Spatial nonlinear conversion of structured light for machine learning based ultra-accurate information networks
Structured light can be encoded to carry information for free-space optical communications with an extended degree of freedom to increase the capacity, however, the accuracy issue along with capacity increase is one of the biggest challenges that prevent practical applications. To achieve high accur...
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sg-ntu-dr.10356-1779422024-06-03T06:15:01Z Spatial nonlinear conversion of structured light for machine learning based ultra-accurate information networks Zhang, Zilong He, Wei Zhao, Suyi Gao, Yuan Wang, Xin Li, Xiaotian Wang, Yuqi Ma, Yunfei Hu, Yetong Shen, Yijie Zhao, Changming School of Electrical and Electronic Engineering School of Physical and Mathematical Sciences Centre for Disruptive Photonic Technologies (CDPT) Engineering Deep learning Information transmission Structured light can be encoded to carry information for free-space optical communications with an extended degree of freedom to increase the capacity, however, the accuracy issue along with capacity increase is one of the biggest challenges that prevent practical applications. To achieve high accuracy with high capacity by a simple method, they propose the spatial nonlinear conversion of structured light into a communication network, especially, realizing an ultra-high-accuracy point-to-multipoint (PtoMP) information transmission link. A series of coherently superposed spatial modes and their spatial nonlinear conversion states are used as information carriers to replace the prior orbital angular momentum beams and greatly expand channel capacity within quite low spatial mode order. Through the spatial nonlinear conversion of simple dual-mode superposition and a very basic neural network for machine learning-based recognition, as high as 99.5% accuracy for more than 500 modes is obtained. By a combination of diffuse reflection screens and multiple CCDs, the large observation angle PtoMP information transmission is also proved to be feasible. This work paves the way for practical large-scale multi-party information networks using structured light. Ministry of Education (MOE) Nanyang Technological University This study was sup-ported by The National Natural Science Foundation of China (NSFC) grant funded by the Chinses government (Grant No. 62375015). Y.S. thanks the support of a start grant from Nanyang Technological University and Singapore Ministry of Education (MOE) AcRF Tier 1 grant (RG157/23). 2024-06-03T06:15:01Z 2024-06-03T06:15:01Z 2024 Journal Article Zhang, Z., He, W., Zhao, S., Gao, Y., Wang, X., Li, X., Wang, Y., Ma, Y., Hu, Y., Shen, Y. & Zhao, C. (2024). Spatial nonlinear conversion of structured light for machine learning based ultra-accurate information networks. Laser and Photonics Reviews, 2301225-. https://dx.doi.org/10.1002/lpor.202301225 1863-8880 https://hdl.handle.net/10356/177942 10.1002/lpor.202301225 2-s2.0-85185333362 2301225 en RG157/23 NTU SUG Laser and Photonics Reviews © 2024 Wiley-VCH GmbH. All rights reserved. |
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Engineering Deep learning Information transmission Zhang, Zilong He, Wei Zhao, Suyi Gao, Yuan Wang, Xin Li, Xiaotian Wang, Yuqi Ma, Yunfei Hu, Yetong Shen, Yijie Zhao, Changming Spatial nonlinear conversion of structured light for machine learning based ultra-accurate information networks |
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Structured light can be encoded to carry information for free-space optical communications with an extended degree of freedom to increase the capacity, however, the accuracy issue along with capacity increase is one of the biggest challenges that prevent practical applications. To achieve high accuracy with high capacity by a simple method, they propose the spatial nonlinear conversion of structured light into a communication network, especially, realizing an ultra-high-accuracy point-to-multipoint (PtoMP) information transmission link. A series of coherently superposed spatial modes and their spatial nonlinear conversion states are used as information carriers to replace the prior orbital angular momentum beams and greatly expand channel capacity within quite low spatial mode order. Through the spatial nonlinear conversion of simple dual-mode superposition and a very basic neural network for machine learning-based recognition, as high as 99.5% accuracy for more than 500 modes is obtained. By a combination of diffuse reflection screens and multiple CCDs, the large observation angle PtoMP information transmission is also proved to be feasible. This work paves the way for practical large-scale multi-party information networks using structured light. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Zhang, Zilong He, Wei Zhao, Suyi Gao, Yuan Wang, Xin Li, Xiaotian Wang, Yuqi Ma, Yunfei Hu, Yetong Shen, Yijie Zhao, Changming |
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Article |
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Zhang, Zilong He, Wei Zhao, Suyi Gao, Yuan Wang, Xin Li, Xiaotian Wang, Yuqi Ma, Yunfei Hu, Yetong Shen, Yijie Zhao, Changming |
author_sort |
Zhang, Zilong |
title |
Spatial nonlinear conversion of structured light for machine learning based ultra-accurate information networks |
title_short |
Spatial nonlinear conversion of structured light for machine learning based ultra-accurate information networks |
title_full |
Spatial nonlinear conversion of structured light for machine learning based ultra-accurate information networks |
title_fullStr |
Spatial nonlinear conversion of structured light for machine learning based ultra-accurate information networks |
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Spatial nonlinear conversion of structured light for machine learning based ultra-accurate information networks |
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spatial nonlinear conversion of structured light for machine learning based ultra-accurate information networks |
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2024 |
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https://hdl.handle.net/10356/177942 |
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1800916112535191552 |