Wavelet Modulus Maxima Research Articles

Research on SNR enhancement method of underwater airgun impulse signal

Abstract Seismic airgun blasting is nowadays widely employed for ocean exploration and marine geophysical study. Being powerful acoustic source showing excellent repeatability and consistency, it’s potentially applicable for long-distance underwater acoustic communication. Given the sound generating mechanism of airguns, the generated signals cannot be modulated compared with sources signals that are more commonly used for communications purposes. Thus the use of time delay shift coding system, which is both necessary and advantageous under such circumstances. The detection of the impulsive signal from airgun blasting and accurate estimation of its arrival time at the receiving end is paramount for carrying out a time delay shift coding communication. Consequentially, augmentation of the signal-to-noise ratio (SNR) of received signal through enhancement is the most direct and effective approach. This manuscript presents an enhancement method of the airgun blasting signals: A recursive wavelet modulus maxima denoising method is introduced, by separating the raw signals into noise components and signal components iteratively, details information that is erroneously erased by conventional wavelet modulus maxima denoising method can thus been preserved. Coupled with the proposed use of a loop coefficient of determination, automatic balancing between denoising effectiveness and distortion of valid signals is acrhieved.

An Information Recognition and Time Extraction Method of Tracking a Flying Target with a Sky Screen Sensor Based on Wavelet Modulus Maxima Theory

Aiming at the problems of big noise, lots of false targets, and accurate time extraction while tracking a flying target in the signal from a sky screen sensor, a flying target recognition and time extraction method is proposed, based on wavelet transformation. The noisy signal output by the sky screen sensor is filtered with wavelet transformation to filter out some high-frequency components; the filter is designed to handle the signal time frequency characteristics of the flying target and noise. To improve the recognition efficiency of whether the signal includes tracking of the flight target, based on a two-class discriminant model, the wavelet Fisher discriminant method is used to construct the feature vector of the false target and the flying target signal, and the recognition method of the flying target signal is studied. According to the wavelet modulus maxima theory, the single target signal is isolated, and the time moment of the flying target passing through the detection screen is calculated. The velocities calculated based on the flying target signal recognition method proposed in this paper and based on the least-mean-squares algorithm of the traditional sky screen sensor velocity measurement system are compared with the net target velocity measurement system. The results show that the velocity data obtained by the method in this paper are closer to the true value of the target flight velocity, and the average error between the velocity value obtained by the method in this paper and the standard net target velocity measurement system is less than 0.954 m/s, which verifies the superiority of the method proposed in this paper.

Improved wavelet modulus maximum method for distributed optical fiber temperature sensing

The temperature measurement accuracy of the Raman distributed optical fiber temperature sensing system is an important metric. Aimed at the reduction of the temperature measurement error, which is caused by the noises in the sensor system, an improved wavelet modulus maximum method, which combine the 3sigm criterion with the traditional wavelet modulus maximum algorithm is proposed. In this proposed algorithm, the 3sigm value is used as the threshold for the highest decomposition layer of the wavelet modulus maximum method to judge whether it is a signal or noise, instead of using the ratio of the modulus maximum of the highest decomposition level to the number of decomposition levels to determine the threshold. We conduct distributed temperature measurement on a 10-km long multimode fiber, and analyze the experimental data by using the improved algorithm. The results show that the temperature measurement fluctuation obtained by improved method is 0.497°C on average. It is better than the 1.13°C that is obtained by the traditional wavelet modulus maximum algorithm. Compared with the traditional algorithms, the 3sigm criterion algorithm has improved the temperature measurement accuracy significantly. The improved algorithm provides a new idea for the selection of the threshold value, which makes the threshold selection more statistically.

Single-phase grounding fault line selection method based on dynamic time warping algorithm

Aiming at the problems of the complex line selection process and slow line selection speed of the existing intelligent classification line selection methods, a single-phase grounding fault line selection method based on the dynamic time warping (DTW) algorithm is proposed. The wavelet modulus maximum truncates the original signal to extract the temporary part. Then the DTW distance matrix between feeders is figured by the DTW algorithm. The fault line selection is realized by comparing the difference between the maximum, sub maximum, and minimum values with the threshold value. The simulation shows that the proposed approach can recognize proper line selection under various fault states and partial loss of recorded data, has high accuracy and has better robustness than the Euclidean distance and person correlation coefficient methods.

