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Compressed sensing and its applications : matheon workshop 2013 / edited by Holger Boche, Robert Calderbank, Gitta Kutyniok, Jan Vybíral.

Colaborador(es): Tipo de material: TextoTextoSeries Applied and Numerical Harmonic AnalysisEditor: Cham : Springer International Publishing : Imprint: Birkhäuser, 2015Descripción: xii, 472 páginas : 105 ilustraciones, 70 ilustraciones en colorTipo de contenido:
  • texto
Tipo de medio:
  • computadora
Tipo de portador:
  • recurso en línea
ISBN:
  • 9783319160429
Formatos físicos adicionales: Edición impresa:: Sin títuloClasificación LoC:
  • Q350-390
Recursos en línea:
Contenidos:
Survey on Compressed Sensing -- Temporal compressive sensing for video -- Compressed Sensing, Sparse Inversion, and Model Mismatch -- Recovering Structured Signals in Noise: Least-Squares Meets Compressed Sensing -- The Quest for Optimal Sampling: Computationally Efficient, Structure-exploiting Measurements for Compressed Sensing -- Compressive Sensing in Acoustic Imaging -- Quantization and Compressive Sensing -- Compressive Gaussian Mixture Estimation -- Two Algorithms for Compressed Sensing of Sparse Tensors -- Sparse Model Uncertainties in Compressed Sensing with Application to Convolutions and Sporadic Communication -- Cosparsity in Compressed Sensing -- Structured Sparsity: Discrete and Convex Approaches -- Explicit Matrices with the Restricted Isometry Property: Breaking the Square-Root Bottleneck -- Tensor Completion in Hierarchical Tensor Representations -- Compressive Classification: Where Wireless Communications Meets Machine Learning.
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Springer eBooks

Survey on Compressed Sensing -- Temporal compressive sensing for video -- Compressed Sensing, Sparse Inversion, and Model Mismatch -- Recovering Structured Signals in Noise: Least-Squares Meets Compressed Sensing -- The Quest for Optimal Sampling: Computationally Efficient, Structure-exploiting Measurements for Compressed Sensing -- Compressive Sensing in Acoustic Imaging -- Quantization and Compressive Sensing -- Compressive Gaussian Mixture Estimation -- Two Algorithms for Compressed Sensing of Sparse Tensors -- Sparse Model Uncertainties in Compressed Sensing with Application to Convolutions and Sporadic Communication -- Cosparsity in Compressed Sensing -- Structured Sparsity: Discrete and Convex Approaches -- Explicit Matrices with the Restricted Isometry Property: Breaking the Square-Root Bottleneck -- Tensor Completion in Hierarchical Tensor Representations -- Compressive Classification: Where Wireless Communications Meets Machine Learning.

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