Sharif Digital Repository

In this paper, a new algorithm for Sparse Component Analysis (SCA) in the noisy underdetermined case (i.e., with more sources than sensors) is presented. The solution obtained by the proposed algorithm is compared to the minimum l1 -norm solution achieved by Linear Programming (LP). Simulation results show that the proposed algorithm is approximately 10 dB better than the LP method with respect to the quality of the estimated sources. It is due to optimality of our solution (in the MAP sense) for source recovery in noisy underdetermined sparse component analysis in the case of spiky model for sparse sources and Gaussian noise. © 2007 IEEE  

There are many depletable resource planning problems associated with convex cost function. The Greedy algorithm has been used to find the optimal solution for such problems. But exploiting that algorithm requires a large bulk of computations to find the optimal solution. Thus, in this paper we have established two piecewise linear estimators for the convex cost function: an inner intersection of the function values and an outer tangent. A practical experiment was conducted to compare the performance of the two presented estimators and it was observed that the outer tangent performs more efficiently than the inner intersection. The execution time of the Greedy Algorithm and the estimation...