Deep Learning-Based Real-Time Mode Decomposition for Multimode Fibers
Deep Learning-Based Real-Time Mode Decomposition for Multimode Fibers
复制标题
DOI：
10.1109/jstqe.2020.2969511
复制
发表时间：
2020-07-01
影响因子：
4.9
通讯作者：
Zhou, Pu
中科院分区：
文献类型：
--
作者：
An, Yi;Huang, Liangjin;Zhou, Pu
模式分解是揭示多模光纤本征模式特性的重要方法。实时分子动力学为分析磁流变液的动力学行为提供了有力的工具。在本文中，我们证明了可以在深度学习技术的帮助下实现实时MD。我们使用大量模拟的MMF光束强度分布来训练卷积神经网络（CNN），然后在模拟和实验数据上评估这个训练好的CNN。在模拟光束剖面上进行测试时，重构方向图与实测方向图的平均相关性在0.9842以上，分解速率可达200 Hz左右。而在实验情况下，平均相关系数在0.8896以上，模态权值的分解率为29.9 Hz，这受到CCD相机最大帧频的限制。仿真和实验结果都表明了基于深度学习的MD方法的实时性。
Mode decomposition (MD) is essential to reveal the intrinsic mode properties of multimode fibers (MMFs). Real-time MD provides a powerful tool to analyze the dynamics in MMFs. In this paper, we demonstrated that real-time MD can be achieved with the help of deep learning technique. We use large amounts of simulated beam intensity profiles of MMFs to train a convolutional neural network (CNN) and then evaluated this trained CNN on both simulation and experimental data. When testing on the simulated beam profiles, the averaged correlation between the reconstructed patterns and measured patterns is above 0.9842 and the decomposing rate can reach about 200 Hz. While for the experimental case, the averaged correlation is above 0.8896 and the decomposing rate for modal weights 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.