Enhancing the Incident Angle Band in Carpet Cloaking using Deep Neural Networks

Metasurface-based carpet cloaks although are very thin and have a good performance, are very sensitive to the incident angles and work only for a very narrow range of incident angles. In this paper, we have addressed this issue using PSO algorithm in combination with a deep neural network. A passive...

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Bibliographic Details
Main Authors: Fallah, Amirhossein, Yousefi, Leila, Kalhor, Ahamd
Format: Conference Proceeding
Language:English
Subjects:
carpet cloaking
Deep learning
deep neural networks
Electrical engineering
evolutionary algorithms
metasurfaces
Neural networks
Numerical simulation
Reliability engineering
Search problems
Stroke (medical condition)
Online Access:Request full text
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Summary:Metasurface-based carpet cloaks although are very thin and have a good performance, are very sensitive to the incident angles and work only for a very narrow range of incident angles. In this paper, we have addressed this issue using PSO algorithm in combination with a deep neural network. A passive single-layer metasurface capable of making objects under it invisible for a wide range of incident angles, 0 to 70 degrees, is designed and optimized using PSO algorithm in combination with a deep neural network. The cloak has been designed and evaluated using full-wave numerical simulations, and the results show a good performance in the mentioned incident angle band.
ISSN:2642-9527
DOI:10.1109/ICEE55646.2022.9827261