The theoretical fundamentals of fiber-based optical parametric amplifiers(OPA) are reviewed,and their applications are discussed in this paper.In the end the future research aspects are expected.

Fiber-based Optical Parametric Amplifiers and Their Applications

Abstract In recent years, machine learning, especially various deep neural networks, as an emerging technique for data analysis and processing, has brought novel insights into the development of fiber lasers, in particular complex, dynamical, or disturbance-sensitive fiber laser systems. This paper highlights recent attractive research that adopted machine learning in the fiber laser field, including design and manipulation for on-demand laser output, prediction and control of nonlinear effects, reconstruction and evaluation of laser properties, as well as robust control for lasers and laser systems. We also comment on the challenges and potential future development. 

https://photonix.springeropen.com/counter/pdf/10.1186/s43074-022-00055-3

Fiber laser development enabled by machine learning: review and prospect

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The general neural networks (NNs) based on classification convert the Brillouin frequency shift (BFS) extraction in Brillouin-based distributed sensing to a problem in which the possible BFS output of the sensing system belongs to a finite number of discrete values. In this paper, we demonstrate a method of applying NNs with adaptive BFS incremental steps to signal processing for Brillouin optical correlation-domain sensing and achieve higher accuracy and operation speed. The comparison with the conventional curving fitting method shows that the NN improves the BFS measurement accuracy by 2–3 times and accelerates the signal processing speed by 1000 times for simulated signals. The experimental results demonstrate the NN provides 1.6–2.7 times enhancement for BFS measurement accuracy and 5000 times acceleration for the BFS extraction speed. This method supplies a potential solution to online signal processing for real-time Brillouin sensing.

Neural network-assisted signal processing in Brillouin optical correlation-domain sensing for potential high-speed implementation.

In this paper we experimentally show parametric amplification and wavelength conversion in a custom manufactured dual-core highly nonlinear fiber. On-off gain <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> 10 dB and conversion efficiencies between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-1$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-8.5$</tex-math></inline-formula> dB were measured for both cores. The estimated effective nonlinear parameter for the cores of the fiber are 6.6 W <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{-1}\mathrm{km}^{-1}$</tex-math></inline-formula> and 6.3 W <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{-1}\mathrm{km}^{-1}$</tex-math></inline-formula> , while the zero-dispersion wavelength for the individual cores is shown to be relatively close from each other. Furthermore, complementary analytical and numerical results show that coupled cores fiber optical parametric amplifier offer the potential of wide-band gain even when they have significantly distinct zero-dispersion wavelengths. 

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Parametric Amplification and Wavelength Conversion in Dual-Core Highly Nonlinear Fibers

Brillouin optical time-domain analyzer (BOTDA) is used to monitor the temperature and strain along a fiber. So far, neural network and machine learning methods have been successfully applied for temperature extraction. But for different frequency scanning steps, different networks should be designed and trained. Here, a BOTDA assisted by kernel extreme learning machine (K-ELM) with high generalization is proposed and experimentally demonstrated. By utilizing K-ELM, the raw Brillouin gain spectra measured from BOTDA system are classified into different temperature classes. The performance of K-ELM is investigated both in simulation and experiment under different cases of signal-to-noise ratios, pump pulse widths, and frequency scanning steps. Compared with curve fitting methods, the K-ELM algorithm has better measurement accuracy of 0.3 °C and it realizes great improvement of the processing speed over 120 times. The ultrafast processing speed, high accuracy and generality make K-ELM become a highly competitive candidate for the high-speed BOTDA sensing system.

Ultrafast and Accurate Temperature Extraction via Kernel Extreme Learning Machine for BOTDA Sensors

Brillouin optical time domain analyzer (BOTDA) assisted by optimized support vector machine (SVM) algorithm for accurate temperature extraction is presented and experimentally demonstrated. Three typical intelligent optimization algorithms, particle swarm optimization algorithm, genetic algorithm and firefly algorithm are explored to optimize the SVM parameters. The performances of optimized SVM algorithms for temperature extraction are investigated in both simulation and experiment under different conditions for Brillouin gain spectrum collection, resulting in the significant enhancement of sensing accuracy. In particular, the extraction accuracy (i.e., smaller root mean square error value) of temperature information is improved about 4 °C compared with the conventional SVM when the signal-to-noise ratio (SNR) as low as 2.5 dB and 40-ns pump pulse width are adopted in the experiment. In addition to the enhanced accuracy with good robustness, the optimized algorithms have faster processing speed than the curve fitting method, over 20-times improvement. That makes the optimized algorithms become a very promising candidate for high performance BOTDA sensors in the future.

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Optimized Support Vector Machine Assisted BOTDA for Temperature Extraction With Accuracy Enhancement

The gain performance of one pump phase sensitive fiber optical parametric amplifier (PS-FOPA) in the dual-core fiber is numerically investigated without pump depleting. The effects of the core coupling coefficient( C ), the pump wavelength and the initial signal phase on the gain of PS-FOPA are analyzed in detail. It shows that, the signal gain is greatly affected by different core coupling coefficients, and a flatter gain is achieved with suitable C value. Also, when the pump wavelength is greater than the zero-dispersion wavelength (ZDW), both the peak gain and gain bandwidth decrease with the increase of C , but the trend is opposite in case of the pump wavelength less than the ZDW. Additionally, the flatter and broader gain spectra are obtained by adjusting the initial signal phase in core 2. This simulation provides a detail analysis for the dual-core PS-FOPA, which is significant for the all optical fiber amplifier systems.

One-Pump Phase Sensitive Fiber Optical Parametric Amplifier With Dual-Core Fiber

The time-frequency analysis (TFA) by wavelet transform is adopted for the laser ultrasonic signal processing, and the effective features extraction of the material defect is obtained. The TFA is adopted here to analyze the laser-generated surface acoustic wave (SAW) signal which contains the defect features, the echo wave features are extracted significantly, especially under the condition of low signal-to-noise rate (SNR). The simulation model by using finite element method (FEM) is set up in an aluminum plate with different surface defect depths in detail, and the defect depths prediction with TFA is also considered. It shows that, without extra denoising process, the echo SAW is extracted significantly in case of defect depths ranging from 0.1mm to 0.9mm at SNR of -3dB by TFA. The TFA for processing the laser ultrasonic signal provides a promising way to get the defect information, with the accuracy increased by 7.9dB in this work, which is extremely meaningful for the ultrasonic signal processing and material evaluation.

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Effective Defect Features Extraction for Laser Ultrasonic Signal Processing by Using Time–Frequency Analysis

Brillouin optical time-domain analyzer (BOTDA) assisted by echo state network (ESN) for fast and accurate temperature extraction is proposed. Compared to curve fitting methods, the processing speed of proposed ESN algorithm is accelerated up to 20 times. Experimental work is carried out and verified with good agreement of the simulation results. © 2020 The Author(s)

Lei Yu

Papers

Ultrafast and Accurate Temperature Extraction via Kernel Extreme Learning Machine for BOTDA Sensors

Optimized Support Vector Machine Assisted BOTDA for Temperature Extraction With Accuracy Enhancement

One-Pump Phase Sensitive Fiber Optical Parametric Amplifier With Dual-Core Fiber

Effective Defect Features Extraction for Laser Ultrasonic Signal Processing by Using Time–Frequency Analysis

Fast temperature extraction via Echo State Network for BOTDA sensors