Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/58143
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dc.creatorSaadi, Muhammad-
dc.creatorBajpai, Ambar-
dc.creatorRodríguez, Demóstenes Zegarra-
dc.date.accessioned2023-07-17T20:10:37Z-
dc.date.available2023-07-17T20:10:37Z-
dc.date.issued2023-
dc.identifier.citationSAADI, M.; BAJPAI, A.; RODRÍGUEZ, D. Z. Traditional and modern techniques for visible light positioning systems. In: APPLICATIONS of 5G and Beyond in Smart Cities. [S.l.]: CRC Press, 2023.pt_BR
dc.identifier.isbn9781003227861pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/58143-
dc.identifier.urihttps://www.taylorfrancis.com/chapters/edit/10.1201/9781003227861-11/traditional-modern-techniques-visible-light-positioning-systems-muhammad-saadi-ambar-bajpai-dem%C3%B3stenes-zegarra-rodr%C3%ADguez-
dc.descriptionIndoor positioning systems have recently gained a lot of attention for multiple reasons, including the modern construction trend of high-rise buildings in metropolitan cities, dominant voice and data services utilization in indoor environments, and the lack of positioning accuracy in traditional positioning techniques. In this chapter, we discuss the need for indoor localization, the hurdles to using outdoor positioning systems for indoor navigation, and the options available for indoor navigation. A detailed comparison of radio frequencies (RFs), and acoustic and visible light-based positioning systems is given. Furthermore, we discuss the traditional approaches which are adopted for indoor positioning. Artificial intelligence and machine learning approaches are penetrating and successfully yielding good results in many walks of life, and indoor positioning is no exception. Finally, we also present current artificial intelligence and machine learning-based techniques for indoor positioning.pt_BR
dc.languageen_USpt_BR
dc.publisherCRC Presspt_BR
dc.rightsrestrictAccesspt_BR
dc.titleTraditional and modern techniques for visible light positioning systemspt_BR
dc.typeCapítulo de livropt_BR
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