geosciences. (Geophysics, 74, R69, 2009) Mostafa Naghizadeh, Mauricio Sacchi, Beyond alias hierarchical scale curvelet interpolation of regularly and irregularly sampled seismic data. (Submitted to Geophysics, December 2009) Gholami A., H.R. Siahkoohi, Regularization of linear and non-linear geophysical ill-posed problems with joint sparsity constraints. (Geophys. J. Int., Vol. 180, 2, 871-882) Ismael Vera Rodriguez, Mauricio D. Sacchi and Yu J. Gu, Toward a near real-time system for event hypocenter and source mechanism recovery via compressive sensing. (SEG Annual Meeting 2010 Expanded Abstracts) H. Yao, P. Gerstoft, P. M. Shearer, and C. Mecklenbräuker , Compressive sensing of the Tohokuâ��Oki Mw 9.0 earthquake: Frequencyâ��dependent rupture modes. (Geophys. Res. Lett., 38, L20310, doi:10.1029/2011GL049223. October 2011) Yi Yang, Jianwei Ma, Stanley Osher, Sesimic data reconstruction via matrix completion. (UCLA, CAM Report 12-14) Hyperspectral Imaging Rebecca Willett, Michael Gehm, and David Brady, Multiscale reconstruction for computational spectral imaging. (Computational Imaging V at SPIE Electronic Imaging, San Jose, California, January 2007) Henry Arguello and Gonzalo R. Arce, Code aperture optimization for spectrally agile compressive imaging. (JOSA A, Vol. 28 Issue 11, pp.2400-2413 (2011)) Compressive Radar Richard Baraniuk and Philippe Steeghs, Compressive radar imaging. (IEEE Radar Conference, Waltham, Massachusetts, April 2007) Sujit Bhattacharya, Thomas Blumensath, Bernard Mulgrew, and Mike Davies, Fast encoding of synthetic aperture radar raw data using compressed sensing. (IEEE Workshop on Statistical Signal Processing, Madison, Wisconsin, August 2007) Matthew Herman and Thomas Strohmer, High-resolution radar via compressed sensing. (To appear in IEEE Trans. on Signal Processing) Lee Potter, Phil Schniter, and Justin Ziniel, Sparse reconstruction for RADAR. (SPIE Algorithms for Synthetic Aperture Radar Imagery XV, 2008) Randy Moses, Müjdat Çetin, and Lee Potter, Wide angle SAR imaging. (SPIE Algorithms for Synthetic Aperture Radar Imagery XI, Orlando, Florida, April, 2004) Kush R. Varshney, Müjdat Çetin, John W. Fisher, and Alan S. Willsky. Sparse representation in structured dictionaries with application to synthetic aperture radar. (IEEE Transactions on Signal Processing, 56(8), pp. 3548 - 3561, August 2008) Albert C. Fannjiang, Penchong Yan and Thomas Strohmer, Compressed Remote Sensing of Sparse Objects. (arXiv:0904.3994) C.R. Berger, S. Zhou, P. Willett, Signal Extraction Using Compressed Sensing for Passive Radar with OFDM Signals. (Proc. of the 11th Int. Conf. on Information Fusion, Cologne, Germany, July 2008) C.R. Berger, S. Zhou, P. Willett, B. Demissie, J. Heckenbach, Compressed Sensing for OFDM/MIMO Radar. (Proc. of the 42nd Annual Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, Oct. 2008) Sibi Raj Bhaskaran, Linda Davis, Alex Grant, Stephen Hanly, Paul Tune, Downlink Scheduling Using Compressed Sensing. (Information Theory Workshop (ITW) 2009, Volos, Greece) Budillon, A. ; Evangelista, A. ; Schirinzi, G. ; , SAR tomography from sparse samples . (Geoscience and Remote Sensing Symposium,2009 IEEE International,IGARSS 2009 ) Graeme E. Smith, Tom Diethe, Zakria Hussain, John Shawe-Taylor, David R. Hardoon,Compressive Sampling for Pulse Doppler Radar. (In Proceedings of the IEEE International Radar Conference, 2010) Xie Xiao-Chun and Zhang Yun-Hua, High-resolution imaging of moving train by