An Introduction to Compressed Sensing

· Computational Science and Engineering āļŦāļ™āļąāļ‡āļŠāļ·āļ­āđ€āļĨāđˆāļĄāļ—āļĩāđˆ 22 · SIAM
eBook
253
āļŦāļ™āđ‰āļē
āļĄāļĩāļŠāļīāļ—āļ˜āļīāđŒ
āļ„āļ°āđāļ™āļ™āđāļĨāļ°āļĢāļĩāļ§āļīāļ§āđ„āļĄāđˆāđ„āļ”āđ‰āļĢāļąāļšāļāļēāļĢāļ•āļĢāļ§āļˆāļŠāļ­āļšāļĒāļ·āļ™āļĒāļąāļ™ Â āļ”āļđāļ‚āđ‰āļ­āļĄāļđāļĨāđ€āļžāļīāđˆāļĄāđ€āļ•āļīāļĄ

āđ€āļāļĩāđˆāļĒāļ§āļāļąāļš eBook āđ€āļĨāđˆāļĄāļ™āļĩāđ‰

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.

āđƒāļŦāđ‰āļ„āļ°āđāļ™āļ™ eBook āļ™āļĩāđ‰

āđāļŠāļ”āļ‡āļ„āļ§āļēāļĄāđ€āļŦāđ‡āļ™āļ‚āļ­āļ‡āļ„āļļāļ“āđƒāļŦāđ‰āđ€āļĢāļēāļĢāļąāļšāļĢāļđāđ‰

āļ‚āđ‰āļ­āļĄāļđāļĨāđƒāļ™āļāļēāļĢāļ­āđˆāļēāļ™

āļŠāļĄāļēāļĢāđŒāļ—āđ‚āļŸāļ™āđāļĨāļ°āđāļ—āđ‡āļšāđ€āļĨāđ‡āļ•
āļ•āļīāļ”āļ•āļąāđ‰āļ‡āđāļ­āļ› Google Play Books āļŠāļģāļŦāļĢāļąāļš Android āđāļĨāļ° iPad/iPhone āđāļ­āļ›āļˆāļ°āļ‹āļīāļ‡āļ„āđŒāđ‚āļ”āļĒāļ­āļąāļ•āđ‚āļ™āļĄāļąāļ•āļīāļāļąāļšāļšāļąāļāļŠāļĩāļ‚āļ­āļ‡āļ„āļļāļ“ āđāļĨāļ°āļŠāđˆāļ§āļĒāđƒāļŦāđ‰āļ„āļļāļ“āļ­āđˆāļēāļ™āđāļšāļšāļ­āļ­āļ™āđ„āļĨāļ™āđŒāļŦāļĢāļ·āļ­āļ­āļ­āļŸāđ„āļĨāļ™āđŒāđ„āļ”āđ‰āļ—āļļāļāļ—āļĩāđˆ
āđāļĨāđ‡āļ›āļ—āđ‡āļ­āļ›āđāļĨāļ°āļ„āļ­āļĄāļžāļīāļ§āđ€āļ•āļ­āļĢāđŒ
āļ„āļļāļ“āļŸāļąāļ‡āļŦāļ™āļąāļ‡āļŠāļ·āļ­āđ€āļŠāļĩāļĒāļ‡āļ—āļĩāđˆāļ‹āļ·āđ‰āļ­āļˆāļēāļ Google Play āđ‚āļ”āļĒāđƒāļŠāđ‰āđ€āļ§āđ‡āļšāđ€āļšāļĢāļēāļ§āđŒāđ€āļ‹āļ­āļĢāđŒāđƒāļ™āļ„āļ­āļĄāļžāļīāļ§āđ€āļ•āļ­āļĢāđŒāđ„āļ”āđ‰
eReader āđāļĨāļ°āļ­āļļāļ›āļāļĢāļ“āđŒāļ­āļ·āđˆāļ™āđ†
āļŦāļēāļāļ•āđ‰āļ­āļ‡āļāļēāļĢāļ­āđˆāļēāļ™āļšāļ™āļ­āļļāļ›āļāļĢāļ“āđŒ e-ink āđ€āļŠāđˆāļ™ Kobo eReader āļ„āļļāļ“āļˆāļ°āļ•āđ‰āļ­āļ‡āļ”āļēāļ§āļ™āđŒāđ‚āļŦāļĨāļ”āđāļĨāļ°āđ‚āļ­āļ™āđ„āļŸāļĨāđŒāđ„āļ›āļĒāļąāļ‡āļ­āļļāļ›āļāļĢāļ“āđŒāļ‚āļ­āļ‡āļ„āļļāļ“ āđ‚āļ›āļĢāļ”āļ—āļģāļ•āļēāļĄāļ§āļīāļ˜āļĩāļāļēāļĢāļ­āļĒāđˆāļēāļ‡āļĨāļ°āđ€āļ­āļĩāļĒāļ”āđƒāļ™āļĻāļđāļ™āļĒāđŒāļŠāđˆāļ§āļĒāđ€āļŦāļĨāļ·āļ­āđ€āļžāļ·āđˆāļ­āđ‚āļ­āļ™āđ„āļŸāļĨāđŒāđ„āļ›āļĒāļąāļ‡ eReader āļ—āļĩāđˆāļĢāļ­āļ‡āļĢāļąāļš

āļ­āđˆāļēāļ™āļ‹āļĩāļĢāļĩāļŠāđŒāļ™āļĩāđ‰āļ•āđˆāļ­

āļĢāļēāļĒāļāļēāļĢāļ­āļ·āđˆāļ™āđ† āļ—āļĩāđˆāđ€āļ‚āļĩāļĒāļ™āđ‚āļ”āļĒ M. Vidyasagar

eBook āļ—āļĩāđˆāļ„āļĨāđ‰āļēāļĒāļāļąāļ™