Classification of optical-sensor response cues with a Bi-dimensional wavelet-transform approach
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In this work is used the two-dimensional discrete wavelet transform as a feature extractor of time responses from a porous silicon optical gas sensor for gas identification. The wavelet decomposition allows us to have a more in-deep sight of the sensor response. In addition, using a linear support vector machine (SVM) as classifier we evaluate our approach for a six-analyte discrimination problem. © 2011 American Institute of Physics.
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Classification; Extraction; Feature; Porous silicon optical gas sensor; Support vector machine; Twodimensional wavelet transform
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