Show simple item record

dc.contributor.advisorNirmal Keshava and Dennis Freeman.en_US
dc.contributor.authorRen, Bobby (Bobby B.)en_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2010-03-25T15:09:02Z
dc.date.available2010-03-25T15:09:02Z
dc.date.copyright2006en_US
dc.date.issued2006en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/53163
dc.descriptionThesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.en_US
dc.descriptionIncludes bibliographical references (p. 87-89).en_US
dc.description.abstractHigh-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.en_US
dc.description.statementofresponsibilityby Bobby Ren.en_US
dc.format.extent89 p.en_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleCalibration, feature extraction and classification of water contaminants using a differential mobility spectrometeren_US
dc.typeThesisen_US
dc.description.degreeM.Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.en_US
dc.identifier.oclc516055607en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record