解调
计算机科学
光纤布拉格光栅
信号(编程语言)
卷积神经网络
信噪比(成像)
噪音(视频)
电子工程
光学
电信
人工智能
物理
光纤
频道(广播)
工程类
图像(数学)
程序设计语言
作者
Baocheng Li,Zhi-Wei Tan,Perry Ping Shum,Chenlu Wang,Yu Zheng,Liang Jie Wong
出处
期刊:Optics Express
[The Optical Society]
日期:2021-01-15
卷期号:29 (5): 7110-7110
被引量:26
摘要
In quasi-distributed fiber Bragg grating (FBG) sensor networks, challenges are known to arise when signals are highly overlapped and thus hard to separate, giving rise to substantial error in signal demodulation. We propose a multi-peak detection deep learning model based on a dilated convolutional neural network (CNN) that overcomes this problem, achieving extremely low error in signal demodulation even for highly overlapped signals. We show that our FBG demodulation scheme enhances the network multiplexing capability, detection accuracy and detection time of the FBG sensor network, achieving a root-mean-square (RMS) error in peak wavelength determination of < 0.05 pm, with a demodulation time of 15 ms for two signals. Our demodulation scheme is also robust against noise, achieving an RMS error of < 0.47 pm even with a signal-to-noise ratio as low as 15 dB. A comparison on our high-performance computer with existing signal demodulation methods shows the superiority in RMS error of our dilated CNN implementation. Our findings pave the way to faster and more accurate signal demodulation methods, and testify to the substantial promise of neural network algorithms in signal demodulation problems.
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