We study the distributed sampling and centralized reconstruction of two correlated signals, modeled as the input and output of an unknown sparse filtering operation. This is akin ...
Ali Hormati, Olivier Roy, Yue M. Lu, Martin Vetter...
Classical sampling records the signal level at pre-determined time instances, usually uniformly spaced. An alternative implicit sampling model is to record the timing of pre-deter...
Abstract—Compressive sampling (CS) has emerged as significant signal processing framework to acquire and reconstruct sparse signals at rates significantly below the Nyquist rate...
Abstract—Compressive sensing is a new signal acquisition technology with the potential to reduce the number of measurements required to acquire signals that are sparse or compres...
One of the fundamental assumptions in traditional sampling theorems is that the signals to be sampled come from a single vector space (e.g. bandlimited functions). However, in many...