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Found 6 papers, 1 papers with code
Deep Learning Framework for the Design of Orbital Angular Momentum Generators Enabled by Leaky-wave Holograms
We can determine the optimal values for each parameter, resulting in the desired radiation pattern, using a total of 77, 000 generated datasets.
Simultaneous estimation of wall and object parameters in TWR using deep neural network
In both cases, we consider the permittivity and thickness for the wall, as well as the two-dimensional coordinates of the target's center and permittivity.
A deep learning approach for inverse design of the metasurface for dual-polarized waves
Here, we have used the Deep Neural Network (DNN) for the generation of desired output unit cell structures in an ultra-wide working frequency band for both TE and TM polarized waves.
Through-the-Wall Radar under Electromagnetic Complex Wall: A Deep Learning Approach
There are two target modes available: single target and double targets.
Deep neural network-based automatic metasurface design with a wide frequency range
Beyond the scope of conventional metasurface which necessitates plenty of computational resources and time, an inverse design approach using machine learning algorithms promises an effective way for metasurfaces design.
EEGsig: an open-source machine learning-based toolbox for EEG signal processing
In the quest to realize a comprehensive EEG signal processing framework, in this paper, we demonstrate a toolbox and graphic user interface, EEGsig, for the full process of EEG signals.
