• Photonics Research
  • Vol. 8, Issue 5, 690 (2020)
Yiqing Chang1、†, Hao Wu†、*, Can Zhao, Li Shen, Songnian Fu, and Ming Tang
Author Affiliations
  • Wuhan National Laboratory for Optoelectronics (WNLO) & National Engineering Laboratory for Next Generation Internet Access System, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
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    DOI: 10.1364/PRJ.389970 Cite this Article Set citation alerts
    Yiqing Chang, Hao Wu, Can Zhao, Li Shen, Songnian Fu, Ming Tang. Distributed Brillouin frequency shift extraction via a convolutional neural network[J]. Photonics Research, 2020, 8(5): 690 Copy Citation Text show less
    Architecture of the proposed BFSCNN.
    Fig. 1. Architecture of the proposed BFSCNN.
    Normalized simulation BGSs. BGS1: BFS=20%, SW=13%, SNR=20 dB; BGS2: BFS=50%, SW=30%, SNR=15 dB; BGS3: BFS=73%, SW=50%, SNR=10 dB.
    Fig. 2. Normalized simulation BGSs. BGS1: BFS=20%, SW=13%, SNR=20  dB; BGS2: BFS=50%, SW=30%, SNR=15  dB; BGS3: BFS=73%, SW=50%, SNR=10  dB.
    Simulation data used for training. (a) Simulation BGSs of random parameters; (b) corresponding BFSs.
    Fig. 3. Simulation data used for training. (a) Simulation BGSs of random parameters; (b) corresponding BFSs.
    Experimental setup of the BOTDA system. EOM, electro-optic modulator; MS, microwave synthesizer; PS, polarization switch; FUT, fiber under test; SOA, semiconductor optical amplifier; AFG, arbitrary function generator; EDFA, erbium-doped fiber amplifier; BPF, bandpass filter; BVTF, bandwidth-variable tunable filter; PD, pin photodetector.
    Fig. 4. Experimental setup of the BOTDA system. EOM, electro-optic modulator; MS, microwave synthesizer; PS, polarization switch; FUT, fiber under test; SOA, semiconductor optical amplifier; AFG, arbitrary function generator; EDFA, erbium-doped fiber amplifier; BPF, bandpass filter; BVTF, bandwidth-variable tunable filter; PD, pin photodetector.
    Normalized BFS RMSE and SD of the simulation data. (a) Normalized RMSE and (b) SD for different SNR data; (c) normalized RMSE and (d) SD for different BFS data; (e) normalized RMSE and (f) SD for different SW data.
    Fig. 5. Normalized BFS RMSE and SD of the simulation data. (a) Normalized RMSE and (b) SD for different SNR data; (c) normalized RMSE and (d) SD for different BFS data; (e) normalized RMSE and (f) SD for different SW data.
    Performance differences in BFSs extracted by the LCF and BFSCNN. (a) Normalized BFS RMSE using the LCF minus the normalized BFS RMSE using the BFSCNN for different simulation data. (b) Normalized BFS SD using the LCF minus the normalized BFS SD using the BFSCNN for different simulation data.
    Fig. 6. Performance differences in BFSs extracted by the LCF and BFSCNN. (a) Normalized BFS RMSE using the LCF minus the normalized BFS RMSE using the BFSCNN for different simulation data. (b) Normalized BFS SD using the LCF minus the normalized BFS SD using the BFSCNN for different simulation data.
    Measurement results when the pump pulse width is 40 ns and the average time is 32. (a) Measured BGSs along an optical fiber; (b) distributed BFSs extracted by LCF and BFSCNN.
    Fig. 7. Measurement results when the pump pulse width is 40 ns and the average time is 32. (a) Measured BGSs along an optical fiber; (b) distributed BFSs extracted by LCF and BFSCNN.
    BFS uncertainty as a function of fiber length. BFS uncertainty traces when the pump pulse width is (a) 20 ns, (b) 30 ns, and (c) 40 ns.
    Fig. 8. BFS uncertainty as a function of fiber length. BFS uncertainty traces when the pump pulse width is (a) 20 ns, (b) 30 ns, and (c) 40 ns.
    Extracted BFSs along the optical fiber when the fiber end is heated. The inset image shows the BFS profiles around the start of the heat section.
    Fig. 9. Extracted BFSs along the optical fiber when the fiber end is heated. The inset image shows the BFS profiles around the start of the heat section.
    ParametersRangeInterval
    SNR5–19 dB2 dB
    BFS10%–90%5%
    SW10%–50%5%
    Table 1. Test Parameters
    Yiqing Chang, Hao Wu, Can Zhao, Li Shen, Songnian Fu, Ming Tang. Distributed Brillouin frequency shift extraction via a convolutional neural network[J]. Photonics Research, 2020, 8(5): 690
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