Maximum Wavelet Coefficients Research Articles

Research on lightning strike location by OPGW optical sensing based on extreme sliding window and wavelet

Abstract Lightning strikes usually affect the safe use and normal operation of the power grid. It is crucial to accurately identify and position the lightning strikes. The lightning current on OPGW near the lightning strike point and the modulated lightning intensity signal waveform were simulated. The polarization state signal of Faraday optical fiber will mutate when it is struck by lightning. The extreme value unit sliding window energy method was used to extract the initial time window of the signal after a lightning strike, and the three alternative windows with the maximum energy were determined. Then the maximum wavelet coefficient is determined to be the first wave time point of the struck signal. Simulation analysis and engineering field application show that the optical polarization signal characteristic of OPGW can be used to accurately locate the lightning strike point, which can meet the needs of practical engineering applications of power systems.

Terahertz Liquid Biosensor Based on A Graphene Metasurface for Ultrasensitive Detection with A Quasi-Bound State in the Continuum.

The concept of a quasi-bound state in a continuum (QBIC) has garnered significant attention in various fields such as sensing, communication, and optical switching. Within metasurfaces, QBICs offer a reliable platform that enables sensing capabilities through potent interactions between local electric fields and matter. Herein, a novel terahertz (THz) biosensor based on the integration of QBIC with graphene is reported, which enables multidimensional detection. The proposed biosensor is distinctive because of its ability to discern concentrations of ethanol and N-methylpyrrolidone in a wide range from 100% to 0%, by monitoring the changes in the resonance intensity and maximum wavelet coefficient. This approach demonstrates an excellent linear fit, which ensures robust quantitative analysis. The remarkable sensitivity of our biosensor enables it to detect minute changes in low-concentration solutions, with the lowest detection concentration value (LDCV) of 0.21pgmL-1 . 2D wavelet coefficient intensity cards are effectively constructed through continuous wavelet transforms, which presents a more accurate approach for determining the concentration of the solution. Ultimately, the novel sensing platform offers a host of advantages, including heightened sensitivity and reusability. This pioneering approach establishes a new avenue for liquid-based terahertz biosensing.

A ‘shape-orientated’ algorithm employing an adapted Marr wavelet and shape matching index improves the performance of continuous wavelet transform for chromatographic peak detection and quantification

A new ‘shape-orientated’ continuous wavelet transform (CWT)-based algorithm employing an adapted Marr wavelet (AMW) with a shape matching index (SMI), defined as peak height normalized wavelet coefficient (C′max/H) for feature filtering, was developed for chromatographic peak detection and quantification. Exploiting the chromatographic profile of a candidate peak, AMW-SMI algorithm emphasizes more on the matching of the overall chromatographic profile to a reference Gaussian shape, while partly alleviates the requirement on the signal intensity derived from single or several data points, thus it allows the detection of low-intensity features from metabolites at low abundance. AMW-SMI imposes maximum and minimum thresholds on the ridgeline width and length to define a valid ridgeline, which corresponds to a more stably shaped chromatographic profile. The maximum wavelet coefficient Cmax′(a0,b0) on the valid ridgeline determines the translation b0 as the peak center. AMW-SMI detects the valley lines to define the peak boundaries, which is important to obtain accurate peak quantification. As a more ‘shape-orientated’ peak detection algorithm, various methods related to the ‘shape’ are introduced for feature filtering, out of which, the effective SNR (eSNR) is defined to evaluate if the shape is strong or good enough relative to the ‘shape noise’, and the SMI, which can quantitatively evaluate the shape quality regardless of the data intensities and peak width, is applied to filter out the poorly shaped false positives. AMW-SMI performs Gaussian fitting of all data points between the defined peak boundaries to refine the peak parameters, and the refined SMI, SNR and peak width can be applied for further feature filtering and reinforce the ‘shape-quality’ of final selected peaks. The performance of AMW-SMI is evaluated qualitatively (by recall, precision and F-score) and quantitatively (by ratio of isotopic features and triplicate RSD) on the LC-MS data of model mixtures of 21 human metabolite standards and 8 plant metabolite standards, and of serum sample spiked with the 21 human metabolite standards, and on the triplicate LC-MS data of the same sample of cell metabolomic extracts. Compared with XCMS (centWave) and MZmine 2 (ADAP), the proposed AMW-SMI algorithm can faithfully identify chromatographic peaks with significantly fewer false positives and demonstrated general superiority in terms of qualitative precision (robustness) and quantitative accuracy (by ratio of isotopic features), and comparable recall (sensitivity) and quantitative stability (by RSD of triplicate measurements).

