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

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.
Authors:
; ; ; ;  [1] ;  [2]
  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)
Publication Date:
OSTI Identifier:
22390866
Resource Type:
Journal Article
Resource Relation:
Journal Name: AIP Conference Proceedings; Journal Volume: 1641; Journal 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)
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; GAS CHROMATOGRAPHY; MARKOV PROCESS; MOLECULAR MODELS; MONTE CARLO METHOD; NEMS; SENSORS; STOCHASTIC PROCESSES