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Nirmal Keshava and Julie Greenberg. Carr, Kristin (Kristin Malia) Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2007-03-12T17:51:19Z 2007-03-12T17:51:19Z 2005 2005 http://hdl.handle.net/1721.1/36761 78618595 Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005. Includes bibliographical references (p. 71-74). Detecting the presence of contaminants in water is a critical mission, but thorough testing often requires extensive time at a remote facility. A MEMS implementation of a FAIMS (High-Field Asymmetric-Waveform Ion Mobility Spectrometry) sensor has recently been developed, and is capable of promptly analyzing water on-site. In this thesis, we apply two well-established statistical target detector algorithms to the detection of chlorite in water. The matched filter and the adaptive cosine estimator (ACE) are subspace detectors that possess complimentary geometric properties. We address several significant challenges in implementing these detectors, including the estimation of the covariance given the limited amount of data available and the design of a target signature subspace in response to the fact that the signature does not scale linearly with the contaminant concentration. In addition, we consider the need for dimension reduction through the use of wavelets. We evaluate each of the detectors on FAIMS data of pure and chlorite-contaminated water. by Kristin Carr M.Eng. 74 p. eng Massachusetts Institute of Technology M.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. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Detection of contaminants using a MEMS FAIMS sensor Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="96b84b1f-6fd6-4bbf-a3ef-178278e3e37a"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Detection of contaminants using a MEMS FAIMS sensor</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2005</PublicationDate> <Authors> <Author> <DisplayName>Carr, Kristin (Kristin Malia)</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>Detecting the presence of contaminants in water is a critical mission, but thorough testing often requires extensive time at a remote facility. A MEMS implementation of a FAIMS (High-Field Asymmetric-Waveform Ion Mobility Spectrometry) sensor has recently been developed, and is capable of promptly analyzing water on-site. In this thesis, we apply two well-established statistical target detector algorithms to the detection of chlorite in water. The matched filter and the adaptive cosine estimator (ACE) are subspace detectors that possess complimentary geometric properties. We address several significant challenges in implementing these detectors, including the estimation of the covariance given the limited amount of data available and the design of a target signature subspace in response to the fact that the signature does not scale linearly with the contaminant concentration. In addition, we consider the need for dimension reduction through the use of wavelets. We evaluate each of the detectors on FAIMS data of pure and chlorite-contaminated water.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>