EFFICIENCY IMPROVEMENT OF SD PROCESSOR BASED ON CRDC ALGORITHM

One such complex algorithm is Singular-value Decomposition (SD) which is an important algorithm with applications in varied domains of signal processing such as direction estimation, spectrum analysis and systems identification. It is a generalized extension to the eigen-decomposition for non-square matrices and is hence of great importance, particularly for subspace based algorithms in signal processing. But SD is known to be a very complicated algorithm with computational complexity ~O (N3) (for a N x N square matrix). For real-time computation of such a complex algorithm the use of a parallel and direct mapped hardware solution is indeed desired. The Singular Value Decomposition (SD) is an important matrix factorization with applications in signal processing, image processing and robotics. It is generally acknowledged that the SD is the only generally reliable method for determining the rank of a matrix numerically. The SD is a very useful tool, for example, in analyzing data matrices from sensor arrays for adaptive beamforming, and low rank approximations to matrices in image enhancement. The wide variety of applications for the SD coupled with its computational complexity justifies dedicating hardware to this computation. Designed 2X2 CRDC based SD processor can be used as basic building block for an array of processors of N X N matrix. Hardware and software resources OS: Windows 9x or upper RAM: Minimum 512 MB Programming Language: XILINX 8.6 or upper Tools: MODELSIM 5.2 or upper Processor: AMD Sempron 1.6 GHz/ Intel P4 2.8 GHz