Otimização da escolha de modelo de propagação por medição de campo e inteligência artificial

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dc.contributor.advisorAkamine, Cristiano
dc.contributor.advisor1Latteshttp://lattes.cnpq.br/0394598624993168por
dc.contributor.authorBotelho, Alberto Leonardo Penteado
dc.creator.Latteshttp://lattes.cnpq.br/1933062035071291por
dc.date.accessioned2019-10-15T18:50:01Z
dc.date.accessioned2020-05-28T18:08:56Z
dc.date.available2020-05-28T18:08:56Z
dc.date.issued2019-02-05
dc.description.abstractThe propagation model to be chosen in the design of a digital terrestrial broadcast station is a critical point for predicting the coverage area. There are several models, with specific characteristics that may be better than others in certain situations. This dissertation presents a study of the choice of propagation model, through the use of artificial intelligence (AI). A brief review of the SBTVD (Brazilian System of Digital Television), the complexity operation in SFN (Single Frequency Network) and the most widely used propagation models in the literature. The comparison of propagation models was elaborated with the field measurements and simulations by the Progira coverage prediction software, which works on an ArcGis geoprocessing platform that considered the criterion of smallest average error (absolute mean deviation, standard deviation and root mean square error) between the field measurement and the software simulation. The propagation model ITUR P. 1812-3 had the best average performance. To optimize the analysis of choice of propagation models, an AI method was developed by machine learning, classification learning, so that the computer can formulate aspects of human intelligence and have the ability to choose the best propagation model for each study area, not restricted to sites measured in the field. The Support Vectors Machines and Nearest Neighbor Classifiers learning models displayed a significant improvement of the average error in comparison to the model of propagation of smallest average erroreng
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dc.identifier.citationBOTELHO, Alberto Leonardo Penteado. Otimização da escolha de modelo de propagação por medição de campo e inteligência artificial. 2019. 189 f. Dissertação (Engenharia Elétrica) - Universidade Presbiteriana Mackenzie, São Paulo, 2019.por
dc.identifier.urihttp://dspace.mackenzie.br/handle/10899/24493
dc.keywordsdigital televisioneng
dc.keywordsSBTVDeng
dc.keywordspropagation modeleng
dc.keywordssingle frequency networkeng
dc.keywordscoverage simulationeng
dc.keywordsfield measurementeng
dc.keywordsartificial intelligenceeng
dc.keywordsmachine learningeng
dc.keywordsclassification learningeng
dc.languageporpor
dc.publisherUniversidade Presbiteriana Mackenziepor
dc.rightsAcesso Abertopor
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjecttelevisão digitalpor
dc.subjectSBTVDpor
dc.subjectmodelo de propagaçãopor
dc.subjectrede de frequência únicapor
dc.subjectpredição de coberturapor
dc.subjectmedição de campopor
dc.subjectinteligência artificialpor
dc.subjectaprendizagem de máquinapor
dc.subjectaprendizagem por classificaçãopor
dc.subject.cnpqCNPQ::ENGENHARIAS::ENGENHARIA ELETRICA::TELECOMUNICACOES::SISTEMAS DE TELECOMUNICACOESpor
dc.thumbnail.urlhttp://tede.mackenzie.br/jspui/retrieve/19957/ALBERTO%20LEONARDO%20PENTEADO%20BOTELHO.pdf.jpg*
dc.titleOtimização da escolha de modelo de propagação por medição de campo e inteligência artificialpor
dc.typeDissertaçãopor
local.contributor.board1Omar, Nizam
local.contributor.board1Latteshttp://lattes.cnpq.br/2067336430076971por
local.contributor.board2Casella, Ivan Roberto Santana
local.contributor.board2Latteshttp://lattes.cnpq.br/3350119903495479por
local.publisher.countryBrasilpor
local.publisher.departmentEscola de Engenharia Mackenzie (EE)por
local.publisher.initialsUPMpor
local.publisher.programEngenharia Elétricapor
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