-
分数阶涡旋光束具有分数轨道角动量（fractional orbital angular momentum，FOAM）模式，理论上可以无限增加传输容量，因此在光通信领域具有巨大的应用前景.然而，分数阶涡旋光束在自由空间传播时，螺旋相位的不连续性使其在实际应用中容易受到衍射的影响，进而影响FOAM阶次识别的准确度，严重制约基于FOAM的实际应用. %如何实现有衍射条件下的分数阶涡旋光的机器学习识别，目前仍是一个亟需解决且仍未见诸报道的问题. 本文提出了一种基于残差网络（residual network, ResNet）的深度学习（deep learning，DL）方法，用于精确识别分数阶涡旋光衍射过程的传播距离和拓扑荷值. 实验结果表明，该方法可以在湍流条件下识别传播距离为100 cm，间隔为5 cm，模式间隔为0.1的FOAM模式，准确率为99.69%. 该技术有助于推动FOAM模式在测距、光通信、微粒子操作等领域的实际应用.Fractional-order vortex beams possess Fractional Orbital Angular Momentum (FOAM) modes, which theoretically have the potential to increase transmission capacity infinitely. Therefore, they have significant application prospects in the field of measurement, optical communication and microparticle manipulation. However, when fractional-order vortex beams propagate in free space, the discontinuity of the helical phase makes them susceptible to diffraction in practical applications, thereby affecting the accuracy of OAM mode recognition and severely limiting the use of FOAM-based optical communication. The problem of achieving machine learning recognition of fractional-order vortex beams under diffraction conditions is currently an urgent and unreported issue. This paper proposes a deep learning (DL) method based on ResNet for accurate recognition of the propagation distance and topological charge of fractional-order vortex beam diffraction process. Utilizing both experimental measured and theoretically simulated intensity distributions, a dataset of vortex beam diffraction intensity patterns in atmospheric turbulence environments was created. An improved 101-layer ResNet structure based on transfer learning was employed to achieve accurate and efficient recognition of the FOAM model at different propagation distances. Experimental results show that the proposed method can accurately recognize FOAM modes with a propagation distance of 100 cm, an interval of 5 cm, and a mode spacing of 0.1 under turbulent conditions, with an accuracy of 99.69%. This method considers the effect of atmospheric turbulence during spatial transmission, allowing the recognition scheme to achieve high accuracy even in special environments. It has the distinguishing capability for ultra-fine FOAM modes and propagation distances that traditional methods cannot achieve. This technology can be applied to multidimensional encoding and sensing measurements based on FOAM beam.
-
Keywords:
- Fractional vortex beams /
- Machine learning /
- Atmosphere turbulence /
- ResNet
-
[1] Shen Y, Wang X, Xie Z, Min C, Fu X, Liu Q, Gong M, Yuan X 2019 Light Sci. Appl. 8 90
[2] Bai Y, Lv H, Fu X, Yang Y 2022 Chin. Opt. Lett. 20 012601
[3] Zhang H, Zeng J, Lu X, Wang Z, Zhao C, Cai Y 2022 Nanophotonics 11 241
[4] Chen X, Wang S, You C, Magaña-Loaiza O S, Jin R B 2022 Phys. Rev. A 106 033521
[5] Guo Z, Chang Z, Meng J, An M, Jia J, Zhao Z, Wang X, Zhang P 2022 Appl. Opt. 61 5269
[6] Nye J F, Berry M V 1974 Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 336 165
[7] Brygndahl O 1973 J. Opt. Soc. Am. 63 1098
[8] Allen L, Beijersbergen M W, Spreeuw R J C, Woerdman J P 1992 Phys. Rev. A 45 8185
[9] Senthilkumaran P, Sato S, Masajada J 2012 Int. J. Opt. 2012 1
[10] Wang J, Yang J Y, Fazal I M, Ahmed N, Yan Y, Huang H, Ren Y, Yue Y, Dolinar S, Tur M, et al. 2012 Nat. Photonics 6 488
[11] Kotlyar V V, Kovalev A A, Nalimov A G, Porfirev A P 2020 Phys. Rev. A 102 023516
[12] Zhu L, Tang M, Li H, Tai Y, Li X 2021 Nanophotonics 10 2487
[13] Nicolas A, Veissier L, Giner L, Giacobino E, Maxein D, Laurat J 2014 Nat. Photonics 8 234
[14] Otte E, Rosales-Guzmán C, Ndagano B, Denz C, Forbes A 2018 Light Sci. Appl. 7 18009
[15] Bu X, Zhang Z, Chen L, Liang X, Tang H, Wang X 2018 IEEE Antennas Wireless Propag. Lett. 17 764
[16] Li X, Tai Y, Lv F, Nie Z 2015 Opt. Commun. 334 235
[17] Leach J, Courtial J, Skeldon K, Barnett S M, Franke-Arnold S, Padgett M J 2004 Phys. Rev. Lett. 92 013601
[18] Beijersbergen M W, Allen L, Van der Veen H, Woerdman J 1993 Opt. Commun. 96 123
[19] Zhou J, Zhang W, Chen L 2016 Appl. Phys. Lett. 108 111108
[20] Krenn M, Fickler R, Fink M, Handsteiner J, Malik M, Scheidl T, Ursin R, Zeilinger A 2014 New J. Phys. 16 113028
[21] Doster T, Watnik A T 2017 Appl. Opt. 56 3386
[22] Liu Z, Yan S, Liu H, Chen X 2019 Phys. Rev. Lett. 123 183902
[23] Jing G, Chen L, Wang P, Xiong W, Huang Z, Liu J, Chen Y, Li Y, Fan D, Chen S 2021 Results Phys. 28 104619
[24] Guo H, Qiu X, Chen L 2022 Phys. Rev. Appl. 17 054019
[25] Gao H, Zhang Z, Yang Y 2023 Appl. Optics 62 5707
[26] Wu Y, Wang A, Zhu L 2023 Opt. Express 31 36078
[27] Zhao Y, Zhong X, Ren G, He S, Wu Z 2017 Opt. Commun. 387 432
[28] Zhou Z Y, Zhu Z H, Shi B S 2023 Quantum Eng. 2023 4589181
[29] Collins S A 1970 J. Opt. Soc. Am. 60 1168
[30] Bos J P, Roggemann M C, Gudimetla V S R 2015 Appl. Opt. 54 2039
[31] Glindemann A, Lane R, Dainty J 1993 J. Mod. Opt. 40 2381
[32] Bhusal N, Lohani S, You C, Hong M, Fabre J, Zhao P, Knutson E M, Glasser R T, Magaña-Loaiza O S 2021 Adv. Quantum Technol. 4 2000103
[33] Lv H, Guo Y, Yang Z X, Ding C, Cai W H, You C, Jin R B 2022 Front. Phys. 168
[34] Fernando B, Habrard A, Sebban M, Tuytelaars T 2014 arXiv:1409.5241
[35] Krizhevsky A, Sutskever I, Hinton G E 2017 Commun. ACM 60 84
[36] Zhang Z, Sabuncu M 2018 Advances in Neural Information Processing Systems 31 8778
[37] Kingma D P, Ba J 2014 arXiv:1412.6980
计量
- 文章访问数: 37
- PDF下载量: 1
- 被引次数: 0
下载:
