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Computing metasurfaces enabled chiral edge image sensing.

ABSTRACT: Computing metasurfaces have shown the extraordinary ability to precisely perform optical analog operations to the input light wave, and therefore exhibit greater potentials toward sensing applications. Here, we propose a unique application of computing metasurface for chiral edge sensing by incorporating a weak-value amplification technique. The computing metasurface performs the spatial differentiation operations of phase objects and extracts the edge-enhanced images, because the phase gradient generally occurs at the edge. The chirality-induced polarization rotation acts as the preselection state and the spatial differentiation operations in the metasurface provide weak coupling. The amplified pointer shift related to the tiny polarization rotation will eventually lead to an asymmetric edge-enhanced image. Owing to the high sensitivity of the weak-value amplification, we experimentally demonstrate a high-contrast recognition of chirality by edge detection, which may have potential applications in real-time measurement and separation of chiral enantiomers.

SUBMITTER: Wang R

PROVIDER: S-EPMC9209724 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Computing metasurfaces enabled chiral edge image sensing.

Wang Ruisi R He Shanshan S Chen Shizhen S Shu Weixing W Wen Shuangchun S Luo Hailu H

iScience 20220608 7

Computing metasurfaces have shown the extraordinary ability to precisely perform optical analog operations to the input light wave, and therefore exhibit greater potentials toward sensing applications. Here, we propose a unique application of computing metasurface for chiral edge sensing by incorporating a weak-value amplification technique. The computing metasurface performs the spatial differentiation operations of phase objects and extracts the edge-enhanced images, because the phase gradient ...[more]

PMID: 35747388

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Project description:In order to cope with the problems of high frequency and multiple causes of mountain fires, it is very important to adopt appropriate technologies to monitor and warn mountain fires through a few surface parameters. At the same time, the existing mobile terminal equipment is insufficient in image processing and storage capacity, and the energy consumption is high in the data transmission process, which requires calculation unloading. For this circumstance, first, a hierarchical discriminant analysis algorithm based on image feature extraction is introduced, and the image acquisition software in the mobile edge computing environment in the android system is designed and installed. Based on the remote sensing data, the land surface parameters of mountain fire are obtained, and the application of image recognition optimization algorithm in the mobile edge computing (MEC) environment is realized to solve the problem of transmission delay caused by traditional mobile cloud computing (MCC). Then, according to the forest fire sensitivity index, a forest fire early warning model based on MEC is designed. Finally, the image recognition response time and bandwidth consumption of the algorithm are studied, and the occurrence probability of mountain fire in Muli county, Liangshan prefecture, Sichuan is predicted. The results show that, compared with the MCC architecture, the algorithm presented in this study has shorter recognition and response time to different images in WiFi network environment; compared with MCC, MEC architecture can identify close users and transmit less data, which can effectively reduce the bandwidth pressure of the network. In most areas of Muli county, Liangshan prefecture, the probability of mountain fire is relatively low, the probability of mountain fire caused by non-surface environment is about 8 times that of the surface environment, and the influence of non-surface environment in the period of high incidence of mountain fire is lower than that in the period of low incidence. In conclusion, the surface parameters of MEC can be used to effectively predict the mountain fire and provide preventive measures in time.

Dataset Information

Computing metasurfaces enabled chiral edge image sensing.

Publications

Computing metasurfaces enabled chiral edge image sensing.

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