Data set from gas sensor array under flow modulation

Recent studies in neuroscience suggest that sniffing, namely sampling odors actively, plays an important role in olfactory system, especially in certain scenarios such as novel odorant detection. While the computational advantages of high frequency sampling have not been yet elucidated, here, in ord...

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Autores: Ziyatdinov, Andrey, Fonollosa, Jordi, Fernández Romero, Lluís, Gutierrez Galvez, Agustín, Marco Colás, Santiago, Perera Lluna, Alexandre|||0000-0001-6427-851X
Formato: artículo
Fecha de publicación:2015
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/85242
Acesso em linha:https://hdl.handle.net/2117/85242
https://dx.doi.org/10.1016/j.dib.2015.02.016
Access Level:acceso abierto
Palavra-chave:Chemical detectors
Gas detectors
Sensors químics
Detectors de gasos
Àrees temàtiques de la UPC::Enginyeria electrònica::Instrumentació i mesura::Sensors i actuadors
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oai_identifier_str oai:upcommons.upc.edu:2117/85242
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spelling Data set from gas sensor array under flow modulationZiyatdinov, AndreyFonollosa, JordiFernández Romero, LluísGutierrez Galvez, AgustínMarco Colás, SantiagoPerera Lluna, Alexandre|||0000-0001-6427-851XChemical detectorsGas detectorsSensors químicsDetectors de gasosÀrees temàtiques de la UPC::Enginyeria electrònica::Instrumentació i mesura::Sensors i actuadorsRecent studies in neuroscience suggest that sniffing, namely sampling odors actively, plays an important role in olfactory system, especially in certain scenarios such as novel odorant detection. While the computational advantages of high frequency sampling have not been yet elucidated, here, in order to motivate further investigation in active sampling strategies, we share the data from an artificial olfactory system made of 16 MOX gas sensors under gas flow modulation. The data were acquired on a custom set up featured by an external mechanical ventilator that emulates the biological respiration cycle. 58 samples were recorded in response to a relatively broad set of 12 gas classes, defined from different binary mixtures of acetone and ethanol in air. The acquired time series show two dominant frequency bands: the low-frequency signal corresponds to a conventional response curve of a sensor in response to a gas pulse, and the high-frequency signal has a clear principal harmonic at the respiration frequency. The data are related to the study in [1], and the data analysis results reported there should be considered as a reference point.Elsevier20152015-06-3020162016-04-05journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/85242https://dx.doi.org/10.1016/j.dib.2015.02.01626217733reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengEuropean Commission http://dx.doi.org/10.13039/100011102 Seventh Framework Programme 216916 Biologically inspired computation for chemical sensingopen accesshttp://purl.org/coar/access_right/c_abf2http://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/852422026-05-27T15:37:01Z
dc.title.none.fl_str_mv Data set from gas sensor array under flow modulation
title Data set from gas sensor array under flow modulation
spellingShingle Data set from gas sensor array under flow modulation
Ziyatdinov, Andrey
Chemical detectors
Gas detectors
Sensors químics
Detectors de gasos
Àrees temàtiques de la UPC::Enginyeria electrònica::Instrumentació i mesura::Sensors i actuadors
title_short Data set from gas sensor array under flow modulation
title_full Data set from gas sensor array under flow modulation
title_fullStr Data set from gas sensor array under flow modulation
title_full_unstemmed Data set from gas sensor array under flow modulation
title_sort Data set from gas sensor array under flow modulation
dc.creator.none.fl_str_mv Ziyatdinov, Andrey
Fonollosa, Jordi
Fernández Romero, Lluís
Gutierrez Galvez, Agustín
Marco Colás, Santiago
Perera Lluna, Alexandre|||0000-0001-6427-851X
author Ziyatdinov, Andrey
author_facet Ziyatdinov, Andrey
Fonollosa, Jordi
Fernández Romero, Lluís
Gutierrez Galvez, Agustín
Marco Colás, Santiago
Perera Lluna, Alexandre|||0000-0001-6427-851X
author_role author
author2 Fonollosa, Jordi
Fernández Romero, Lluís
Gutierrez Galvez, Agustín
Marco Colás, Santiago
Perera Lluna, Alexandre|||0000-0001-6427-851X
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Chemical detectors
Gas detectors
Sensors químics
Detectors de gasos
Àrees temàtiques de la UPC::Enginyeria electrònica::Instrumentació i mesura::Sensors i actuadors
topic Chemical detectors
Gas detectors
Sensors químics
Detectors de gasos
Àrees temàtiques de la UPC::Enginyeria electrònica::Instrumentació i mesura::Sensors i actuadors
description Recent studies in neuroscience suggest that sniffing, namely sampling odors actively, plays an important role in olfactory system, especially in certain scenarios such as novel odorant detection. While the computational advantages of high frequency sampling have not been yet elucidated, here, in order to motivate further investigation in active sampling strategies, we share the data from an artificial olfactory system made of 16 MOX gas sensors under gas flow modulation. The data were acquired on a custom set up featured by an external mechanical ventilator that emulates the biological respiration cycle. 58 samples were recorded in response to a relatively broad set of 12 gas classes, defined from different binary mixtures of acetone and ethanol in air. The acquired time series show two dominant frequency bands: the low-frequency signal corresponds to a conventional response curve of a sensor in response to a gas pulse, and the high-frequency signal has a clear principal harmonic at the respiration frequency. The data are related to the study in [1], and the data analysis results reported there should be considered as a reference point.
publishDate 2015
dc.date.none.fl_str_mv 2015
2015-06-30
2016
2016-04-05
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/85242
https://dx.doi.org/10.1016/j.dib.2015.02.016
26217733
url https://hdl.handle.net/2117/85242
https://dx.doi.org/10.1016/j.dib.2015.02.016
identifier_str_mv 26217733
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission http://dx.doi.org/10.13039/100011102 Seventh Framework Programme 216916 Biologically inspired computation for chemical sensing
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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