A new method for extracting the Brillouin frequency shift (BFS) from the Brillouin gain spectrum (BGS), the modified backward correlation method (MBWC), is presented. The possibilities of using MBWC, and MBWC in combination with the Lorentzian curve fitting (LCF) based on Levenberg–Marquardt (LM) method, are studied. The effectiveness of the new method, and its combination with LM, has been demonstrated for processing spectra with a low signal-to-noise ratio (SNR). The experiments, which were in good agreement with the performed simulation, showed that at SNR = 0 dB, the combined (MBWC + LM) method provided the BFS extraction error of less than 4 MHz, while the state-of-the-art LM algorithm extracted it with the error greater than 4.5 MHz. The advantage of correlation methods becomes more significant with the decreasing SNR: at SNR = −2 dB, the LM’s error is 14.3 MHz, and that of the combined one is 8.1 MHz.

/pdf/a-refinement-of-backward-correlation-technique-for-precise-hal2mf5s.pdf

A Refinement of Backward Correlation Technique for Precise Brillouin Frequency Shift Extraction

Brillouin optical time-domain reflectometry (BOTDR) is widely used for strain and temperature measurements in various fields. However, the accuracy and reliability of the measurements are often limited by the noise in the sensor signals. Dynamic measurement of BOTDR requires small averaging number and fast measurement, and hence noise reduction is more significant in dynamic measurement. Small gain stimulated Brillouin scattering (SBS) can enhance the Brillouin signal power in BOTDR to realize dynamic measurement, but noise reduction is still important in system. In this work, we investigate the denoising of Brillouin gain spectrum (BGS) images using convolutional neural networks (DnCNN) to improve the accuracy of the small gain SBS STFT-BOTDR measurement of strain vibration. It is shown that the denoising of BGS images along the time axis can result in better detection of the strain vibration compared with denoising of BGS images along the fiber length. The denoising performance was evaluated using frequency uncertainties and R-squared values. The best denoising performance was achieved with a DnCNN network with 8 layers and 200 epochs, leading to a frequency uncertainty of 2.32 MHz and an R-squared value of 0.907. The frequency uncertainty is improved to about 45% of the original value.

Denoising of Brillouin Gain Spectrum Images for Improved Dynamic Measurements of BOTDR

Brillouin optical time domain reflectometry (BOTDR) detects fiber temperature and strain data and represents one of the most critical ways of identifying abnormal conditions such as ice coverage and lightning strikes on optical fiber composite overhead ground wire (OPGW) cable. Existing BOTDR extracts brillouin frequency shift (BFS) features with cumulative averaging and curve fitting. BFS feature extraction is slow for long-distance measurements, making realizing real-time measurements on fiber optic cables challenging. We propose a fast feature extraction method for block matching and 3D filtering (BM3D) + Sobel brillouin scattering spectroscopy (BGS). BM3D takes the advantage of non-local means (NLM) and wavelet denoising (WD) and utilizes the spatial-domain non-local principle to enhance the denoising in the transform domain. The global filtering capability of BM3D is utilized to filter out the low cumulative average BGS noise and the BFS feature extraction is completed using Sobel edge detection. Simulation verifies the feasibility of the algorithm, and the proposed method is embedded in BOTDR to measure 30 km of actual OPGW line. The experimental results show that under the same conditions, the processing time of this method is reduced by 37 times compared to that with the 50,000-time cumulative averaging + levenberg marquardt (LM) algorithm without severe distortion of the reference resolution. The method improves the sensor demodulation speed by using image processing technology without changing the existing hardware equipment, which is expected to be widely used in the new generation of BOTDR.

Fast Feature Extraction Method for Brillouin Scattering Spectrum of OPGW Optical Cable Based on BOTDR

This paper demonstrates how the processing of Brillouin gain spectra (BGS) by two-dimensional correlation methods improves the accuracy of Brillouin frequency shift (BFS) extraction in distributed fiber optic sensor systems based on the BOTDA/BOTDR (Brillouin optical time domain analysis/reflectometry) principles. First, the spectra corresponding to different spatial coordinates of the fiber sensor are resampled. Subsequently, the resampled spectra are aligned by the position of the maximum by shifting in frequency relative to each other. The spectra aligned by the position of the maximum are then averaged, which effectively increases the signal-to-noise ratio (SNR). Finally, the Lorentzian curve fitting (LCF) method is applied to the spectrum with improved characteristics, including a reduced scanning step and an increased SNR. Simulations and experiments have demonstrated that the method is particularly efficacious when the signal-to-noise ratio does not exceed 8 dB and the frequency scanning step is coarser than 4 MHz. This is particularly relevant when designing high-speed sensors, as well as when using non-standard laser sources, such as a self-scanning frequency laser, for distributed fiber-optic sensing. 

An Image Processing-Based Correlation Method for Improving the Characteristics of Brillouin Frequency Shift Extraction in Distributed Fiber Optic Sensors

基于块匹配三维滤波算法的长距离botdr系统研究

The Brillouin optical time domain reflectometry (BOTDR) system measures the distributed strain and temperature information along the optic fibre by detecting the Brillouin gain spectra (BGS) and finding the Brillouin frequency shift profiles. By introducing small gain stimulated Brillouin scattering (SBS), dynamic measurement using BOTDR can be realized, but the performance is limited due to the noise of the detected information. An image denoising method using the convolutional neural network (CNN) is applied to the derived Brillouin gain spectrum images to enhance the performance of the Brillouin frequency shift detection and the strain vibration measurement of the BOTDR system. By reducing the noise of the BGS images along the length of the fibre under test with different network depths and epoch numbers, smaller frequency uncertainties are obtained, and the sine-fitting R-squared values of the detected strain vibration profiles are also higher. The Brillouin frequency uncertainty is improved by 24% and the sine-fitting R-squared value of the obtained strain vibration profile is enhanced to 0.739, with eight layers of total depth and 200 epochs.

/pdf/denoising-of-botdr-dynamic-strain-measurement-using-18gz40fr.pdf

Denoising of BOTDR Dynamic Strain Measurement Using Convolutional Neural Networks

The estimation and monitoring of SpO2 are crucial for assessing lung function and treating chronic pulmonary diseases. The COVID-19 pandemic has highlighted the importance of early detection of changes in SpO2, particularly in asymptomatic patients with clinical deterioration. However, conventional SpO2 measurement methods rely on contact-based sensing, presenting the risk of cross-contamination and complications in patients with impaired limb perfusion. Additionally, pulse oximeters may not be available in marginalized communities and undeveloped countries. To address these limitations and provide a more comfortable and unobtrusive way to monitor SpO2, recent studies have investigated SpO2 measurement using videos. However, measuring SpO2 using cameras in a contactless way, particularly from smartphones, is challenging due to weaker physiological signals and lower optical selectivity of smartphone camera sensors. The system includes three main steps: 1) extraction of the region of interest (ROI), which includes the palm and back of the hand, from the smartphone-captured videos; 2) spatial averaging of the ROI to produce R, G, and B time series; and 3) feeding the time series into an optophysiology-inspired CNN for SpO2 estimation. Our proposed method can provide a more efficient and accurate way to monitor SpO2 using videos captured from consumer-grade smartphones, which can be especially useful in telehealth and health screening settings.

https://doi.org/10.22541/au.167907495.58726050/v1

Model Explainability in Physiological and Healthcare-based Neural Networks

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Denoising of BOTDR Dynamic Strain Measurement Using Convolutional Neural Networks

Denoising of Brillouin Gain Spectrum Images for Improved Dynamic Measurements of BOTDR

Model Explainability in Physiological and Healthcare-based Neural Networks