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 us to 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 at dsp.rice.edu/cs/submit. To submit a resource that isn't a paper, please email

- Joel Tropp and Anna Gilbert, Signal recovery from random measurements via orthogonal matching pursuit. (IEEE Trans. on Information Theory, 53(12) pp. 4655-4666, December 2007)
- Shriram Sarvotham, Dror Baron, and Richard Baraniuk, Sudocodes - Fast measurement and reconstruction of sparse signals. (IEEE Int. Symposium on Information Theory (ISIT), Seattle, Washington, July 2006)
- David Donoho and Yaakov Tsaig, Fast solution of ell-1-norm minimization problems when the solution may be sparse. (Stanford University Department of Statistics Technical Report 2006-18, 2006)
- Massimo Fornasier and Holger Rauhut, Iterative thresholding algorithms. (Preprint, 2007)
- Rick Chartrand, Exact reconstructions of sparse signals via nonconvex minimization. (IEEE Signal Proc. Lett., 14(10) pp. 707-710, 2007)
- Mário A. T. Figueiredo, Robert D. Nowak, and Stephen J. Wright, Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems. (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. (IEEE Trans. on Image Processing, 16(12), pp. 2992 - 3004, Dec. 2007)
- Mark Iwen, A deterministic sub-linear time sparse Fourier algorithm via non-adaptive compressed sensing methods. (Preprint, 2007)
- Elaine T. Hale, Wotao Yin, and Yin Zhang, A fixed-point continuation method for ell-1 regularized minimization with applications to compressed sensing. (Preprint, 2007)
- Petros Boufounos, Marco F. Duarte, and Richard G. Baraniuk, Sparse signal reconstruction from noisy compressive measurements using cross validation. (Proc. IEEE Workshop on Statistical Signal Processing, Madison, Wisconsin, August 2007)
- Wotao Yin, Stanley Osher, Donald Goldfarm, and Jerome Darbon, Bregman iterative algorithms for ell-1 minimization with applications to compressed sensing. (Preprint, 2007)
- Roland Griesse, Dirk A. Lorenz, A semismooth Newton method for Tikhonov functionals with sparsity constraints. (Preprint, 2007)
- Emmanuel Candès, Michael Wakin, and Stephen Boyd, Enhancing sparsity by reweighted ell-1 minimization. (Preprint, 2008)
- Rick Chartrand and Wotao Yin, Iteratively reweighted algorithms for compressive sensing. (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, Nevada, April 2008)
- Sadegh Jokar and Marc E. Pfetsch, Exact and approximate sparse solutions of underdetermined linear equations. (Preprint, 2007)
- Deanna Needell and Roman Vershynin, Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit. (Preprint, 2007)
- Nam H. Nguyen and Trac D. Tran, The stability of regularized orthogonal mathcing pursuit. (Preprint, 2007)
- Ewout van den Berg and Michael P. Friedlander, Probing the Pareto frontier for basis pursuit solutions. (Preprint, January 2008)
- I.F. Gorodnitsky and B.D. Rao, Sparse signal reconstruction from limited data using FOCUSS: A re-weighted norm minimization algorithm. (IEEE Trans. on Signal Processing, 45, pp. 600 - 616, March 1997)
- B.D. Rao and K. Kreutz-Delgado, An affine scaling methodology for best basis selection. (IEEE Trans. on Signal Processing, 47, pp. 187 - 200, January 1999)
- S. F. Cotter, J. Adler, B. D. Rao, K. Kreutz-Delgado, Forward sequential algorithms for best basis selection. (Proc. Vision, Image, and Signal Processing, pp. 235 - 244, October 1999)
- B.D Rao, K. Engan, S.F. Cotter, J. Palmer, K, Kreutz-Delgado, Subset selection in noise based on diversity measure minimization. (IEEE Trans. on Signal Processing, 51(3), pp. 760 - 770, March 2003)
- S. F. Cotter, B. D. Rao, K. Engan, and K. Kreutz-Delgado, Sparse solutions to linear inverse problems with multiple measurement vectors . (IEEE Trans. on Signal Processing, 53(9), pp. 2477 - 2488, July 2005)
