Ergebnisse für: Sparse Signal Recovery
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Data analysis is fundamental to many parts of modern society. This work examines how we can use sparsity to extract meaning and structure from complex data sets.
We begin by establishing a general framework for imposing a model on the sparse representation of a signal. This not only encompasses m...
The introduction of compressive sensing (CS) led to a new paradigm in signal processing. Traditionally, signals are sampled above their Nyquist rate. Using CS, the same information is acquired with much fewer measurements, provided a sparse representation of the signal exists. This makes CS a very p...
The present collection is the very first contribution of this type in the field of sparse recovery. Compressed sensing is one of the important facets of the broader concept presented in the book, which by now has made connections with other branches such as mathematical imaging, inverse problems, nu...
This volume provides a comprehensive overview of recent research advances in the upcoming field of sparse control and state estimation of linear dynamical systems. The contents offer a detailed introduction to the subject by combining classical control theory and compressed sensing. It covers concep...
At the intersection of mathematics, engineering, and computer science sits the thriving field of compressive sensing. Based on the premise that data acquisition and compression can be performed simultaneously, compressive sensing finds applications in imaging, signal processing, and many other domai...
An important problem in statistics, machine learning, and modern signal processing is to recover information of limited complexity, or, more specifically, of low-dimensional structure, from seemingly few data.
Often, this amounts to recover a sparse signal, i.e., a signal which is non-zero at few ...
The present collection is the very first contribution of this type in the field of sparse recovery. Compressed sensing is one of the important facets of the broader concept presented in the book, which by now has made connections with other branches such as mathematical imaging, inverse problem...
The present collection is the very first contribution of this type in the field of sparse recovery. Compressed sensing is one of the important facets of the broader concept presented in the book, which by now has made connections with other branches such as mathematical imaging, inverse problems, nu...
At the intersection of mathematics, engineering, and computer science sits the thriving field of compressive sensing. Based on the premise that data acquisition and compression can be performed simultaneously, compressive sensing finds applications in imaging, signal processing, and many other domai...
This volume provides a comprehensive overview of recent research advances in the upcoming field of sparse control and state estimation of linear dynamical systems. The contents offer a detailed introduction to the subject by combining classical control theory and compressed sensing. It covers concep...
At the intersection of mathematics, engineering, and computer science sits the thriving field of compressive sensing. Based on the premise that data acquisition and compression can be performed simultaneously, compressive sensing finds applications in imaging, signal processing, and many other domai...
This volume provides a comprehensive overview of recent research advances in the upcoming field of sparse control and state estimation of linear dynamical systems. The contents offer a detailed introduction to the subject by combining classical control theory and compressed sensing. It covers concep...