Harmonic noise cancellation of magnetic resonance sounding signal based on wavelet modulus maximum

Magnetic resonance sounding (MRS) signals are often severely distorted by noise, primarily the power-line harmonics which originates from anthropogenic interferences. Noise cancellation is a particularly important issue for the application of the MRS method. In order to reduce the harmonic peaks as well as the noise floor between the 50 Hz harmonics effectively, a method based on the wavelet modulus maximum is proposed. The method is based on marking the local modulus maximum points in the wavelet transform coefficients and removed the modulus maximum contributed by noise for MRS signal reconstruction. The optimum wavelet basis for noise cancelling is determined through simulations with synthetic signals. The accuracy and stability of the proposed method are investigated using synthetic signals embedded in both artificial and field-recorded noise. In addition, the results of the test on field data show the proposed method can effectively improve the fitting accuracy of the signal parameters and obtain stable inversion results.

Seismic Signal Denoising Based on Adaptive Wavelet Modulus Maximum Method

Seismic noise suppression plays an important role in seismic data processing and interpretation. Aiming at remedying the problem of low quality of seismic data acquired by a seismometer, a novel denoising method based on wavelet maximum modulus and an adaptive threshold is designed. This adaptive wavelet maximum modulus (ATWMM) seismic signal noise reduction method is using the opposite polarity of the Lipschitz index with seismic signal and noise to extract the seismic signal of original data. Setting adaptive thresholding function related to wavelet decomposition scale to solve the problem of effective signal losing on a small decomposition scale. The experimental results show that the ATWMM method can extract more seismic signal from the noisy seismic data. Using RMSE and SNR evaluation, the synthetic Ricker seismic dataset experimental results show that the indexes are 0.0502 and 20.1617 dB. Compared with the wavelet modulus maximum (WMM) method, it has a 25.7% reduction and 14.6% increase. The real-field seismic data came from the JZW51 seismic monitoring station in China experimental results indicate that the proposed ATWMM method is effective in seismic signal denoising with SNR above 30.1855 dB of approximately 15.8% enhancement compared with the WMM method, that has improvement for quality of seismic data.

Scorecard Model-Based Motion Stability Evaluation of Manipulators for Robot Training

Motion stability is vital for the industrial application of robotic manipulators; therefore, its evaluation becomes an important task for robot training. This paper proposes an evaluation system based on the scorecard model to evaluate the stability performance of manipulators. Firstly, the wavelet modulus maximum filtering method is employed to divide the vibration signal into structural vibration components and motion-generated vibration components. Secondly, the evaluation index (EI) is designed based on the processed data to judge whether the vibration signal of the moving process is stable. Finally, the scorecard model is proposed to evaluate the stability of vibration samples, including the process of attribute extraction, binning, WOE coding, feature selection, logistic regression, and score transformation. Additionally, an endpoint localization method is employed to segment the vibration signal of a continuously moving manipulator into single-step motion segments, making it possible to score the motion subprocesses. Experiments are conducted on physical robotic arms to verify the effectiveness of the proposed scheme, and the results show that the model can reasonably score the motion stability of robotic arms for robot training.

Magnetic anomaly of long track detection method based on wavelet combining with fractal for high speed maglev transit

A scheme of detecting magnetic field anomaly is proposed to find local short circuit faults in long stator cores of high speed maglev transit. And a composite magnetic anomaly signal processing method is designed based on wavelet with fractal, screening out a little anomalous data from massive data. First, suspicious data are quickly recognized by applying multiple fractal spectra, then being confirmed by wavelet modulus maxima calculation. The experiment results show that the method is immune to the fluctuation of 8–12 mm suspension gap and its hit rate can be improved by more than 66%.

