轨道角动量介导的机器学习用于高精度的模式特征编码
Xinyuan Fang1, Xiaonan Hu2,3, Baoli Li2
1Institute of Photonic Chips, University of Shanghai for Science and Technology, Shanghai, 200093, China. xinyuan.fang@usst.edu.cn.
Light, science & applications
|February 14, 2024
概括
这项研究引入了机器学习的轨道角动量 (OAM),使全光卷积神经网络 (CNN) 在MNIST数据集上达到97.2%的准确性. 这一进步提高了光学信息处理和安全性.
科学领域:
- 光电学是指光电子产品.
- 机器学习 机器学习
- 信息光学是指信息光学.
背景情况:
- 光学神经网络通过利用光学维度提供高速和低能耗.
- 由于特征提取的局限性,利用神经网络节点的轨道角动量 (OAM) 状态仍然是一个挑战.
- 拉盖尔-高斯 (Laguerre-Gaussian,LG) 波束模式提供了在光学信息处理中增加自由度的潜力.
研究的目的:
- 通过全光卷积神经网络 (CNN) 使用轨道角动量 (OAM) 演示机器学习.
- 利用OAM状态在光学神经网络中进行增强的特征提取和分类.
- 探索安全通信和异常检测中的应用,使用基于OAM的光学机器学习.
主要方法:
- 开发一种全光学CNN,使用拉盖尔-高斯 (Laguerre-Gaussian,LG) 束模式.
- 实现可训练的OAM模式分散脉冲作为特征提取的卷积内核.
- 利用深度学习的衍射层进行分类,并使用编码的OAM模式的能量加权系数进行检测.
主要成果:
- 在MNIST数据库上通过OAM模式分散选择性实现了97.2%的准确性,用于信息模式特征编码.
- 在点对点自由空间传输中表现出对窃听的抵抗力.
- 实现全光学尺寸缩小,用于使用多重 OAM 状态检测异常,精度为 85%.
结论:
- 通过OAM介导的机器学习为光学信息处理提供了一种新的方法.
- 拟议的全光学CNN架构有效地提取特征,并使用OAM状态对数据进行分类.
- 这项研究为改进机器视觉任务和安全的光通信系统开辟了道路.
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