- S. D. Howard, A. R. Calderbank, and S. J. Searle, A fast reconstruction algortihm for deterministic compressive sensing using second order Reed-Muller codes. (Conf. on Info. Sciences and Systems (CISS), Princeton, New Jersey, March 2008)
- Wei Dai, Olgica Milenkovic, Subspace pursuit for compressive sensing: Closing the gap between performance and complexity. (Preprint, 2008)
- D. Needell, J. A. Tropp, CoSaMP: Iterative signal recovery from incomplete and inaccurate samples. (Preprint, 2008)
- Lorne Applebaum, Stephen Howard, Stephen Searle, and Robert Calderbank, Chirp sensing codes: Deterministic compressed sensing measurements for fast recovery. (Preprint, 2008)
- T. Blumensath, M. E. Davies, Iterative hard thresholding for compressed sensing. (Preprint, 2008)
- T. Blumensath, M. E. Davies, Stagewise weak gradient pursuits. Part I: Fundamentals and numerical studies. (Preprint, 2008)
- T. Blumensath, M. E. Davies, Stagewise weak gradient pursuits. Part II: Theoretical properties. (Preprint, 2008)
- Stéphane Chrétien, An alternating ell-1 approach to the compressed sensing problem. (Preprint, 2008)
- Thong T. Do, Lu Gan, Nam Nguyen, Trac D. Tran, Sparsity adaptice matching pursuit algorithm for practical compressed sensing. (Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, California, October 2008)
- Patrick Combettes, Valérie Wajs, Signal recovery by proximal forward-backward splitting. (Multiscale Modeling and Simulation, 4(4), pp. 1168 - 1200, November 2005)
- Caroline Chaux, Patrick Combettes, Jean-Christophe Pesquet, Valérie Wajs, A variational formulation for frame-based inverse problems. (Inverse Problems, 23, pp. 1495 - 1518, June 2007)
- Patrick Combettes, Jean-Christophe Pesquet, Proximal thresholding algorithm for minimization over orthonormal bases. (SIAM Journal on Optimization, 18(4), pp. 1351 - 1376, November 2007)
- Kai Tobias Block, Martin Uecker, and Jens Frahm, Undersampled Radial MRI with Multiple Coils. Iterative Image Reconstruction Using a Total Variation Constraint. (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. Allerton Conference on Communication, Control, and Computing, Monticello, IL, September 2008)
- F. Malgouyres, T. Zeng, A predual proximal point algorithm solving a non negative basis pursuit denoising model. (International Journal of Computer Vision (IJCV), vol. 83, num.3, pp 294-311, July 2009)
- Sangkyun Lee, Stephen Wright, Implementing algorithms for signal and image reconstruction on graphical processing units. (Preprint, 2008)
- Albert Cohen, Wolfgang Dahmen, Ronald DeVore, Instance optimal decoding by thresholding in compressed sensing. (Preprint, 2008)
- Salman Asif, Justin Romberg, Streaming measurements in compressive sensing: ell-1 filtering. (Preprint, 2008)
- Dror Baron, Shriram Sarvoham, Richard G. Baraniuk, Bayesian compressive sensing via belief propagation. (To appear in IEEE Trans. Signal Processing, 2009)
- Sergio D. Cabrera, J. Gerardo Rosiles, Alejandro E. Brito, Affine scaling transformation algorithms for harmonic retrieval in a compressive sensing framework. (Proc. Wavelets XIII, SPIE, San Diego, California, August 2007)
- Sergio D. Cabrera, Rufino Dominguez, J. Gerardo Rosiles, Javier Vega-Pineda, Variable-p affine scaling transformation algorithms for improved compressive sensing. (Proc. Sensor, Signal, and Info. Proc. Workshop (SenSIP), Sedona, Arizona, May 2008)
- Rufino J. Dom�nguez, Sergio D. Cabrera, J. Gerardo Rosiles, Javier Vega-Pineda, Reconstruction in compressive sensing using affine scaling transformations with variable-p diversity measure. (Proc. IEEE DSP Workshop, Marco Island, Florida, January 2009)
- Hoa V. Pham, Wei Dai, Olgica Milenkovic, Sublinear compressive sensing reconstruction via belief propagation decoding. (Preprint, January 2009)
