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dc.creatorCoy, Stephen L.en_US
dc.creatorJacobson, Matthew P.en_US
dc.creatorField, R. W.en_US
dc.date.accessioned2006-06-15T15:34:13Z
dc.date.available2006-06-15T15:34:13Z
dc.date.issued1996en_US
dc.identifier1996-TC-07en_US
dc.identifier.urihttp://hdl.handle.net/1811/13626
dc.descriptionAuthor Institution: Department of Chemistry, Massachusetts Institute of Technologyen_US
dc.description.abstractThe traditional approach to understanding the information encoded in spectroscopic data has been first to assign each transition observed and then to relate the positions and intensities of the transitions to a model that allows insight into the system being studied. In complex and congested spectra, however, the process of assignment may be difficult, tedious, or even impossible. In such a situation, it is often desirable to be able to recognized pattern recognition techniques, which we refer to as Extended Autocorrelation (XAC) and Extended Cross-Correlation (XCC). The XAC can be used to locate patterns that are parameterized in a complex way within a congested spectrum. The XCC recognizes patterns that are common among two or more spectra. Tests of these techniques using synthetic spectra will be presented, as well as application to the mass spectra of large molecules (XAC) and FTIR spectra of mixtures of ammonia isotopemers (XCC).en_US
dc.format.extent93019 bytes
dc.format.mimetypeimage/jpeg
dc.language.isoEnglishen_US
dc.publisherOhio State Universityen_US
dc.titlePATTERN RECOGNITION BY EXTENDED AUTO- AND CROSS CORRELATIONen_US
dc.typearticleen_US


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