Show simple item record

dc.contributor.authorZiyatdinov, Andrey
dc.contributor.authorFonollosa, Jordi
dc.contributor.authorFernández Romero, Lluís
dc.contributor.authorGutierrez Galvez, Agustín
dc.contributor.authorMarco Colás, Santiago
dc.contributor.authorPerera Lluna, Alexandre
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.identifier.citationZiyatdinov, A., Fonollosa, J., Fernández, L., Gutierrez, A., Marco, S., Perera, A. Data set from gas sensor array under flow modulation. "Data in Brief", 30 Juny 2015, vol. 3, p. 131-136.
dc.description.abstractRecent 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.
dc.format.extent6 p.
dc.subjectÀrees temàtiques de la UPC::Enginyeria electrònica::Instrumentació i mesura::Sensors i actuadors
dc.subject.lcshChemical detectors
dc.subject.lcshGas detectors
dc.titleData set from gas sensor array under flow modulation
dc.subject.lemacSensors químics
dc.subject.lemacDetectors de gasos
dc.contributor.groupUniversitat Politècnica de Catalunya. SISBIO - Senyals i Sistemes Biomèdics
dc.rights.accessOpen Access
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/FP7/216916/EU/Biologically inspired computation for chemical sensing/NEUROCHEM
upcommons.citation.authorZiyatdinov, A., Fonollosa, J., Fernández, L., Gutierrez, A., Marco, S., Perera, A.
upcommons.citation.publicationNameData in Brief

Files in this item


This item appears in the following Collection(s)

Show simple item record

Except where otherwise noted, content on this work is licensed under a Creative Commons license: Attribution-NonCommercial-NoDerivs 3.0 Spain