- Pierre J. Garrigues, Laurent El Ghaoui, An homotopy algorithm for the lasso with online observations. (Neural Information Processing Systems (NIPS), Vancouver, Canada, December 2008)
- Suvrit Sra, Joel Tropp, Row-action methods for compressed sensing (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Toulouse, France, May 2006)
- Namrata Vaswani, Analyzing least squares and kalman filtered compressed sensing (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Taipei, Taiwan, April 2009)
- Hadi Zayyani, Massoud Babaie-Zadeh, Christian Jutten, Bayesian pursuit algorithm for sparse representation (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Taipei, Taiwan, April 2009)
- Daniele Angelosante, Georgios B. Giannakis, RLS-weighted lasso for adaptive estimation of sparse signals (IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), Taipei, Taiwan, April 2009)
- Thomas Blumensath, Mike E. Davies, Normalised iterative hard thresholding; guaranteed stability and performance (Preprint, 2009)
- Mark Iwen, Combinatorial sublinear-time Fourier algorithms (Preprint, 2009)
- A. Dogandžić and K. Qiu, Variance-component based sparse signal reconstruction and model selection. (To appear in IEEE Trans. Signal Processing, vol. 58, 2010)
- Behtash Babadi, Nicholas Kalouptsidis, Vahid Tarokh, SPARLS: A low complexity recursive ell1-regularized least squares algorithm. (Preprint, January 2009).
- M. Salman Asif, Justin Romberg, Dynamic updating for sparse time varying signals. (CISS 2009, Baltimore, MD)
- Namrata Vaswani, Wei Lu, Modified-CS: Modifying compressive sensing for problems with partially known support. (IEEE Intl. Symp. Info. Theory (ISIT), 2009)
- Rahul Garg, Rohit Khandekar, Gradient descent with sparsification: An iterative algorithm for sparse recovery with restricted isometry property. (ICML 2009, Montreal, Canada)
- Gerlind Plonka, Jianwei Ma, Curvelet-wavelet regularized split Bregman iteration for compressed sensing. (Preprint, June 2009)
- Mark Davenport and Michael Wakin, Analysis of orthogonal matching pursuit using the restricted isometry property. (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. (IEEE International Conference on Image Processing, September 2010)
- Zvika Ben-Haim, Yonina C. Eldar, and Michael Elad, Coherence-based performance guarantees for estimating a sparse vector under random noise. (to appear in IEEE Trans. Signal Process., 2010)
- Ryota Tomioka and Masashi Sugiyama, Dual Augmented Lagrangian Method for Efficient Sparse Reconstruction. (IEEE Signal Processing Letters, 16(12), pp. 1067 - 1070, 2009) [[Software]]
- Zhiqiang Xu, A remark about orthogonal matching pursuit algorithm. (arXiv:1005.3093)
- Balakrishnan Varadarajan, Sanjeev Khudanpur and Trac Tran, Stepwise Optimal Subspace Pursuit for Improving Sparse Recovery. (IEEE Letters on Signal Processing, 18(1), pp. 27-30, Jan. 2011 ) [http://sites.google.com/site/balakrishnanvaradarajan/pubs/SPL-08761-2010(extended).pdf]
- Avi Septimus and Raphael Steinberg, Compressive Sampling Hardware Reconstruction. (Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on. IEEE, 2010, pp. 3316â��3319.)
- Shisheng Huang, Jubo Zhu, Recovery of sparse signals using OMP and its variants: convergence analysis based on RIP . (Inverse Problems, 2011, 27(3))
- Stephen Becker, E. J. CandÃ¨s and M. Grant, Templates for convex cone problems with applications to sparse signal recovery. (submitted, September 2010)
- Daniel H. Chae, Parastoo Sadeghi, and Rodney A. Kennedy, Effects of Basis-mismatch in Compressive Sampling of Continuous Sinusoidal Signals. (IEEE International Conference on Future Computer and Communication (ICFCC), 2010 )
- Y. Kopsinis, K. Slavakis, S. Theodoridis, Online Sparse System Identification and Signal Reconstruction Using Projections Onto Weighted $ell_{1}$ Balls. (IEEE Trans. on Signal Processing, pp. 936-952, Mar. 2011.)
