Signal analysis of NEMS sensors at the output of a chromatography column

Journal Article · Tue Jan 13 00:00:00 EST 2015 · AIP Conference Proceedings

DOI:https://doi.org/10.1063/1.4905995· OSTI ID:22390866

Bertholon, François; Harant, Olivier; Bourlon, Bertrand; Gerfault, Laurent; Grangeat, Pierre ^[1]; Jutten, Christian ^[2]

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1. Université Grenoble Alpes, F-38000 Grenoble (France)
2. Université Grenoble Alpes, F-38000 Grenoble, France and Grenoble Image Parole Signal Automatique lab, GIPSA lab UMR 5216 CNRS, 11 Rue des Mathématiques, 38400 Saint-Martin-d'Hères (France)

This article introduces a joined Bayesian estimation of gas samples issued from a gas chromatography column (GC) coupled with a NEMS sensor based on Giddings Eyring microscopic molecular stochastic model. The posterior distribution is sampled using a Monte Carlo Markov Chain and Gibbs sampling. Parameters are estimated using the posterior mean. This estimation scheme is finally applied on simulated and real datasets using this molecular stochastic forward model.

• OSTI ID: 22390866
• Journal Information: AIP Conference Proceedings, Vol. 1641, Issue 1; Conference: MAXENT 2014: Conference on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Clos Luce, Amboise (France), 21-26 Sep 2014; Other Information: (c) 2015 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA); ISSN 0094-243X
• Country of Publication: United States
• Language: English

Related Subjects

71 CLASSICAL AND QUANTUM MECHANICS
GENERAL PHYSICS
GAS CHROMATOGRAPHY
MARKOV PROCESS
MOLECULAR MODELS
MONTE CARLO METHOD
NEMS
SENSORS
STOCHASTIC PROCESSES