Wavelet Detail Coefficients Research Articles

Specific emitter identification by wavelet residual network based on attention mechanism

AbstractSpecific emitter identification technology plays a very important role in spectrum resource management, wireless network security, cognitive radio etc. However, in complex electromagnetic environments, the variability and uncertainty of signals make it very difficult to extract representative feature representations of the signals. At the same time, the feature extraction capability of the recognition model is also a factor that needs to be considered. To address these issues, a wavelet residual neural network model based on attention mechanism is proposed for specific emitter identification. First, multi‐level wavelet decomposition is performed on all received signals to obtain their wavelet detail coefficients at different scales. Next, all the wavelet detail coefficients are used as the feature input for the attention‐based residual network, and perform parallel feature extraction at multi scales. Finally, the feature representation capability of all coefficients are compared, and the model's recognition results based on it are obtained. The recognition rates on the three datasets are 94.7%, 93.21%, and 86.1%, respectively, all of which are superior to recent state‐of‐the‐art algorithms. In addition, through ablation experiment, the validity of each component of the model has been verified.

Damage detection of road domain waveform guardrail structure based on machine learning multi-module fusion.

The current highway waveform guardrail recognition technology has encountered problems with low segmentation accuracy and strong noise interference. Therefore, an improved U-net semantic segmentation model is proposed to improve the efficiency of road maintenance detection. The model training is guided by mixed expansion convolution and mixed loss function, while the presence of guardrail shedding is investigated by using partial mean values of gray values in ROI region based on segmentation results, while the first-order detail coefficients of wavelet transform are applied to detect guardrail defects and deformation. It has been determined that the Miou and Dice of the improved model are improved by 8.63% and 17.67%, respectively, over the traditional model, and that the method of detecting defects in the data is more accurate than 85%. As a result of efficient detection of highway waveform guardrail, the detection process is shortened and the effectiveness of the detection is improved later on during road maintenance.

Detecting damages in metallic beam structures using a novel wavelet selection criterion

Structural damage detection using wavelet transform is effective if a suitable wavelet function is selected. However, the selection of the appropriate wavelet function can be a challenging task. This paper introduces a novel and efficient criterion for selecting the proper wavelet function. It is emphasized that there is a relationship between the accuracy of damage detection using wavelet detail coefficients and the correctness of the approximation of the original function. Accordingly, we define a ratio function and then a novel wavelet selection criterion called WSC in this study. After applying the proposed WSC criterion for 128 wavelet functions, it is found that the wavelet functions selected via the proposed criterion (both in numerical and experimental scenarios) can detect the position of damages in the beam structure with high accuracy.

A Daubechies wavelet analysis on coupled radiation and conduction in absorbing and scattering medium under pulsed laser

This study examines how the wavelet is affected by the wavelet function and thermal parameters by combining radiative and conductive heat transfer with Daubechies discrete wavelet decomposition for signal processing. By comparing time-frequency information under different sampling time interval, we can deduce that the large sampling time interval causes the detail coefficients to focus on the high frequency area. Regarding the wavelet coefficient, the radiation characteristics, heating power, heating time, and actual random errors will affect the wavelet frequency. The thermal conductivity and extinction coefficient have a great influence on the detail coefficient, while the albedo has an impact on the temperature and the amplitude of the wavelet detail coefficient in the range of 0.5-0.9, and the asymmetry factor has a significant effect on the time of the temperature peak. The wavelet detail coefficient of the heating side temperature changes more dramatically than that of the cooling side. The detail coefficient of the low-frequency region is more affected by thermal physical factors whereas the high-frequency portion is more affected by random errors and heating power. This law can improve the accuracy of acquiring physical attributes by filtering out random errors in practical applications.

A wavelet analysis method to retrieve thermal conductivity and radiative properties of intense extinction and low conductive medium

The accurate acquisition of the physical properties of strongly extinction materials limits their application development. In order to solve the problem of large errors in the high temperature measurement of these materials, this paper constructs an inversion model for the radiative and conductive coupled heat transfer through wavelet decomposition coefficients, and compares it with traditional methods. The radiative and conductive coupled heat transfer model is solved by finite volume method and Monte Carlo method. Daubechies nine wavelet is used for signal wavelet analysis. It has been discovered that the wavelet decomposition parameters of the temperature field are mainly concentrated in the 4–8 wavelet frequency band of wavelet, it can separate from the random error that is mainly concentrated in the 1–3 frequency band of wavelet. Most of physical properties like the heating power, convective heat transfer coefficient, material thermal conductivity, extinction coefficient has a great impact on the waveform and amplitude of wavelet detail coefficient and approximation coefficient, and the change trend of approximation coefficient and detail coefficient amplitude is consistent with the trend of temperature field. However, the effect of scattering albedo on wavelet parameters is tiny. Through inversion, it is found that the inversion model based on wavelet decomposition parameters of 5–7 frequency band has higher accuracy when random error exists.

