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Low Complexity Iterative Algorithms for Channel Coding and Compressed Sensing
Iterative algorithms are now widely used in all areas of signal processing and digital communications. In modern communication systems, iterative algorithms are used... -
A greedy algorithm to extract sparsity degree for l1/l0-equivalence in a dete...
This paper investigates the problem of designing a deterministic system matrix, that is measurement matrix, for sparse recovery. An efficient greedy algorithm is... -
A greedy algorithm to extract sparsity degree for l1/l0-equivalence in a dete...
International audience -
Dictionary learning for sparse decomposition: a new criterion and algorithm
International audience -
A novel effective compressed sensing based sparse channel estimation in OFDM ...
International audience -
On optimal Sampling in low and high dimension
During my PhD, I had the chance to learn and work under the great supervision of my advisor R emi (Munos) in two elds that are of particular interest to me. These... -
Task-Driven Dictionary Learning
final draft post-refereeing -
Blind Multilinear Identification
International audience -
A new algorithm for learning overcomplete dictionaries
International audience -
Efficient Limited Data Multi-Antenna Compressed Spectrum Sensing Exploiting A...
International audience -
Near-optimal Binary Compressed Sensing Matrix
Manuscript submitted to IEEE Transaction on Information Theory, 2013. -
An evaluation of the sparsity degree for sparse recovery with deterministic m...
International audience -
Selective l1 minimization for sparse recovery
International audience -
Acoustic source identification: Experimenting the l(1) minimization approach
International audience -
Spike detection from inaccurate samplings
Revised version, minor changes, 16pages -
Projection onto the Cosparse Set is NP-Hard
to appear in ICASSP 2014 -
Compressive Pattern Matching on Multispectral Data
International audience -
Balancing Sparsity and Rank Constraints in Quadratic Basis Pursuit
We investigate the methods that simultaneously enforce sparsity and low-rank structure in a matrix as often employed for sparse phase retrieval problems or phase... -
Neural Networks and Sparse Information Acquisition
Cette thèse traite de mémoires associatives neuro-inspirées. Une extension des réseaux de neurones récurrents et binaires introduits par Gripon et Berrou a été étudiée... -
Feature detection in a multispectral image by compressed sensing
Multi- and hyper-spectral sensors generate a huge stream of data. A way around this problem is to use a compressive acquisition of the multi- and hyper-spectral...
