Linear and Nonlinear Deterministic Compressed Sensing
Arash Amini, Guest PhD Student, BIG

Seminar • 01 December 2009 • BM 4.233

Abstract
The developing field of compressed sensing which studies the sampling-reconstruction problem for the class sparse signals based on their linear projections onto spaces with lower dimension, is mainly based on the random structure of the measurements. However, for practical applications, random samplers should be replaced by deterministic methods both for the storage purposes and reconstruction procedures. Unlike the vast amount of literature in random sensing theory, deterministic approaches are hardly studied. Moreover, the current known deterministic approaches fail to achieve the predicted asymptotic bound by random measurements. In this talk, beside a brief introduction to compressed sensing and RIP condition, I will discuss some of the known deterministic sampling matrices which were part of my previous works. I will also introduce a class of nonlinear sensing functions which are more efficient considering sampling and reconstruction tasks, but are sensitive to additive noise.