ground-based radar with compressive sensing. (Electron Lett, 2010, 46, (7), pp. 529-531, April 2010) Joachim H.G. Ender, On compressive sensing applied to radar. (Signal Processing, 90(5), pp. 1402-1414, May 2010) W.U. Bajwa, K. Gedalyahu, and Y.C. Eldar, Identification of underspread linear systems with application to super-resolution radar. (Submitted for journal publication, Aug. 2010.) Mahesh C. Shastry, Ram M. Narayanan, and Muralidhar Rangaswamy, Compressive Radar Imaging Using White Stochastic Waveforms. (5th IEEE International Conference on Waveform Diversity and Design, Niagara Falls, ON, Canada, August 2010) Xiao Xiang Zhu & Richard Bamler, Tomographic SAR Inversion by L1 Norm Regularization - The Compressive Sensing Approach. (IEEE Transactions on Geoscience and Remote Sensing, 48(10), pp. 3839-3846) Xiao Xiang Zhu & Richard Bamler, Compressive sensing for high resolution differential SAR tomography-the SL1MMER algorithm. (Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International, pp.17-20) Xiao Xiang Zhu & Richard Bamler, Super-resolution for 4-D SAR Tomography via Compressive Sensing. (EUSAR 2010 - 8th European Conference on Synthetic Aperture Radar, Aachen, Germany) Budillon, A.; Evangelista, A.; Schirinzi, G.; , Three-Dimensional SAR Focusing From Multipass Signals Using Compressive Sampling . (IEEE Trans on Geoscience and Remote Sensing, vol. 49, pp. 488-499, Jan. 2011) Xiao Xiang Zhu, Very High Resolution Tomographic SAR Inversion for Urban Infrastructure Monitoring â�� A Sparse and Nonlinear Tour. (Deutsche Geodätische Kommission, Reihe C, Nr. 666, Verlag der Bayerischen Akademie der Wissenschaften, ISBN 978-3-7696-5078-5) Astronomy J. Bobin, J.-L. Starck, and R. Ottensamer, Compressed sensing in astronomy. (Preprint, 2008) Y. Wiaux, L. Jacques, G. Puy, A. M. M. Scaife, and P. Vandergheynst, Compressed sensing imaging techniques for radio interferometry (To appear, Monthly Notices of the Royal Astronomical Society, 2009) Communications S.F. Cotter and B.D. Rao, Sparse channel estimation via matching pursuit with application to equalization. (IEEE Trans. on Communications, 50(3), March 2002) Georg Taubŏck and Franz Hlawatsch, A compressed sensing technique for OFDM channel estimation in mobile environments: Exploiting channel sparsity for reducing pilots. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, Nevada, April 2008) Waheed U. Bajwa, Jarvis Haupt, Gil Raz, and Robert Nowak, Compressed channel sensing. (Conf. on Info. Sciences and Systems (CISS), Princeton, New Jersey, March 2008) Waheed U. Bajwa, Akbar M. Sayeed, and Robert Nowak, Learning sparse doubly-selective channels. (Proc. Allerton Conference on Communication, Control, and Computing, Monticello, IL, September 2008) [See also relatedtechnical report] Yasamin Mostofi and Pradeep Sen, Compressed mapping of communication signal strength. (Military Communications Conference, San Diego, CA, November 2008) Waheed U. Bajwa, Akbar Sayeed, and Robert Nowak, Compressed sensing of wireless channels in time, frequency, and space. (Asilomar Conf. on Signals, Systems, and Computers, Pacific Grove, California, October 2008) Waheed U. Bajwa, Akbar Sayeed, and Robert Nowak, Sparse multipath channels: Modeling and estimation. (IEEE Digital Signal Proc. Workshop, Marco Island, Florida, January 2009) Alyson K. Fletcher, Sundeep Rangan, and Vivek K Goyal, On-Off Random Access Channels: A Compressed Sensing Framework. (Submitted to IEEE Trans. Information