A novel Machine Learning (ML) method based on Neural Networks (NN) is proposed to
assess radio‐frequency (RF) exposure generated by WiFi sources in indoor scenarios. The …

This study focused on the assessment of radio-frequency electromagnetic fields (RF-EMF)
exposure in a realistic apartment due to the presence of a WiFi source deployed in uncertain …

This study presents an evaluation of human exposure levels due to WiFi signals. Up to now,
human exposure to electromagnetic fields has been measured using methodologies and …

In this study, the impact of Wi-Fi access points is investigated in terms of electromagnetic
field exposure evaluations. Installations, typically present in common living contexts, have …

In the last decade the number of wireless devices operating at the frequency band of 2.4
GHz has increased in several settings, such as healthcare, occupational, and household. In …

This paper focuses on the study of emissions in Wi-Fi networks in a typical indoor place,
inside a building, by quantifying exposure levels detected in different locations of the house …

Paving the path toward the fifth generation (5G) of wireless networks with a huge increase in
the number of user equipment has strengthened public concerns on human exposure to …

The present work was performed to expand the knowledge on human RF-EMF exposure,
considering the use of mm-wave spectrum in mobile communication applications, due to the …

This work provides an intensive and comprehensive in-depth study from an empirical and
modeling approach of the environmental radiofrequency electromagnetic fields (RF-EMF) …

Radiofrequency fields are usually measured in order to be compared with electromagnetic
exposure limits defined by international standardization organizations with the aim of …