Sparse recovery using sparse random matrices. (IEEE Information Theory Workshop, Lake Tahoe, September 2007)Д¬omputation and relaxation of conditions for equivalence between ell-1 and ell-0 minimization.ДЇast compressive sampling with structurally random matrices. (IEEE Workshop on Statistical Signal Processing (SSP), Madison, Wisconsin, August 2007)Д®fficient compressive sensing with determinstic guarantees using expander graphs. Toeplitz-structured compressed sensing matrices. Waheed Bajwa, Jarvis Haupt, Gil Raz, Stephen Wright, and Robert Nowak,.(Rice CAAM Department Technical Report TR05-15, 2005)

(Rice CAAM Department Technical Report TR05-10, 2005) (Rice CAAM Department Technical Report TR05-09, 2005)ДЄ simple proof for recoverability of ell-1-minimization (II): the nonnegative case. 918 - 925, August 2007)Д®xplicit constructions for compressed sensing of sparse signals.ДЄ simple proof for recoverability of ell-1-minimization: go over or under? 969-985, 2007)Дeterministic constructions of compressed sensing matrices. Sparsity and incoherence in compressive sampling.

Holger Rauhut, Karin Schnass, and Pierre Vandergheynst,Д¬ompressed sensing and redundant dictionaries.

Allerton Conference on Communication, Control, and Computing, Monticello, IL, September 2006) Sharp thresholds for high-dimensional and noisy recovery of sparsity

Albert Cohen, Wolfgang Dahmen, and Ronald DeVore,Д¬ompressed sensing and best k-term approximation.

Richard Baraniuk, Mark Davenport, Ronald DeVore, and Michael Wakin,ДЄ simple proof of the restricted isometry property for random matrices.

The Dantzig Selector: Statistical estimation when p is much larger than n Signal reconstruction from noisy random projections. (Communications on Pure and Applied Mathematics, 59(8), pp. Stable signal recovery from incomplete and inaccurate measurements. Practical signal recovery from random projections. Near optimal signal recovery from random projections: Universal encoding strategies? Quantitative robust uncertainty principles and optimally sparse decompositions. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information.

Emmanuel CandГЁs, Justin Romberg, and Terence Tao,.

See below for tutorial talks on compressive sensing.

Hong Huang, Satyajayant Misra, Wei Tang, Hajar Barani, and Hussein Al-Azzawi, Applications of Compressed Sensing in Communications Networks.

Kazunori Hayashi, Masaaki Nagahara, and Toshiyuki Tanaka, A User's Guide to Compressed Sensing for Communications Systems.

, To Appear in Journal of Communication and Networks, July, 2013)

Saad Qaisar, Rana Muhammad Bilal, Wafa Iqbal, Muqaddas Naureen and Sungyoung Lee, Compressive Sensing: From Theory to Applications, A Survey.

Jordan Ellenberg, Fill in the Blanks: Using Math to Turn Lo-Res Datasets Into Hi-Res Samples.

Sergios Theodoridis, Yannis Kopsinis, Konstantinos Slavakis, Sparsity-Aware Learning and Compressed Sensing: An Overview.

Gitta Kutyniok, Theory and Applications of Compressed Sensing.

Vandergheynst, 'Compressed Sensing: When sparsity meets sampling'.

Rebecca Willett, Roummel Marcia, and Jonathan Nichols, Compressed sensing for practical optical imaging systems: a tutorial.

(To appear in IEEE Transactions on Signal Processing)

Marco Duarte and Yonina Eldar, Structured compressed sensing: Theory and applications.

(Chapter in Compressed Sensing: Theory and Applications, Cambridge University Press, 2012) Mark Davenport, Marco Duarte, Yonina Eldar, and Gitta Kutyniok,.(Chapter in Part 2 of the Handbook of Mathematical Methods in Imaging (O. Massimo Fornasier and Holger Rauhut, Compressive sensing.Richard Baraniuk, More Is less: Signal processing and the data deluge.(Mackenzie, Dana (2009), 'Compressed sensing makes every pixel count', What's Happening in the Math. Dana Mackenzie, Compressed Sensing Makes Every Pixel Count.(IEEE Signal Processing Magazine, 25(2), pp.

(IEEE Signal Processing Magazine, 24(4), pp.

Emmanuel CandГЁs, Compressive Sampling.

Bu matlab 2009