Theory) G. Tauböck and F. Hlawatsch, Compressed sensing based estimation of doubly selective channels using a sparsity-optimized basis expansion. (in Proceedings of EUSIPCO 2008, (Lausanne, Switzerland), Aug. 2008) Georg Tauböck, Franz Hlawatsch, Daniel Eiwen, and Holger Rauhut, Compressive estimation of doubly selective channels in multicarrier systems: Leakage effects and sparsity-enhancing processing. (IEEE J. Sel. Top. Signal Process., vol. 4, no. 2, Apr. 2010, pp. 255-271) P. Zhang, Z. Hu, R. C. Qiu and B. M. Sadler, Compressive Sensing Based Ultra-wideband Communication System. (IEEE ICC'09, Dresden, Germany, Jun. 14-18, 2009) C.R. Berger, S. Zhou, J. Preisig, Peter Willett, Sparse Channel Estimation for Mutlicarrier Underwater Acoustic Communications: From Subspace Methods to Compressed Sensing. (MTS/IEEE Oceans, Bremen, Germany, May 2009) C.R. Berger, S. Zhou, W. Chen, Peter Willett, Sparse Channel Estimation for OFDM: Over-Complete Dictionaries and Super-Resolution Methods. (IEEE Intl. Workshop on Signal Process. Advances in Wireless Comm., Perugia, Italy, June 2009) H. Zayyani, M. Babaie-Zadeh, C. Jutten, Compressed Sensing Block MAP-LMS Adaptive Filter for Sparse Channel Estimation and a Bayesian Cramer-Rao Bound. (MLSP 2009) J. Meng, J. Ahmadi-Shokouh, H. Li, E. J. Charlson, Z. Han, S. Noghanian, E. Hossain,Sampling Rate Reduction for 60 GHz UWB Communication using Compressive Sensing. (Asilomar Conf. on Signals, Systems, and Computers, Monterey, California, November 2009) J. Meng, W. Yin, H. Li, E. Houssain, Z. Han, Collaborative spectrum sensing from sparse observations using matrix completion for cognitive radio networks. (ICASSP, Dallas, TX, March 2010) Waheed U. Bajwa, Jarvis Haupt, Akbar M. Sayeed, and Robert Nowak, Compressed Channel Sensing: A New Approach to Estimating Sparse Multipath Channels (Proc. IEEE, June 2010) A. Oka and L. Lampe, Compressed Sensing Reception of Bursty UWB Impulse Radio is Robust to Narrow-band Interference. (IEEE Global Communications Conference (GLOBECOM 2009), Honolulu, Hawaii, USA, November-December 2009) [] A. Oka and L. Lampe, A Compressed Sensing Receiver for Bursty Communication with UWB Impulse Radio. (IEEE International Conference on Ultra-Wideband (ICUWB), Vancouver, BC, Canada, September 2009) [] Daniel Eiwen, Georg Tauböck, Franz Hlawatsch, Holger Rauhut, and Nicolai Czink,Multichannel-compressive estimation of doubly selective channels in MIMO-OFDM systems: Exploiting and enhancing joint sparsity. (in Proc. IEEE ICASSP-10, Dallas, TX, Mar. 2010, pp. 3082-3085) Daniel Eiwen, Georg Tauböck, Franz Hlawatsch, and Hans Georg Feichtinger, Group sparsity methods for compressive channel estimation in doubly dispersive multicarrier systems. (in Proc. IEEE SPAWC-10, Marrakech, Morocco, June 2010) Moshe Mishali and Yonina C. Eldar, Wideband Spectrum Sensing at Sub-Nyquist Rates. (to appear in IEEE Signal Processing Magazine; [Online] arXiv 1009.1305.) Wotao Yin, Zaiwen Wen, Shuyi Li, Jia (Jasmine) Meng, and Zhu Han, Dynamic Compressive Spectrum Sensing for Cognitive Radio Networks. (Rice CAAM Technical Report TR11-04) Christian R. Berger, Shengli Zhou, James C. Preisig, and Peter Willett, Sparse Channel Estimation for Multicarrier Underwater Acoustic Communication: From Subspace Methods to Compressed Sensing. (IEEE Trans. Signal Process., vol. 58, no. 3, pp. 1708--1721, Mar. 2010 ) Christian R. Berger, Zhaohui Wang, Jianzhong Huang, and Shengli Zhou, Application of Compressive Sensing