Design and analysis of ultrathin polarization-insensitive metamaterial absorber for stealth technology applications

Singh, Neelam; Yadav, Sanjeev; Chahar, Rekha

doi:10.1109/spin.2017.8049942

Cited by 6 publications

(1 citation statement)

References 14 publications

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“…Metamaterials are artificial materials, designed with special structures, and can exhibit controllable electromagnetic properties [ 1 ], which are entirely different from their constituent materials. The broad application of metamaterials in (bio)sensing [ 2 , 3 , 4 ], imaging [ 5 , 6 ], cloaking [ 7 , 8 , 9 , 10 , 11 ], radar [ 12 ], and telecommunications [ 13 ] have drawn extensive attention. The development history of metamaterials has experience from equivalent medium metamaterials and surface plasmon metamaterials to information and smart metamaterials [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Secure Deep Learning for Intelligent Terahertz Metamaterial Identification

Liu

Zhang

Sun

et al. 2020

Sensors

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Metamaterials, artificially engineered structures with extraordinary physical properties, offer multifaceted capabilities in interdisciplinary fields. To address the looming threat of stealthy monitoring, the detection and identification of metamaterials is the next research frontier but have not yet been explored. Here, we show that the crypto-oriented convolutional neural network (CNN) makes possible the secure intelligent detection of metamaterials in mixtures. Terahertz signals were encrypted by homomorphic encryption and the ciphertext was submitted to the CNN directly for results, which can only be decrypted by the data owner. The experimentally measured terahertz signals were augmented and further divided into training sets and test sets using 5-fold cross-validation. Experimental results illustrated that the model achieved an accuracy of 100% on the test sets, which highly outperformed humans and the traditional machine learning. The CNN took 9.6 s to inference on 92 encrypted test signals with homomorphic encryption backend. The proposed method with accuracy and security provides private preserving paradigm for artificial intelligence-based material identification.

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