Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1887
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dc.contributorDepartment of Electrical Engineering-
dc.creatorWong, KP-
dc.creatorFeng, DD-
dc.creatorSiu, WC-
dc.date.accessioned2014-12-11T08:26:44Z-
dc.date.available2014-12-11T08:26:44Z-
dc.identifier.isbn0-7803-3679-8-
dc.identifier.urihttp://hdl.handle.net/10397/1887-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights© 1996 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_US
dc.rightsThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.en_US
dc.subjectDiscrete time systemsen_US
dc.subjectEigenvalues and eigenfunctionsen_US
dc.subjectLeast squares approximationsen_US
dc.subjectMedical signal processingen_US
dc.subjectParameter estimationen_US
dc.subjectPhysiological modelsen_US
dc.subjectSignal samplingen_US
dc.titleFast system identification algorithm for non-uniformly sampled noisy biomedical signalen_US
dc.typeConference Paperen_US
dc.description.otherinformationAuthor name used in this publication: Dagan Fengen_US
dc.identifier.doi10.1109/TENCON.1996.608402-
dcterms.abstractThe recently developed generalized linear least squares (GLLS) algorithm has been found very useful in non-uniformly sampled biomedical signal processing and parameter estimation. In this paper, the algorithm is used for the identification of a compartmental model with a pair of repeated eigenvalues based on the non-uniformly sampled noisy data. A case study is presented, which demonstrates that the algorithm is able to select the most suitable model for the system from the non-uniformly sampled noisy signals.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitation1996 IEEE TENCON Digital Signal Processing Applications proceedings : The University of Western Australia, Perth, Western Australia, 26-29 November, 1996, v. 2, p. 559-564-
dcterms.issued1996-
dc.identifier.isiWOS:A1996BJ42V00103-
dc.identifier.scopus2-s2.0-0030315520-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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