Blind signal separation and independent component analysis

This special issue on ICA and BSS is a timely publication covering important recent 0ndings in ICA theory as well as new applications of ICA in data analysis.

Abstract

Radiotracers can be used in several industrial applications by injecting the radiotracer into the industrial system and monitoring the radiation using radiation detectors for obtaining signals. These signals are analyzed to obtain indications about what is happening within the system or to determine the problems that may be present in the system. For multi-phase system analysis, more than one radiotracer is used and the result is a mixture of radiotracers signals. The problem is in such cases is how to separate these signals from each other. The paper presents a proposed method based on Principle Component Analysis (PCA) for separating mixed two radiotracer signals from each other. Two different radiotracers (Technetium-99m (Tc99m) and Barium-137m (Ba137m)) were injected into a physical model for simulation of chemical reactor (PMSCR-MK2) for obtaining the radiotracer signals using radiation detectors and Data Acquisition System (DAS). The radiotracer signals are mixed and signal processing steps are performed include background correction and signal de-noising, then applying the signal separation algorithms. Three separation algorithms have been carried out; time domain based separation algorithm, Independent Component Analysis (ICA) based separation algorithm, and Principal Components Analysis (PCA) based separation algorithm. The results proved the superiority of the PCA based separation algorithm to the other based separation algorithm, and PCA based separation algorithm and the signal processing steps gives a considerable improvement of the separation process.