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Comparison of filtering effects of two algorithms in terms of total harmonic distortion (THD) and signal-to-noise ratio (SNR).
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Adaptive filtering has the advantages of real-time processing, small computational complexity, and good adaptability and robustness. It has been widely used in communication, navigation, signal processing, optical fiber sensing, and other fields. In this paper, by adding an interferometer with the same parameters as the signal interferometer as the...
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Context 1
... THD and SNR vary with the signal, the average of 100 sets of THD and 100 sets of SNR is calculated to reflect the overall filtering performance in terms of THD and SNR. The difference before and after filtering is taken as the improvement extent, and the comparison of the filtering effects of the two algorithms is shown in Figure 6. See Tables A1 and A2 of Appendix A for detailed results. ...
Context 2
... Tables A1 and A2 of Appendix A for detailed results. It can be seen from Figure 6 that the overall performance of the adaptive filtering of the LMS algorithm and NLMS algorithm is good, but the filtering effect of the two algorithms on the harmonics within 50 Hz is poor. As the low-frequency signal has many harmonics, filtering out the harmonics and filtering out some of the effective signals results in an increase in THD. ...
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Citations
... To get the best estimation of the system noise, with an adaptive algorithm, the adaptive filtering technique modifies the filter parameters to complete the optimum noise cancellation result. It is appropriate for real-time processing because it requires a small calculation [6]. ...
... The conditions on the step size μ1'=1⁄μ1 of the linear part are similar to those of the NLMS algorithm using a fixed step size. So, the VSS-NLMS algorithm will converge when μ 1 meets the condition 0<μ1<2 [4,6,[24][25][26]. ...
... To get the best estimation of the system noise, with an adaptive algorithm, the adaptive filtering technique modifies the filter parameters to complete the optimum noise cancellation result. It is appropriate for real-time processing because it requires a small calculation [6]. ...
... The conditions on the step size μ1'=1⁄μ1 of the linear part are similar to those of the NLMS algorithm using a fixed step size. So, the VSS-NLMS algorithm will converge when μ 1 meets the condition 0<μ1<2 [4,6,[24][25][26]. ...
Noise cancellation remains a significant challenge in signal processing, particularly when addressing non-stationary and time-varying noise sources. Traditional approaches, such as the Normalized Least Mean Square (NLMS) algorithm, are often limited by the fixed step size parameter, which dictates the trade-off between convergence rate and system robustness. In this study, an innovative Variable Step Size NLMS (VSS-NLMS) algorithm is introduced, designed to dynamically adjust the step size parameter, thereby optimizing performance criteria including precision, robustness, convergence rate, and tracking ability. Employing system identification techniques within an adaptive filtering framework, this research advances the NLMS algorithm by incorporating a variable step size parameter that adapts in real-time to the noise environment. The proposed VSS-NLMS algorithm is evaluated through extensive simulations, demonstrating a significant enhancement in the balance between Mean Square Error (MSE) reduction and convergence rate over both the conventional NLMS and Recursive Least Squares (RLS) algorithms, whilst maintaining computational simplicity. In the context of adaptive filters, the VSS-NLSM algorithm represents a substantial improvement for noise cancellation applications, particularly in scenarios characterized by variable noise dynamics. The results presented herein confirm that the VSS-NLMS algorithm not only achieves a superior trade-off between accuracy/robustness and convergence rate/tracking but also sets a new benchmark for adaptive noise cancellation strategies in complex acoustic environments.
... Light in the wavelength range of the optical window of biological tissues can penetrate biological tissues at a greater depth [5,7]. However, the system performance (signal-to-noise ratio or sensitivity) can differ from the performance optimization of the two core components: the detection sensor and light source [8,9]. ...
Vein puncture is commonly used for blood sampling, and accurately locating the blood vessel is an important challenge in the field of diagnostic tests. Imaging systems based on near-infrared (NIR) light are widely used for accurate human vein puncture. In particular, segmentation of a region of interest using the obtained NIR image is an important field, and research for improving the image quality by removing noise and enhancing the image contrast is being widely conducted. In this paper, we propose an effective model in which the relative total variation (RTV) regularization algorithm and contrast-limited adaptive histogram equalization (CLAHE) are combined, whereby some major edge information can be better preserved. In our previous study, we developed a miniaturized NIR imaging system using light with a wavelength of 720–1100 nm. We evaluated the usefulness of the proposed algorithm by applying it to images acquired by the developed NIR imaging system. Compared with the conventional algorithm, when the proposed method was applied to the NIR image, the visual evaluation performance and quantitative evaluation performance were enhanced. In particular, when the proposed algorithm was applied, the coefficient of variation was improved by a factor of 15.77 compared with the basic image. The main advantages of our algorithm are the high noise reduction efficiency, which is beneficial for reducing the amount of undesirable information, and better contrast. In conclusion, the applicability and usefulness of the algorithm combining the RTV approach and CLAHE for NIR images were demonstrated, and the proposed model can achieve a high image quality.
... T HE 3×3 interrogation algorithm is a popular method to demodulate the phase signal in the optical fiber sensing system [1]- [5]. In particular, it has the unique advantages of possessing a high dynamic range and simple implementation architecture, making it well-suited to the interferometric optical fiber sensing system (e.g., optical fiber hydrophone system). ...
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... As for the situation where P = P 1 , the complex amplitudes of the three outputs can be obtained from (5), which is given by ⎡ ...
