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Bayesian Inference using Spike Latency Codes for Quantification of Health Endangering Formaldehyde

Authors:
Muhammad Hassan
Amine Bermak
Amine Ait Si Ali
Abbes Amira

Keywords: Formaldehyde exposure; Sensor array; Spike latency coding; Bayesian inference.

Abstract:
Recently, the exposure to formaldehyde has appeared as a major concern since it is listed as a human carcinogen. Conventional methods for its long-term monitoring are not feasible due to their high operational cost, long analysis time and the requirement of specialized equipment and staff. In this paper, we use an electronic nose, containing an array of commercially available Figaro gas sensors, to estimate formaldehyde concentration. A hardware friendly bio-inspired spike latency coding scheme has recently been employed for gas classification by using relative time between spikes. We use this scheme to estimate formaldehyde concentration by utilizing absolute spike timings. However, there is no straightforward relationship between the spike latency and the formaldehyde concentration. Instead, stochastic variability in the sensor array response, corresponding to repeated exposure to the same formaldehyde concentration, implies that latency patterns of the sensor array encode probability distribution over the formaldehyde strength. We use a Bayesian inference approach to estimate the formaldehyde concentration, and its performance is successfully validated by acquiring data for formaldehyde with our sensor array at twenty different concentrations in the laboratory environment.

Pages: 79 to 82

Copyright: Copyright (c) IARIA, 2015

Publication date: August 23, 2015

Published in: conference

ISSN: 2308-3514

ISBN: 978-1-61208-426-8

Location: Venice, Italy

Dates: from August 23, 2015 to August 28, 2015