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dc.contributor.authorNedjah, O.
dc.contributor.authorHussein, A.M.
dc.contributor.authorKrishnan, S.
dc.contributor.authorSotudeh, R.
dc.date.accessioned2013-01-10T15:29:04Z
dc.date.available2013-01-10T15:29:04Z
dc.date.issued2010
dc.identifier.citationNedjah , O , Hussein , A M , Krishnan , S & Sotudeh , R 2010 , ' Comparative study of adaptive techniques for denoising CN Tower lightning current derivative signals ' , Digital Signal Processing , vol. 20 , no. 2 , pp. 607-618 . https://doi.org/10.1016/j.dsp.2009.08.002
dc.identifier.issn1051-2004
dc.identifier.otherPURE: 113357
dc.identifier.otherPURE UUID: e2977b9d-f8d7-4907-b662-51e7d02c8891
dc.identifier.otherdspace: 2299/5496
dc.identifier.otherScopus: 76349106684
dc.identifier.urihttp://hdl.handle.net/2299/9546
dc.descriptionOriginal article can be found at: http://www.sciencedirect.com/ Copyright Elsevier
dc.description.abstractThe lightning current derivative data recorded at the CN Tower during the past 18 years contain different kinds of noise and needs to be denoised for accurately determining the lightning current waveform parameters. It is usually a challenging task to denoise transient signals having large bandwidth without altering their waveshapes or shrinking their amplitudes. This paper deals with denoising the CN Tower lightning current derivative signals using several adaptive techniques. A new adaptive denoising approach (Divide-and-Conquer) has been successfully used to denoise a vast variety of CN Tower lightning current derivative waveshapes. The supremacy of the new technique over the existing ones is outlined for a signal with a poor signal-to-noise ratio (SNR). While keeping the signal amplitude unchanged and preserving its waveshape, the new denoising technique improved its SNR from −22.93 dB to 71.41 dB.en
dc.format.extent12
dc.language.isoeng
dc.relation.ispartofDigital Signal Processing
dc.rightsOpen
dc.subjectCN Tower
dc.subjectlightning current derivative
dc.subjectadaptive denoising techniques
dc.titleComparative study of adaptive techniques for denoising CN Tower lightning current derivative signalsen
dc.contributor.institutionSchool of Engineering and Technology
dc.contributor.institutionScience & Technology Research Institute
dc.description.statusPeer reviewed
dc.description.versiontypeFinal Accepted Version
dcterms.dateAccepted2010
rioxxterms.versionAM
rioxxterms.versionofrecordhttps://doi.org/10.1016/j.dsp.2009.08.002
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeOpen


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