A Multifractal Wavelet Model for Positive Processes

dc.citation.bibtexNameinproceedingsen_US
dc.citation.conferenceNameIEEE-SP International Symposium on Time-frequency and Time-scale Analysisen_US
dc.citation.firstpage341en_US
dc.citation.lastpage344en_US
dc.citation.locationPittsburgh, PAen_US
dc.contributor.authorCrouse, Matthewen_US
dc.contributor.authorRiedi, Rudolf H.en_US
dc.contributor.authorRibeiro, Vinay Josephen_US
dc.contributor.authorBaraniuk, Richard G.en_US
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)en_US
dc.date.accessioned2007-10-31T00:40:59Zen_US
dc.date.available2007-10-31T00:40:59Zen_US
dc.date.issued1998-10-01en_US
dc.date.modified2006-07-19en_US
dc.date.note2004-11-08en_US
dc.date.submitted1998-10-01en_US
dc.descriptionConference Paperen_US
dc.description.abstractIn this paper, we develop a new multiscale modeling framework for characterizing positive-valued data with long-range-dependent correlations (1/f noise). Using the Haar wavelet transform and a special multiplicative structure on the wavelet and scaling coefficients to ensure positive results, the model provides a rapid O(N) cascade algorithm for synthesizing N-point data sets. We study both the second-order and multifractal properties of the model, the latter after a tutorial overview of multifractal analysis. We derive a scheme for matching the model to real data observations and, to demonstrate its effectiveness, apply the model to network traffic synthesis. The flexibility and accuracy of the model and fitting procedure result in a close fit to the real data statistics (variance-time plots and moment scaling) and queuing behavior. Although for illustrative purposes we focus on applications in network traffic modeling, the multifractal wavelet model could be useful in a number of other areas involving positive data, including image processing, finance, and geophysics.en_US
dc.identifier.citationM. Crouse, R. H. Riedi, V. J. Ribeiro and R. G. Baraniuk, "A Multifractal Wavelet Model for Positive Processes," 1998.en_US
dc.identifier.urihttps://hdl.handle.net/1911/19818en_US
dc.language.isoengen_US
dc.subjectmultifractalen_US
dc.subjectwavelet modelen_US
dc.subject.keywordmultifractalen_US
dc.subject.keywordwavelet modelen_US
dc.subject.otherSignal Processing for Networkingen_US
dc.subject.otherMultifractalsen_US
dc.titleA Multifractal Wavelet Model for Positive Processesen_US
dc.typeConference paperen_US
dc.type.dcmiTexten_US
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