Fiber Optic Signal Research Articles

Cerebral Microcirculation: Progress and Outlook of Laser Doppler Flowmetry in Neurosurgery and Neurointensive Care.

Laser Doppler flowmetry (LDF) is a well-established technique for the investigation of tissue microcirculation. Compared to skin, the use in the human brain is sparse. The measurement of cerebral microcirculation in neurointensive care and during neurosurgery is challenging and requires adaptation to the respective clinical setting. The aim of the review is to present state of the art and progress in neurosurgery and neurointensive care where LDF has proven useful and can find clinical importance in the investigation of cerebral microcirculation. The literature in the field is summarized and recent technical improvements regarding LDF systems and fiber optical probe designs for neurosurgical and neurocritical care described. By combining two signals from the LDF unit, the measurement of the microcirculation (Perfusion) and gray whiteness (TLI) of the brain tissue, the full potential of the device is achieved. For example, a forward-looking LDF-probe detects high-risk hemorrhage areas and gray-white matter boundaries along intraoperative trajectories during stereotactic neurosurgery. Proof of principles are given for LDF as a guidance tool in deep brain stimulation implantation, brain tumor needle biopsies, and as long-term monitoring device in neurocritical care. With well-designed fiber optical probes, surgical fixation, and signal processing for movement reduction, LDF monitoring of the cerebral microcirculation is successful up to 10 days. The use of LDF can be combined with other physiological measurement techniques, for example, fluorescence spectroscopy for identification of glioblastoma during tumor surgery. Fiber optics can also be used during magnetic resonance imaging (MRI). Despite the many advantages, fiber optical LDF has not yet reached its full potential in clinical neuro-applications. Multicenter studies are required to further evaluate LDF in neurosurgery and neurointensive care. In conclusion, the present status of LDF in neurosurgery and neurointensive care has been reviewed. By combining Perfusion and TLI with tailored probe designs the full potential of LDF can be achived in measuring cerebral microcirculation. This includes guidance during DBS implantation and needle biopsies, and long-term monitoring in neurocritical care.

A comprehensive study on geometric shape optical soliton solutions to the time-factional nonlinear Schrödinger-Hirota equation

In this study, we investigate the analytical soliton solutions of a fundamental model, namely the nonlinear Schrödinger-Hirota equation, in the context of beta time-fractional derivative. We adopt the (ω′/ω, 1/ω)-expansion method, which is a reliable and straightforward approach to extract fresh and general soliton solutions in terms of hyperbolic, trigonometric, and rational functions. The solitons include anti-kink, anti-bell-shaped, bell-shaped, and periodic solitons. These solitons have significant applications in various scientific fields, such as optical fiber communications, signal processing, plasma physics, and trans-oceanic data transfer. This study demonstrates the significance of fractional-order differentiation in revealing new solitons. We also provide a comprehensive comparison with existing literature in normal and anomalous dispersion regions, highlighting the uniqueness of the solutions. Moreover, the graphical representations are used to illustrate the properties and potential applications of these solitons. This research might contribute to the advancement of nonlinear optical research and technology.

Design and Implementation of an Optical Measuring Interference System for Noise Signals in Optical Fiber Based on Neural Networks

Design and Implementation of an Optical Measuring Interference System for Noise Signals in Optical Fiber Based on Neural Networks

Deep Integration of Fiber-Optic Communication and Sensing Systems Using Forward-Transmission Distributed Vibration Sensing and on-off Keying.

The deep integration of communication and sensing technology in fiber-optic systems has been highly sought after in recent years, with the aim of rapid and cost-effective large-scale upgrading of existing communication cables in order to monitor ocean activities. As a proof-of-concept demonstration, a high-degree of compatibility was shown between forward-transmission distributed fiber-optic vibration sensing and an on-off keying (OOK)-based communication system. This type of deep integration allows distributed sensing to utilize the optical fiber communication cable, wavelength channel, optical signal and demodulation receiver. The addition of distributed sensing functionality does not have an impact on the communication performance, as sensing involves no hardware changes and does not occupy any bandwidth; instead, it non-intrusively analyzes inherent vibration-induced noise in the data transmitted. Likewise, the transmission of communication data does not affect the sensing performance. For data transmission, 150 Mb/s was demonstrated with a BER of 2.8 × 10-7 and a QdB of 14.1. For vibration sensing, the forward-transmission method offers distance, time, frequency, intensity and phase-resolved monitoring. The limit of detection (LoD) is 8.3 pε/Hz1/2 at 1 kHz. The single-span sensing distance is 101.3 km (no optical amplification), with a spatial resolution of 0.08 m, and positioning accuracy can be as low as 10.1 m. No data averaging was performed during signal processing. The vibration frequency range tested is 10-1000 Hz.

