Variational Mode Decomposition-Informed Empirical Wavelet Transform for Electric Vibrator Noise Analysis

Electric vibrators find wide applications in reliability testing, waveform generation, and vibration simulation, making their noise characteristics a topic of significant interest. While Variational Mode Decomposition (VMD) and Empirical Wavelet Transform (EWT) offer valuable support for studying signal components, they also present certain limitations. This article integrates the strengths of both methods and proposes an enhanced approach that integrates VMD into the frequency band division principle of EWT. Initially, the method decomposes the signal using VMD, determining the mode count based on residuals, and subsequently employs EWT decomposition based on this information. This addresses mode aliasing issues in the original method while capitalizing on VMD’s adaptability. Feasibility was confirmed through simulation signals and ultimately applied to noise signals from vibrators. Experimental results demonstrate that the improved method not only resolves EWT frequency band division challenges but also effectively decomposes signal components compared to the VMD method.

ELECTROCHEMICAL NOISE ANALYSIS OF PURE ALUMINUM IN SODIUM CHLORIDE SOLUTION WITH WAVELET TRANSFORM TECHNIQUE

Wavelet transforms (WT) are proposed as an alternative tool to overcome the limitations of Fourier transforms (FFT) in the analysis of electrochemical noise (EN) data. The most relevant feature of this method of analysis is its capability of decomposing electrochemical noise records into different sets of wavelet coefficients, which contain information about the time scale characteristic of the associated corrosion event. In this context, the potential noise fluctuations during the free corrosion of pure aluminum in sodium chloride solution was recorded and analyzed with wavelet transform technique. The typical results showed that the EN signal is composed of distinct type of events, which can be classified according to their scales, i.e. their time constants. Meanwhile, the energy distribution plot (EDP) can be used as 'fingerprints' of EN signals and can be very useful for analyzing EN data in the future.

WAVELET TRANSFORM THRESHOLD NOISE REDUCTION METHODS IN THE OIL PIPELINE LEAKAGE MONITORING AND POSITIONING SYSTEM

This letter investigates the wavelet transform, as well as the principle and the method of the noise reduction based on wavelet transform, it chooses the threshold noise reduction, and discusses in detail the principles, features and design steps of the threshold method. Rigrsure, heursure, sqtwolog and minimization four kinds of threshold selection method are compared qualitatively, and quantitatively. The wavelet analysis toolbox of MATLAB helps to realize the computer simulation of the signal noise reduction. The graphics and calculated standard deviation of the various threshold noise reductions show that, when dealing with the actual pressure signal of the oil pipeline leakage, sqtwolog threshold selection method can effectively remove the noise. Aiming to the pressure signal of the oil pipeline leakage, the best choice is the wavelet threshold noise reduction with sqtwolog threshold. The leakage point is close to the actual position, with the relative error of less than 1%.

THE WAVELET TRANSFORM BASED PROCESSING MEANS FOR 1/f NOISE IN MILLIMETER WAVE FOCAL PLANE ARRAY

After briefly introducing the characteristics of 1/f noise in millimeter wave focalplane array detectors, the paper analyses the relation of wavelet transform and 1/f noise in detail, suggests the fashion of decorrelating 1/f noise using the wavelet transform and deduces the relative expressions. The results of computer simulation show good effectiveness.

Improving the Autoregressive Modeling Method in Random Noise Suppression of GPR Data Using Undecimated Discrete Wavelet Transform

Geophysics has played a significant and efficient role in studying geological structures over the past decades as the goal of geophysical data acquisition is to investigate underground phenomena with the highest possible level of accuracy. The ground penetrating radar (GPR) method is used as a nondestructive method to reveal shallow structures by beaming electromagnetic waves through the Earth and recording the received reflections, albeit inevitably, along with random noise. Various types of noise affect GPR data, among the most important of which are random noise resulting from arbitrary motions of particles during data acquisition. Random noise which exists always and at all frequencies, along with coherent noise, reduces the quality of GPR data and must be reduced as much as possible. Over the recent years, discrete wavelet transform has proved to be an efficient tool in signal processing, especially in image and signal compressing and noise suppression. It also allows for obtaining an accurate understanding of the signal properties. In this study, we have used the autoregression in both wavelet and f-x domains to suppress random noise in synthetic and real GPR data. Finally, we compare noise suppression in the two domains. Our results reveal that noise suppression is conducted more efficiently in the wavelet domain due to decomposing the signal into separate subbands and exclusively applying the method parameters in autoregression modeling for each subband.

Characteristics analysis of muzzle impulse noise based on wavelet energy spectrum

Because muzzle impulse noise could cause damage to or have an intluence on the operator, tiae ettecnve protecnve measures should be taken. Therefore, correct analysis of impulse noise characteristics is very significant. Considering the shortcomings of fast Fourier transform method （FFT） in analysis of muzzle impulse noise frequency characteristics, wavelet energy spectrum method is put forward. Based on specific experiment data, the frequency characteristics and spectral energy dis tribution can be obtained. The experiment results show that wavelet energy spectrum method is applicable in muzzle impulse noise characteristic analysis.