- Jaewook Kang, Heung-No Lee, Kiseon Kim, Message passing aided least square recovery for compressive sensing. (accepted by SPARS '11, Edinburgh, Scotland, UK, Apr., 2011)
- Wei Deng, Wotao Yin, and Yin Zhang, Group Sparse Optimization by Alternating Direction Method. (Technical Report TR11-06, Department of Computational and Applied Mathematics, Rice University, 2011)
- Jiao Wu, Fang Liu, LC Jiao and Xiaodong Wang, Compressive Sensing SAR Image Reconstruction Based on Bayesian Framework and Evolutionary Computation. (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)
- Hung-Wei Chen, Chun-Shien Lu, and Soo-Chang Pei, Fast Compressive Sensing Recovery with Transform-based Sampling. (Proc. Workshop on Signal Processing with Adaptive Sparse Structured Representations, June 27-30, Edinburgh, UK, 2011.)
- Dirk A. Lorenz, Marc. E. Pfetsch, Andreas M. Tillmann, Solving Basis Pursuit: Subgradient Algorithm, Heuristic Optimality Check, and Solver Comparison. (Optimization Online E-Print ID 2011-07-3100) [http://www.math.nat.tu-bs.de/mo/spear/]
- Jaewook Kang, Heung-No Lee, and Kiseon Kim, Bayesian Hypothesis Test for Sparse Support Recovery using Belief Propagation. ( IEEE Statistical Signal Processing Workshop (SSP), Ann Arbor, Aug. 2012.)
- Jian Zhang, Debin Zhao, Chen Zhao, Ruiqin Xiong, Siwei Ma, Wen Gao, Compressed Sensing Recovery via Collaborative Sparsity. (Proc. of IEEE Data Compression Conference (DCC), pp. 287-296, Snowbird, Utah, USA, Apr. 2012.)
- Gholami, A., Hosseini, S.M., , A General Framework for Sparsity-based Denoising and Inversion. (IEEE Transactions on Signal Processing. vol. 59, no. 11, pp. 5202-5211.)
- C. Herzet and A. Dremeau, Bayesian Pursuit Algorithms. (EUSIPCO'2010, European Signal Processing conference, Aalborg, Danemark, August 2010) [technical report]
- Kimon Fountoulakis, Jacek Gondzio, Pavel Zhlobich, Matrix-free Interior Point Method for Compressed Sensing Problems. (Technical Report ERGO 12-006, School of Mathematics and Maxwell Institute, The University of Edinburgh, June/2012)
- Entao Liu and Vladimir N. Temlyakov, The Orthogonal Super Greedy Algorithm and Applications in Compressed Sensing. (IEEE Trans. on Information Theory, 58(4), pp. 2040-2047, April 2012)
- K.Fountoulakis, J. Gondzio and P. Zhlobich, Matrix-free interior point method for compressed sensing problems. (Preprint 2012)
- Xueqin Zhou, Xiangchu Feng, and Mingli Jing, Sparse Recovery by Semi-Iterative Hard Thresholding Algorithm. (Mathematical Problems in Engineering, vol. 2013, Article ID 498589, 6 pages, 2013.)
- Mohammad-Khaninezhad M.R., Jafarpour B., Sparse Bayesian inversion for reservoir history matching and uncertainty quantification. (SPE-163656-MS presented at the Society of Petroleum Engineers Reservoir Simulation Symposium, Woodlands, TX, 18-20 February 2013)
- Li L., Jafarpour B. , Effective solution of nonlinear subsurface flow inverse problems in sparse bases. (Inverse Problems, 26(10),105-016 (24pp).)
- Gonzalo H Otazu and Christian Leibold, A corticothalamic circuit model for sound identification in complex scenes.. (PLoS One. 2011;6(9):e24270. doi: 10.1371/journal.pone.0024270. Epub 2011 Sep 13.)
- Dmitry Dovnar, Yury Lebedinsky, Igor Zakharov, New Concept of Image Restoring. (3rd IEEE Benelux Signal Processing Symposium (SPS-2002), Leuven, Belgium, March 21-22, 2002) [See, please other related publications at http://losi.itgo.com]
- Haixiao Liu, Bin Song, Hao Qin, and Zhiliang Qiu, An Adaptive-ADMM Algorithm With Support and Signal Value Detection for Compressed Sensing. (IEEE Trans. on Signal Processing Letters, 20 (4), pp. 315-318, April 2013)
- Mingrui Yang; Frank de Hoog, Coherence and RIP Analysis for Greedy Algorithms in Compressive Sensing. (arxiv:1307.1949)