... The 3 × 3 demodulation algorithm is a common methods to demodulate the phase signal of the interferometric optical fiber sensing system [1][2][3][4][5], owning a great deal of advantages such as possessing a high dynamic range and a simple structure compared with the phase generated carrier (PGC) method. However, an effective 3 × 3 demodulation algorithm relies greatly on the strictly symmetric splitting ratio of the 3 × 3 coupler, which can hardly be guaranteed that can lead to the distortion of the retrieved phase signals and the deterioration of the demodulation algorithm [6], limiting the further application of it as a result. ...
The 3 × 3 demodulation algorithm has been widely used in retrieving the phase information in the optical fiber sensing system, while the asymmetry of the 3 × 3 coupler can introduce some distortions. In this situation, the parameters of the 3 × 3 coupler can be calibrated by the ellipse fitting method to remove the distortions. Conducting a frequency modulation on the laser, together with an unbalanced Michelson interferometer, makes the ellipse fitting method implemented, which is more appropriate for all-optical sensing. Unexpectedly, the auxiliary amplitude modulation of the laser induced by the frequency modulation is inevitable, leading to the deterioration of the calibration. In this paper, the influence of the auxiliary amplitude modulation on the calibration of the asymmetric parameters is analyzed theoretically and verified experimentally, on the basis of which a convenient but highly efficient method for acquiring the output of the laser and removing related items from the interferometric signals is put forward. The feasibility and robustness of the proposed solution is tested experimentally, and the results show that the mean square errors of the fittings and the variation coefficients of the calibrated parameter sequences are at the scale of 10−5 and 10−4, respectively, indicating that the method performs very well.
In the realm of acoustic signal detection, the identification of weak signals, particularly in the presence of negative signal-to-noise ratios, poses a significant challenge. This challenge is further heightened when signals are acquired through fiber-optic hydrophones, as these signals often lack physical significance and resist clear systematic modeling. Conventional processing methods, e.g., low-pass filter (LPF), require a thorough understanding of the effective signal bandwidth for noise reduction, and may introduce undesirable time lags. This paper introduces an innovative feedback control method with dual Kalman filters for the demodulation of phase signals with noises in fiber-optic hydrophones. A mathematical model of the closed-loop system is established to guide the design of the feedback control, aiming to achieve a balance with the input phase signal. The dual Kalman filters are instrumental in mitigating the effects of signal noise, observation noise, and control execution noise, thereby enabling precise estimation for the input phase signals. The effectiveness of this feedback control method is demonstrated through examples, showcasing the restoration of low-noise signals, negative signal-to-noise ratio signals, and multi-frequency signals. This research contributes to the technical advancement of high-performance devices, including fiber-optic hydrophones and phase-locked amplifiers.
The temperature resolution of Fiber Optic Sensor is greatly limited by background noise. In this paper, we propose a noise suppression method, based on variational mode decomposition (VMD), to enhance the temperature resolution. The VMD algorithm is employed to decompose the original signal into several intrinsic mode function (IMF) components, each limited by a certain bandwidth. We calculate the correlation coefficient between each IMF component and the original sensing signal, and filter out IMFs with correlation coefficients less than the average, which primarily contain noise signal. The IMFs with correlation coefficients greater than the average are retained for signal reconstruction. The experimental and comparative results demonstrate the exceptional performance of the proposed algorithm in denoising temperature signals, effectively removing background and environmental noise while preserving temperature information. Furthermore, by analyzing the power spectrum of the temperature signal before and after noise suppression, we observe that the proposed algorithm can achieve a temperature resolution of 10
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°C, which is two orders of magnitude higher than that before noise suppression (10
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°C). These results validate the efficacy of our method in reducing system noise and improving the accuracy and reliability of interferometric temperature sensors.
Percussion-based inspection of structures has attracted widespread attention in recent years. However, the percussion acoustic signals collected in the marine environment usually have a low signal-to-noise ratio (SNR) and are difficult to use directly due to the interference by a multitude of marine noises. The frequency contents of the ambient noises usually overlap with those of the percussion acoustic signals, thus limiting the denoising using traditional methods. This paper proposes a denoising method using the least mean square (LMS) algorithm to obtain the approximate percussion signal. The noisy percussion signals and marine noise are recorded synchronously by two hydrophones. Then the LMS algorithm processes the collected signals and provides the frequency peaks that cannot be extracted with conventional methods. The proposed method is validated by experiments conducted in a noiseless laboratory environment and a noisy, naturally occurring marine environment. The results reveal that the proposed method is excellent in denoising the raw signal, and the error is about 3% in terms of differences in the estimated value of the primary peak frequency. This study demonstrates the broad potential for the method to be applied toward damage detection for underwater structures.
The phase-generated-carrier (PGC) method is one of the popular interrogation methods for the interferometric fiber-optic sensor systems. Among varieties of specifications, the noise characteristic is a key one that determines the minimum detectable signal. However, the mechanism of the noise transfer process in the PGC-based interferometric fiber-optic sensor has not been reported to the best knowledge. In this paper, the influences of three different types of noise sources on the noise floor of the PGC-based interferometric fiber-optic sensor system are studied systematically. On the basis of that, the phase noise transfer model is built and verified experimentally. The results depict that the theoretical model can well predict the experimental result. With the proposed theoretical model, the prediction and analysis of the output phase noise in the PGC-based interferometric fiber-optic sensor is available. In addition, the proposed model provides a potential support for locking the initial phases to suppress the common-mode noise in the PGC-based system. In this sense, the proposed model is a significant tool for the noise-target design and construction of the PGC-based in terferometric fiber-optic sensor system.