A Comparative Investigation of Wavelet Families for Classification of EOG Signals Related to Healthy and ADHD Children.

Based on previous research, there are differences between eye movements of people with attention-deficit hyperactivity disorder (ADHD) and of healthy people, as a result, the existence of differences regarding the electrooculogram (EOG) signals of the 2 groups exists. Thus, this study aimed to examine the recorded EOG signals of 30 ADHD children and 30 healthy children while performing an attention-related task. For this purpose, the EOG signals of these 2 groups were decomposed utilizing various wavelet functions. Afterward, features, including mean, energy, and standard deviation (SD) of approximation and detail wavelet coefficients were calculated. The Davies-Bouldin (DB) index was used for the evaluation of the feature space quality. Finally, the 2 groups were classified using one-dimensional feature vector and support vector machine (SVM). The SD of detail coefficients (db4) was selected as the most effective feature for separating the 2 groups. Statistical analysis revealed that the values of energy and SD of EOG signals' detail coefficients were significantly lower in the ADHD group in comparison with the healthy group (P<.001). These results showed that the speed of the ADHD group's eye movements was slower due to the fact that the high-frequency band activity of EOG signals in the healthy group was higher. In addition, the EOG signals were classified with a detection accuracy of 83.42 ± 3.8%. The results of this study can be applied in designing an EOG biofeedback protocol to treat or mitigate the symptoms of ADHD patients.

Monitoring effects of heavy metal stress on biochemical and spectral parameters of cotton using hyperspectral reflectance.

Monitoring heavy metal pollution in agricultural ecosystems is crucial to ensure environmental safety. Heavy metals interfere with plants' biochemical characteristics, such as chlorophyll content and photosynthesis, andalso influence leaves' spectral properties. Spectral changes caused by heavy metal stress can easily be measured using proximal sensing or in-field spectroscopy. This research utilizes a combined approach of biochemical and spectral characteristics to evaluate cottoncrops' performance under different heavy metal (Pb & Cd)stress after artificial contamination with the metal under study. A detailed study of spectroscopy and lab-based measurements for chlorophyll and heavy metal content during the crop's growth cycle revealed some significant findings. Results indicated that the chlorophyll pigments decreased significantly with increased heavy metal levels. Pb accumulation is high in cotton as compared to Cd. The most sensitive stage for the accumulation of Pb is the initial vegetative stageof cotton. The transfer factor from soil to plant was higher for Pb, indicating the feasibility of growing cotton in Pb-contaminated soil. The spectral measurement showed no characteristic changes in standard reflectance spectra due to heavy metal stress. Wavelet decomposition of reflectance spectra amplified the changes indicating Pb stress in cotton during the initial vegetative stage. The significant correlation of greater than - 0.70 between the reconstructed detail wavelet coefficients at the third level of the decomposition in the wavelength range of 651-742nm suggested that Pb stress caused spectral changes in near-infrared and visible ranges in cotton plants. The effects of Cd stress on the cotton plant were negligible due to less absorption. Thus, detailed wavelet coefficients at the third level of decomposition in the range of 651-742nm are a potential indicator of Pb stress. The results of this study can provide a basis for quantifying heavy metal stress in a particular region.

A Wavelet-Based Steganographic Method for Text Hiding in an Audio Signal.

The developed method of steganographic hiding of text information in an audio signal based on the wavelet transform acquires a deep meaning in the conditions of the use by an attacker of deliberate unauthorized manipulations with a steganocoded audio signal to distort the text information embedded in it. Thus, increasing the robustness of the stego-system by compressing the steganocoded audio signal subject to the preservation of the integrity of text information, taking into account the features of the psychophysiological model of sound perception, is the main objective of this scientific research. The task of this scientific research is effectively solved using a multilevel discrete wavelet transform using adaptive block normalization of text information with subsequent recursive embedding in the low-frequency component of the audio signal and further scalar product of the obtained coefficients with the Daubechies wavelet filters. The results of the obtained experimental studies confirm the hypothesis, namely that it is proposed to use recursive embedding in the low-frequency component (approximating wavelet coefficients) followed by their scalar product with wavelet filters at each level of the wavelet decomposition, which will increase the average power of hidden data. It should be noted that upon analyzing the existing method, which is based on embedding text information in the high-frequency component (detailed wavelet coefficients), at the last level of the wavelet decomposition, we obtained the limit CR = 6, and in the developed, CR = 20, with full integrity of the text information in both cases. Therefore, the resistance of the stego-system is increased by 3.3 times to deliberate or passive compression of the audio signal in order to distort the embedded text information.

