Paral·lelització d’algorismes de processat digital mitjançant targetes GPU

The objective of this paper is to program the DFT algorithm, which is widely used in signal processing, using the CUDA language developed by Nvidia. Additionally, the time needed to process the signal will be compared to the same algorithm written in C language. This paper is aimed to the utilization of Graphic Processing Units (GPU) to parallelize calculations.L’objectiu del següent treball és programar l’algorisme de la DFT, àmpliament utilitzat en processat de senyal, en el llenguatge CUDA desenvolupat per Nvidia. A més comparar els temps de processament necessaris amb el mateix algorisme programat en C. El treball està orientat a la utilització de targetes gràfiques (GPU) per a realitzar processat de senyal en paral·lel

Sparse Representation of Astronomical Images

Sparse representation of astronomical images is discussed. It is shown that a significant gain in sparsity is achieved when particular mixed dictionaries are used for approximating these types of images with greedy selection strategies. Experiments are conducted to confirm: i)Effectiveness at producing sparse representations. ii)Competitiveness, with respect to the time required to process large images.The latter is a consequence of the suitability of the proposed dictionaries for approximating images in partitions of small blocks.This feature makes it possible to apply the effective greedy selection technique Orthogonal Matching Pursuit, up to some block size. For blocks exceeding that size a refinement of the original Matching Pursuit approach is considered. The resulting method is termed Self Projected Matching Pursuit, because is shown to be effective for implementing, via Matching Pursuit itself, the optional back-projection intermediate steps in that approach.Comment: Software to implement the approach is available on http://www.nonlinear-approx.info/examples/node1.htm