<ul><li>Understanding the spatial and temporal patterns of environmental exposure to radio-frequency electromagnetic fields is crucial for risk assessments.</li><li>A comparative analysis of finite and infinite-width convolutional network-based methods for estimating and assessing RF-EMF exposure levels was conducted.</li><li>Real-world datasets from 70 sensors in Lille, France, were used for the analysis.</li><li>The evaluation criterion, Root Mean Square Error (RMSE), was used to compare the performance of the deep learning models.</li></ul>

Comparative analysis of Realistic EMF Exposure Estimation from Low Density Sensor Network by Finite & Infinite Neural Networks

Discover more