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High-resolution recognition of FOAM modes via an improved EfficientNet V2 based convolutional neural network

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Abstract

Vortex beam with fractional orbital angular momentum (FOAM) is the excellent candidate for improving the capacity of free-space optical (FSO) communication system due to its infinite modes. Therefore, the recognition of FOAM modes with higher resolution is always of great concern. In this work, through an improved EfficientNetV2 based convolutional neural network (CNN), we experimentally achieve the implementation of the recognition of FOAM modes with a resolution as high as 0.001. To the best of our knowledge, it is the first time this high resolution has been achieved. Under the strong atmospheric turbulence (AT) \((C_n^2 = {10^{ - 15}}\,{{\rm{m}}^{ - 2/3}})\), the recognition accuracy of FOAM modes at 0.1 and 0.01 resolution with our model is up to 99.12% and 92.24% for a long transmission distance of 2000 m. Even for the resolution at 0.001, the recognition accuracy can still remain at 78.77%. This work provides an effective method for the recognition of FOAM modes, which may largely improve the channel capacity of the free-space optical communication.

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References

  1. L. Allen, M. W. Beijersbergen, R. J. C. Spreeuw, and J. P. Woerdman, Orbital angular momentum of light and the transformation of Laguerre–Gaussian laser modes, Phys. Rev. A 45(11), 8185 (1992)

    Article  ADS  Google Scholar 

  2. G. C. G. Berkhout, M. P. J. Lavery, J. Courtial, M. W. Beijersbergen, and M. J. Padgett, Efficient sorting of orbital angular momentum states of light, Phys. Rev. Lett. 105(15), 153601 (2010)

    Article  ADS  Google Scholar 

  3. K. Liu, Y. Q. Cheng, X. Li, and Y. Gao, Microwave-sensing technology using orbital angular momentum: Overview of its advantages, IEEE Veh. Technol. Mag. 14(2), 112 (2019)

    Article  Google Scholar 

  4. L. Yan, P. Kristensen, and S. Ramachandran, Vortex fibers for STED microscopy, APL Photonics 4(2), 022903 (2019)

    Article  ADS  Google Scholar 

  5. X. W. Zhuang, Unraveling DNA condensation with optical tweezers, Science 305(5681), 188 (2004)

    Article  Google Scholar 

  6. Z. Y. Zhou, D. S. Ding, Y. K. Jiang, Y. Li, S. Shi, X. S. Wang, and B. S. Shi, Orbital angular momentum light frequency conversion and interference with quasi-phase matching crystals, Opt. Express 22(17), 20298 (2014)

    Article  ADS  Google Scholar 

  7. S. J. Li, Z. Y. Li, G. S. Huang, X. B. Liu, R. Q. Li, and X. Y. Cao, Digital coding transmissive metasurface for multi-OAM-beam, Front. Phys. 17(6), 62501 (2022)

    Article  ADS  Google Scholar 

  8. L. Zou, L. Wang, and S. M. Zhao, Turbulence mitigation scheme based on spatial diversity in orbital-angular-momentum multiplexed system, Opt. Commun. 400, 123 (2017)

    Article  ADS  Google Scholar 

  9. E. M. Amhoud, M. Chafii, A. Nimr, and G. Fettweis, OFDM with index modulation in orbital angular momentum multiplexed free space optical links, in: IEEE 93rd Vehicular Technology Conference (VTC-Spring), Electr Network, 2021

  10. A. E. Willner, K. Pang, H. Song, K. H. Zou, and H. B. Zhou, Orbital angular momentum of light for communications, Appl. Phys. Rev. 8(4), 041312 (2021)

    Article  ADS  Google Scholar 

  11. X. H. Zhang, T. **a, S. B. Cheng, and S. H. Tao, Freespace information transfer using the elliptic vortex beam with fractional topological charge, Opt. Commun. 431, 238 (2019)

    Article  ADS  Google Scholar 

  12. V. V. Kotlyar, A. A. Kovalev, A. G. Nalimov, and A. P. Porfirev, Evolution of an optical vortex with an initial fractional topological charge, Phys. Rev. A 102(2), 023516 (2020)

    Article  ADS  MathSciNet  Google Scholar 

  13. S. S. Li, B. F. Shen, W. P. Wang, Z. G. Bu, H. Zhang, H. Zhang, and S. H. Zhai, Diffraction of relativistic vortex harmonics with fractional average orbital angular momentum, Chin. Opt. Lett. 17(5), 050501 (2019)

    Article  ADS  Google Scholar 

  14. M. I. Dedo, Z. Wang, K. Guo, Y. Sun, F. Shen, H. Zhou, J. Gao, R. Sun, Z. Ding, and Z. Guo, Retrieving performances of vortex beams with GS algorithm after transmitting in different types of turbulences, Appl. Sci. (Basel) 9(11), 2269 (2019)

