2026-05-31T07:33:26Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/100552026-02-03T11:59:25Zcom_10630_2254col_10630_37959

00925njm 22002777a 4500 dc González-Monroy, Javier author González-Jiménez, Antonio Javier author 2015-07-08 The classification of volatiles substances with an e-nose is still a challenging problem, particularly when working under real-time, out-of-the-lab environmental conditions where the chaotic and highly dynamic characteristics of the gas transportation induce an almost permanent transient state in the e-nose readings. In this work, a sequential Bayesian filtering approach is proposed to efficiently integrate information from previous e-nose observations while updating the belief about the gas class on a real-time basis. We validate our proposal with two real olfaction datasets composed of dynamic time-series experiments (gas transitions are Considered, but no mixture of gases), showing an improvement in the classification rate when compared to a stand-alone probabilistic classifier. http://hdl.handle.net/10630/10055 Olores - Control - Automatización Real-Time odor classification through sequential bayesian filtering