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Data-driven modeling of nano-nose gas sensor arraysFormat | Member Price | Non-Member Price |
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Paper Abstract
We present a data-driven approach to classification of Quartz Crystal Microbalance (QCM) sensor data. The sensor
is a nano-nose gas sensor that detects concentrations of analytes down to ppm levels using plasma polymorized
coatings. Each sensor experiment takes approximately one hour hence the number of available training data is
limited. We suggest a data-driven classification model which work from few examples. The paper compares
a number of data-driven classification and quantification schemes able to detect the gas and the concentration
level. The data-driven approaches are based on state-of-the-art machine learning methods and the Bayesian
learning paradigm.
Paper Details
Date Published: 28 April 2010
PDF: 12 pages
Proc. SPIE 7697, Signal Processing, Sensor Fusion, and Target Recognition XIX, 76970U (28 April 2010); doi: 10.1117/12.850314
Published in SPIE Proceedings Vol. 7697:
Signal Processing, Sensor Fusion, and Target Recognition XIX
Ivan Kadar, Editor(s)
PDF: 12 pages
Proc. SPIE 7697, Signal Processing, Sensor Fusion, and Target Recognition XIX, 76970U (28 April 2010); doi: 10.1117/12.850314
Show Author Affiliations
Tommy S. Alstrøm, Technical Univ. of Denmark (Denmark)
Jan Larsen, Technical Univ. of Denmark (Denmark)
Jan Larsen, Technical Univ. of Denmark (Denmark)
Claus H. Nielsen, Technical Univ. of Denmark (Denmark)
Univ. of Copenhagen (Denmark)
Niels B. Larsen, Technical Univ. of Denmark (Denmark)
Univ. of Copenhagen (Denmark)
Niels B. Larsen, Technical Univ. of Denmark (Denmark)
Published in SPIE Proceedings Vol. 7697:
Signal Processing, Sensor Fusion, and Target Recognition XIX
Ivan Kadar, Editor(s)
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