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...
This paper describes a low-complexity approach for reconstructing average packet arrival rates and instantaneous packet counts at a router in a communication network, where the arr...
Maxim Raginsky, Sina Jafarpour, Rebecca Willett, A...
We propose a quantization design technique (estimator) suitable for new compressed sensing sampling systems whose ultimate goal is classification or detection. The design is base...
We provide a novel achievability proof of the Slepian-Wolf theorem for i.i.d. sources over finite alphabets. We demonstrate that random codes that are linear over the real field ac...
Bikash Kumar Dey, Sidharth Jaggi, Michael Langberg
The 2- 1 compressed sensing minimization problem can be solved efficiently by gradient projection. In imaging applications, the signal of interest corresponds to nonnegative pixel...
Zachary T. Harmany, Daniel Thompson, Rebecca Wille...