Publication: Research - peer-review › Book chapter – Annual report year: 2010
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.
|Title of host publication||Signal Processing, Sensor Fusion, and Target Recognition Xix|
|Place of Publication||Bellingham|
|Publisher||Spie-int Soc Optical Engineering|
|State||Published - 2010|
|Series||Proceedings of SPIE, the International Society for Optical Engineering|
- 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)