Solar flare detection system based on tolerance near sets in a GPU–CUDA framework

This article presents a unique application of tolerance near sets (TNS) for detecting solar flare events in solar images acquired using radio astronomy techniques. In radio astronomy (RA) applications, the interferometric array processing of data streams presents algorithmic and response time challenges as well as a high volume of data. The radio interferometer is an RA instrument composed of an array of antennas. Radio signals emitted by a celestial object are captured by the antennas and are subsequently processed in such a way that each pair of antennas produces correlated data. The overall correlated data is then accumulated and, after an integration period, the spectral image of the observed object is obtained. The process of deconvolution of the spectral image produces the desired spatial image of the celestial object. The proposed solar flare detection system is embedded in a computational platform framework suitable for dealing with huge volumes of data, based on a cluster of CPU–GPU pairs. The experimental results presented in the paper include comparison of the TNS-based algorithm (implemented as the SOL-FLARE system) with the K-means algorithm using significant samples of test images to validate the detection system. The performances of both systems are comparatively analyzed using Receiver Operating Characteristic (ROC) curves. The images used in the experiments were selected from a data repository produced by the Nobeyama Radioheliograph, in Japan, during the years 2004 up to 2013. The main contribution of the article is a novel approach to solar flare detection in a GPU–CUDA framework.