Feature reduction using Support Vector Machines for binary gas detection

  • Authors:

  • S. Maldonado-Bascón;S. Al-Khalifa;F. López-Ferreras

  • Affiliations:

  • Dpto. de Teoría de la Señal y Comunicaciones, Universidad de Alcalá, Alcalá de Henares (Madrid), Spain 28871;School of Engineering, University of Warwick, Coventry, UK CV4 7 AL;Dpto. de Teoría de la Señal y Comunicaciones, Universidad de Alcalá, Alcalá de Henares (Madrid), Spain 28871

  • Venue:
  • IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
  • Year:
  • 2009

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Abstract

Gas sensor (electronic nose) has many different applications, such as fire detection, food quality control or medical application as well as the detection of atmospheric gases. We describe in this paper a signal processing technique using wavelet transform and Support Vector Machines (SVM) for COand NO2gas detection and to obtain gas concentration. We propose a low complexity algorithm which can be implemented in a low cost palmtop gas monitor. SVM were used in a twofold way. First, SVM were used to classify the type of gas and then for the estimation of gas concentration.