Damage Detection in Prestressed Concrete Slabs Using Wavelet Analysis of Vibration Responses in the Time Domain

Detection of damages in structures during their service life is of vital importance and under attention of researchers. In this paper, it is attempted to identify damages in prestressed concrete slabs using vibration responses obtained from modal testing in the time domain. For this purpose, first some damage scenarios with various geometric shapes and at different locations of numerical models, corresponding to a prestressed concrete slab, were created. Next, the impact hammer force in the modal test was simulated and the accelerations time histories at different degrees of freedom corresponding to the numerical models per two states of damaged and undamaged structure were selected as the inputs for a number of damage indices to identify the damage locations. Some of these damage scenarios have been located at the middle of prestressed concrete slabs and some at the corners. The proposed damage indices in this research are obtained based on the area under the diagram of acceleration time histories, maximum and also the area under diagram of detail coefficients of the wavelet transform using the three wavelet families of Daubechies, Biorthogonal and Reverse Biorthogonal. The results showed that using damage index obtained from the area under diagram of detail coefficients of wavelet transform with the mother wavelet db2 could detect the damage scenarios at the middle and corners of the slab with a well precision. Furthermore, the damage scenarios at the corners of numerical models could be detected properly by using the mother wavelet rbio2.2 in the proposed damage index.

Effect of fault classification and detection in transmission line using wavelet detail coefficient

This paper presents the progress of fault detection and classification analysis in transmission line using wavelet transform with high-speed protective digital relay. These techniques are applicable for real time data analysis. The MATLAB simulation model obtains the voltage and current signals that have been measured in both end of the transmission line. These signals are used in a discrete wavelet transform to extract the original signals and to measure the sharp variation of multi-resolution analysis (MRA). The analysis for various types of faults in transmission lines is detected and classified with the help of power protection techniques. In this paper, the wavelet transform technique is used for the detection of faults. This methodology delivers signals in the time and frequency domain. The wavelet transform will be examined to determine the effect of the detail coefficients db-3 and db-4 and provide an acquired time and frequency signal. The performance of wavelet transform will be more suitable, effective, and adequate for high impedance faults in high voltage transmission lines. The power transmission lines are interconnected with the generating station at 400 kv via consumer load through 300 km distance to obtain by MATLAB/Simulink.

Effect of fault classification and detection in transmission line using wavelet detail coefficient

Effect of fault classification and detection in transmission line using wavelet detail coefficient

Wavelet Frequency Separation Attention Network for Chest X-ray Image Super-Resolution.

Medical imaging is widely used in medical diagnosis. The low-resolution image caused by high hardware cost and poor imaging technology leads to the loss of relevant features and even fine texture. Obtaining high-quality medical images plays an important role in disease diagnosis. A surge of deep learning approaches has recently demonstrated high-quality reconstruction for medical image super-resolution. In this work, we propose a light-weight wavelet frequency separation attention network for medical image super-resolution (WFSAN). WFSAN is designed with separated-path for wavelet sub-bands to predict the wavelet coefficients, considering that image data characteristics are different in the wavelet domain and spatial domain. In addition, different activation functions are selected to fit the coefficients. Inputs comprise approximate sub-bands and detail sub-bands of low-resolution wavelet coefficients. In the separated-path network, detail sub-bands, which have more sparsity, are trained to enhance high frequency information. An attention extension ghost block is designed to generate the features more efficiently. All results obtained from fusing layers are contracted to reconstruct the approximate and detail wavelet coefficients of the high-resolution image. In the end, the super-resolution results are generated by inverse wavelet transform. Experimental results show that WFSAN has competitive performance against state-of-the-art lightweight medical imaging methods in terms of quality and quantitative metrics.

Multiresolution wavelet analysis of noisy datasets with different measures for decomposition coefficients

The possibility of distinguishing between different types of complex oscillations using datasets contaminated with measurement noise is studied based on multiresolution wavelet analysis (MWA). Unlike the conventional approach, which characterizes the differences in terms of standard deviations of detail wavelet coefficients at independent resolution levels, we consider ways to improve the separation between complex motions by applying several measures for the decomposition coefficients. We show that MWA’s capabilities in diagnosing dynamics can be expanded by applying detrended fluctuation analysis (DFA) to sets of detail wavelet coefficients or by computing the excess of the probability density function of these sets.

