Acceleration of Digital Stochastic Measurement Simulation Based on Concurrent Programming

Abstract

A/D conversion methods improved through stochastic signal superposition, along with oversampling techniques present significant research direction in the area of signal processing and measurement. Concerning that accuracy of those methods rises with length of measurement interval, i.e. integration time; it turns them appropriate for calculation / measurement of the orthogonal transformations. Simulation and validation of above mentioned digital stochastic methods, requires significant computing resource allocation. Long measurement intervals assigned for processing of numerous arithmetic operations over oversampled input signals presents the most demanding computing requirements.

In this paper, a novel digital stochastic measurement simulation approach is presented and validated. Simulation approach is based on Concurrent Programming technique. General orthogonal transformations are analysed through the stochastic measurement technique. As a reference test case Discrete Fourier Transform is calculated over several periodic input signals converted by the stochastic A/D converter. Time required for a simulation test case accomplishment is analysed as a main performance metric. Final results have proven that Concurrent Programming technique improves simulation speed, without other consequences on measurement performance.

DOI: http://dx.doi.org/10.5755/j01.eie.24.6.22284