INTRODUCTION TO COMPRESSED SENSING

An Introduction to Compressed Sensing

Soft cover. Zustand: New. Contents: Preface. 1. Mathematical Preliminaries. 2. Compressed Sensing: Problem Formulations. 3. Null Space Based Conditions. 4. Concepts from Graph Theory. 5. Deterministic Construction of Measurement Matrices. 6. Probabilistic Construction of Measurement Matrices. 7. Matrix Recovery and Completion. 8. Some Recent Approaches to Vector Recovery. 9. Optimization Algorithms. 10. Case Studies. Bibliography. Index. Compressed sensing is a relatively recent area of research that refers to the recovery of high-dimensional but low-complexity objects from a limited number of measurements. The topic has applications to signal/image processing and computer algorithms, and it draws from a variety of mathematical techniques such as graph theory, probability theory, linear algebra, and optimization. The author presents significant concepts never before discussed as well as new advances in the theory, providing an in-depth initiation to the field of compressed sensing. An Introduction to Compressed Sensing contains substantial material on graph theory and the design of binary measurement matrices, which is missing in recent texts despite being poised to play a key role in the future of compressed sensing theory covers several new developments in the field and is the only book to thoroughly study the problem of matrix recovery; and supplies relevant results alongside their proofs in a compact and streamlined presentation that is easy to navigate. The core audience for this book is engineers, computer scientists, and statisticians who are interested in compressed sensing. Professionals working in image processing, speech processing, or seismic signal processing will also find the book of interest. Artikel-Nr. 135743A

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