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Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
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Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
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is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.
Instructions to authors
English 日本語
Instructions for manuscript preparation
English 日本語
Template
English
Publisher
MYU K.K.
Sensors and Materials
1-23-3-303 Sendagi,
Bunkyo-ku, Tokyo 113-0022, Japan
Tel: 81-3-3827-8549
Fax: 81-3-3827-8547
MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.
MYU Research
(proofreading and recording)
MYU K.K.
(translation service)
The Art of Writing Scientific Papers
(How to write scientific papers)
(Japanese Only)
Sensors and Materials, Volume 16, Number 6 (2004)
Copyright(C) MYU K.K.
Copyright(C) MYU K.K.
|
pp. 291-299
S&M566 Research Paper in Biosensors and Related Areas Published: 2004 High-Order Statistics for Fluctuation-Enhanced Gas Sensing [PDF] Janusz M. Smulko and Laszlo B. Kish (Received January 23, 2004; Accepted May 26, 2004) Keywords: gas detection, gas recognition, stochastic processes, sensor signal processing, pattern generation
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.
Corresponding author: Laszlo B. Kish Cite this article Janusz M. Smulko and Laszlo B. Kish, High-Order Statistics for Fluctuation-Enhanced Gas Sensing, Sens. Mater., Vol. 16, No. 6, 2004, p. 291-299. |
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