- 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)
- Feng Wu, Jingjing Fu, Zhouchen Lin, Bing Zeng, Analysis on rate-distortion performance of compressive sensing for binary sparse source. (Data Compression Conference, pp. 113-122, 2009)
- Zhi Li, Feng Wu, John Wright, On the systematic measurement matrix for compressed sensing in presence of gross errors. (Data Compression Conference, pp. 356-365, 2010)
- 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)
- Xiaolin Liu, Chong Luo, Feng Wu, Formulating binary compressive sensing decoding with asymetrical property. (Data Compression Conference, pp. 213-222, 2011)

- 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)
- Mahdi Cheraghchi, Derandomization and Group Testing. (In Proceedings of 48th Annual Allerton Conference on Communication, Control, and Computing, 2010.)
- Gitta Kutyniok,
Data separation by sparse representations.
(Chapter in
*Compressed Sensing: Theory and Applications*, Cambridge University Press, 2012) - Mahdi Cheraghchi, Coding-Theoretic Methods for Sparse Recovery. (In Proceedings of 49th Annual Allerton Conference on Communication, Control, and Computing, 2011.)

- 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]

- 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 a technical report]
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- 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)
- 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)
- 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)
- Bijit Kumar Das, Mrityunjoy Chakraborty, Soumitra Banerjee, Adaptive Identification of Sparse Systems with Variable Sparsity. (IEEE International Symposium on Circuits and Systems, 2011)
- Dinesh Ramasamy, Sriram Venkateswaran and Upamanyu Madhow, Compressive estimation in AWGN: general observations and a case study. (Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, November 2012)
- Daniel Eiwen, Compressive Channel Estimation - Compressed Sensing Methods for Estimating Doubly Selective Channels in Multicarrier Systems. (PhD thesis, University of Vienna, 2012)
- Daito Akimura, Yoshihiro Kawahara, and Tohru Asami, Browse Conference Publications > Networked Sensing Systems (IN ... Help Compressed sensing method for human activity sensing. (Proceddings of 2012 Ninth International Conference on Networked Sensing Systems (INSS), pp.1-4, June 2012) [http://www.akg.t.u-tokyo.ac.jp/]
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- 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 related technical 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)
- 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]
- Balazs Pinter, Gyula Voros, Zoltan Szabo, and Andras Lorincz, Automated Word Puzzle Generation via Topic Dictionaries. (ICML-2012 -- Sparsity, Dictionaries and Projections in Machine Learning and Signal Processing Workshop, 30 June 2012) [website of the workshop]

- 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]
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- Shihao Ji, David Dunson, and Lawrence Carin, Multi-task compressive sensing. (Preprint, 2007)
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- 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)
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- 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)
- Ahmed A. Quadeer and Tareq Y. Al-Naffouri, Structure-Based Bayesian Sparse Reconstruction. (IEEE Transactions on Signal Processing, vol. 60, no. 12, pp. 6354 - 6367, 2012)
- Li L., Jafarpour B. , A sparse Bayesian framework for conditioning uncertain geologic models to nonlinear flow measurements. (Advances in Water Resources, 33(9),1024-1042, DOI: 10.1016 /j.advwatres.2010.06.005.)

- 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)

- 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)

- 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)

- 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)

- 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)

- 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 paper here]
- 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]

- 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.)
- Abdolreza Abdolhosseini Moghadam, Mohammad Aghagolzadeh, Mrityunjay Kumar, and Hayder Radha, Compressive Demosaicing. (IEEE International Workshop on Multimedia Signal Processing (MMSP), pp.105-110, October 2010)
- 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)
- Siyuan Xiang and Lin Cai, Scalable Video Coding with Compressive Sensing for Wireless Videocast. (IEEE ICC'11)
- 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�Î£-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.)
- S. Xiang and L. Cai, Scalable Video Coding with Compressive Sensing for Wireless Videocast. (IEEE ICC'11, Kyoto, Japan)
- Kaihua Zhang, Lei Zhang, Ming-Hsuan Yang, Real-Time Compressive Tracking. (European Conference on Computer Vision (ECCV 2012), Florence, Italy, October, 2012. )
- Giulio Coluccia, Enrico Magli, A Novel Progressive Image Scanning and Reconstruction Scheme Based on Compressed Sensing and Linear Prediction. (IEEE International Conference on Multimedia & Expo (ICME 2012), pp- 866-871, Melbourne (Australia), July 2012)
- Giulio Coluccia, Simeon Kamden-Kuiteng, Andrea Abrardo, Mauro Barni and Enrico Magli, Progressive Compressed Sensing and Reconstruction of Multidimensional Signals Using Hybrid Transform/Prediction Sparsity Model. (Emerging and Selected Topics in Circuits and Systems, IEEE Journal on , vol.PP, no.99, pp.1-13, 0 doi: 10.1109/JETCAS.2012.22148)
- Hongzhi Chen, Ning Xi, Bo Song, Liangliang Chen, Jianguo Zhao, King Wai Chiu Lai, Ruiguo Yang, Infrared Camera Using a Single Nano-photodetector. (Chen, H.; etc "Infrared Camera Using a Single Nano-photodetector," Sensors Journal, IEEE , vol.PP, no.99, pp.1, 0, 2012)
- Chen, H.; Xi, N.; Song, B.; Chen, L.; Zhao, J.; Lai, K.W.C.; Yang, R.;, Infrared Camera Using a Single Nano-photodetector. (Sensors Journal, IEEE , vol.PP, no.99, pp.1, 0)