    Article  Google Scholar 

  15. X. Yan, P. F. Zhang, J. H. Zhang, X. X. Feng, C. H. Qiao, and C. Y. Fan, Effect of atmospheric turbulence on entangled orbital angular momentum three-qubit state, Chin. Phys. B 26(6), 064202 (2017)

    Article  ADS  Google Scholar 

  16. Y. J. Yang, Q. Zhao, L. L. Liu, Y. D. Liu, C. Rosales-Guzman, and C. W. Qiu, Manipulation of orbital-angular-momentum spectrum using pinhole plates, Phys. Rev. Appl. 12(6), 064007 (2019)

    Article  ADS  Google Scholar 

  17. Z. C. Zhang, J. C. Pei, Y. P. Wang, and X. G. Wang, Measuring orbital angular momentum of vortex beams in optomechanics, Front. Phys. 16(3), 32503 (2021)

    Article  ADS  Google Scholar 

  18. A. Forbes, A. Dudley, and M. McLaren, Creation and detection of optical modes with spatial light modulators, Adv. Opt. Photonics 8(2), 200 (2016)

    Article  ADS  Google Scholar 

  19. J. Yu and Z. F. Wang, 3D facial motion tracking by combining online appearance model and cylinder head model in particle filtering, Sci. China Inf. Sci. 57(7), 029101 (2014)

    Google Scholar 

  20. N. Uribe-Patarroyo, A. Fraine, D. S. Simon, O. Minaeva, and A. V. Sergienko, Object identification using correlated orbital angular momentum states, Phys. Rev. Lett. 110(4), 043601 (2013)

    Article  ADS  Google Scholar 

  21. J. Zhu, P. Zhang, D. Z. Fu, D. X. Chen, R. F. Liu, Y. N. Zhou, H. Gao, and F. L. Li, Probing the fractional topological charge of a vortex light beam by using dynamic angular double slits, Photon. Res. 4(5), 187 (2016)

    Article  Google Scholar 

  22. D. Deng, M. C. Lin, Y. Li, and H. Zhao, Precision measurement of fractional orbital angular momentum, Phys. Rev. Appl. 12(1), 014048 (2019)

    Article  ADS  Google Scholar 

  23. S. Zheng and J. Wang, Measuring orbital angular momentum (OAM) states of vortex beams with annular gratings, Sci. Rep. 7(1), 40781 (2017)

    Article  ADS  Google Scholar 

  24. K. Bayoudh, R. Knani, F. Hamdaoui, and A. Mtibaa, A survey on deep multimodal learning for computer vision: Advances, trends, applications, and datasets, Vis. Comput. 38(8), 2939 (2022)

    Article  Google Scholar 

  25. N. O’Mahony, S. Campbell, A. Carvalho, S. Harapana-halli, G. V. Hernandez, L. Krpalkova, D. Riordan, and J. Walsh, Deep learning vs. traditional computer vision, in: Computer Vision Conference (CVC), Springer International Publishing Ag, Las Vegas, NV, 2019, pp 128–144

    Google Scholar 

  26. J. Long, E. Shelhamer, and T. Darrell, Fully convolutional networks for semantic segmentation, in: IEEE Conference on Computer, Vision and Pattern Recognition (CVPR), IEEE, Boston, MA, 2015, pp 3431–3440

    Google Scholar 

  27. N. Le, V. S. Rathour, K. Yamazaki, K. Luu, and M. Savvides, Deep reinforcement learning in computer vision: a comprehensive survey, Artif. Intell. Rev. 55(4), 2733 (2022)

    Article  Google Scholar 

  28. R. Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, Convolutional neural networks: An overview and application in radiology, Insights Imaging 9(4), 611 (2018)

    Article  Google Scholar 

  29. P. Michalski, B. Ruszczak, and M. Tomaszewski, Convolutional neural networks implementations for computer vision, in: 3rd International Scientific Conference on Brain-Computer Interfaces (BCI), Springer International Publishing Ag, Opole Univ Technol, Opole, POLAND, 2018, pp 98–110

    Google Scholar 

  30. Z. W. Liu, S. Yan, H. G. Liu, and X. F. Chen, Super-high-resolution recognition of optical vortex modes assisted by a deep-learning method, Phys. Rev. Lett. 123(18), 183902 (2019)

    Article  ADS  Google Scholar 

  31. M. Cao, Y. L. Yin, J. W. Zhou, J. H. Tang, L. P. Cao, Y. **a, and J. P. Yin, Machine learning based accurate recognition of fractional optical vortex modes in atmospheric environment, Appl. Phys. Lett. 119(14), 141103 (2021)

    Article  ADS  Google Scholar 

  32. J. Zhou, Y. Yin, J. Tang, C. Ling, M. Cao, L. Cao, G. Liu, J. Yin, and Y. **a, Recognition of high-resolution optical vortex modes with deep residual learning, Phys. Rev. A 106(1), 013519 (2022)

