scholarly journals Information Encoding with Optical Dielectric Metasurface via Independent Multichannels

ACS Photonics ◽  
2018 ◽  
Vol 6 (1) ◽  
pp. 230-237 ◽  
Author(s):  
Fengliang Dong ◽  
Hang Feng ◽  
Lihua Xu ◽  
Bo Wang ◽  
Zhiwei Song ◽  
...  
Keyword(s):  
Information Encoding ◽  
Optical Dielectric ◽  
Dielectric Metasurface

Frequency Spectrum Evolution of Quasi-Optical Dielectric Resonators with Conducting Endplates

2002 ◽  
Vol 57 (12) ◽  
pp. 10 ◽  
Author(s):  
Nikolay T. Cherpak ◽  
A. A. Barannik ◽  
Yu.V. Prokopenko ◽  
Yu. F. Filippov ◽  
T.A. Smirnova
Keyword(s):  
Frequency Spectrum ◽  
Dielectric Resonators ◽  
Optical Dielectric

Q-FACTOR AND EXCITATION EFFICIENCY OF LAMINAR QUASI-OPTICAL DIELECTRIC RESONATORS

2014 ◽  
Vol 73 (1) ◽  
pp. 73-81 ◽  
Author(s):  
A. Ya. Kirichenko ◽  
G. V. Golubnichaya ◽  
I. G. Maximchuk ◽  
V. B. Yurchenko
Keyword(s):  
Dielectric Resonators ◽  
Q Factor ◽  
Excitation Efficiency ◽  
Optical Dielectric

Reflecting Focusing Dielectric Metasurface

2018 ◽  
Author(s):  
A. Sayanskiy ◽  
M. Odit ◽  
V. Asadchy ◽  
P. Kapitanova ◽  
P. Belov
Keyword(s):  
Dielectric Metasurface

scholarly journals All-Dielectric Metasurface for Sensing Microcystin-LR

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1363
Author(s):  
Binze Ma ◽  
Ao Ouyang ◽  
Juechen Zhong ◽  
Pavel A. Belov ◽  
Ravindra Kumar Sinha ◽  
...  
Keyword(s):  
Quality Factor ◽  
Bound States ◽  
High Sensitivity ◽  
Concentration Limit ◽  
Specific Recognition ◽  
Low Concentrations ◽  
Freshwater Bodies ◽  
Toxic Pollutant ◽  
Small Change ◽  
Dielectric Metasurface

Sensing Microcystin-LR (MC-LR) is an important issue for environmental monitoring, as the MC-LR is a common toxic pollutant found in freshwater bodies. The demand for sensitive detection method of MC-LR at low concentrations can be addressed by metasurface-based sensors, which are feasible and highly efficient. Here, we demonstrate an all-dielectric metasurface for sensing MC-LR. Its working principle is based on quasi-bound states in the continuum mode (QBIC), and it manifests a high-quality factor and high sensitivity. The dielectric metasurface can detect a small change in the refractive index of the surrounding environment with a quality factor of ~170 and a sensitivity of ~788 nm/RIU. MC-LR can be specifically identified in mixed water with a concentration limit of as low as 0.002 μg/L by a specific recognition technique for combined antigen and antibody. Furthermore, the demonstrated detection of MC-LR can be extended to the identification and monitoring of other analytes, such as viruses, and the designed dielectric metasurface can serve as a monitor platform with high sensitivity and high specific recognition capability.

scholarly journals Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics

2021 ◽  
Vol 11 (7) ◽  
pp. 885
Author(s):  
Maher Abujelala ◽  
Rohith Karthikeyan ◽  
Oshin Tyagi ◽  
Jing Du ◽  
Ranjana K. Mehta
Keyword(s):  
Machine Learning ◽  
Environmental Conditions ◽  
Brain Activity ◽  
Memory Task ◽  
Classification Problem ◽  
Brain Regions ◽  
Training Data ◽  
Information Encoding ◽  
Machine Learning Approach ◽  
Encoding And Retrieval

The nature of firefighters` duties requires them to work for long periods under unfavorable conditions. To perform their jobs effectively, they are required to endure long hours of extensive, stressful training. Creating such training environments is very expensive and it is difficult to guarantee trainees’ safety. In this study, firefighters are trained in a virtual environment that includes virtual perturbations such as fires, alarms, and smoke. The objective of this paper is to use machine learning methods to discern encoding and retrieval states in firefighters during a visuospatial episodic memory task and explore which regions of the brain provide suitable signals to solve this classification problem. Our results show that the Random Forest algorithm could be used to distinguish between information encoding and retrieval using features extracted from fNIRS data. Our algorithm achieved an F-1 score of 0.844 and an accuracy of 79.10% if the training and testing data are obtained at similar environmental conditions. However, the algorithm’s performance dropped to an F-1 score of 0.723 and accuracy of 60.61% when evaluated on data collected under different environmental conditions than the training data. We also found that if the training and evaluation data were recorded under the same environmental conditions, the RPM, LDLPFC, RDLPFC were the most relevant brain regions under non-stressful, stressful, and a mix of stressful and non-stressful conditions, respectively.

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