liu.seSearch for publications in DiVA
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
Fusing Cyclic Sensor Data with Different Cycle Length
Linköping University, Department of Physics, Chemistry and Biology. Linköping University, Faculty of Science & Engineering. Saarland University, Germany.
Lab Measurement Technology, Germany.
Lab Measurement Technology, Germany.
2016 (English)In: 2016 IEEE INTERNATIONAL CONFERENCE ON MULTISENSOR FUSION AND INTEGRATION FOR INTELLIGENT SYSTEMS (MFI), IEEE , 2016, 72-77 p.Conference paper, Published paper (Refereed)
Abstract [en]

Cyclic modulation of sensor parameters can improve sensitivity and selectivity of gas sensors. If the modulated parameter influences the sens environment, several readings can be gained, eventually resulting in a multi-dimensional response which can be analyzed with, e.g., principal component analysis. In certain cases, e.g. temperature modulated gas sensors with different thermal time constants, the length of the used cycles, and, thus, the temporal resolution of the sensors can differ. As a consequence, different sensors can produce datasets with an unequal number of observations which, nevertheless, cover the same interval of time. In this work, we explore three different strategies which enable combination of those datasets in order to retain the maximum amount of information from two sensors when used in parallel. Simulated data show that simple combination of a short cycle with the last complete long cycle can improve correct classification rate by 15 percent points while maintaining the better temporal resolution. On the other hand, performance can be further increased at the expense of temporal resolution by adding either several of the short cycles, or their mean, to a long cycle, effectively reducing noise. The proposed combination strategies and their dependence on preprocessing are validated with a real dataset of two gas sensors. Overall, and taking into account differences in data performance for simulated and real data is observed.

Place, publisher, year, edition, pages
IEEE , 2016. 72-77 p.
National Category
Medical Laboratory and Measurements Technologies
Identifiers
URN: urn:nbn:se:liu:diva-139678DOI: 10.1109/MFI.2016.7849469ISI: 000405714400012ISBN: 978-1-4673-9709-4 (print)ISBN: 978-1-4673-9708-7 (electronic)OAI: oai:DiVA.org:liu-139678DiVA: diva2:1130452
Conference
IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)
Available from: 2017-08-09 Created: 2017-08-09 Last updated: 2017-08-09

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Bastuck, Manuel
By organisation
Department of Physics, Chemistry and BiologyFaculty of Science & Engineering
Medical Laboratory and Measurements Technologies

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 16 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