Speckle filtering of ultrasonic images using weighted nuclear norm minimization in wavelet domain

Medical images such as ultrasound images suffer from multiplicative speckle noise which reduces the contrast of ultrasound images and adversely affects the diagnosis process. Discrete Wavelet transform (DWT) is widely accepted in the area of image processing due to its time localization, multi-resolution and sparseness properties. DWT decomposes the noisy image into approximation coefficients and detail coefficients. The detail wavelet coefficients of clean image have sparsity property, whereas in the case of noisy image, sparsity of detail coefficients is reduced due to the presence of speckle noise. Thus, in the proposed method, low rank based Weighted Nuclear Norm Minimization (WNNM) is applied on detail coefficients to reveal the sparsity property of DWT. WNNM is applied on the group matrix of non-local similar patches of detail subbands of DWT to approximate the low-rank denoised version of the subbands. Moreover, less amount of edge and structure information is present in the approximation coefficients of DWT. Thus, in the proposed method, Non-local Means (NLM) filter with Square-Chord distance is also used to denoise speckle noise from approximation coefficients of DWT. Exhaustive experiments are conducted on various images such as real US images, simulated kidney image and synthetic image. It is observed that the mean improvement in terms of PSNR and CNR values of the proposed hybrid method are 1.50% and 3.81% respectively over WNNM based Despeckling using Low-Rank Approximation (DLRA) method. Qualitative and quantitative analyses show that the proposed hybrid method performed better than existing speckle reduction methods in terms of both speckle reduction and edge preservation.

Recognition of fish acoustic patterns at monitoring of freshwater ecosystems

The basic sources of contamination and obstruction of reservoirs are cleared not enough sewer water of industrial and communal enterprises, large stock-raising complexes, wastes of production; upcast of water and railway transport; wastes of roughing-out of flax, pesticides and other. Сontaminents, getting in natural reservoirs, result in the quality changes of water, that, mainly, appear in the change of physical properties of water, in the change of chemical composition of water, in a presence floating substances on the surface of water and laying of them on the bottom of reservoirs. The increases of population, expansion of old and origin of new cities considerably increased entering of domestic flows internal reservoirs. Synthetic cleansers that is widely used in the way of life contaminate reservoirs in a yet greater degree. In the total the capacity of waters goes down for oxigenating, activity of bacteria that mineralize organic substances is paralysed. The unfavorable ecological state of many freshwater ecosystems inflicts substantial harm to the fish resources of reservoirs and puts under a threat possibility not only to develop fish industry, conducting fish artificially, but also simply to catch her. All of it stimulate to do events in relation to the improvement of the ecological state of fresh reservoirs. Voice vibrations are the important constituent of the ecological monitoring of the biota state of fresh reservoirs. Information is about formation of sound in a reservoir part of that is activity of fishes turns out by means of acoustic sensors, that farther yields to computer treatment. The modern methods of recognition of fish acoustic patterns are based on the standards of signals, with properties of average estimations, or on comparisons of acoustic signals with a standard. It is shown that for creation of standards, as a rule, executed: previous signal processing, extraction of features of acoustic signal. Acoustic signals that act from movable objects – fishes can change depending on objective external terms and physical state of reservoirs. The hard algorithms of recognition of acoustic patterns are characterized high probability of error. In this connection repressing are adaptive algorithms of recognition of acoustic patterns. In the process of forming of standards clarification of software comes true according to the features of acoustic signal. Realization of process of creation of standards allows to determine the measure of functional readiness of parameters and knowledge base for the decision of recognition tasks of acoustic signals. In the process of recognition the probability terms of the correct comparing are set to the standard, on default of that an algorithm stops to be executed and requires additional studies. It requires creation of standards that reflect the characteristic features of fish signals. Presently for authentication mostly choose such pattern of acoustic signals, as period length of signal fundamental wave. It can be determined or by the search of maximal value in an autocorrelation function, or by the search of minimum value in the function of mean value of difference of signal amplitudes, or by the search of difference of two maximal values in the sequence of going into detail wavelet-coefficients. It is shown that for the tasks of recognition of fish acoustic patterns, most exact and requiring the least studies there is presentation of acoustic signal as a set of sign vectors of frames. In detail methodologies of the period selection of fundamental wave of acoustic signal were analysed: SIFT, EFT-А and EFT-WT. Methodology of EFT-WT is characterized absence of the thresholds set in good time; by the rapid search of period of fundamental wave; by absence of dependence on a noise-level, as a certain range of frequencies is investigated. At the same time calculable complication of wavelet transform is relatively high, in this connection it is necessary optimization of calculation algorithms.