Multi-Scale Edge Detection of Crack in Extra-High Arch Dam Based on Orthogonal Wavelet Construction

This paper conducts a research on the wavelet construction and application of image edge detection. Taking the image edge detection algorithm based on wavelet modulus maxima as the research subject, this paper discusses the problem of dislocation phenomenon, threshold selection, multi-scale edge fusion and evaluation criterion in the algorithm, and proposes an improved self-adaptive hierarchical threshold algorithm based on information amount and vanishing moment. From the angle of wavelet symmetry, filter composition and vanishing moment, the influence of wavelet property on image edge detection is studied, and the construction requirements of wavelet filter banks for image edge detection are proposed. The constant-length compactly supported biorthogonal wavelet parameterized construction method is used to construct the biorthogonal wavelet featuring odd symmetry for high-pass decomposition filter and unidirectionality for filter sequence, and the improved algorithm of wavelet construction and threshold is applied to the simulation of image edge detection of concrete cracks in ultra-high arch dam.

An Improved Wavelet Modulus Algorithm Based on Fusion of Light Intensity and Degree of Polarization

Edge detection is the basis of image analysis and image processing. The wavelet modulus maxima algorithm is a widely used edge-detection algorithm. The algorithm has the advantages of strong anti-noise ability and high precision of edge location, but it still cannot accurately obtain edge information for low-contrast images. Therefore, this paper proposes an improved wavelet mode maximum edge algorithm for the fusion of light intensity and degree of polarization. The improved wavelet mode maximum algorithm was used to extract the edges of a light intensity image and degree of polarization image, and then refine and fuse the two edges to obtain the final edge information. Simulation experiments showed that the edge image obtained by the edge-detection algorithm in this paper had a clearer outline and better connectivity.

Combined evaluation of Young modulus and fracture toughness in small specimens of fine grained nuclear graphite using 3D image analysis

The fracture toughness of the fine-grained nuclear graphite SNG742 has been investigated by observation of stable crack propagation in double cleavage drilled compression specimens. The three-dimensional displacement fields were obtained by digital volume correlation (DVC) of in situ laboratory X-ray computed tomographs. The crack tip location and crack opening displacements were determined using an image edge detection algorithm based on the wavelet modulus maxima. The Young modulus was estimated by fitting a finite element model to DVC displacement field data measured before crack initiation. Using the 3D crack geometry and the surrounding full-field 3D displacement fields as boundary conditions, the elastic strain energy release rate J and the three-dimensional stress intensity factors KI to KIII were then evaluated via the contour integral method. Constant mode I critical stress intensity factor was obtained along the curved crack fronts, with negligible shearing modes. This method allows evaluation of the fracture toughness without prior knowledge of elastic properties, and has potential applications to assess the effects of high temperature, oxidation and irradiation in small specimens of nuclear graphite.

Wavelet packet coefficient of transient first half wave line selection method for single-phase grounding fault

Overvoltage will occur after single-phase grounding fault in distribution network, endangering system insulation. It is of great significance to find out the fault line as soon as possible to ensure the safe operation of the system. Wavelet modulus maxima can be used to detect the sudden change singularity of transient current. However, the judgement of single mode maximum value is easily influenced by the form of fault and interference, leading to misjudgement. In this paper, the transient characteristics of single-phase grounding fault are analyzed, and the magnitude and phase characteristics of the first half wave are given; Based on wavelet packet algorithm, several key problems such as boundary effect, wavelet selection and decomposition scale are explored; Taking the zero sequence voltage as the reference to determine the fault interval, the zero sequence current of each line is decomposed by three-scale wavelet packet using db6 wavelet, and the wavelet coefficients of the characteristic frequency band are extracted for comparison. The fault line is the one with the largest amplitude and different phase from others. The distribution system model of neutral grounding through arc suppression coil is built. Simulation is carried out for several cases with great difficulty in line selection, such as short circuit when phase voltage crosses zero, high resistance grounding and long line fault. The experimental results prove the reliability of this method.

