Compressive sensing

Compressive sensing (2007)

by Richard Baraniuk

Venue:	IEEE Signal Processing Mag
Citations:	146 - 27 self

Datum	Value	Source
TITLE	Compressive sensing	INFERENCE
AUTHOR NAME	Richard Baraniuk	SVM HeaderParse 0.2
AUTHOR AFFIL	Rice University	SVM HeaderParse 0.2
ABSTRACT	The Shannon/Nyquist sampling theorem tells us that in order to not lose information when uniformly sampling a signal we must sample at least two times faster than its bandwidth. In many applications, including digital image and video cameras, the Nyquist rate can be so high that we end up with too many samples and must compress in order to store or transmit them. In other applications, including imaging systems (medical scanners, radars) and high-speed analog-to-digital converters, increasing the sampling rate or density beyond the current state-of-the-art is very expensive. In this lecture, we will learn about a new technique that tackles these issues using compressive sensing [1, 2]. We will replace the conventional sampling and reconstruction operations with a more general linear measurement scheme coupled with an optimization in order to acquire certain kinds of signals at a rate significantly below Nyquist. 2	SVM HeaderParse 0.2
YEAR	2007	INFERENCE
VENUE	IEEE Signal Processing Mag	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	118--120	INFERENCE
CITATIONS	9 found	ParsCit 1.0