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Volume 8
Issue 2
10.3390/photonics8020051
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Article

Comparing Performance of Deep Convolution Networks in Reconstructing Soliton Molecules Dynamics from Real-Time Spectral Interference

by
Caiyun Li
,
Jiangyong He
,
Yange Liu
,
Yang Yue
,
Luhe Zhang
,
Longfei Zhu
,
Mengjie Zhou
,
Congcong Liu
,
Kaiyan Zhu
and
Zhi Wang
*
Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, Institute of Modern Optics, Nankai University, Tianjin 300350, China
*
Author to whom correspondence should be addressed.
Photonics 2021, 8(2), 51; https://doi.org/10.3390/photonics8020051
Submission received: 30 December 2020 / Revised: 5 February 2021 / Accepted: 9 February 2021 / Published: 13 February 2021
(This article belongs to the Special Issue Advanced Technique and Future Perspective for Next Generation Optical Fiber Communications)
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Abstract

Deep neural networks have enabled the reconstruction of optical soliton molecules with more complex structures using the real-time spectral interferences obtained by photonic time-stretch dispersive Fourier transformation (TS-DFT) technology. In this paper, we propose to use three kinds of deep convolution networks (DCNs), including VGG, ResNets, and DenseNets, for revealing internal dynamics evolution of soliton molecules based on the real-time spectral interferences. When analyzing soliton molecules with equidistant composite structures, all three models are effective. The DenseNets with layers of 48 perform the best for extracting the dynamic information of complex five-soliton molecules from TS-DFT data. The mean Pearson correlation coefficient (MPCC) between the predicted results and the real results is about 0.9975. Further, the ResNets in which the MPCC achieves 0.9906 also has the better ability of phase extraction than VGG which the MPCC is about 0.9739. The general applicability is demonstrated for extracting internal information from complex soliton molecule structures with high accuracy. The presented DCNs-based techniques can be employed to explore undiscovered mechanisms underlying the distribution and evolution of large numbers of solitons in dissipative systems in experimental research.
Keywords: fiber nonlinearities; deep learning (DL); artificial intelligence (AI) fiber nonlinearities; deep learning (DL); artificial intelligence (AI)

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MDPI and ACS Style

Li, C.; He, J.; Liu, Y.; Yue, Y.; Zhang, L.; Zhu, L.; Zhou, M.; Liu, C.; Zhu, K.; Wang, Z. Comparing Performance of Deep Convolution Networks in Reconstructing Soliton Molecules Dynamics from Real-Time Spectral Interference. Photonics 2021, 8, 51. https://doi.org/10.3390/photonics8020051

AMA Style

Li C, He J, Liu Y, Yue Y, Zhang L, Zhu L, Zhou M, Liu C, Zhu K, Wang Z. Comparing Performance of Deep Convolution Networks in Reconstructing Soliton Molecules Dynamics from Real-Time Spectral Interference. Photonics. 2021; 8(2):51. https://doi.org/10.3390/photonics8020051

Chicago/Turabian Style

Li, Caiyun, Jiangyong He, Yange Liu, Yang Yue, Luhe Zhang, Longfei Zhu, Mengjie Zhou, Congcong Liu, Kaiyan Zhu, and Zhi Wang. 2021. "Comparing Performance of Deep Convolution Networks in Reconstructing Soliton Molecules Dynamics from Real-Time Spectral Interference" Photonics 8, no. 2: 51. https://doi.org/10.3390/photonics8020051

APA Style

Li, C., He, J., Liu, Y., Yue, Y., Zhang, L., Zhu, L., Zhou, M., Liu, C., Zhu, K., & Wang, Z. (2021). Comparing Performance of Deep Convolution Networks in Reconstructing Soliton Molecules Dynamics from Real-Time Spectral Interference. Photonics, 8(2), 51. https://doi.org/10.3390/photonics8020051

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