Data-driven solutions and parameter discovery of the extended higher-order nonlinear Schrödinger equation in optical fibers

In this paper, we investigate an extended higher-order nonlinear Schrödinger equation that includes third-order and fourth-order dispersion terms. By considering higher-order effects, the equation can more accurately simulate and predict the propagation behavior of ultrashort pulses in optical fibers, e.g., pulse splitting, compression and stability. In the optical fiber sensors, the sensitivity and resolution of the sensor can be improved by utilizing nonlinear effects. The analysis of the extended higher-order nonlinear Schrödinger equation helps to understand how nonlinear effects affect the sensing signal, thereby designing more accurate sensors. Specific coefficients of each term in the equation are studied for different scenarios, such as optical fiber design and signal propagation in optical communications. Under certain conditions, the extended higher-order nonlinear Schrödinger equation can be degenerated into the Hirota equation or the Lakshmanan–Porsezian–Daniel equation. With the development of numerical algorithms, the utilization of deep neural networks for solving equations has become increasingly attractive. With given initial conditions and boundary conditions, the physical information is embedded into the neural network in the form of loss functions. We efficiently derive numerical solutions to the extended equation for one-soliton, two-soliton and rogue-wave cases by training weights and biases in physics-informed neural networks. Compared with traditional numerical methods, physics-informed neural networks yield lower prediction errors with less data volume, thus will reduce the cost and time consumptions for data sampling in practical engineering. Furthermore, we solve the inverse problem associated with the extended equation. This approach is critical for extracting fundamental parameters of optical systems from observable data, thereby deepening our understanding of complex optical behavior and helping to improve the performance of optical fiber communication technologies.

Enhanced Discrete Wavelet Transform–Non-Local Means for Multimode Fiber Optic Vibration Signal

Real-time monitoring of heartbeat signals using multimode fiber optic microvibration sensing technology is crucial for diagnosing cardiovascular diseases, but the heartbeat signals are very weak and susceptible to noise interference, leading to inaccurate diagnostic results. In this paper, a combined enhanced discrete wavelet transform (DWT) and non-local mean estimation (NLM) denoising method is proposed to remove noise from heartbeat signals, which adaptively determines the filtering parameters of the DWT-NLM composite method using objective noise reduction quality assessment metrics by denoising different ECG signals from multiple databases with the addition of additive Gaussian white noise (AGW) with different signal-to-noise ratios (SNRs). The noise reduction results are compared with those of NLM, enhanced DWT, and conventional DWT combined with NLM method. The results show that the output SNR of the proposed method is significantly higher than the other methods compared in the range of −5 to 25 dB input SNR. Further, the proposed method is employed for noise reduction of heartbeat signals measured by fiber optic microvibration sensing. It is worth mentioning that the proposed method does not need to obtain the exact noise level, but only the adaptive filtering parameters based on the autocorrelation nature of the denoised signal. This work greatly improves the signal quality of the multimode fiber microvibration sensing system and helps to improve the diagnostic accuracy.

Hardware and Software Design of Programmable Medium and High-Speed Data Acquisition (DAQ) Board of Fiber Optic Signal for Partial Discharge Acquisition

The anti-electromagnetic interference capability of partial discharge (PD) acoustic signal conversion and collection circuits severely restrict the sensitivity of PD detection. The data acquisition (DAQ) systems available in the current market are costly and have limited functionality, making it difficult to satisfy the acquisition requirements for PD detection. This paper proposes a medium to high-speed fiber optic signal acquisition board with an adjustably controlled sampling rate and filter cutoff frequency. The circuit achieves a higher signal-to-noise (SNR) ratio by distributing the noise in each part of the signal acquisition chain reasonably. The temperature characteristics of the acquisition module are improved by utilizing the programmable T-type structure for transimpedance amplification of photocurrent. The DAQ card performs data acquisition and processing using STM32H743 internal ADC and caches data in bulk with an SRAM and SD card. A data uploading method based on time reference has been proposed, which enables full, effective information signal upload through a low-cost transmission interface. The research ultimately achieves a stable sampling of three channels at 1 MSps, SNR of 63 dB, and programmable gain amplification of the photocurrent with 0–60 dB. Finally, the system is used for PD acoustic signal acquisition in the frequency range of 20 Hz to 100 kHz.

