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dc.contributor.advisorBrady, David
dc.contributor.authorChen, Evan Xuguang
dc.date.accessioned2015-09-01T19:50:32Z
dc.date.available2015-12-08T05:30:07Z
dc.date.issued2015
dc.identifier.urihttp://hdl.handle.net/10161/10452
dc.descriptionDissertation
dc.description.abstract<p>Conventional mass spectrometry sensing has isomorphic nature, which means measure the input mass spectrum abundance function by a resemble of delta function to avoid ambiguity. However, the delta function nature of traditional mass spectrometry sensing approach imposes trade-offs between mass resolution and throughput/mass analysis time. This dissertation proposes a new field of mass spectrometry sensing which combines both computational signal processing and hardware modification to break the above trade-offs. We introduce the concept of generalized sensing matrix/discretized forward model in mass spectrometry filed. The presence of forward model can bridge the cap between sensing system hardware design and computational sensing algorithm including compressive sensing, feature/variable selection machine learning algorithms, and stat-of-art inversion algorithms. </p><p>Throughout this dissertation, the main theme is the sensing matrix/forward model design subject to the physical constraints of varies types of mass analyzers. For quadrupole ion trap systems, we develop a new compressive and multiplexed mass analysis approach mutli Resonant Frequency Excitation (mRFE) ejection which can reduce mass analysis time by a factor 3-6 without losing mass spectra specificity for chemical classification. A new information-theoretical adaptive sensing and classification framework has proposed on quadrupole mass filter systems, and it can significantly reduces the number of measurements needed and achieve a high level of classification accuracy. Furthermore, we present a coded aperture sector mass spectrometry which can yield a order-of-magnitude throughput gain without compromising mass resolution compare to conventional single slit sector mass spectrometer.</p>
dc.subjectElectrical engineering
dc.subjectComputational mass spectrometry
dc.subjectComputational sensing
dc.subjectmagnetic sector spectrometer
dc.subjectMass spectrometry
dc.subjectQuadrupole ion trap
dc.subjectQuadrupole mass filter
dc.titleComputational Mass Spectrometry
dc.typeDissertation
dc.departmentElectrical and Computer Engineering
duke.embargo.months3


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