Influence of wavelet characteristics on single-phase grounding fault line selection

It is difficult to select the fault line in distribution network because of the small single-phase grounding fault current. Wavelet transform is especially suitable for fault transient analysis because of its unique time-frequency localization performance. However, the characteristics of different wavelets have a direct impact on the line selection results. Improper selection of wavelets will directly lead to misjudgment. Firstly, the transient characteristics of single-phase grounding fault and the relationship between wavelet transform modulus maxima and singularity are analysed, and the line selection criterion of wavelet modulus maxima is given. Then, the influence of wavelet vanishing moment order, support length, symmetry, and orthogonality on the detection effect of signal singularity is analysed, and four principles for selecting wavelet in fault line selection are proposed. The larger the order of vanishing moment and support length, the more conducive to detection. The influence of vanishing moment order is much greater than that of support length. The 10kV system simulation model is built, five representative wavelets are selected to decompose the zero- sequence current of the fault line in four scales respectively, and the different modulus maxima corresponding to the sudden change point are compared. The experimental results prove the reliability of the wavelet selection principles proposed in this paper.

Projectile Explosion Position Parameters Data Fusion Calculation and Measurement Method Based on Distributed Multi-Acoustic Sensor Arrays

In conventional weapon test field, the parameter of projectile explosion position is an important index to measure the control ability and damage efficiency of fuze. Due to the random deviation of gun firing and the difference of detection performance of fuze itself, the projectile explosion position is uncertain and relatively scattered in terminal ballistic, which is easy to miss the projectile explosion information. In order to obtain the parameters of projectile explosion position in large area of in terminal ballistic, this paper constructs a projectile explosion position testing system with distributed multiple acoustic sensor basic arrays, establishes the calculation model of projectile explosion position by a basic array, researches a multi-source data fusion algorithm based on the measurement data of multiple array acoustic basic arrays and gets real projectile explosion coordinates. To improve the measurement accuracy, we use the wavelet transform filtering method to filter the output signal of the acoustic sensors, and propose the time extraction algorithm of the projectile explosion acoustic signal with the wavelet modulus maximum. Through experiment and analysis, we collect the sound signal of the actual projectile explosion and verify the rationality and correctness of the proposed calculation model and algorithm.

Design of optical system of photoelectric detection sensor and flare signal processing with array lens

AbstractBecause the flare signal formed by projectile explosion is affected by the external environment, it is difficult for multi‐acoustic sensor array to obtain accurate synchronization signal. In order to solve this problem, this paper proposes a design method of ultra‐wide‐angle photoelectric detection sensor by using circular curved surface array lens, analyze the detection ability of photoelectric detection sensor with array lens, and establish the output signal function calculation model. According to the output signal of flare detection circuit, the feature points detection algorithm of modulus maxima based on wavelet transform is studied. Based on this algorithm, the signal is processed on the hardware processor to output the actual flare trigger control signal. The processing and recognition algorithm of signal mainly uses the relationship among wavelet modulus maxima, scale, Lipschitz index, and signal singularity to select the appropriate wavelet basis to process the flare signal formed by projectile explosion. Thus, the feature points of the flare signal singularity can be obtained. When the detection conditions of the feature points of the signal singularity are satisfied, the hardware outputs pulse signals to control the acoustic sensor array. The experimental results show that the designed detection algorithm of wavelet filtering and modulus maxima feature points can effectively detect the flare signal.

Research on an Lost Circulation Zone Location Method Based on Transient Pressure Wave.

Accurately identifying the location of loss zone after lost circulation is the key to subsequent plugging operation. In view of the difficulty of identifying the location of lost circulation zone, a method of identifying the location of loss zone by transient pressure wave signal is proposed. When lost circulation occurs, transient back pressure is applied to the wellhead at the surface choke manifold to produce transient pressure wave. The transient pressure wave propagates downward from the wellhead. The propagation process of transient pressure wave in an annulus system is analyzed, and the position of loss zone is determined according to the change of pressure signal at the choke manifold. Based on the simulation of this method, relevant experiments are also carried out. Aiming at the problem of excessive noise of the pressure wave signal collected in the experiment, variational modal decomposition (VMD) is used to decompose the signal into multiple band-limited intrinsic mode function (BIMF) components. Combined with a Hilbert spectrum, the time–frequency characteristics and energy distribution of each BIMF component are analyzed in turn. The main frequency component is selected to reconstruct the signal to achieve the denoising effect. On this basis, a wavelet modulus maxima method is used to decompose the denoised signal, extract the characteristic points of the signal, identify the loss circulation information in the signal, and then identify the thief zone position by a time–domain method. Through experimental verification, the existence of loss zone will affect the change trend of pressure wave; a VMD–wavelet modulus maxima algorithm can effectively remove the noise of the pressure wave signal and locate the pressure change point. The experimental recognition error range of this method is 0.10–9.22%, which has certain guiding significance for field application.

