An Introduction to Compressed Sensing
Seiten
2020
Society for Industrial & Applied Mathematics,U.S. (Verlag)
978-1-61197-611-3 (ISBN)
Society for Industrial & Applied Mathematics,U.S. (Verlag)
978-1-61197-611-3 (ISBN)
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. This book presents significant concepts never before discussed and new advances in the theory, providing an in-depth initiation to the field of compressed sensing.
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. It also covers several new developments in the field and is the only book to thoroughly study the problem of matrix recovery. The book 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.
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. It also covers several new developments in the field and is the only book to thoroughly study the problem of matrix recovery. The book 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.
M. Vidyasagar is a Science and Engineering Research Board (SERB) Distinguished Fellow at the Indian Institute of Technology Hyderabad. During his 50-year career he has worked in a variety of areas including control theory, robotics, statistical learning theory, computational cancer biology, and compressed sensing. He has received many awards and honors in recognition of his research, including the Fellowship of The Royal Society and the IEEE Control Systems Technical Field Award.
| Erscheinungsdatum | 31.01.2020 |
|---|---|
| Reihe/Serie | Computational Science and Engineering 22 |
| Verlagsort | New York |
| Sprache | englisch |
| Gewicht | 750 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Angewandte Mathematik |
| Mathematik / Informatik ► Mathematik ► Finanz- / Wirtschaftsmathematik | |
| Mathematik / Informatik ► Mathematik ► Graphentheorie | |
| ISBN-10 | 1-61197-611-1 / 1611976111 |
| ISBN-13 | 978-1-61197-611-3 / 9781611976113 |
| Zustand | Neuware |
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