to Sparse Channel Estimation. (IEEE Communications Magazine, (invited), vol. 48, no. 11, pp. 164-174, Nov. 2010) Ahmad Gomaa, K.M. Zahidul Islam, and Naofal Al-Dhahir , Two novel compressed-sensing algorithms for NBI detection in OFDM systems. (IEEE ICASSP 2010, Dallas, TX, USA) Ahmad Gomaa and Naofal Al-Dhahir, A Compressive Sensing Approach to NBI Cancellation in Mobile OFDM Systems . (IEEE GLOBECOM 2010, Miamim, FL, USA) Ahmad Gomaa and Naofal Al-Dhahir, A Sparsity-Aware Approach for NBI Estimation in MIMO-OFDM. (To appear in IEEE Transactions on Wireless Communications) Ahmad Gomaa and Naofal Al-Dhahir, A New Design Framework for Sparse FIR MIMO Equalizers. (To appear in IEEE Transactions on Communications) Ahmad Gomaa and Naofal Al-Dhahir, Low-Complexity Sparse FIR Channel Shortening. (IEEE GLOBECOM 2010, Miami, FL, USA) Daniel Eiwen, Georg Tauböck, Franz Hlawatsch, Hans Georg Feichtinger, Compressive Tracking of Doubly Selective Channels in Multicarrier Systems Based on Sequential Dealy-Doppler Sparsity. (Proc. IEEE ICASSPâ��11, Prague, Czech Republic, May 2011) Jia Meng, Wotao Yin, Yingying Li, Nam T. Nguyen, and Zhu Han, Compressive Sensing Based High Resolution Channel Estimation for OFDM System. (IEEE Journal of Selected Topics in Signal Processing, Special Issue on Robust Measures and Tests Using Sparse Data, accepted) Yao Xie, Yonina C. Eldar, Andrea Goldsmith, Reduced-dimension multiuser detection. (Submitted to IEEE Trans. on Information Theory, Oct. 2011) A. Sen Gupta, J. Preisig, A geometric mixed norm approach to shallow water acoustic channel estimation and tracking. (Physical Communication, 5 (2), pp. 119-128, June 2012) Surface Metrology Jianwei Ma, Compressed sensing for surface characterization and metrology. (IEEE Transactions on Instrument and Measurement, to appear) Jianwei Ma, Compressed Sensing for Surface Characterization and Metrology . ((IEEE Transactions on Instrument and Measurement, 2010, 59 (6), 1600-1615.) Acoustics, Audio, and Speech Processing Y. N. Lilis, D. Angelosante, G. B. Giannakis, Sound Field Reproduction using the Lasso. (Accepted to IEEE Trans. on Audio, Speech and Language Processing, 2010) Anthony Griffin, Toni Hirvonen, Christos Tzagkarakis, A. Mouchtaris, and P. Tsakalides,Single-channel and Multi-channel Sinusoidal Audio Coding Using Compressed Sensing. (IEEE Trans. on Audio, Speech, and Language Processing (in Press)) Remote Sensing Jianwei Ma and Francois-Xavier Le Dimet, Deblurring from highly incomplete measurements for remote sensing. (IEEE Trans. Geoscience and Remote Sensing, 47 (3), 792-802, 2009) Jianwei Ma, Single-pixel remote sensing (IEEE Geoscience and Remote Sensing Letters, 6(2), pp. 199-203, 2009) Atul Divekar, Okan Ersoy, Image Fusion by Compressive Sensing. (Geoinformatics 2009, to appear) Jianwei Ma, Improved iterative curvelet thresholding for compressed sensing and measurement. (IEEE Trans. on Intrumentation and Measurement, 2010, to appear) A. Shaharyar Khwaja, Jianwei Ma, Applications of Compressed Sensing for SAR Moving-Target Velocity Estimation and Image Compression. (IEEE Trans. on Instrumentation and Measurement, to appear.) Jianwei Ma, M. Yousuff Hussaini, Extensions of Compressed Imaging: Flying Sensor, Coded Mask, and Fast Decoding. (IEEE Trans. on Instrumentation and Measurement, to appear.) Pierre Borgnat and Patrick Flandrin, Time-frequency localization from sparsity constraints. