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Ultra-Low-Power, High-Accuracy 434 MHz Indoor Positioning System for Smart Homes Leveraging Machine Learning Models.
Nawaz, Haq; Tahir, Ahsen; Ahmed, Nauman; Fayyaz, Ubaid U; Mahmood, Tayyeb; Jaleel, Abdul; Gogate, Mandar; Dashtipour, Kia; Masud, Usman; Abbasi, Qammer.
Afiliação
  • Nawaz H; Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan.
  • Tahir A; Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan.
  • Ahmed N; James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK.
  • Fayyaz UU; Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan.
  • Mahmood T; Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan.
  • Jaleel A; Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan.
  • Gogate M; Department of Computer Science, Rachna College of Engineering and Technology (RCET), University of Engineering and Technology, Lahore 54890, Pakistan.
  • Dashtipour K; School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK.
  • Masud U; School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK.
  • Abbasi Q; Department of Electronics Engineering, University of Engineering and Technology, Taxila 47080, Pakistan.
Entropy (Basel) ; 23(11)2021 Oct 25.
Article em En | MEDLINE | ID: mdl-34828099

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Entropy (Basel) Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Paquistão

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Entropy (Basel) Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Paquistão