Demystifying compressive sensing

Laue, Heinrich Edgar Arnold

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dc.contributor.author	Laue, Heinrich Edgar Arnold
dc.date.accessioned	2017-08-15T07:58:38Z
dc.date.available	2017-08-15T07:58:38Z
dc.date.issued	2017-07
dc.description.abstract	The conventional Nyquist-Shannon sampling theorem has been fundamental to the acquisition of signals for decades, relating a uniform sampling rate to the bandwidth of a signal. However, many signals can be compressed after sampling, implying a high level of redundancy. The theory of compressive sensing/sampling (CS) presents a sampling framework based on the ‘rate of information’ of a signal and not the bandwidth, thereby minimising redundancy during sampling. This means that a signal can be recovered from far fewer samples than conventionally required.	en_ZA
dc.description.department	Electrical, Electronic and Computer Engineering	en_ZA
dc.description.librarian	hj2017	en_ZA
dc.description.uri	http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=79	en_ZA
dc.identifier.citation	H.E.A. Laue, “Demystifying Compressive Sensing [Lecture Notes],” in IEEE Signal Processing Magazine, vol. 34, no. 4, pp. 171–176, July 2017. DOI:10.1109/MSP.2017.2693649.	en_ZA
dc.identifier.issn	1053-5888 (print)
dc.identifier.issn	1558-0792 (online)
dc.identifier.other	10.1109/MSP.2017.2693649
dc.identifier.uri	http://hdl.handle.net/2263/61643
dc.language.iso	en	en_ZA
dc.publisher	Institute of Electrical and Electronics Engineers	en_ZA
dc.rights	© 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.	en_ZA
dc.subject	Sampling	en_ZA
dc.subject	Compressive sensing/sampling (CS)	en_ZA
dc.subject	Redundancy	en_ZA
dc.subject	Rate of information	en_ZA
dc.title	Demystifying compressive sensing	en_ZA
dc.type	Postprint Article	en_ZA