A Kernel based approach for classification of electromagnetic interference signals

Ender Luzardo, José Paredes, Jaime Ramírez


This paper introduces Electromagnetic Interference signal classification methods for signals obtained on ribbon cables with different crosstalk configurations. The proposed method comprises two stages. The first one is a preprocessing stage that applies either Principal Components Analysis (PCA), Kernel Principal Components Analysis (KPCA) or Independent Components Analysis (ICA) to reduce the data dimension and, at the same time, to obtain the most relevant information from the raw data. The second stage, the classification one, uses Support Vector Machine (SVM) to classify the kind of electromagnetic coupling. We compare the performance of the different classification structures obtained by combining a preprocessing method with SVM, namely PCA+SVM, KPCA+SVM, ICA+SVM as well as SVM in the time domain.

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Universidad del Zulia /Venezuela/ Revista Técnica de la Facultad de Ingeniería/ revistatecnica@gmail.com /

p-ISSN: 0254-0770 / e-ISSN: 2477-9377 


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Este obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 3.0 Unported.