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Multi-Sensor Integration to Map Odor Distribution for the Detection of Chemical Sources

This paper addresses the problem of mapping odor distribution derived from a chemical source using multi-sensor integration and reasoning system design. Odor localization is the problem of finding the source of an odor or other volatile chemical. Most localization methods require a mobile vehicle to...

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Detalles Bibliográficos
Autores principales: Gao, Xiang, Acar, Levent
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4970083/
https://www.ncbi.nlm.nih.gov/pubmed/27384568
http://dx.doi.org/10.3390/s16071034
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author Gao, Xiang
Acar, Levent
author_facet Gao, Xiang
Acar, Levent
author_sort Gao, Xiang
collection PubMed
description This paper addresses the problem of mapping odor distribution derived from a chemical source using multi-sensor integration and reasoning system design. Odor localization is the problem of finding the source of an odor or other volatile chemical. Most localization methods require a mobile vehicle to follow an odor plume along its entire path, which is time consuming and may be especially difficult in a cluttered environment. To solve both of the above challenges, this paper proposes a novel algorithm that combines data from odor and anemometer sensors, and combine sensors’ data at different positions. Initially, a multi-sensor integration method, together with the path of airflow was used to map the pattern of odor particle movement. Then, more sensors are introduced at specific regions to determine the probable location of the odor source. Finally, the results of odor source location simulation and a real experiment are presented.
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spelling pubmed-49700832016-08-04 Multi-Sensor Integration to Map Odor Distribution for the Detection of Chemical Sources Gao, Xiang Acar, Levent Sensors (Basel) Article This paper addresses the problem of mapping odor distribution derived from a chemical source using multi-sensor integration and reasoning system design. Odor localization is the problem of finding the source of an odor or other volatile chemical. Most localization methods require a mobile vehicle to follow an odor plume along its entire path, which is time consuming and may be especially difficult in a cluttered environment. To solve both of the above challenges, this paper proposes a novel algorithm that combines data from odor and anemometer sensors, and combine sensors’ data at different positions. Initially, a multi-sensor integration method, together with the path of airflow was used to map the pattern of odor particle movement. Then, more sensors are introduced at specific regions to determine the probable location of the odor source. Finally, the results of odor source location simulation and a real experiment are presented. MDPI 2016-07-04 /pmc/articles/PMC4970083/ /pubmed/27384568 http://dx.doi.org/10.3390/s16071034 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. https://creativecommons.org/licenses/by/4.0/This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ).
spellingShingle Article
Gao, Xiang
Acar, Levent
Multi-Sensor Integration to Map Odor Distribution for the Detection of Chemical Sources
title Multi-Sensor Integration to Map Odor Distribution for the Detection of Chemical Sources
title_full Multi-Sensor Integration to Map Odor Distribution for the Detection of Chemical Sources
title_fullStr Multi-Sensor Integration to Map Odor Distribution for the Detection of Chemical Sources
title_full_unstemmed Multi-Sensor Integration to Map Odor Distribution for the Detection of Chemical Sources
title_short Multi-Sensor Integration to Map Odor Distribution for the Detection of Chemical Sources
title_sort multi-sensor integration to map odor distribution for the detection of chemical sources
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4970083/
https://www.ncbi.nlm.nih.gov/pubmed/27384568
http://dx.doi.org/10.3390/s16071034
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