Signal Feature Extraction Using Granular Computing: Comparative Analysis with Frequency and Time Descriptors Applied to Dynamic Laser Speckle Patterns

Abstract

The laser dynamic speckle is a phenomenon caused by the fluctuant interference of the laser light reflected from an illuminated surface where some kind of activity is taking place. Signals generated by the intensity changes in each pixel through the sequence are processed with the finality of identifying underlying activity in each point. In this work we compare the performance of a Rough Fuzzy Granular Descriptor (previously published) against a set of dynamic speckle descriptors based in time and frequency processing. To perform this evaluation a numerical simulation is proposed to explore their linearity, robustness, sensitivity related to the samples quantity, as well as also by their computing time. Also the robustness to inhomogeneous spatial intensity was evaluated in an experiment performed with the illuminated surface of an actual biological object.