- 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 $\alpha$-Stable Distributions. (Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010), Buenos Aires, Argentina)
- Mehmet SÃ¼zen 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)
- Angshul Majumdar and Rabab K. Ward, Exploiting Rank Deficiency and Transform Domain Sparsity for MR Image Reconstruction. (Magnetic Resonance Imaging, Vol. 30 (1), pp. 9â��18, 2012)
- V. M. Patel, R. Maleh, A. C. Gilbert, and R. Chellappa, Gradient-based image recovery methods from incomplete Fourier measurements. (IEEE Transactions on Image Processing, vol. 21, no. 1, pp. 94-105, Jan. 2012)
- Martin F. Schiffner, Timo Jansen, and Georg Schmitz, ComÂpresÂsed SenÂsing for Fast Image AcÂquiÂsiÂtiÂon in PulÂse-Echo UlÂtraÂsound. (BioÂmed. Tech., vol. 57 (suppl. 1), 2012, pp. 192 - 195)
- Sajan Goud Lingala, Mathews Jacob, Blind compressive sensing dynamic MRI. (IEEE Transactions on Medical Imaging (in press), 2013. )
- Lv, Yi Wu, Wentao , Compressive Ultrasound Imaging Based on Analog to Information Conversion. (International Conference on Biomedical Engineering and Biotechnology (iCBEB), 2012 )
- Mohammad Pourhomayoun, Mark Fowler, Zhanpeng Jin, A Novel Method for Tumor Localization and Tracking in Radiation Therapy. (IEEE Asilomar Conference on Signals, Systems, and Computers, Monterey, CA, Nov 2012)
- Mohammad Pourhomayoun, Mark Fowler, Zhanpeng Jin, Robustness Analysis of Sparsity Based Tumor Localization under Tissue Configuration Uncertainty. (IEEE Signal Processing in Medicine and Biology Symposium (SPMB12), New York, Dec 2012.)
- Sajan Goud Lingala, and Mathews Jacob, Blind compressive sensing dynamic MRI. (IEEE Transactions on Medical Imaging (early view))

- 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 in
*Compressed 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)
- Chenchi Luo, Milind A. Borkar, Arthur J. Redfern, James H. McClellan, Compressive Sensing for Sparse Touch Detection on Capacitive Touch Screens. (Emerging and Selected Topics in Circuits and Systems, IEEE Journal on , vol.PP, no.99, pp.1-10)

- 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)
- Mohammad Pourhomayoun, M. L. Fowler, and Z. Jin, A Novel Method for Medical Implant In-Body Localization. (34th Intnl. Conf. of IEEE Engineering in Medicine and Biology (EMBC2012), San Diego, August 2012.)

- 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. MecklenbrÃ¤uker , 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)
- Nadia Kreimer and Mauricio D. Sacchi, A tensor higher-order singular value decomposition for prestack seismic data noise reduction and interpolation. (Geophysics 77, V113, April 2012 , doi:10.1190/geo2011-0399.1)
- Gholami, A., Sacchi, M.D., , A Fast and Automatic Sparse Deconvolution in the Presence of Outliers. (IEEE Transactions on Geoscience and Remote Sensing, accepted.)
- Mohammad-Khaninezhad M.R., Jafarpour B., Li L., Sparse geologic dictionaries for subsurface flow model calibration: Part I. Inversion formulation. (Advances in Water Resources, Vol. 39, PP 106-121.)
- Mohammad-Khaninezhad M.R., Jafarpour B., Li L., Sparse geologic dictionaries for subsurface flow model calibration: Part II. Robustness to uncertainty. (Advances in Water Resources, Vol. 39, PP 122-136.)