Deep learning-assisted structural health monitoring: acoustic emission analysis and domain adaptation with intelligent fiber optic signal processing

Structural health monitoring aims to detect damage progression in materials. This study focuses on categorizing crack stages, a critical aspect of monitoring structural integrity. By leveraging acoustic emission (AE) monitoring, cracks can be analyzed in a data-driven manner. However, applying AE analysis poses several challenges, including discrepancies between simulated AE data from models and experimental data from the field, as well as class imbalance in crack progression data, with a scarcity of late-stage data. To bridge the gap between theory and experiments, our approach employs domain adaptation to synchronize simulated and actual AE data. The model learns robust domain-invariant features through meticulous experimentation across training epochs. Quantitative analysis of the model’s performance provides key insights. F1 scores vary with feature counts, and domain adaptation outperforms by 20% on highly imbalanced datasets. This emphasizes the model’s adaptability for precise crack classification, even with underrepresented damage classes. In summary, this study advances structural health monitoring by offering a solid AE analysis approach. Core contributions include reconciling simulated and experimental data discrepancies, tackling class imbalance, optimizing feature extraction, and demonstrating robust crack stage categorization. The insights gained highlight the merits of domain adaptation and data-driven AE analysis for predicting crack progression.

Research on the processing and interpretation methods of distributed fiber optic vibration signal logging injection profiles

The oil and gas industry currently faces the dual challenges of improving extraction efficiency and reducing environmental impacts. Traditional well monitoring technologies are increasingly unable to meet the demands of modern oil and gas extraction due to technological limitations and environmental concerns. Distributed Acoustic Sensing (DAS) technology, which utilizes optical fibers as sensing elements, enables real-time and accurate monitoring of the CO2 injection process in wells. However, DAS technology encounters issues such as weak amplitude signals and noise interference in practical applications. In response, a comprehensive method for processing and interpreting DAS logging data for CO2 injection wells has been proposed. This method involves restoring amplitude signals through wavefront energy compensation and applying Gaussian filtering and wavelet threshold denoising algorithms for noise reduction. The processed results are classified using the K-Medoids clustering algorithm and the CO2 absorption volume in the injection layer is calculated using the integral area method. The accuracy and practicality of this method have been validated through comparative analysis with pulse neutron oxygen activation logging results. This approach aims to address the challenges faced by DAS technology in the oil and gas industry, thereby providing technical support for the efficient, environmentally friendly, and intelligent management of oil and gas fields.

Contact acoustic nonlinearity and damping properties of completely free vibrating curved and delaminated CFRP plates

This study reports an experimental investigation on the effect of plate curvature and circular inter-ply delamination on the completely free vibration properties of carbon fiber reinforced polymer (CFRP) plates. Impact tests on wire suspended plates were performed and variations in measured natural frequencies and modal damping are reported. Delamination clapping type contact acoustic nonlinearity (CAN) was observed in the presence of a series of higher harmonics in the vibration spectrum. These higher harmonics consisted of five to seven equally distanced peaks grouped in a frequency range of around the double of odd mode M (5,0). Modal damping was highly sensitive to delamination which caused a 33% average increase in damping loss factors. Odd mode M (3,0) was the most affected with an average increase of 51% in damping loss factor, explained by the delamination location relative to the wavelength of that mode shape. Natural frequencies were less sensitive to delamination, on average decreasing 1.0% in flat plates and increasing 3.1% in curved plates. The modal frequencies calculated from embedded fiber optic sensor (FOS) or electrical resistance strain gauge signals agreed with finite element modeling (FEM) to within a 3.1% difference for all modes considered. The damping loss factors deducted from suspended plate tests were consistent with complementary results from dynamic mechanical analysis (DMA). The measured delamination clapping CAN and its related influence on damping can serve to enhance structural health monitoring (SHM) methods.