    Article  ADS  Google Scholar 

  33. W. W. Song, S. T. Li, L. Y. Fang, and T. Lu, Hyperspectral image classification with deep feature fusion network, IEEE Trans. Geosci. Remote Sens. 56(6), 3173 (2018)

    Article  ADS  Google Scholar 

  34. M. X. Tan and Q. V. Le, EfficientNetV2: Smaller models and faster training, in: International Conference on Machine Learning (ICML), Electr Network, 2021, pp 7102–7110

  35. M. L. Huang and Y. C. Liao, A lightweight CNN-based network on COVID-19 detection using X-ray and CT images, Comput. Biol. Med. 146, 105604 (2022)

    Article  Google Scholar 

  36. R. Karthik, T. S. Vaichole, S. K. Kulkarni, O. Yadav, and F. Khan, Eff2Net: An efficient channel attention-based convolutional neural network for skin disease classification, Biomed. Signal Process. Control 73, 103406 (2022)

    Article  Google Scholar 

  37. H. Zhang, J. Zeng, X. Y. Lu, Z. Y. Wang, C. L. Zhao, and Y. J. Cai, Review on fractional vortex beam, Nanophotonics 11(2), 241 (2022)

    Article  ADS  Google Scholar 

  38. A. Belafhal and L. Dalil-Essakali, Collins formula and propagation of Bessel-modulated Gaussian light beams through an ABCD optical system, Opt. Commun. 177(1–6), 181 (2000)

    Article  ADS  Google Scholar 

  39. Y. J. Yang, Y. Dong, C. L. Zhao, and Y. J. Cai, Generation and propagation of an anomalous vortex beam, Opt. Lett. 38(24), 5418 (2013)

    Article  ADS  Google Scholar 

  40. P. H. F. Mesquita, A. J. Jesus-Silva, E. J. S. Fonseca, and J. M. Hickmann, Engineering a square truncated lattice with light’s orbital angular momentum, Opt. Express 19(21), 20616 (2011)

    Article  ADS  Google Scholar 

  41. B. Rodenburg, M. P. J. Lavery, M. Malik, M. N. O’Sullivan, M. Mirhosseini, D. J. Robertson, M. Padgett, and R. W. Boyd, Influence of atmospheric turbulence on states of light carrying orbital angular momentum, Opt. Lett. 37(17), 3735 (2012)

    Article  ADS  Google Scholar 

  42. S. Y. Fu and C. Q. Gao, Influences of atmospheric turbulence effects on the orbital angular momentum spectra of vortex beams, Photon. Res. 4(5), B1 (2016)

    Article  Google Scholar 

  43. L. C. Andrews, An analytical model for the refractive index power spectrum and its application to optical scintillations in the atmosphere, J. Mod. Opt. 39(9), 1849 (1992)

    Article  ADS  Google Scholar 

  44. W. Cheng, J. W. Haus, and Q. W. Zhan, Propagation of vector vortex beams through a turbulent atmosphere, Opt. Express 17(20), 17829 (2009)

    Article  ADS  Google Scholar 

  45. S. M. Zhao, J. Leach, L. Y. Gong, J. Ding, and B. Y. Zheng, Aberration corrections for free-space optical communications in atmosphere turbulence using orbital angular momentum states, Opt. Express 20(1), 452 (2012)

    Article  ADS  Google Scholar 

  46. Y. Kim, I. Ohn, and D. Kim, Fast convergence rates of deep neural networks for classification, Neural Netw. 138, 179 (2021)

    Article  Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 62271332, 12374273, and 62275162), the Guangdong Basic and Applied Basic Research Foundation (No. 2023A1515030152), the Shenzhen Government’s Plan of Science and Technology (Nos. JCYJ20180305124927623 and JCYJ20190808150205481), and the Training Program for Excellent Young innovators of Changsha (No. kq2107013).

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Authors and Affiliations

  1. International Collaborative Laboratory of 2D Materials for Optoelectronics Science and Technology, Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, 518060, China

    Youzhi Shi, Zuhai Ma, Hongyu Chen & Yu Chen

  2. Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education, Synergetic Innovation Center for Quantum Effects and Applications, School of Physics and Electronics, Hunan Normal University, Changsha, 410081, China

    **nxing Zhou

  3. Key Laboratory of Hunan Province on Information Photonics and Freespace Optical Communications, School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, 414006, China

    Yougang Ke

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  1. Youzhi Shi
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Correspondence to Yu Chen or **nxing Zhou.

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Shi, Y., Ma, Z., Chen, H. et al. High-resolution recognition of FOAM modes via an improved EfficientNet V2 based convolutional neural network. Front. Phys. 19, 32205 (2024). https://doi.org/10.1007/s11467-023-1373-4

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