oru.sePublikationer
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
On the performance of gas sensor arrays in open sampling systems using inhibitory support vector machines
University of California, San Diego. (BioCircuits Institute)
University of California, San Diego. (BioCircuits Institute)
University of California, San Diego. (BioCircuits Institute)
Örebro University, School of Science and Technology. (AASS Research Centre, Mobile Robotics and Olfaction Lab)ORCID iD: 0000-0003-0195-2102
Show others and affiliations
2013 (English)In: Sensors and actuators. B, Chemical, ISSN 0925-4005, E-ISSN 1873-3077, Vol. 185, no August 2013, 462-477 p.Article in journal (Refereed) Published
Abstract [en]

Chemo-resistive transduction presents practical advantages for capturing the spatio-temporal and structural organization of chemical compounds dispersed in different human habitats. In an open sampling system, however, where the chemo-sensory elements are directly exposed to the environment being monitored, the identification and monitoring of chemical substances present a more difficult challenge due to the dispersion mechanisms of gaseous chemical analytes, namely diffusion, turbulence, and advection. The success of such actively changeable practice is influenced by the adequate implementation of algorithmically driven formalisms combined with the appropriate design of experimental protocols. On the basis of this functional joint-formulation, in this study we examine an innovative methodology based on the inhibitory processing mechanisms encountered in the structural assembly of the insect's brain, namely Inhibitory Support Vector Machine (ISVM) applied to training a sensor array platform and evaluate its capabilities relevant to odor detection and identification under complex environmental conditions. We generated - and made publicly available - an extensive and unique dataset with a chemical detection platform consisting of 72 conductometric metal-oxide based chemical sensors in a custom-designed wind tunnel test-bed facility to test our methodology. Our findings suggest that the aforementioned methodology can be a valuable tool to guide the decision of choosing the training conditions for a cost-efficient system calibration as well as an important step toward the understanding of the degradation level of the sensory system when the environmental conditions change.

Place, publisher, year, edition, pages
2013. Vol. 185, no August 2013, 462-477 p.
Keyword [en]
Metal-oxide sensors, Support Vector Machines, System calibration, Open sampling system, Sensor array, Electronic nose
National Category
Robotics Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-30223DOI: 10.1016/j.snb.2013.05.027OAI: oai:DiVA.org:oru-30223DiVA: diva2:641040
Available from: 2013-08-15 Created: 2013-08-15 Last updated: 2017-12-06Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Authority records BETA

Trincavelli, Marco

Search in DiVA

By author/editor
Trincavelli, Marco
By organisation
School of Science and Technology
In the same journal
Sensors and actuators. B, Chemical
RoboticsComputer Science

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 304 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf