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A Novel Approach for Gas Discrimination in Natural Environments with Open Sampling Systems
Örebro University, School of Science and Technology. (AASS MRO Lab)ORCID iD: 0000-0001-5061-5474
Örebro University, School of Science and Technology. (AASS MRO Lab)
Institute of Bioengineering of Catalonia, Spain.
Örebro University, School of Science and Technology. (AASS MRO Lab)ORCID iD: 0000-0003-0217-9326
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2014 (English)In: Proceedings of the IEEE Sensors Conference 2014, IEEE conference proceedings, 2014, -2049 p.Conference paper, Published paper (Refereed)
Abstract [en]

This work presents a gas discrimination approachfor Open Sampling Systems (OSS), composed of non-specificmetal oxide sensors only. In an OSS, as used on robots or insensor networks, the sensors are exposed to the dynamics of theenvironment and thus, most of the data corresponds to highlydiluted samples while high concentrations are sparse. In addition,a positive correlation between class separability and concentra-tion level can be observed. The proposed approach computes theclass posteriors by coupling the pairwise probabilities betweenthe compounds to a confidence model based on an estimation ofthe concentration. In this way a rejection posterior, analogous tothe detection limit of the human nose, is learned. Evaluation wasconducted in indoor and outdoor sites, with an OSS equippedrobot, in the presence of two gases. The results show that theproposed approach achieves a high classification performancewith a low sensitivity to the selection of meta parameters.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2014. -2049 p.
National Category
Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-40889DOI: 10.1109/ICSENS.2014.6985437OAI: oai:DiVA.org:oru-40889DiVA: diva2:779039
Conference
IEEE Sensors 2014. November 2-5, Valencia, Spain
Available from: 2015-01-12 Created: 2015-01-12 Last updated: 2017-10-18Bibliographically approved

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CiteExportLink to record
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Citation style
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