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Machine learning approach to radio frequency interference(RFI) classification in Radio Astronomy

dc.contributor.advisorNiesler, T. R.en_ZA
dc.contributor.advisorDavidson, D. B.en_ZA
dc.contributor.authorWolfaardt, Cornelis Johannesen_ZA
dc.contributor.otherStellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.en_ZA
dc.date.accessioned2016-03-09T14:22:12Z
dc.date.available2016-03-09T14:22:12Z
dc.date.issued2016-03
dc.identifier.urihttp://hdl.handle.net/10019.1/98464
dc.descriptionThesis (MEng)--Stellenbosch University, 2016.en_ZA
dc.description.abstractENGLISH ABSTRACT: Radio frequency interference (RFI) presents a large problem for radio telescopes. Interference prevents observations from being made, or extends the duration required for observations. This thesis investigates different methods to automatically classify RFI signals. Data from different sources was cap- tured at the SKA site. Both Gaussian Mixture Model (GMM) and K-nearest neighbors (KNN) classifiers were used to analyse the data. Both performed adequately, with the KNN slightly outperforming the GMM. Different feature extraction methods were also investigated.en_ZA
dc.description.abstractAFRIKAANSE OPSOMMING: Radio frekwensie steurseine verteenwoordig `n groot probleem vir radio tele- skope. Steurseine verhoed teleskope om waarnemings te maak. Hierdie tesis ondersoek verskeie metodes om steurseine automaties te identifiseer en klasi- fiseer. Data van bekende steurseine op die SKA terrein is versamel. Verkeie voorverwerkingtegnieke word ondersoek en dan geannaliseer met bekende sta- tistiese modelle soos `n GMM en KNN. Beide lewer aanvaarbare resultate. Verskeie metodes om kenmerke te onttrek word ook ondersoek.af_ZA
dc.format.extentxii, 75 pages : illustrationsen_ZA
dc.language.isoen_ZAen_ZA
dc.publisherStellenbosch : Stellenbosch Universityen_ZA
dc.subjectRadio astronomyen_ZA
dc.subjectUCTDen_ZA
dc.subjectRadio -- Interferenceen_ZA
dc.subjectElectromagnetic interferenceen_ZA
dc.subjectRadio -- Transmitters and transmissionen_ZA
dc.subjectRadio -- Antennasen_ZA
dc.subjectRadio - Recievers and receptionen_ZA
dc.subjectRadio frequency interferenceen_ZA
dc.titleMachine learning approach to radio frequency interference(RFI) classification in Radio Astronomyen_ZA
dc.typeThesisen_ZA
dc.rights.holderStellenbosch Universityen_ZA


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