- 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))

- 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 GeodÃ¤tische Kommission, Reihe C, Nr. 666, Verlag der Bayerischen Akademie der Wissenschaften, ISBN 978-3-7696-5078-5)

- J. Bobin, J.-L. Starck, and R. Ottensamer, Compressed sensing in astronomy. (Preprint, 2008)
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- Borhan M. Sanandaji, Tyrone L. Vincent, Michael B. Wakin, Exact Topology Identification of Large-Scale Interconnected Dynamical Systems from Compressive Observations. (2011 American Control Conference, San Francisco, California, June 2011, pp. 649â��656)

- 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 related technical 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)
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- 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) []
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- 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 H. Chae, Parastoo Sadeghi, and Rodney A. Kennedy, Robustness of Compressive Sensing under Multiplicative Perturbations: The Challenge of Fading Channels. (IEEE MILITARY COMMUNICATIONS CONFERENCE (MILCOM) , pp. 1199-1204, 2010 )
- Daniel Eiwen, Georg TaubÃ¶ck, 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)
- H. Schepker, A. Dekorsy, Sparse Multi-User Detection for CDMA Transmission using Greedy Algorithms. (8th International Symposium on Wireless Communication Systems (ISWCS 11), Aachen, Deutschland, 6. - 9. November 2011)
- S. Dhakal, and A. Bayesteh, Sparse space codes for multi-antenna systems. (12th Canadian Workshop In Information Theory (CWIT), pp. 159-164, May 2011.)
- Byonghyo Shim and Byungkwen Song, Multiuser Detection via Compressive Sensing. (IEEE Communications Letters, vol.16, no.7, pp.972-974, July 2012)
- Evangelos Vlachos, Aris Lalos, Kostas Berberidis, Stochastic Gradient Pursuit for Adaptive Equalization of Sparse Multipath Channels. (IEEE Journal on Emerging and Selected Topics in Circuits and Systems)
- Dinesh Ramasamy, Sriram Venkateswaran and Upamanyu Madhow, Compressive tracking with 1000-element arrays: a framework for multi-Gbps mm wave cellular downlinks. (Proc. of 50th Allerton Conference on Communication Control and Computing, Monticello IL, September 2012)
- Payam Siyari, Hamid R. Rabiee, Mostafa Salehi, Motahareh Eslami, Network Reconstruction under Compressive Sensing. (Proceedings of ASE International Conference on Social Informatics, IEEE Computer Society, pp. 19-25, December 2012)
- Changyong Pan, Linglong Dai, and Zhaocheng Wang, Time Domain Synchronous OFDM Based on Compressive Sensing: A New Perspective. (in Proc. IEEE Global Communications Conference (GLOBECOMâ��12), Anaheim, USA, Dec. 2012.)
- Zhaocheng Wang, Linglong Dai, and Jun Wang, Priori information aided compressive sensing for time domain synchronous OFDM. (Electronics Letters, vol. 48, no. 13, pp. 800-801, Jun. 2012.)
- Daniel Eiwen, Compressive Channel Estimation - Compressed Sensing Methods for Estimating Doubly Selective Channels in Multicarrier Systems. (PhD thesis, University of Vienna, Vienna, 2012)
- Borhan M. Sanandaji, Tyrone L. Vincent, Kameshwar Poolla, Michael B. Wakin, A Tutorial on Recovery Conditions for Compressive System Identification of Sparse Channels. (51th IEEE Conference on Decision and Control, Maui, Hawaii, December 2012, pp. 6277-6283)
- H. Schepker, A. Dekorsy, Compressive Sensing Multi-User Detection with Block-Wise Orthogonal Least Squares. (2012 IEEE 75th Vehicular Technology Conference: VTC2012-Spring, Yokohama, Japan, May 2012)
- F. Monsees, C. Bockelmann, D. WÃ¼bben, A. Dekorsy, Sparsity Aware Multiuser Detection for Machine to Machine Communication. (Second International Workshop on Machine-to-Machine Communication at IEEE Globecom 2012, Anaheim, USA, December 2012)
- Guan Gui, Wei Peng, and Fumiyuki Adachi, Sub-Nyquist rate ADC sampling-based compressive channel estimation. (Wireless Communication and Mobile Computing, 10 pages, DOl:10.1002/wcm2372, Apr. 2013)
- Guan Gui, Wei Peng, and Fumiyuki Adachi, Improved Adaptive Sparse Channel Estimation Based on the Least Mean Square Algorithm. (2013 IEEE Wireless Communications and Networking Conference (WCNC 2013), Shanghai, China, 7-10 Apr. 2013.)
- Guan Gui, Wei Peng, and Fumiyuki Adachi, Sparse least mean fourth algorithm for adaptive channel estimation in low signal-to-noise ratio region. ( International Journal of Communication System (Wiley), Apr. 2013. 11 pages, DOl:10.1002/dac.2531)
- Guan Gui, Wei Peng, and Fumiyuki Adachi, High-resolution compressive channel estimation for broadband wireless communication systems. ( International Journal of Communication Systems, Article first published online: 14 Dec. 2012, doi: 10.1002/dac.2483.)
- Yahya H. Ezzeldin, Radwa Aly Sultan, Karim G Seddik, Sparse Reconstruction-based Detection of Spatial Dimension Holes in Cognitive Radio Networks. (To appear in PIMRC 2013)

