Prof.  Zohre  Aminifard
Semnan University,  Iran

Title: Compressed sensing models: Algorithms and applications

Abstract:

Recent years have seen an explosion of research activities in a fascinating area called compressed sensing. The area of compressed sensing, at the intersection of mathematics, electrical engineering, computer science, and physics, takes its name from the premise that data acquisition and compression can be performed simultaneously. This is possible because many real-world signals are sparse, and even though they are acquired with seemingly too few measurements, exploiting sparsity enables one to solve the resulting underdetermined systems of linear equations. The reconstruction of sparse signals is not only feasible in theory, but efficient algorithms also exist to perform the reconstruction in practice. In consideration of different methodologies, optimization techniques play a central role. These approaches can be roughly divided into three optimization strategies: convex relaxation, greedy algorithms, and combinational methods. These realizations, together with their potential applications, have triggered the interest of the scientific.

Biography: