Titlebook: Compressed Sensing and its Applications; MATHEON Workshop 201 Holger Boche,Robert Calderbank,Jan Vybíral Book 2015 Springer International P

出汗

Compressed Sensing and its Applications978-3-319-16042-9Series ISSN 2296-5009 Series E-ISSN 2296-5017

不遵守

Eigene Führungsrolle in China verstehene object to recover, but also on its structure. This chapter is about understanding this phenomenon, and demonstrating how it can be fruitfully exploited by the design of suitable sampling strategies in order to outperform more standard compressed sensing techniques based on random matrices.

撤退

Range-Of-Motion

Temporal Compressive Sensing for Video,ough modern imagers are capable of both simultaneous spatial and temporal resolutions at micrometer and microsecond scales, the power required to sample at these rates is undesirable. The field of compressive sensing (CS) has recently suggested a solution to this design challenge. By exploiting phys

結(jié)果

Confidential

Recovering Structured Signals in Noise: Least-Squares Meets Compressed Sensing,ges, gene expression data from a DNA microarray, social network data, etc.), yet is such that its desired properties lie in some low dimensional structure (sparsity, low-rankness, clusters, etc.). In the modern viewpoint, the goal is to come up with efficient algorithms to reveal these structures an

Dysplasia

The Quest for Optimal Sampling: Computationally Efficient, Structure-Exploiting Measurements for Coe object to recover, but also on its structure. This chapter is about understanding this phenomenon, and demonstrating how it can be fruitfully exploited by the design of suitable sampling strategies in order to outperform more standard compressed sensing techniques based on random matrices.

Landlocked

Osteoarthritis

Quantization and Compressive Sensing,lores the interaction of quantization and compressive sensing and examines practical quantization strategies for compressive acquisition systems. Specifically, we first provide a brief overview of quantization and examine fundamental performance bounds applicable to any quantization approach. Next,

遍及

Compressive Gaussian Mixture Estimation,d at estimating the parameters of a density mixture on training data in a compressive manner by computing a low-dimensional . of the data. The sketch represents empirical moments of the underlying probability distribution. Instantiating the framework on the case where the densities are isotropic Gau