Laue, Heinrich Edgar Arnold2017-08-152017-08-152017-07H.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.2017.2693649.1053-5888 (print)1558-0792 (online)10.1109/MSP.2017.2693649http://hdl.handle.net/2263/61643The 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© 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.SamplingCompressive sensing/sampling (CS)RedundancyRate of informationPostprint Article