Wavelet denoising of fiber optic monitoring signals in permafrost regions.

To address the noise issue in fiber optic monitoring signals in frozen soil areas, this study employs wavelet denoising techniques to process the fiber optic signals. Since existing parameter choices for wavelets are typically based on conventional environments, selecting suitable parameters for frozen soil regions becomes crucial. In this work, an index library is constructed based on commonly used wavelet basis functions in civil engineering. An optimal wavelet basis function is objectively selected through specific criteria. Considering the characteristic of small root mean square error in fiber optic signals in frozen soil areas, a multi-index fusion approach is applied to determine the optimal decomposition level. Field observations validate that denoised signals, with parameters set appropriately, can more accurately identify locations where settlement occurs.

Distributed magnetic field sensor using nickel-acrylate composite coated silica optical fiber

This paper reports on the development and characterization of a distributed magnetic field sensor utilizing magnetostrictive material and the Rayleigh backscattered signal in optical fiber. The sensor consists of a single-mode fiber (SMF) coated with nickel-acrylate (Ni-Acryl) composite, fabricated using the conventional fiber drawing method. The surface morphology and the coating thickness of the Ni-Acryl were analysed using SEM. The sensor is capable of measuring variations in the strain of the fiber as the external magnetic field strength changes, with a spatial resolution of 20 cm. The experimental results revealed a linear relationship between magnetic field strength and Rayleigh frequency shift, with an average sensitivity of 22.85 MHz/mT. The magnetostrictive strain of the Ni-Acryl composite coated fiber is measured to be 36.4 με in response to the 245 mT applied magnetic field. This study highlights the potential of the developed sensor to be utilized in magnetic field sensing applications, particularly for remote sensing applications in a harsh environment.

Distributed Fiber Optic Vibration Signal Logging Well Production Fluid Profile Interpretation Method Research

Traditional logging methods need a lot of data support such as suction profile information, reservoir geological information, and production information of injection and extraction wells to calculate oil and gas production, which is a tedious and complicated process with low interpretation accuracy. Distributed fiber optic vibration signal logging is a technology that uses fiber optics to sense the vibration signals returned from different formations or well walls to analyze the surrounding formation characteristics or downhole events, which has the advantages of strong real-time monitoring results and high reliability of interpretation results. However, the currently distributed fiber optic vibration signal logging also fails to fully utilize the technical advantages to form a systematic production calculation process. Therefore, this paper proposes to use the K-means++ algorithm to divide the vibration signal frequency bands to represent different downhole events and use the amplitude mean curve envelope area of the reservoir-related frequency bands to calculate the relative production of each production formation. The experimental results correspond well with the relative water absorption data interpreted by conventional production logging, and the accuracy of production interpretation is high, which fills the gap of a production calculation method in the field of distributed fiber optic vibration signal logging in China and strongly promotes the development of the intelligent construction of oil and gas fields.

Research on denoising the temperature monitoring signal of a distributed optical fiber downhole cable

Aiming at the noise mixed in the downhole cable skin temperature signal measured by the BOTDR distributed fiber optic sensing temperature measurement system, a new improved wavelet threshold function is proposed, and denoising experiments are completed based on the MATLAB platform for the measured cable skin temperature signal. The denoising results of several threshold function methods are compared and analyzed from the aspects of graphical intuitive comparison as well as quantitative evaluation indicators. The results show that the improved threshold function denoising fiber optic temperature monitoring signal has a higher SNR and lower RMSE, there is no obvious distortion phenomenon, and the denoising performance is significantly improved compared with the traditional threshold function, which has a high application value.

Recognition of Fiber Optic Vibration Signals Based on Laplace Wavelet Transform and Deep Learning

AbstractConvolutional neural networks possess the capability of feature learning and nonlinear mapping, which has significant advantages in classifying and recognizing optical fiber vibration signals. In order to further enhance the recognition rate of vibration signals, this paper combines wavelet transform with convolutional neural networks and designs a convolutional layer based on parameterized wavelets. In this layer, the initial signal is convolved with parameterized Laplace wavelet dictionaries to complete the wavelet transform. Such customized filters make more sense than filters with randomly initialized parameters for traditional CNNs. Simultaneously, we introduce the channel attention mechanism to enhance the features of the filtered signals. Subsequently, standard CNNs are employed to extract and process signal features, ultimately utilizing a multi‐layer perceptron for recognition and classification. The experimental results show that the network model possesses better recognition refinement. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Fiber optic vibration signal recognition based on IMFCC and CDET (July 2024)

