@Article{PoliLlCeSaPeRaNi:2014:SoFlDe,
               author = "Poli, G. and Llapa, E. and Cecatto, Jos{\'e} Roberto and Saito, 
                         J. H. and Peters, J. F. and Ramanna, S. and Nicoletti, M. C.",
          affiliation = "{} and {} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Solar flare detection system based on tolerance near sets in a 
                         GPU-CUDA framework",
              journal = "Knowledge Based Systems",
                 year = "2014",
               volume = "70",
                pages = "345--360",
                 note = "{Setores de Atividade: Pesquisa e desenvolvimento 
                         cient{\'{\i}}fico.}",
             keywords = "Tolerance near sets, Flare detection, Pattern recognition, 
                         GPU-CUDA.",
             abstract = "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 CPUGPU 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 
                         GPUCUDA framework.",
                  doi = "10.1016/j.knosys.2014.07.012",
                  url = "http://dx.doi.org/10.1016/j.knosys.2014.07.012",
                 issn = "0950-7051 and 1872-7409",
                label = "lattes: 6513046926936437 3 PoliLlCeSaPeRaNi:2014:SoFlDe",
             language = "en",
           targetfile = "1-s2.0-S095070511400269X-main.pdf",
        urlaccessdate = "27 abr. 2024"
}