Damage detection in precast full panel building by continuous wavelet analysis analytical method

In this research, the free vibrations of a four-story building with specific boundary conditions have been investigated and the health of the building is monitored based on experimental results using the analytical method of continuous wavelet; then, the damage that may occur to these structures has been evaluated and analyzed. The finite element software was used to model the building by the sandwich model. In this four-story building, a sandwich panel with eight layers of material (polystyrene, concrete, steel, and concrete) was used symmetrically. Then, the fourteen natural frequencies of the sandwich structure are compared with the experimental model and the shape of the main modes of the building, which is effective in monitoring the health of the structure, has been obtained. An error of less than 2.5% reveals a good match between the results of the two models. According to experimental results, the damage location was successfully identified using the coif5 function with a scale parameter of 8 compared to other wavelet functions and shows more perturbation of coefficients at the damage locations. Thus, the relative maximum and minimum jumps in the wavelet coefficients occurred at the location of the damage and by considering the maximum or minimum coefficients of the wavelet generated at the damage location as the center of damage, the damage center can be identified with an error of less than 8%. Moreover, the effects of higher modes in displaying the damage intensity are more noticeable, so that in the torsional mode of the structure, the maximum wavelet coefficients are greater and the intensity of the damage is more visible.

A Novel Image Registration Algorithm Using Wavelet Transform and Matrix-Multiply Discrete Fourier Transform

Nearly all existing image registration algorithms are based on the full information (e.g., intensity and/or features) carried by the images being registered. Such a full-image-based strategy can achieve high-precision registration (e.g., at the subpixel level) but at the expense of long computation times, especially for high spatial resolution remote-sensing (RS) images. This letter presents a novel subimage-based registration method that is expected to dramatically reduce the time consumption but still meet subpixel accuracy requirements. The core idea behind this method is that the transform pattern of the reference and sensed images under a global transformation is theoretically similar to that of subimages derived from the full image, and vice versa. Hence, this method first divides the original reference image into subimages with nearly equal sizes, and extracts the subimage with the maximum wavelet coefficient summation to serve as a new reference image using the wavelet transform technique. Then, the two-step methods based on the phase correlation and matrix-multiply discrete Fourier transform (DFT) algorithms are applied to register the sensed image to the reference subimage. Finally, the proposed method is tested with simulated data sets. The results indicate that this method can perform subpixel registration as achieved by full-image-based methods but with the benefit of much shorter computation times (e.g., approximately 89% compared with a widely used rapid registration algorithm named enhanced phase correlation).

A new potential field shape descriptor using continuous wavelet transforms

Potential field characterization aims at determining source depths, inclination, and type, preferably without a priori information. For ideal sources, the type is often defined from the field’s degree of homogeneity, derived from its expression in the space domain. We have developed a new shape descriptor for potential field source functions, stemming from spectral-domain parameters, which manifest clearly when using continuous wavelet transforms (CWTs). We generalize the use of the maximum wavelet coefficient points in the CWT diagram for the analysis of all types of potential fields (gravity, magnetic, and self-potential). We interpret the CWT diagram as a similarity diagram between the wavelet and the analyzed signal, which has fewer limitations than its interpretation as a weighted and upward-continued field projection. We develop new formulas for magnetic source depth prediction, as well as for effective inclination estimation, using various kinds of wavelets. We found that the potential field source functions exhibit precise behaviors in the CWT analysis that can be predicted using a single parameter [Formula: see text], which is related to their Fourier transforms. This parameter being scale and rotation-invariant can be used as a source-body shape descriptor similar to the commonly used structural index (SI). An advantage of the new descriptor is an increased level of discrimination between sources because it takes different values to describe the horizontal or the vertical cylinder structures. Our approach is illustrated on synthetic examples and real data. The method can be applied directly with the native form of the CWT without scaling factor modification, negative plane diagram extension, or downward plotting. This framework offers an alternative to existing wavelet-like projection methods or other classic deconvolution techniques relying on SI for determining the source depth, dip, and type without a priori information, with an increased level of differentiation between source structures thanks to the new shape descriptor.

