Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/23130
DC FieldValueLanguage
dc.contributorDepartment of Computing-
dc.creatorZhang, L-
dc.creatorBao, P-
dc.creatorWu, X-
dc.date.accessioned2015-08-28T04:31:08Z-
dc.date.available2015-08-28T04:31:08Z-
dc.identifier.issn0018-9448 (print)-
dc.identifier.issn1557-9654 (online)-
dc.identifier.urihttp://hdl.handle.net/10397/23130-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.titleWavelet estimation of fractional Brownian motion embedded in a noisy environmenten_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage2194-
dc.identifier.epage2200-
dc.identifier.volume50-
dc.identifier.issue9-
dc.identifier.doi10.1109/TIT.2004.833357-
dcterms.abstractThis correspondence proposes a wavelet-based fractional Brownian motion (fBm) signal estimation scheme. Despite the fact that wavelet transform approximately whitens the fBm processes, it is observed that statistical dependencies still exist across adjacent wavelet scales and between neighboring wavelet coefficients. These dependencies can be exploited to improve the estimation of fBm signals embedded into noise. The idea is to reorganize the wavelet coefficients into a scale-time mixture model and then carry out the minimum mean-square-error estimation (MMSE) using the model. Experiments show that the proposed scheme obtains better estimates than Wornell and Oppenheim's algorithm, in which the wavelet dependencies are not utilized.-
dcterms.bibliographicCitationIEEE transactions on information theory, 2004, v. 50, no. 9, p. 2194-2200-
dcterms.isPartOfIEEE transactions on information theory-
dcterms.issued2004-
dc.identifier.rosgroupidr22192-
dc.description.ros2004-2005 > Academic research: refereed > Publication in refereed journal-
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