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, Nevada, April 2008) G. Oliveri, P. Rocca, and A. Massa, A Bayesian-Compressive-Sampling-Based Inversion for Imaging Sparse Scatterers. (IEEE Trans. Geoscience Remote Sensing, vol 49, no. 10, pp. 3993 - 4006, Oct. 2011) [www.eledia.ing.unitn.it] Xiao Xiang Zhu and Richard Bamler, Super-Resolution Power and Robustness of Compressive Sensing for Spectral Estimation With Application to Spaceborne Tomographic. (IEEE Transaction on Geoscience and Remote Sensing, in press, 2011) Xiao Xiang Zhu, Xuan Wang and Richard Bamler, Compressive Sensing for Image Fusion â�� with Application to Pan-Sharpening. (Proceedings of the IEEE International Geoscience & Remote Sensing Symposium (IGARSS), Vancouver, Canada, 2011) Xiao Xiang Zhu and Richard Bamler, Within The Resolution Cell: Super-Resolution in Tomographic SAR Imaging. (Proceedings of the IEEE International Geoscience & Remote Sensing Symposium (IGARSS), Vancouver, Canada, 2011) Jianwei Ma, Improved iterative curvelet thresholding for compressed sensing and measurement. (IEEE Trans. on Intrumentation and Measurement, 2011, 60 (1), 126-136.) A. Shaharyar Khwaja, Jianwei Ma, Applications of Compressed Sensing for SAR Moving-Target Velocity Estimation and Image Compression. (IEEE Trans. on Instrumentation and Measurement, 2011, 60 (8), 2848-2860.) Jianwei Ma, M. Yousuff Hussaini, Extensions of Compressed Imaging: Flying Sensor, Coded Mask, and Fast Decoding. (IEEE Trans. on Intrumentation and Measurement, 2011, 60 (9), 3128-3139.) Siwei Yu, A. Shaharyar Khwaja, Jianwei Ma, Compressed sensing of complex-valued data. (Signal Processing, 2012, 92 (2), 357-362) Computer Engineering Davood Shamsi, Petros Boufounos, and Farinaz Koushanfar, Noninvasive leakage power tomography of integrated circuits by compressive sensing. (Preprint, 2008) Tomas Tuma, Sean Rooney, Paul Hurley, On the Applicability of Compressive Sampling in Fine Grained Processor Performance Monitoring. (14th IEEE International Conference on Engineering of Complex Computer Systems, 2009) Computer Graphics Pradeep Sen and Soheil Darabi, Compressive dual photography. (Computer Graphics Forum, March 2009) Pradeep Sen, Soheil Darabi, Compressive Rendering: A Rendering Application of Compressed Sensing. (Accepted, IEEE Trans. on Visualization and Comp. Graphics, 2010) Robotics & Control Yasamin Mostofi, Pradeep Sen, Compressive Cooperative Sensing and Mapping in Mobile Networks. (Proceedings of American Control Conference (ACC), Page(s):3397 - 3404, June 2009) Sourabh Bhattacharya and Tamer Basar, Sparsity Based Feedback Design: A New Paradigm in Opportunistic Sensing. (American Control Conference, pp 3704-3709, 2011) Masaaki Nagahara and Daniel E. Quevedo, Sparse Representations for Packetized Predictive Networked Control. (IFAC 18th World Congress, pp. 84-89, August 2011) Content Based Retrieval Atul Divekar, Okan Ersoy, Compact Storage of Correlated Data for Content Based Retrieval. (accepted to the 43rd Asilomar Conference on Signals,Systems and Computers) Optics and Holography David J. Brady, Kerkil Choi, Daniel L. Marks, Ryoichi Horisaki, Sehoon Lim, Compressive Holography. (Opt. Express 17, 13040-13049, 2009) Loïc Denis, Dirk Lorenz, Eric Thiébaut, Corinne Fournier, Dennis Trede, Inline hologram reconstruction with sparsity constraints. (Opt.Lett. 34, 3475-3477, 2009) A. Bourquard, F. Aguet, M. Unser, Optical Imaging with Binary Sensors. (In press, OSA journal Optics Express, 2010) Snir Gazit, Alexander Szameit, Yonina C. Eldar, and