Recovery of Transient Signals in Noise by OptimalThresholding in Wavelet Domain

：研究用离散子波变换复原被加性高斯白噪声污染的瞬态信号．在子波域中提出了一种最佳门限方法，该方法涉及到对于波系数的假设检验，利用似然比、奈曼，皮尔逊准则和最小均方差设计该门限，计算机仿真证明，该方法在较低信噪比下复原信号的有效性．

Wavelet domain adaptive filtering algorithm for removing the seamless pipe noise contained in the magnetic flux leakage data

With the widespread application and fast development of gas and oil pipeline network in China, the pipeline inspection technology has been used more extensively. The magnetic flux leakage （MFL） method has established itself as the most widely used in-line inspection technique for the evaluation of gas and oil pipelines. The MFL data obtained from seamless pipeline inspection is usually contaminated by the seamless pipe noise （SPN）. SPN can in some cases completely mask MFL signals from certain type of defects, and therefore considerably reduces the detectability of the defect signals. In this paper, a new de-noising algorithm called wavelet domain adaptive filtering is proposed for removing the SPN contained in the MFL data. The new algorithm results from combining the wavelet transform with the adaptive filtering technique. Results from application of the proposed algorithm to the MFL data from field tests show that the proposed algorithm has good performance and considerably improves the detectability of the defect signals in the MFL data.

Denoising of an Image Using Discrete Stationary Wavelet Transform and Various Thresholding Techniques

Image denoising has remained a fundamental problem in the field of image processing. With Wavelet transforms, various algorithms for denoising in wavelet domain were introduced. Wavelets gave a superior performance in image denoising due to its properties such as multi-resolution. The problem of estimating an image that is corrupted by Additive White Gaussian Noise has been of interest for practical and theoretical reasons. Non-linear methods especially those based on wavelets have become popular due to its advantages over linear methods. Here I applied non-linear thresholding techniques in wavelet domain such as hard and soft thresholding, wavelet shrinkages such as Visu-shrink (non-adaptive) and SURE, Bayes and Normal Shrink (adaptive), using Discrete Stationary Wavelet Transform (DSWT) for different wavelets, at different levels, to denoise an image and determine the best one out of them. Performance of denoising algorithm is measured using quantitative performance measures such as Signal-to-Noise Ratio (SNR) and Mean Square Error (MSE) for various thresholding techniques.

Technology of signal de-noising and singularity elimination based on wavelet transform

Based on wavelet transform theory,a method for signal de-noising and singularity detection and elimination is proposed,which can reduce the noises and express local singularity.Each singularity can also be detected and located through the local modulus maxima of wavelet transform.Simulation experiments are conducted with MATLAB software.The experimental results demonstrate that the method proposed in this paper is effective and feasible.

Partial Discharge Source Classification and De-Noising in Rotating Machines Using Discrete Wavelet Transform and Directional Coupling Capacitor

This paper introduces a new method to separate PD1 from other disturbing signals present on the high voltage genera-tors and motors. The method is based on combination of a pattern classifier, the Discrete Wavelet Transform (DWT), to de-noise PD and Time-Of-Arrival method to separate PD sources. Furthermore, it will be shown that it can recognize PD sources including rotating machine’s internal and external discharge pulses (e.g. on the bus bar).

Research on mine noise sources analysis based on least squares wave-let transform

In order to determine the characteristics of noise source accurately, the noisedistribution at different frequencies was determined by taking the differences into accountbetween aerodynamic noises, mechanical noise, electrical noise in terms of in frequencyand intensity.Designed a least squares wavelet with high precision and special effects forstrong interference zone (multi-source noise), which is applicable to strong noise analysisproduced by underground mine, and obtained distribution of noise in different frequencyand achieves good results.According to the results of decomposition, the characteristicsof noise sources production can be more accurately determined, which lays a good foundationfor the follow-up focused and targeted noise control, and provides a new methodthat is greatly applicable for testing and analyzing noise control.

Wavelet diagnosis of ECG signals with kaiser based noise diminution

The evaluation of distortion diagnosis using Wavelet function for Electrocardiogram (ECG), Electroencephalogram (EEG) and Phonocardiography (PCG) is not novel. However, some of the technological and economic issues remain challenging. The work in this paper is focusing on the reduction of the noise interferences and analyzes different kinds of ECG signals. Furthermore, a physiological monitoring system with a programming model for the filtration of ECG is presented. Kaiser based Finite Impulse Response (FIR) filter is used for noise reduction and identification of R peaks based on Peak Detection Algorithm (PDA). Two approaches are implemented for detecting the R peaks;Amplitude Threshold Value (ATV) and Peak Prediction Technique (PPT). Daubechies wavelet transform is applied to analyze the ECG of driver under stress, arrhythmia and sudden cardiac arrest signals. From the obtained results, it was found that the PPT is an effective and efficient technique in detecting the R peaks compared to ATV.

