Decoding by linear programming
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
A deterministic sub-linear time sparse fourier algorithm via non-adaptive compressed sensing methods
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Explicit constructions for compressed sensing of sparse signals
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Almost Euclidean subspaces of ℓN1 via expander codes
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
IEEE Transactions on Image Processing
Relaxed conditions for sparse signal recovery with general concave priors
IEEE Transactions on Signal Processing
On the sparseness of 1-norm support vector machines
Neural Networks
Sparse approximate solution of partial differential equations
Applied Numerical Mathematics
Bayesian compressive sensing using Laplace priors
IEEE Transactions on Image Processing
Compact storage of correlated data for content based retrieval
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Reed muller sensing matrices and the LASSO
SETA'10 Proceedings of the 6th international conference on Sequences and their applications
Performance analysis for sparse support recovery
IEEE Transactions on Information Theory
Toeplitz compressed sensing matrices with applications to sparse channel estimation
IEEE Transactions on Information Theory
Sparse representations and approximation theory
Journal of Approximation Theory
Exact optimization for the l1-Compressive Sensing problem using a modified Dantzig-Wolfe method
Theoretical Computer Science
Deterministic sampling of sparse trigonometric polynomials
Journal of Complexity
Breaking the k2 barrier for explicit RIP matrices
Proceedings of the forty-third annual ACM symposium on Theory of computing
Deterministic construction of a high dimensional lp section in l1n for any p
Proceedings of the forty-third annual ACM symposium on Theory of computing
Efficient Deterministic Compressed Sensing for Images with Chirps and Reed-Muller Codes
SIAM Journal on Imaging Sciences
Journal of Approximation Theory
On the Design of Deterministic Matrices for Fast Recovery of Fourier Compressible Functions
SIAM Journal on Matrix Analysis and Applications
Strengthening hash families and compressive sensing
Journal of Discrete Algorithms
SETA'12 Proceedings of the 7th international conference on Sequences and Their Applications
A Simple Compressive Sensing Algorithm for Parallel Many-Core Architectures
Journal of Signal Processing Systems
Analysis of adaptive sampling techniques for underwater vehicles
Autonomous Robots
An Evaluation of the Sparsity Degree for Sparse Recovery with Deterministic Measurement Matrices
Journal of Mathematical Imaging and Vision
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Compressed sensing is a new area of signal processing. Its goal is to minimize the number of samples that need to be taken from a signal for faithful reconstruction. The performance of compressed sensing on signal classes is directly related to Gelfand widths. Similar to the deeper constructions of optimal subspaces in Gelfand widths, most sampling algorithms are based on randomization. However, for possible circuit implementation, it is important to understand what can be done with purely deterministic sampling. In this note, we show how to construct sampling matrices using finite fields. One such construction gives cyclic matrices which are interesting for circuit implementation. While the guaranteed performance of these deterministic constructions is not comparable to the random constructions, these matrices have the best known performance for purely deterministic constructions.