文献类型：期刊文献

英文题名：Recognizing Optical Vortex Modes in Ultralow Illuminating Power With Convolutional Neural Network

作者：Zhang, Bin[1,2];Qi, Yi-Hong[3];Ouyang, Hong-Lin[1];Zhang, Xiao-Gang[1]

机构：[1]Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Peoples R China;[2]Hunan Univ Humanities Sci & Technol, Sch Energy & Electromech Engn, Loudi 417000, Peoples R China;[3]East China Univ Sci & Technol, Sch Phys, Shanghai 200237, Peoples R China

年份：2023

卷号：15

期号：5

外文期刊名：IEEE PHOTONICS JOURNAL

收录：;EI(收录号：20233514638975);WOS:【SCI-EXPANDED(收录号:WOS:001068876100001)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grants 62171184 and 12274123 and in part by the Scientific Research Project of Hunan Provincial Department of Education under Grant 21C0786.

语种：英文

外文关键词：Orbital angular momentum (OAM); topological charge; free space optical communication; convolutional neural network (CNN); pattern recognition

摘要：Vortex beams' orbital angular momentum (OAM) has important application value in optical communication and other fields. Accurate measurement of their OAM topological charges is required for the application of vortex beams in the field of optical communication. As a type of convolutional neural network, DenseNet which combines the "Data Augmentation with Expansion" method, was introduced to measure the fractional topological charge of OAM images in the case of few samples. The OAM images used in this paper generally do not have high brightness, which simulates ultra-long-distance transmission situations in reality. The experiment shows that the classification accuracy of DenseNet network reaches 98.77%, with an average training time of about 1100 seconds, which is short. Our results showcase the potential of convolutional neural network approach to further study the OAM light in the application of free-space optical communication.