{"id":581,"date":"2016-07-18T15:48:25","date_gmt":"2016-07-18T18:48:25","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=581"},"modified":"2016-07-18T15:48:25","modified_gmt":"2016-07-18T18:48:25","slug":"vol10-no1-art4","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no1\/vol10-no1-art4\/","title":{"rendered":"Independent Component Analysis and Blind Signal Separation: Theory, Algorithms and Applications"},"content":{"rendered":"<p><strong>T\u00edtulo:<\/strong> Independent Component Analysis and Blind Signal Separation: Theory, Algorithms and Applications<\/p>\n<p><strong>Autores:<\/strong> Simas Filho, Eduardo F.; Seixas, Jos\u00e9 M. de; Moura, Natanael N.; Haddad, Diego B.; Faier, Jos\u00e9 M.; Albuquerque, Maria C. S.<\/p>\n<p align=\"justify\"><strong>Resumo:<\/strong> This paper reviews Independent Components Analysis (ICA) and Blind Signal Separation (BSS) problems. An overview on the main statistical principles that guide the search for the independent components is formulated, methods for blind signal separation that require both high-order and second-order statistics are also illustrated. Some of the most successful algorithms for both ICA and BSS are derived. Experimental applications in different signal processing tasks such as passive sonar, nondestructive ultrasound inspection and electrical-load time series are presented.<\/p>\n<p><strong>Palavras-chave:<\/strong> ICA; Blind Source Separation; Signal processing; Feature extraction<\/p>\n<p><strong>P\u00e1ginas:<\/strong> 19<\/p>\n<p><strong>C\u00f3digo DOI:<\/strong> <a href=\"http:\/\/dx.doi.org\/10.21528\/lnlm-vol10-no1-art4\">10.21528\/lmln-vol10-no1-art4<\/a><\/p>\n<p><strong>Artigo em PDF:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2016\/07\/vol10-no1-art4.pdf\" rel=\"\">vol10-no1-art4.pdf<\/a><\/p>\n<p><strong>Arquivo BibTex:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2016\/07\/vol10-no1-art4.bib\" rel=\"\">vol10-no1-art4.bib<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>T\u00edtulo: Independent Component Analysis and Blind Signal Separation: Theory, Algorithms and Applications Autores: Simas Filho, Eduardo F.; Seixas, Jos\u00e9 M. de; Moura, Natanael N.; Haddad, Diego B.; Faier, Jos\u00e9 M.; Albuquerque, Maria C. S. Resumo: This paper reviews Independent Components <a href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no1\/vol10-no1-art4\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":565,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-581","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Independent Component Analysis and Blind Signal Separation: Theory, Algorithms and Applications - Learning and NonLinear Models<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no1\/vol10-no1-art4\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Independent Component Analysis and Blind Signal Separation: Theory, Algorithms and Applications - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"T\u00edtulo: Independent Component Analysis and Blind Signal Separation: Theory, Algorithms and Applications Autores: Simas Filho, Eduardo F.; Seixas, Jos\u00e9 M. de; Moura, Natanael N.; Haddad, Diego B.; Faier, Jos\u00e9 M.; Albuquerque, Maria C. S. 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