Benefits of Spectral Property Engineering in Distributed Brillouin Fiber Sensing

Feng, Cheng; Schneider, Thomas

doi:10.3390/s21051881

Cited by 10 publications

(3 citation statements)

References 69 publications

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“…Moreover, quantum control of light and sound can be implemented using the SBS effect in microresonators [23]. Note that the SBS effect is successfully implemented in optical fibers for sensing and other important applications [24][25][26][27][28]. In the case of fibers, enormous lengths are required, but the Brillouin powers are also much higher than in the case of microresonators.…”

Section: Introductionmentioning

confidence: 99%

Cascade Brillouin Lasing in a Tellurite-Glass Microsphere Resonator with Whispering Gallery Modes

Anashkina

Marisova

Дорофеев

et al. 2022

Sensors

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Brillouin microlasers based on microresonators with whispering gallery modes (WGMs) are in high demand for different applications including sensing and biosensing. We fabricated a microsphere resonator with WGMs from a synthesized high-quality tellurite glass with record high Q-factors for tellurite microresonators (Q ≥ 2.5 × 107), a high Brillouin gain coefficient (compared to standard materials, e.g., silica glasses), and a Brillouin frequency shift of 9 ± 0.5 GHz. The high density of excited resonance modes and high loaded Q-factors allowed us to achieve experimentally cascade Stokes-Brillouin lasing up to the 4th order inclusive. The experimental results are supported by the results of the theoretical analysis. We also theoretically obtained the dependences of the output Brillouin powers on the pump power and found the pump-power thresholds for the first five Brillouin orders at different values of pump frequency detuning and Q-factors, and showed a significant effect of these parameters on the processes under consideration.

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Section: Introductionmentioning

confidence: 99%

Cascade Brillouin Lasing in a Tellurite-Glass Microsphere Resonator with Whispering Gallery Modes

Anashkina

Marisova

Дорофеев

et al. 2022

Sensors

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“…Meanwhile, systems based on these principles are applied in SHM quite often [ 17 , 18 ]. One of the serious problems (especially at low signal-to-noise ratios) is the inability to accurately determine the frequency maximum of the Brillouin gain spectrum [ 19 ]. The present work is focused on solving this problem using a combination of various methods, including machine learning.…”

Section: Introductionmentioning

confidence: 99%

Improving Prediction Accuracy and Extraction Precision of Frequency Shift from Low-SNR Brillouin Gain Spectra in Distributed Structural Health Monitoring

Nordin

Abdullah

Zan

et al. 2022

Sensors

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In this paper, we studied the possibility of increasing the Brillouin frequency shift (BFS) detection accuracy in distributed fibre-optic sensors by the separate and joint use of different algorithms for finding the spectral maximum: Lorentzian curve fitting (LCF, including the Levenberg–Marquardt (LM) method), the backward correlation technique (BWC) and a machine learning algorithm, the generalized linear model (GLM). The study was carried out on real spectra subjected to the subsequent addition of extreme digital noise. The precision and accuracy of the LM and BWC methods were studied by varying the signal-to-noise ratios (SNRs) and by incorporating the GLM method into the processing steps. It was found that the use of methods in sequence gives a gain in the accuracy of determining the sensor temperature from tenths to several degrees Celsius (or MHz in BFS scale), which is manifested for signal-to-noise ratios within 0 to 20 dB. We have found out that the double processing (BWC + GLM) is more effective for positive SNR values (in dB): it gives a gain in BFS measurement precision near 0.4 °C (428 kHz or 9.3 με); for BWC + GLM, the difference of precisions between single and double processing for SNRs below 2.6 dB is about 1.5 °C (1.6 MHz or 35 με). In this case, double processing is more effective for all SNRs. The described technique’s potential application in structural health monitoring (SHM) of concrete objects and different areas in metrology and sensing were also discussed.

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“…Scanning the required range of pump-probe frequency difference is used to construct BGS. BFS, the peak of BGS, is a function of local temperature and strain in the fiber [4,5]. Hence, one of the key technologies for the sensors is technology of estimating BFS from BGS, which can be realized using the traditional method, Lorentzian Curve Fitting (LCF).…”

Section: Introductionmentioning

confidence: 99%

Fast Measurement of Brillouin Frequency Shift in Optical Fiber Based on a Novel Feedforward Neural Network

Xiao

2021

Photonics

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Brillouin scattering-based distributed optical fiber sensors have been successfully employed in various applications in recent decades, because of benefits such as small size, light weight, electromagnetic immunity, and continuous monitoring of temperature and strain. However, the data processing requirements for the Brillouin Gain Spectrum (BGS) restrict further improvement of monitoring performance and limit the application of real-time measurements. Studies using Feedforward Neural Network (FNN) to measure Brillouin Frequency Shift (BFS) have been performed in recent years to validate the possibility of improving measurement performance. In this work, a novel FNN that is 3 times faster than previous FNNs is proposed to improve BFS measurement performance. More specifically, after the original Brillouin Gain Spectrum (BGS) is preprocessed by Principal Component Analysis (PCA), the data are fed into the Feedforward Neural Network (FNN) to predict BFS.

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Benefits of Spectral Property Engineering in Distributed Brillouin Fiber Sensing

Cited by 10 publications

References 69 publications

Cascade Brillouin Lasing in a Tellurite-Glass Microsphere Resonator with Whispering Gallery Modes

Cascade Brillouin Lasing in a Tellurite-Glass Microsphere Resonator with Whispering Gallery Modes

Improving Prediction Accuracy and Extraction Precision of Frequency Shift from Low-SNR Brillouin Gain Spectra in Distributed Structural Health Monitoring

Fast Measurement of Brillouin Frequency Shift in Optical Fiber Based on a Novel Feedforward Neural Network

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