ABSTRACT
This paper focuses on quantifying the uncertainty in the specific absorption rate valuesof the brain induced by the uncertain positions of the electroencephalography electrodes placed onthe patient's scalp. To avoid running a large number of simulations, an artificial neural networkarchitecture for uncertainty quantification involving high-dimensional data is proposed in this paper.The proposed method is demonstrated to be an attractive alternative to conventional uncertaintyquantification methods because of its considerable advantage in the computational expense andspeed.
Subject(s)
Electroencephalography , Neural Networks, Computer , Brain , Data Analysis , Electrodes , Electroencephalography/methods , Humans , UncertaintyABSTRACT
Metasurfaces enable us to control the fundamental properties of light with unprecedented flexibility. However, most metasurfaces realized to date aim at modifying plane waves. While the manipulation of nonplanar wavefronts is encountered in a diverse number of applications, their control using metasurfaces is still in its infancy. Here we design a metareflector able to reflect a diverging Gaussian beam back onto itself with efficiency over 90% and focusing at an arbitrary distance. We outline a clear route towards the design of complex metareflectors that can find applications as diverse as optical tweezing, lasing, and quantum optics.