Fiber optic vibration signal recognition based on IMFCC and CDET (July 2024)

Discovery of asymmetric distribution of fine particles in fluidization using signal deflection reconstruction measurement

Bubbles in fluidization contribute to efficient gas–solid interaction and excellent transfer behavior. The fines addition effectively improves fluidization quality by prioritizing bubble control. Our study delves into the dynamic behavior of these added fines through cold-flow fluidization experiments, employing coarse glass particle (Geldart Group B, 186 μm in diameter) with varying contents of fines (Geldart Group A, 60 μm in diameter). To overcome the challenge of measuring the solid volume fractions of individual components in this bi-disperse system, a novel measurement technology, coupled with an optical fiber probe and signal deflection reconstruction method, is established. This technology could sensitively capture the preferential entry of fines into bubbles, leading to an asymmetric distribution of fines between bubbles and emulsion phase. Furthermore, our proposed flux mode, considering both the capability to maintain mono-disperse phase segregation and the competitive occupation of bi-disperse particles through bubble interface, effectively describes the observed asymmetry.

TFF-CNN: Distributed optical fiber sensing intrusion detection framework based on two-dimensional multi-features

Distributed fiber optic vibration sensing system has been widely used in safety monitoring with distinct advantages, and the feature extraction and classification methods of fiber optic signals directly determine the real-time performance and reliability of the monitoring system. The existing research feature extraction methods are unidimensional and time consuming, which cannot balance the goals of high accuracy and low time consumption for safety monitoring systems. In this paper, we propose an efficient recognition framework for fusing signal time–frequency features, called TFF-CNN, based on Gramian Angular Difference Fields(GADF) and FFT co-generation matrix(FFTT) to manually extract two-dimensional time–frequency image features of fiber optic signals, taking the significant advantages of CNN in image processing and combining a two-channel model and a fusion module that simulates human decision-making behavior. The accuracy of TFF-CNN is 99.30 % and the detection response time is only 0.6 s. Compared with other methods in this field, TFF-CNN has the advantages of low false alarm rate and short time consumption, which is more suitable for deployment in security monitoring field with distributed fiber optic sensing system.Index Terms: Distributed optical fiber vibration sensing system(DVS), Two-dimensional multi-feature, CNN, intrusion detection, real-time monitoring.

Pipeline Threat Event Identification Based on GAF of Distributed Fiber Optic Signals

Pipeline Threat Event Identification Based on GAF of Distributed Fiber Optic Signals

Preliminary Test for 3D Surface Strain Measurement in the Tower and Foundation of Offshore Wind Turbines Using DOFS.

Subjected to the relentless impacts of typhoons and rough seas, offshore wind turbines' structures, particularly the tower, foundation, and blade, are at constant risk of damage. Full-field strain monitoring helps to discover potential structural defects, thereby reducing disasters caused by overall structural failure. This study introduces a novel method for assessing strain and temperature fields on these kinds of 3D surfaces of cylindrical structures. The method harnesses the capabilities of a high spatial resolution (0.65 mm) Optical Frequency Domain Reflectometer (OFDR)-based Distributed Optical Fiber Sensor (DOFS) in conjunction with a unique helical wiring layout. The core process begins with mapping the fiber optic path onto a plane corresponding to the unfolded cylinder. Fiber optic signals are then differentiated on this plane, deriving a two-dimensional strain distribution. The plane strain field is subsequently projected onto the 3D side of the cylinder. An experiment was carried out in which a 3.5 m long optical fiber was helically wound with a 10 mm pitch on the surface of a cantilever beam of a cylinder shell with a diameter of 36 mm and a length of 300 mm. The experiment collected about 5400 measurement points on the cylindrical surface of 340 cm2, approximately 15.9 measurement points per square centimeter. The reconstructed results successfully reveal the strain field of the pipe cantilever beam under bending and torsional loads, as well as the palm-shaped temperature field. This experimental validation of the method's efficacy lays the theoretical groundwork for its application to real wind turbines.