Minimizing Noise in Sinusiodal Function Signal using Wavelet Transform

Resistances that occur in retrieving and processing signal is caused by the interference (noise) on the data signal measurement results. The resistance will raise uncertainties in determining the value of the frequency. This is due to the signal which is mixed with the noise in the original signal. In general, the process of signal analysis uses Fast Fourier Transformation (FFT). However, by using FFT in analyzing and reconstructing there are still doubts in determining the real frequency due to the still visible noise in the signal. In this study the signal function used is a sinusiodal function, Y = 2 sinπf1 t + 2 sin πf2 t, with a given noise value of 2 DB. The specified frequency value of f1 and f2 equal to 0.25 Hz and 5 Hz, respectively. This research proposed wavelet transforms to analyze and in filtering original signal with noise. By using the transformation wavelet, signal with noise filtered with the high pass and low pass filter method and also using the Haar wavelet function in analyzing. Once the signal is decomposed using wavelet transformation, the wavelet coefficients value will be obtained. The wavelet coefficient values will then threshold within a range of 5-50%. The purposed in determining the treshold value is to reduce the signal data identified as a noise signal data. If the value of wavelet coefficient below the treshold percentage value multiplied by the maximum wavelet coefficient, it is identified as a noise signal data, and the value of coefficient wavelet will be zero. The wavelet coefficient will then be reconstructed in order to obtain the data signal with the new sinusoidal function. In determining the value of the reconstructed frequency signal, the Fast Fourier Transform (FTT) method is used. The results of the study is signals with noise can be analyzed and filtered using wavelet transforms, by changing the signal into wavelet coefficients. Furthermore, the threshold of 5% is capable in reducing of noise in signal so that the graph of frequency and amplitude showed a clearer value of frequency.

Quantifying near fault pulses using generalized Morse wavelets

Recently, a family of analytic wavelets known as generalized Morse wavelets (GMW) has been established in the literature. This versatile wavelet family uses two parameters (γ and β) to adjust its frequency or time domain properties. By varying these two parameters, this wavelet family includes many commonly used analytic wavelets. In this paper, GMW are examined, along with the continuous wavelet transform (CWT), to determine their suitability for extraction of ground motion pulses from a suite of acceleration records. GMW are compared to a popular pulse model that has recently been adapted for wavelet analysis in the literature. It is shown, based on two different metrics, that GMW performs comparably with the other model for this suite of ground motions. Since CWT is repeated with different wavelet parameters for each ground motion, the parameter selection and pulse extraction process are examined. It is shown that using the maximum wavelet coefficient alone does not allow for the extraction of pulses that best match the time series or the spectral response. Alternative criteria are proposed in order to optimize pulse selection using performance-based metrics.

Analysis of Back Propagation Neural Network Method for Heart Disease Recognition

ECG is the recording of the electrical activity of the heart, and has become one of the most important tools in the diagnosis of heart diseases. ECG signal is shaped by P wave, QRS complex, and T wave. In the normal ECG beat, the main parameters including shape, duration, R-R interval and relationship between P wave, QRS complex, and T wave components are inspected. Any change in these parameters indicates an illness of the heart. This article introduces an electrocardiogram (ECG) pattern recognition method based on wavelet transform and standard BP neural network classifier. Experiment analyzes wavelet transform of ECG to extract the maximum wavelet coefficients of multi-scale firstly. This article then inputs them into BP to classify for different kinds of ECGs. The experimental result shows that the standard BP neural network classifier's overall pattern recognition rate is well.

Fault Sensing Using Fractal Dimension and Wavelet

A new fusion sensing (FS) method was proposed by using the improved fractal box dimension (IFBD) and a developed maximum wavelet coefficient (DMWC) for fault sensing of an online power cable. There are four strategies that were used. Firstly, the traditional fractal box dimension was improved to enlarge the feature distances between the different fault classes. Secondly, the IFBD recognition algorithm was proposed by using the improved fractal dimension feature extracted from the three-phase currents for the first stage of fault recognition. Thirdly, the DMWC recognition algorithm was developed based on the K-transform and wavelet analysis to establish the relationship between the maximum wavelet coefficient and the fault class. Fourthly, the FS method was formed by combining the IFBD algorithm and the DMWC algorithm in order to recognize the 10 types of short circuit faults of online power. The designed test system proved that the FS method increased the fault recognition accuracy obviously. In addition, the parameters of the initial angle, transient resistance, and fault distance had no influence on the FS method.

An empirical law for wavelet maxima interpretation of potential fields: Application to the Uinta Mountains range

Wavelet methods have been used in potential fields study to estimate source properties such as depth or structural index, through the analysis of Wavelet Transform Modulus Maxima Lines (WTMML) intersections and slopes at high scales. Little has been done on the study of maximum points of the wavelet diagram, that we call here Maximum Wavelet Coefficient Scales (MWCS). Previous works have shown interesting correlations between MWCS and source depths, depending on the wavelet used in regards to the source nature and the data derivative order. In this paper, we introduce an empirical law involving spectral parameters that have not been studied so far, which allows analytical calculation of the MWCS, knowing the source characteristics and using certain wavelets. In return, the study of MWCS allows recovering source characteristics from the use of a single wavelet, without prior knowledge on the source.We demonstrate through synthetic models that the new capability of predicting the source type and depth according to the wavelet coefficient behaviour allows new ways of potential fields' sources characterization and identification. We show an application of the formula on a real case example in the Uinta Mountains (Utah, USA).

