On compressed sampling applications and its implementation
type: Dataset
contact point: Organization
  fn: CCSD
keyword: sparse-sampling
keyword: minimisation-l1
keyword: l1-minimization
keyword: codage-parcimonieux
keyword: compressed-sampling
keyword: inverse-problem
keyword: theses
keyword: probleme-inverse
keyword: spiotherengineering-sciences-physicsother
keyword: infoeu-reposemanticsdoctoralthesis
landing page: https://theses.hal.science/tel-00934823
description: 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.
identifier: NNT: 2013GRENT009
issued: 2026-05-07T07:27:59.292010
language: fr
modified: 2026-05-07T07:27:59.292016

test_moissonnage_selune
type: Agent

On compressed sampling applications and its implementation
type: Distribution
access URL: https://theses.hal.science/tel-00934823
format: HTML
issued: 2026-05-07T07:27:59.301590
modified: 2026-05-07T07:27:59.281281