- 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.)

- 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))
- Bertrand Fontaine and Herbert Peremans, Compressive sensing: a strategy for fluttering target discrimination employed by bats emitting broadband calls. (J. Acoust. Soc. Am. Volume 129, Issue 2, pp. 1100-1110 (2011))

- 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)
- V. M. Patel, G. R. Easley, D. M. Healy, Jr. and R. Chellappa, Compressed synthetic aperture radar. (IEEE Journal of Selected Topics in Signal Processing: Special Issue on Compressive Sensing, vol. 4, no. 2, pp. 244-254, April 20)
- Mohammad Pourhomayoun , Mark Fowler , N.Eva Wu, Spatial Sparsity Based Emitter Localization. (IEEE Conference on Information Sciences and Systems, Princeton University, March 2012.)

- 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)

- 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)

- 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)
- Masaaki Nagahara, Takahiro Matsuda, and Kazunori Hayashi, Compressive Sampling for Remote Control Systems. (IEICE Trans. on Fundamentals, Vol. E95-A, No. 4, pp. 713-722, April 2012)
- J. Zhaoï¼�B. Song, N. Xi, K. Lai, H. Chen, and C. Qu, Compressive Feedback Based Non-vector Space Control. (Zhao, Jianguo, Bo Song, Ning Xi, King Wai Chiu Lai, Hongzhi Chen, and Chengeng Qu. "Compressive feedback based non-vector space )
- Michael B. Wakin, Borhan M. Sanandaji, Tyrone L. Vincent, On the Observability of Linear Systems from Random, Compressive Measurements. (in Proceedings of the 49th IEEE Conference on Decision and Control â�� CDC 2010, Atlanta, Georgia, USA, Dec. 2010, pp. 4447â��4454)

- Atul Divekar, Okan Ersoy,
Compact Storage of Correlated Data for Content Based Retrieval.
(accepted to the 43rd Asilomar Conference on Signals,Systems and Computers)
- Tao Hu and Dmitri B. Chklovskii, Reconstruction of Sparse Circuits using Multi-neuronal Excitation (RESCUME). (in Advances in Neural Information Processing Systems 22, pp. 790--798, 2009.)
- Alyson K. Fletcher, Sundeep Rangan, Lav R. Varshney, and Aniruddha Bhargava, Neural Reconstruction with Approximate Message Passing (NeuRAMP) . (in Advances in Neural Information Processing Systems 24, pp. 2555--2563, 2011.)
- Yuriy Mishchenko and Liam Paninski, A Bayesian compressed-sensing approach for reconstructing neural connectivity from subsampled anatomical data. (Journal of Computational Neuroscience, 2012.)

- 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)

- 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))
- J. Almeida, J. Prior, M. B. Plenio, Computation of 2-D spectra assisted by compressed sampling . (J. Phys. Chem. Lett., 3 (18), 2692 (2012))

- 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

- 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

- 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 related technical 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
- CCE: A MATLAB Toolbox for Compressive Channel Estimation and/or Tracking in SISO or MIMO Systems

- 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)

- 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

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