35 Transient Multiexponential Data Selection Using Cramer Rao Lower Bound

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dc.contributor.authorJibia, Abdussamad U.
dc.contributor.authorSalami, Momoh-Jimoh E.
dc.date.accessioned2019-11-04T11:50:35Z
dc.date.available2019-11-04T11:50:35Z
dc.date.issued2012
dc.description.abstractPreviously, analysis of transient multiexponential data using a combination of Gardner transform and parametric methods was shown to yield good results. However, one problem that remains unsolved is that of the nonstationarity of the data resulting from the associated deconvolution. Hitherto, trial and error methods have been used to select the qualitative length of the deconvolved data. In this paper, Cramer Rao Lower Bound (CRLB) is used to select the data truncation points for use with the MUSIC (Multiple Signal Classification), minimum norm and ARMA (autoregressive moving average) methods. Several simulations are made based on which truncation points are recommended for each of the three parametric methods.en_US
dc.identifier.citationRotinwa-Akinbile, M. O., Aibinu, A. M., & Salami, M. J. E. (2011, December). A novel palmprint segmentation technique. In 2011 First International Conference on Informatics and Computational Intelligence (pp. 235-239). IEEE.en_US
dc.identifier.urihttps://doi.org/10.1115/1.859940.paper35
dc.identifier.urihttp://repository.elizadeuniversity.edu.ng/handle/20.500.12398/619
dc.language.isoenen_US
dc.publisherASME Pressen_US
dc.subjectTransient Multiexponentialen_US
dc.subjectData Selectionen_US
dc.subjectLower Bounden_US
dc.subjectCramer Raoen_US
dc.title35 Transient Multiexponential Data Selection Using Cramer Rao Lower Bounden_US
dc.typeArticleen_US
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