An electromagnetic parameter retrieval method based on deep learning

2020 ◽  
Vol 127 (22) ◽  
pp. 224902
Author(s):  
Siqi Huang ◽  
Zilong Cao ◽  
Helin Yang ◽  
Zhaoyang Shen ◽  
Xiaoxia Ding
Keyword(s):  
Deep Learning ◽  
Electromagnetic Parameter ◽  
Retrieval Method ◽  
Parameter Retrieval

Deep learning based remote sensing technique for environmental parameter retrieval and data fusion from physical models

2021 ◽  
Vol 14 (13) ◽  
Author(s):  
Ratna Kumari Vemuri ◽  
Pundru Chandra Shaker Reddy ◽  
B S Puneeth Kumar ◽  
Jayavadivel Ravi ◽  
Sudhir Sharma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Data Fusion ◽  
Environmental Parameter ◽  
Physical Models ◽  
Remote Sensing Technique ◽  
Parameter Retrieval

An Ultrathin and Ultrawideband Metamaterial Absorber and an Equivalent-Circuit Parameter Retrieval Method

2020 ◽  
Vol 68 (5) ◽  
pp. 3739-3746 ◽  
Author(s):  
Jose Bruno O. de Araujo ◽  
Glaucio L. Siqueira ◽  
Erich Kemptner ◽  
Mauricio Weber ◽  
Cynthia Junqueira ◽  
...  
Keyword(s):  
Equivalent Circuit ◽  
Metamaterial Absorber ◽  
Circuit Parameter ◽  
Retrieval Method ◽  
Parameter Retrieval ◽  
Equivalent Circuit Parameter

scholarly journals Predicting double negativity using transmitted phase in space coiling metamaterials

2018 ◽  
Vol 5 (5) ◽  
pp. 171042 ◽  
Author(s):  
Santosh K. Maurya ◽  
Abhishek Pandey ◽  
Shobha Shukla ◽  
Sumit Saxena
Keyword(s):  
Acoustic Waves ◽  
Negative Refraction ◽  
Acoustic Properties ◽  
Propagation Path ◽  
Elastic Response ◽  
Acoustic Metamaterials ◽  
Effective Response ◽  
Retrieval Method ◽  
S Parameter ◽  
Parameter Retrieval

Metamaterials are engineered materials that offer the flexibility to manipulate the incident waves leading to exotic applications such as cloaking, extraordinary transmission, sub-wavelength imaging and negative refraction. These concepts have largely been explored in the context of electromagnetic waves. Acoustic metamaterials, similar to their optical counterparts, demonstrate anomalous effective elastic properties. Recent developments have shown that coiling up the propagation path of acoustic wave results in effective elastic response of the metamaterial beyond the natural response of its constituent materials. The effective response of metamaterials is generally evaluated using the ‘S’ parameter retrieval method based on amplitude of the waves. The phase of acoustic waves contains information of wave pressure and particle velocity. Here, we show using finite-element methods that phase reversal of transmitted waves may be used to predict extreme acoustic properties in space coiling metamaterials. This change is the difference in the phase of the transmitted wave with respect to the incident wave. This method is simpler when compared with the more rigorous ‘S’ parameter retrieval method. The inferences drawn using this method have been verified experimentally for labyrinthine metamaterials by showing negative refraction for the predicted band of frequencies.

A single-shot phase retrieval method for phase measuring deflectometry based on deep learning

2020 ◽  
Vol 476 ◽  
pp. 126303
Author(s):  
Gang Qiao ◽  
Yiyang Huang ◽  
Yiping Song ◽  
Huimin Yue ◽  
Yong Liu
Keyword(s):  
Deep Learning ◽  
Phase Retrieval ◽  
Single Shot ◽  
Retrieval Method

A privacy-preserving image retrieval method based on deep learning and adaptive weighted fusion

2019 ◽  
Vol 17 (1) ◽  
pp. 161-173 ◽  
Author(s):  
Jiaohua Qin ◽  
Jianhua Chen ◽  
Xuyu Xiang ◽  
Yun Tan ◽  
Wentao Ma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Retrieval ◽  
Privacy Preserving ◽  
Retrieval Method ◽  
Weighted Fusion
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