2 citations · 2 across the 1 of their papers we have counts for
4 papers
B-ExposNet: Bayesian Deep Learning for EMF Exposure Mapping with Uncertainty Quantification
Yukun Liu, Shanshan Wang, Paul Lagouanelle +2
Electromagnetic field exposure in urban environments is highly variable, and conventional exposure maps provide a deterministic estimate, without considering its uncertainty. To ad…
UrbanEMF: City-Scale EMF Mapping over a Continuous Urban Area with Real-World Base-Station Deployment
Shuangning Li, Chenxin Luo, Shanshan Wang +3
Electromagnetic Fields (EMF) mapping is essential for wireless propagation modeling and are fundamental to a wide range of applications, including spectrum awareness, network plann…
ExposNet: A Deep Learning Framework for EMF Exposure Prediction in Complex Urban Environments
Yarui Zhang, Shanshan Wang, Joe Wiart
The prediction of the electric field (E-field) plays a crucial role in monitoring radiofrequency electromagnetic field (RF-EMF) exposure induced by cellular networks. In this paper…
A Gaussian process based approach for validation of multi-variable measurement systems: application to SAR measurement systems
C. Bujard, E. Neufeld, M. Douglas +2
Resource-efficient and robust validation of systems designed to measure a multi-dimensional parameter space is an unsolved problem as it would require millions of test permutations…