Mordechai Segev, Super-resolution and reconstruction of sparse sub-wavelength images. ( Opt. Express 17, 23920-23946 (2009) ) Shechtman, Yoav; Gazit, Snir; Szameit, Alexander; Eldar, Yonina C; Segev, Mordechai,Super-resolution and reconstruction of sparse images carried by incoherent light. (Optics Letters 35, 1148-1150 (2010)) Lei Tian, Justin Lee, Se Baek Oh, George Barbastathis, Experimental verification of compressive reconstruction of correlation functions in Ambiguity space. (eprint arXiv:1109.1322) J. Oliver, WoongBi Lee, SangJunPark, and Heung-No Lee, Improving resolution of miniature spectrometers by exploiting sparse nature of signals. (Optics Express, Accepted for Publication, Jan 2012) Yair Rivenson, Adrian Stern and Bahram Javidi, Compressive Fresnel Holography. (IEEE/OSA Display Technology, Journal of , vol.6, no.10, pp.506-509) Yair Rivenson, Adrian Stern and Joseph Rosen, Compressive multiple view projection incoherent holography. (Opt. Express 19, 6109-6118 (2011)) Yair Rivenson and Adrian Stern, Conditions for practicing compressive Fresnel holography. (Opt. Lett. Vol. 36 (17) pp. 3365â��3367 (2011)) Yair Rivenson, Alon Rot, Sergey Balber, Adrian Stern and Joseph Rosen, Recovery of partially occluded objects by applying compressive Fresnel holography. (Opt. Lett., accepted (avilable on early positng) ) Lei Tian, Justin Lee, Se Baek Oh, and George Barbastathis, Experimental compressive phase space tomography. (Lei Tian, Justin Lee, Se Baek Oh, and George Barbastathis, "Experimental compressive phase space tomography," Opt. Express 20, 8) Physics D. Gross, Y.-K. Liu, S.T. Flammia, S. Becker, J. Eisert, Quantum state tomography via compressed sensing. (Preprint, 2010) A. Shabani, R. L. Kosut, H. Rabitz, Compressed Quantum Process Tomography. (Preprint, 2009) A. Shabani, M. Mohseni, S. Lloyd, R. L. Kosut, H. Rabitz, Efficient estimation of many-body quantum Hamiltonians via random measurements. (Preprint, 2010) Rapid Measurement of Ultrasound Transducer Fields in Water Employing Compressive Sensing,Applications of Compressive Sensing, Physics. (IEEE International Ultrasonics Symposium 2010, pp. 1849-1852) Massimo Fornasier, Jan Haskovec, and Jan Vybiral, Particle Systems and Kinetic Equations Modeling Interacting Agents in High Dimension. (preprint 2011) A. Shabani, R. L. Kosut, M. Mohseni, H. Rabitz, M. A. Broome, M. P. Almeida, A. Fedrizzi, and A. G. White,Efficient Measurement of Quantum Dynamics via Compressive Sensing. (Phys. Rev. Lett. 106, 100401 (2011)) David Gross, Yi-Kai Liu, Steven T. Flammia, Stephen Becker, and Jens Eisert, Quantum State Tomography via Compressed Sensing. (Phys. Rev. Lett. 105, 150401 (2010)) Fault Identification Danny Bickson, Dror Baron, Alex T. Ihler, Harel Avissar, Danny Dolev, Fault Identification via Non-parametric Belief Propagation. (Submitted to Trans. on Signal Processing, July, 2010) Mark Accepted Talks Richard Baraniuk, Justin Romberg, and Michael Wakin, Tutorial on compressive sensing (2008 Information Theory and Applications Workshop) Petros Boufounos, Justin Romberg and Richard Baraniuk, Compressive sensing - Theory and applications (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, Nevada, April 2008) Richard Baraniuk, Theory and applications of compressive sensing (EUSIPCO, Lausanne, Switzerland, August 2008) Yonina Eldar, Beyond bandlimited sampling: Nonideal sampling, smoothness, and sparsity (EUSIPCO, Lausanne, Switzerland, August 2008) Duke Compressive Sensing