The design of a recognition system for multi-function electrical rubber hand

There is an issue with the timely application of artificial intelligence to humankind, particularly to the disabled. In this paper, we propose a surface electromyography signal recognition system for prosthetic application to persons with disabled hands, which achieves simplicity, reliability, accuracy and a short response time by increasing the performance of each part of the system and the coordination between the interconnected components. First, a surface electromyography signal acquisition system was designed on the basis of the cost and processing speed. Second, a method called the ‘extreme value’ was carried out on the original signal containing five continuous movements, by separating the signal into isolated segments representing different postures, which made application of the system for daily use possible. Third, on the basis of time-domain, chaos-theory and time–frequency-domain analysis methods, four features, namely the average amplitude, fractal dimension, maximum Lyapunov exponent and wavelet coefficient, were extracted from four possible arm locations to be determined. Furthermore, the average amplitude from the extensor digitorum and the wavelet coefficient from the flexor pollicis longus were determined as the final features after comparing the clustering effects of the extracted features. Finally, a new strategy for classifying the different postures based on a back-propagation neural network was introduced to obtain an average system accuracy of 82.77% for five continuous movements.

Application of Frequency B-Spline Wavelets for Detection of Defects in Rolling Bearings

Frequency B-spline (fbsp) wavelets have been applied in the present work for detection of localized defects in the inner race of a rolling element bearing. The theoretical response from the bearing system due to an inner race defect coupled with additive noise of varying magnitude form the simulated signal. Methodology of defect detection includes three steps. Firstly, wavelet transformation of noisy vibration signal has been obtained using fbsp wavelets with appropriate parameters. Then, maximum wavelet coefficients were retained from the result of transformation on which a threshold level was applied. Finally fast Fourier transformation was applied on these retained coefficients to identify the presence of components at characteristic frequency corresponding to the defect location. The results show that the method could specifically identify bearing defects even from noisy vibration signals.

A Robust Watermarking Scheme Based on Maximum Wavelet Coefficient Modification and Optimal Threshold Technique

Digital watermarking has received extensive attention as a new method for copyright protection. This paper proposes a robust watermarking algorithm based on maximum wavelet coefficient modification and optimal threshold technique. The medium wavelet coefficients are randomly permutated according to a secret key and divided into subgroups. We modify the maximum wavelet in each subgroup according to the embedded watermark bits, which can effectively resist attacks. During the extraction process, an optimal threshold value calculated by iterative computation is used to extract the watermark from the watermarked image under different attacks, without using the original image or watermark. Numerous experiments are conducted to evaluate the watermarking performance. Experimental results demonstrate the superiority of our scheme on robustness against content-preserving operations and incidental distortions such as JPEG compression, Gaussian noise, median filter, resizing, cropping, and sharpening.

G0400601 超音波法によるレール軸力の評価に関する研究 : レール軸力とウェーブレット係数の関係について

The part of rail joint becomes the cause of the disadvantage points such as ride discomfort, undesired sound and track maintenance, etc. For restraining those points, the long rail made by welding the standard length rails has been developed. However, the occurrence of buckling is pointed out. The problem of the overhang or breakage by buckling is caused by the axial force produced in the rail. The stress produced by the force is called thermal stress. The stress is brought out by the weather conditions or the friction heat between wheel and rail. Though some evaluation techniques for the buckling management are produced, the development of the technique with non-destructive testing which quantitatively evaluates the force at the good accuracy and reduces the labor for measurement worker is desired. In this study, the relationship between the increase in compressive stress and the behaviors of characteristics of ultrasonic echo was researched. The stress was increased in 10 MPa interval, and the echo at that time was detected. From that experimental results, it was found that the frequency analysis results of detected echoes fluctuate with the increases in the stress. Especially, it became clear that there is the strong correlation between the ratio of the maximum wavelet coefficients (RMWC) of a wavelet transform result (i.e. a kind of time-frequency analysis) of each echo and applied stress. Therefore, it was possible to show the possibility of comparatively accurately predicting the force by comparing the transition of the RMWC to that of the initial state.

