Demystifying compressive sensing
| 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 |