Calibration, feature extraction and classification of water contaminants using a differential mobility spectrometer
Name
516055607-MIT.pdf
Description
Full printable version
Size
34.67 MB
Format
Adobe PDF
Checksum (MD5)
12696a0a50b6a1a6f48ff72298ef79e1
Author(s)
Ren, Bobby (Bobby B.)
Advisor(s)
Nirmal Keshava and Dennis Freeman.
Date Issued
2006
Publisher
Massachusetts Institute of Technology
Abstract
High-Field Asymmetric Waveform Ion Mobility Spectrometry (FAIMS) is a chemical sensor that separates ions in the gaseous phase based on their mobility in high electric fields. A threefold approach was developed for both chemical type classification and concentration classification of water contaminants for FAIMS signals. The three steps in this approach are calibration, feature extraction, and classification. Calibration was carried out to remove baseline fluctation and other variations in FAIMS data sets. Four feature extraction algorithms were used to extract subsets of the signal that had high separation potential between two classes of signals. Finally, support vector machines were used for binary classification. The success of classification was measured both by using separability metrics to evaluate the separability of extracted features, and by the percent of correct classification (Pcc) in each task.
Description
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
Includes bibliographical references (p. 87-89).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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