Evaluation of Wavelet–Functions for Broken Rotor Bar Detection of Induction Machine Using Coefficient–Related Features

Early fault detection of the induction machine is necessary in order to guarantee its stable and high performance. To evaluate the motor's health and detect existence of any failure in it, any motor parameter is first measured using condition monitoring techniques. The raw signal acquired is then interpret applying signal processing and data analysis procedures. Wavelet analysis of the motor current has been considered as an effective fault detection method. However, there are different types of the wavelet function that can be used for signal decomposition. This paper intends to investigate the ability of different types of wavelet functions for early broken rotor bar detection. Different harmonic components introduced by this fault such as maximum wavelet coefficient, left and right gradients of the maximum coefficient, were extracted and used as a characteristic signature for fault detection. The results indicate that the reliability of the fault detection depends on the type of wavelet function applied for decomposition of the signal.

Characterizing emotional response to music in the prefrontal cortex using near infrared spectroscopy

Known to be involved in emotional processing the human prefrontal cortex (PFC), can be non-invasively monitored using near-infrared spectroscopy (NIRS). As such, PFC NIRS can serve as a means for studying emotional processing by the PFC. Identifying patterns associated with emotions in PFC using NIRS may provide a means of bedside emotion identification for nonverbal children and youth with severe physical disabilities. In this study, NIRS was used to characterize the PFC hemodynamic response to emotional arousal and valence in a music-based emotion induction paradigm in 9 individuals without disabilities or known health conditions. In particular, a novel technique based on wavelet-based peak detection was used to characterize chromophore concentration patterns. The maximum wavelet coefficients extracted from oxygenated hemoglobin concentration waveforms from all nine recording locations on the PFC were significantly associated with emotional valence and arousal. Specifically, high arousal and negative emotions were associated with larger maximum wavelet coefficients.

Dynamic wavelet-based tool for gearbox diagnosis

This paper proposes a novel wavelet-based technique for detecting and localizing gear tooth defects in a noisy environment. The proposed technique utilizes a dynamic windowing process while analyzing gearbox vibration signals in the wavelet domain. The gear vibration signal is processed through a dynamic Kaiser's window of varying parameters. The window size, shape, and sliding rate are modified towards increasing the similarity between the non-stationary vibration signal and the selected mother wavelet. The window parameters are continuously modified until they provide maximum wavelet coefficients localized at the defected tooth. The technique is applied on laboratory data corrupted with high noise level. The technique has shown accurate results in detecting and localizing gear tooth fracture with different damage severity.

Wavelet-Based Disturbance Analysis for Power System Wide-Area Monitoring

This paper proposes a wavelet-based method to obtain the characteristics of frequency and voltage derivatives for disturbance analysis. Frequency and voltage derivatives are important indicators to reflect the degree of disturbances and manifest power system dynamics. However, current computational methods for the indicators have noticeable drawbacks with respect to the accuracy. Wavelet transform-based multiresolution analysis (WT-based MRA) is introduced to obtain the characteristics of the indicators by computed maximum wavelet coefficients (WCs). Results from numerical experiments show a superior performance of WT-based MRA over the existing methods. Generation loss and load change as two major types of disturbances are studied to verify the proposed method. The disturbances are simulated in PSS/E for IEEE New England 39-bus system. The relationship of maximum WCs and power variation is discussed. Maximum WCs can provide enough information to distinguish the location of generation loss and estimate the load power variation.

Crack Detection and Quantification in Beams Using Wavelets

Abstract: A new method has been proposed to detect the location and also to quantify the crack using the deflection response of the damaged beams alone. The deflection is measured at a particular point for various locations of a concentrated load on the beam. This static deflection profile is used as the input signal for wavelet (Symlet) analysis. Due to variation in deflection at some points, compared to their adjacent points, peaks are seen in the wavelet coefficient (WC) plot. These peak points are identified as damage points along the length of the beam. The peaks can also be seen at sensor point and supports. These can be eliminated by performing wavelet analysis for the deflection profile measured at another point. In a real damaged structure, it is very difficult to measure deflection at several points, as a large amount of instrumentation needs to be installed to measure the response. This practical difficulty can be avoided by minimizing the number of measuring points in the field as explained in the present work. A parametric study has been carried out by varying the damage, location of damage, intensity of load, flexural rigidity, and length of the beam. It has been observed that the WCs change with variations in damage, location of damage, intensity of load, flexural rigidity, and length of the beam. A generalized curve has been proposed to quantify the damage in a fixed beam by taking envelop of all maximum WCs of the deflection response measured at damage points.