Fault location method based on edge detection for low SNR traveling waves

Accurate fault location methods can greatly reduce the power recovery time, which has an important effect on the power supply reliability. Fault location methods have been extensively studied; however, location for high impedance faults and faults with near-0° fault angles or low signal-to-noise ratio (SNR) transient waves remain challenging. In this paper, a fault location method based on edge detection is proposed. This approach extracts fault features using the Canny operator and determines the fault inception time using the Otsu threshold. The algorithm is described using the noise-free and noisy fault waveforms as examples. The parameters in the algorithm can be adaptively adjusted according to the frequency and SNR of the fault waveforms. A 500 kV AC transmission line was built to compare the performance of the proposed method with that of the fault location method based on the wavelet modulus maximum under different conditions of fault resistance, fault position and fault angle. The results show that the proposed method not only has a better location accuracy under typical fault conditions, but is also more robust for faults with low SNR traveling waves such as those with high fault resistances and near-0° fault angles.

ECG signal denoising using a novel approach of adaptive filters for real-time processing

Electrocardiogram (ECG) is considered as the main signal that can be used to diagnose different kinds of diseases related to human heart. During the recording process, it is usually contaminated with different kinds of noise which includes power-line interference, baseline wandering and muscle contraction. In order to clean the ECG signal, several noise removal techniques have been used such as adaptive filters, empirical mode decomposition, Hilbert-Huang transform, wavelet-based algorithm, discrete wavelet transforms, modulus maxima of wavelet transform, patch based method, and many more. Unfortunately, all the presented methods cannot be used for online processing since it takes long time to clean the ECG signal. The current research presents a unique method for ECG denoising using a novel approach of adaptive filters. The suggested method was tested by using a simulated signal using MATLAB software under different scenarios. Instead of using a reference signal for ECG signal denoising, the presented model uses a unite delay and the primary ECG signal itself. Least mean square (LMS), normalized least mean square (NLMS), and Leaky LMS were used as adaptation algorithms in this paper.

Changes in long-term properties and natural cycles of the Danube river level and flow induced by damming

In this paper we assessed changes in scaling properties of the river Danube level and flow data, associated with building of Djerdap/Iron Gates hydrological power plants positioned on the border of Romania and Serbia. We used detrended fluctuation analysis (DFA), wavelet transform spectral analysis (WTS) and wavelet modulus maxima method (WTMM) to investigate time series of measurements from hydrological stations in the vicinity of dams and in the area of up to 480 km upstream from dams, and time series of simulated NOAA-CIRES 20th Century Global Reanalysis precipitation data for the Djerdap/Iron Gates region. By comparing river dynamics during the periods before and after construction of dams, we were able to register changes in scaling that are different for recordings from upstream and from downstream (from dams) areas. We found that damming caused appearance of human-made or enhancement of natural cycles in the small time scales region, which largely influenced the change in temporal scaling in downstream recording stations. We additionally found disappearance or decline in the amplitude of large-time-scale cycles as a result of damming, which changed the dynamics of upstream data. The most prominent finding of our paper is a demonstration of a complete or partial loss of annual cycles in the upstream stations’ data that stems from the operation of the artificial water reservoir and extends as far as 220 km from dams. We discussed probable sources of such found changes in scaling, aiming to provide explanations that could be of use in future environmental assessments.

Evolutionary intelligence for breast lesion detection in ultrasound images: A wavelet modulus maxima and SVM based approach

Intelligent lesion detection system for medical ultrasound images are aimed at reducing physicians’ effort during cancer diagnosis process. Automatic separation and classification of tumours in ultrasound images is challenging owing to the low contra