Workshop (February 2009) Online talks Software l1-Magic SparseLab GPSR ell-1 LS: Simple Matlab Solver for ell-1-Regularized Least Squares Problems sparsify MPTK: Matching Pursuit Toolkit [See also related conference publication:ICASSP 2006] Bayesian Compressive Sensing SPGL1: A solver for large scale sparse reconstruction sparseMRI FPC Chaining Pursuit Regularized OMP SPARCO: A toolbox for testing sparse reconstruction algorithms [See also relatedtechnical report] TwIST Compressed Sensing Codes Fast CS using SRM FPC_AS Fast Bayesian Matching Pursuit (FBMP) SL0 Sparse recovery using sparse matrices PPPA Compressive sensing via belief propagation SpaRSA KF-CS: Kalman Filtered CS (and other sequential CS algorithms) Fast Bayesian CS with Laplace Priors YALL1 TVAL3 RecPF Basis Pursuit DeQuantization (BPDQ) k-t FOCUSS Sub-Nyquist sampling: The Modulated Wideband Converter Threshold-ISD A Sparse Learning Package Model-based Compressive Sensing Toolbox Sparse Modeling Software Spectral Compressive Sensing Toolbox CS-CHEST: A MATLAB Toolbox for Compressive Channel Estimation DictLearn: A MATLAB Implementation for Dictionary Learning TFOCS: Templates for First Order Conic Solvers Links Call for Papers Special Issue on Compressive Sensing, IEEE Journal of Special Topics in Signal Processing Special Session in DSP2009, Compressed Sensing and Sub-Nyquist Sampling Open Positions Postdoc Postions in Signal and Image Processing at Rice University Additional job postings Conferences and Workshops SPARS 2011 - Workshop on Signal Processing with Adaptive Sparse Structured Representations (June 27-30, 2011) 2011 LMS Invited Lectures, Emmanuel Candes, Cambridge (March, 2011) Summer School: Theoretical Foundations and Numerical Methods for Sparse Recovery (RICAM, Aug. 31 - Sept. 4) DSP 2009 (July, 2009) SAMPTA 2009 - Internation Conference on Sampling Theory and Applications (May, 2009) SPARS 2009 - Workshop on Signal Processing with Adaptive Sparse/Structured Representations (April, 2009) Compressive Sensing Workshop (February, 2009) IEEE International Conference on Acoustics, Speech, and Signal Processing (April, 2008) Conference on Information Sciences and Systems (March, 2008) Information Theory and Applications Workshop (January, 2008) IEEE Statistical Signal Processing Workshop (August, 2007) MADALGO Summer School on Data Stream Algorithms (August, 2007) 2007 von Neumann Symposium on Sparse Representations and High-Dimensional Geometry (July, 2007) IMA New Directions Short Course: Compressive Sampling and Frontiers in Signal Processing (June, 2007) IPAM Short Course on Sparse Representations and High-Dimensional Geometry (June, 2007) IEEE International Conference on Acoustics, Speech, and Signal Processing (April, 2007) IITK Data Streams Workshop (December, 2006) Sparse Approximation Workshop (November, 2006) Signal Processing with Adaptative Sparse Structured Representations (November, 2005) Dagstuhl Workshop on Sublinear Algorithms (July, 2005) Blogs Nuit Blanche Geomblog What's new? Other Related Links Face Recognition via Sparse Representation Low-Rank Matrix Recovery via Convex Optimization Distilled Sensing Preprints in sparse recovery / Summary of properties of random matrices Resources on Geophysical Data Reconstruction and Inversion using Sparsity Norms Compressive sensing hardware Compressive sensing calendar This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported License.
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