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http://hdl.handle.net/10397/1887
| Title: | Fast system identification algorithm for non-uniformly sampled noisy biomedical signal | Authors: | Wong, KP Feng, DD Siu, WC |
Issue Date: | 1996 | Source: | 1996 IEEE TENCON Digital Signal Processing Applications proceedings : The University of Western Australia, Perth, Western Australia, 26-29 November, 1996, v. 2, p. 559-564 | Abstract: | The 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. | Keywords: | Discrete time systems Eigenvalues and eigenfunctions Least squares approximations Medical signal processing Parameter estimation Physiological models Signal sampling |
Publisher: | IEEE | ISBN: | 0-7803-3679-8 | DOI: | 10.1109/TENCON.1996.608402 | 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. This 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. |
| Appears in Collections: | Conference Paper |
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| Wong_Feng_Siu_Noisy_Biomedical_Signal.pdf | 492.72 kB | Adobe PDF | View/Open |
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