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...
Consider a scenario where a distributed signal is sparse and is acquired by various sensors that see different versions. Thus, we have a set of sparse signals with both some commo...
Image reconstruction problems in radio astronomy and other fields like biomedical imaging are often ill-posed and some form of regularization is required. This imposes user speci...
We introduce the data model BM, which specifies kernels of motifs by means of Boolean matrices. Different from position frequency matrices these only specify which bases can appea...
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...