Noise Detection in the Photogrammetric Images Using Wavelets

The paper presents the method of the valuation of random noise in the photogrammetric images, based on wavelets. The proposed method involves the analysis of the dynamics of the components of wavelet decomposition on several resolution levels. The hypothesis was made that the noise-free images are characterized by systematically growing variances of the single components with growing decomposition. This hypothesis was. studied on several dozen fragments of airborne images recorded both with a photogrammetric analogue camera and digital camera. For all the studied photos taken with a digital camera, the hypothesis of growing variances of details was confirmed. The images from an analogue camera had different dynamics of variance, and the cause was recognized as random noise, caused by the grains from of the photographs. Referring to earlier applications of wavelets to noise evaluation, the proposed method is characterized by smaller dependence upon the structure and texture of the image.

Single Channel Speech Enhancement by De-noising Using Stationary Wavelet Transform

A method of single channel speech enhancement is proposed by de-noising using stationary wavelet transform. The approach developed herein processes multi-resolution wavelet coefficients individually and then recovery signal is reconstructed. The time invariant characteristics of stationary wavelet transform is particularly useful in speech de-noising. Experimental results show that the proposed speech enhancement by de-noising algorithm is possible to achieve an excellent balance between suppresses noise effectively and preserves as many target characteristics of original signal as possible. This de-noising algorithm offers a superior performance to speech signal noise suppress.

New Wavelet Thresholding Algorithm in Dropping Ambient Noise from Underwater Acoustic Signals

Underwater Wireless Communication, largely dependent on the acoustic communication between the machines, is largely affected by various types of noise in the shallow and deep water. However ambient noise which is due to multiple sources (e.g. shipping, wind) and no one source dominates. Ambient noise masks the acoustic signal to a large extent. Hence today it has drawn the attention of the experts to reduce its effect on the received signal. This paper discusses ambient noise problem and devises a new wavelet thresholding method to reduce its effect. Afterwards a comparative study on statistical parameters is shown to prove the efficiency of the devised method.

Coefficients de-noising with wavelet transform for magnetic flux leakage data obtained from oil pipeline

This paper considers the problem of noise cancellation for the magnetic flux leakage (MFL) data obtained from the inspection of oil pipelines. MFL data is contaminated by various sources of noise, and the noise can considerably reduce the detectability of flaw signals in MFL data. This paper presents a new de-noising approach for removing the system noise contained in the MFL data by using the coefficients de-noising with wavelet transform. Experimental results are presented to demonstrate the advantages of this de-noising approach over the conventional wavelet de-noising method.

基于SVC和wavelet-transform的图像脉冲噪声自适应新滤波器

利用小波变换可以检测信号奇异点的原理,提出了一种基于WT的脉冲噪声检测方法,并把这一方法与支持向量分类器SVC脉冲噪声检测方法相结合,提出了一种改进的SVC图像脉冲噪声滤波器。实验表明,这一改进的SVC脉冲噪声滤波器的滤波效果比原先的SVC滤波器有明显的改善。

High-resolution Rayleigh wave phase velocity maps from ambient noise tomography in North China

We presented high-resolution Rayleigh wave phase velocity maps at periods ranging from 5 s to 30 s in the northeast part of the North China Craton (NNCC). Continuous time-series of vertical component between October 2006 and December 2008, recorded by 187 broadband stations temporarily deployed in the NNCC region, have been cross-correlated to obtain estimated fundamental mode Rayleigh wave Green’s functions. Using the frequency and time analysis technique based on continuous wavelet transformation, we measured 3 667 Rayleigh wave phase velocity dispersion curves. High-resolution phase velocity maps at periods of 5, 10, 20 and 30 s were reconstructed with grid size 0.25°× 0.25°, which reveal lateral heterogeneity of shear wave structure in the crust and upper mantle of NNCC. For periods shorter than 10 s, the phase velocity variations are well correlated with the principal geological units in the NNCC, with low-speed anomalies corresponding to the major sedimentary basins and high-speed anomalies coinciding with the main mountain ranges. Within the period range from 20 s to 30 s, high phase velocity observed in eastern NCC is coincident with the thin crust, whereas low phase velocities imaged in central NCC is correlated to the thick crust. However, the low-velocity anomaly in the Beijing-Tianjin-Tangshan region displayed in the 20 s and 30 s phase maps may be associated with fluids.

Study on Method of Removing Stripe Noise in CBERS-02 Image

Based on the project of land macroscopical monitoring by CBERS,a remote sensing image of Arongqi in Inner Mongolia was studied by different methods such as histogram matching,principal component analysis,moment matching,low-pass filter and wavelet transform.A qualitative analysis and quantitative assessment was also carried out.The results showed that wavelet transform could effectively remove stripe noise,and also kept its advantages in the details.Moment matching had a better strip removal,but it changed features in its spectrum easily and it was not fit for CBERS-02 image processing.Principal component analysis could not remove stripe noise,but also strengthened it in a certain extent.