An Introduction to Compressed Sensing is popular PDF and ePub book, written by M. Vidyasagar in 2019-12-03, it is a fantastic choice for those who relish reading online the Technology & Engineering genre. Let's immerse ourselves in this engaging Technology & Engineering book by exploring the summary and details provided below. Remember, An Introduction to Compressed Sensing can be Read Online from any device for your convenience.
An Introduction to Compressed Sensing Book PDF Summary
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.
Detail Book of An Introduction to Compressed Sensing PDF
- Author : M. Vidyasagar
- Release : 03 December 2019
- Publisher : SIAM
- ISBN : 9781611976120
- Genre : Technology & Engineering
- Total Page : 341 pages
- Language : English
- PDF File Size : 13,7 Mb
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