Compressed sensing allows to reconstruct a signal from a few linear projections, under the assumption that the signal can be sparsely represented, that is, with only a few coefficients, on a known dictionary. Coding is very simple and all the complexity is gathered on the reconstruction. After more detailed explanations of the principle of compressed sensing, some theoretic resultats from literature and a few simulations allowing to get an idea of expected performances, we focusson three problems: First, the study for the building of a system using compressed sensing with a binary matrix and the obtained benefits. Then, we have a look at the building of a dictionary for sparse representations of the signal. And lastly, we discuss the possibility of processing signal without reconstruction, with an example in classification.
On compressed sampling applications and its implementation