2026-09-19T00:27:52Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/531632022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Nirmal Keshava and Dennis Freeman.
Ren, Bobby (Bobby B.)
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2010-03-25T15:09:02Z
2010-03-25T15:09:02Z
2006
2006
http://hdl.handle.net/1721.1/53163
516055607
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
Includes bibliographical references (p. 87-89).
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.
by Bobby Ren.
M.Eng.
89 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Calibration, feature extraction and classification of water contaminants using a differential mobility spectrometer
Thesis
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<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.</Abstract>
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