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Proceedings Paper

Deep learning enabled real-time modal analysis for fiber beams
Author(s): Yi An; Liangjin Huang; Jun Li; Jinyong Leng; Lijia Yang; Pu Zhou
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Paper Abstract

Mode decomposition (MD) is essential to reveal the intrinsic mode properties of fiber beams. However, traditional numerical MD approaches are relatively time-consuming and sensitive to the initial values. To solve these problems, deep learning technique is introduced to perform non-iterative MD. In this paper, we focus on the real-time MD ability of the pre-trained convolutional neural network. The numerical simulation indicates that the averaged correlation between the reconstructed patterns and measured patterns is 0.9987 and the decomposing rate can reach about 125 Hz. As for the experimental case, the averaged correlation is 0.9719 and the decomposing rate is 29.9 Hz, which is restricted by the maximum frame rate of the CCD camera. The results of both simulation and experiment show the superb real-time ability of the deep learning-based MD methods.

Paper Details

Date Published: 18 December 2019
PDF: 5 pages
Proc. SPIE 11342, AOPC 2019: AI in Optics and Photonics, 1134203 (18 December 2019); doi: 10.1117/12.2541268
Show Author Affiliations
Yi An, National Univ. of Defense Technology (China)
Liangjin Huang, National Univ. of Defense Technology (China)
Jun Li, National Univ. of Defense Technology (China)
Jinyong Leng, National Univ. of Defense Technology (China)
Lijia Yang, National Univ. of Defense Technology (China)
Pu Zhou, National Univ. of Defense Technology (China)


Published in SPIE Proceedings Vol. 11342:
AOPC 2019: AI in Optics and Photonics
John Greivenkamp; Jun Tanida; Yadong Jiang; HaiMei Gong; Jin Lu; Dong Liu, Editor(s)

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