High-order statistics for fluctuation-enhanced gas sensing Academic Article

abstract

• The stochastic component of a chemical sensor signal contains valuable information that can be visualized not only by spectral analysis but also by methods of high-order statistics (HOS). The analysis of HOS enables the extraction of nonconventional features and may lead to significant improvements in selectivity and sensitivity. We pay particular attention to the bispectrum that characterizes the non-Gaussian component and detects nonstationarity in analyzed noise. The results suggest that the bispectrum can be applied to gas recognition. The analysis of bispectra and the reproducibility statistics of skewness and kurtosis indicate that the measured time records were stationary.

authors

• Kish, Laszlo

published proceedings

• SENSORS AND MATERIALS

author list (cited authors)

• Smulko, J. M., & Kish, L. B.

complete list of authors

• Smulko, JM||Kish, LB

publication date

• January 2004

published in

• SENSORS AND MATERIALS  Journal

keywords

• Gas Detection
• Gas Recognition
• Pattern Generation
• Sensor Signal Processing
• Stochastic Processes

start page

• 291

end page

• 299

volume

• 16

issue

• 6