Data-driven modeling of nano-nose gas sensor arrays

Publication: Research - peer-reviewBook chapter – Annual report year: 2010

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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.
Original languageEnglish
Title of host publicationSignal Processing, Sensor Fusion, and Target Recognition Xix
Place of publicationBellingham
PublisherSpie-int Soc Optical Engineering
Publication date2010
StatePublished
NameProceedings of Spie-the International Society For Optical Engineering
Number7697
ISSN (Print)0277-786X

Keywords

  • Concentration Level Estimation, Gaussian Process Regression (GPR), Classification, Principal Component Analysis (PCA), Polymer Coated Quartz Crystal Microbalance Sensor (QCM), Non-negative Matrix Factorization (NMF), Principal Component Regression (PCR), Artificial Neural Network (ANN)
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