Advanced agent identification with fluctuation-enhanced sensing

Chiman Kwan, Gabor Schmera, Janusz M. Smulko, Laszlo B. Kish, P. Heszler, Claes Göran Granqvist

Research output: Article

35 Citations (Scopus)

Abstract

Conventional agent sensing methods normally use the steady state sensor values for agent classification. Many sensing elements (Hines et al., 1999; Ryan et al., 2004; Young et al., 2003; Qian et al., 2004; Qiane et al., 2006; Carmel et al., 2003) are needed in order to correctly classify multiple agents in mixtures. Fluctuation-enhanced sensing (FES) looks beyond the steady-state values and extracts agent information from spectra and bispectra. As a result, it is possible to use a single sensor to perform multiple agent classification. This paper summarizes the application of some advanced algorithms that can classify and estimate concentrations of different chemical agents. Our tool involves two steps. First, spectral and bispectral features will be extracted from the sensor signals. The features contain unique agent characteristics. Second, the features are fed into a hyperspectral signal processing algorithm for agent classification and concentration estimation. The basic idea here is to use the spectral/bispectral shape information to perform agent classification. Extensive simulations have been performed by using simulated nanosensor data, as well as actual experimental data using commercial sensor (Taguchi). It was observed that our algorithms are able to accurately classify different agents, and also can estimate the concentration of the agents. Bispectra contain more information than spectra at the expense of high-computational costs. Specific nanostructured sensor model data yielded excellent performance because the agent responses are additive with this type of sensor. Moreover, for measured conventional sensor outputs, our algorithms also showed reasonable performance in terms of agent classification.

Original languageEnglish
Article number4529196
Pages (from-to)706-713
Number of pages8
JournalIEEE Sensors Journal
Volume8
Issue number6
DOIs
Publication statusPublished - jún. 2008

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Sensors
sensors
Nanosensors
Chemical elements
estimates
Signal processing
signal processing
costs
output
Costs
simulation

ASJC Scopus subject areas

  • Engineering(all)
  • Electrical and Electronic Engineering

Cite this

Kwan, C., Schmera, G., Smulko, J. M., Kish, L. B., Heszler, P., & Granqvist, C. G. (2008). Advanced agent identification with fluctuation-enhanced sensing. IEEE Sensors Journal, 8(6), 706-713. [4529196]. https://doi.org/10.1109/JSEN.2008.923029

Advanced agent identification with fluctuation-enhanced sensing. / Kwan, Chiman; Schmera, Gabor; Smulko, Janusz M.; Kish, Laszlo B.; Heszler, P.; Granqvist, Claes Göran.

In: IEEE Sensors Journal, Vol. 8, No. 6, 4529196, 06.2008, p. 706-713.

Research output: Article

Kwan, C, Schmera, G, Smulko, JM, Kish, LB, Heszler, P & Granqvist, CG 2008, 'Advanced agent identification with fluctuation-enhanced sensing', IEEE Sensors Journal, vol. 8, no. 6, 4529196, pp. 706-713. https://doi.org/10.1109/JSEN.2008.923029
Kwan C, Schmera G, Smulko JM, Kish LB, Heszler P, Granqvist CG. Advanced agent identification with fluctuation-enhanced sensing. IEEE Sensors Journal. 2008 jún.;8(6):706-713. 4529196. https://doi.org/10.1109/JSEN.2008.923029
Kwan, Chiman ; Schmera, Gabor ; Smulko, Janusz M. ; Kish, Laszlo B. ; Heszler, P. ; Granqvist, Claes Göran. / Advanced agent identification with fluctuation-enhanced sensing. In: IEEE Sensors Journal. 2008 ; Vol. 8, No. 6. pp. 706-713.
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