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• W3099794172 abstract "We introduce deep learning technique to perform complete mode decomposition for few-mode optical fiber for the first time. Our goal is to learn a fast and accurate mapping from near-field beam profiles to the complete mode coefficients, including both modal amplitudes and phases. We train the convolutional neural network with simulated beam patterns, and evaluate the network on both of the simulated beam data and the real beam data. In simulated beam data testing, the correlation between the reconstructed and the ideal beam profiles can achieve 0.9993 and 0.995 for 3-mode case and 5-mode case respectively. While in the real 3-mode beam data testing, the average correlation is 0.9912 and the mode decomposition can be potentially performed at 33 Hz frequency on Graphic Processing Unit, indicating real-time processing ability. The quantitative evaluations demonstrate the superiority of our deep learning based approach." @default.
• W3099794172 created "2020-11-23" @default.
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• W3099794172 date "2019-03-27" @default.
• W3099794172 modified "2023-10-17" @default.
• W3099794172 title "Learning to decompose the modes in few-mode fibers with deep convolutional neural network" @default.
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• W3099794172 doi "https://doi.org/10.1364/oe.27.010127" @default.
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