Enhanced multiresolution wavelet analysis of complex dynamics in nonlinear systems.

Multiresolution wavelet analysis (MWA) is a powerful data processing tool that provides a characterization of complex signals over multiple time scales. Typically, the standard deviations of wavelet coefficients are computed depending on the resolution level and such quantities are used as measures for diagnosing different types of system behavior. To enhance the capabilities of this tool, we propose a combination of MWA with detrended fluctuation analysis (DFA) of detail wavelet coefficients. We find that such an MWA&DFA approach is capable of revealing the correlation features of wavelet coefficients in independent ranges of scales, which provide more information about the complex organization of datasets compared to variances or similar statistical measures of the standard MWA. Using this approach, we consider changes in the dynamics of coupled chaotic systems caused by transitions between different types of complex oscillations. We also demonstrate the potential of the MWA&DFA method for characterizing different physiological conditions by analyzing the electrical brain activity in mice.

Event detection and classification through wavelet-based method in low voltage wide-area monitoring systems

This paper presents an approach to automatically analyse events on electric power systems using data from phasor measurement units installed at low voltage level. The proposed approach is based on the application of multiresolution analysis through the wavelet transform, decomposing signals into functions of both time and frequency domains, aiming to detect and characterize systemic events. Detection and classification algorithms are designed to recognize systemic events according to the energy extracted from wavelet detail coefficients, taking into account decomposition levels, thresholds and decision variables which are set using a particle swarm optimization algorithm. Numerical results, validated with low voltage phasor measurements of the Brazilian and Chilean power systems, show that the approach can be used effectively for event detection and classification.

Fault Diagnosis of Commutation Failure Using Wavelet Transform and Wavelet Neural Network in HVDC Transmission System

Commutation failure is the most common fault on the inverter side of the high voltage direct current (HVDC) transmission system. In the actual engineering operation, there are many reasons for the commutation failure. In order to diagnose the causes in real-time and effectively, this article proposes a new method for the fault diagnosis of the commutation failure based on the combination of the wavelet transform and wavelet neural network (WNN). First, by analyzing the changes in electrical characteristics caused by the failure of commutation, the voltage signal of the inverter side commutation bus is finally selected as the feature signal for diagnosis. Then, the wavelet transform is used as a tool for extracting fault feature quantities, and the root mean square (rms) value of the wavelet detail coefficients, wavelet energy skewness, and wavelet energy spectrum information entropy of the inverter side commutation bus voltage signal after wavelet decomposition are used as three feature parameters. At the same time, wavelet packet decomposition is performed on the inverter side commutation bus voltage signal, and the wavelet packet subband energy, energy entropy, and energy skewness are extracted as the other three feature vectors. Finally, the simulation test data shows that the proposed fault diagnosis method can accurately identify the specific fault causes and has high reference value and specific engineering guiding significance for improving the reliability of the HVDC transmission system. Compared with the traditional BP neural network method for fault diagnosis of commutation failure, the proposed diagnosis method considers the fault type more comprehensively, the diagnosis speed is faster, and the recognition accuracy rate is higher in the case of a small number of samples.

Fault Detection of the Power System Based on the Chaotic Neural Network and Wavelet Transform

The safety and stability of the power supply system are affected by some faults that often occur in power system. To solve this problem, a criterion algorithm based on the chaotic neural network (CNN) and a fault detection algorithm based on discrete wavelet transform (DWT) are proposed in this paper. MATLAB/Simulink is used to establish the system model to output fault signals and travelling wave signals. Db4 wavelet decomposes the travelling wave signals into detail signals and approximate signals, and these signals are combined with the two-terminal travelling wave location method to achieve fault location. And the wavelet detail coefficients are extracted to input to the proposed chaotic neural network. The results show that the criterion algorithm can effectively determine whether there are faults in the power system, the fault detection algorithm has the capabilities of locating the system faults accurately, and both algorithms are not affected by fault type, fault location, fault initial angle, and transition resistance.

Fluid and rock heterogeneity assessment of gas condensate reservoirs by wavelet transform of pressure-transient responses

The response of gas condensate reservoirs is directly influenced by heterogeneity. In such reservoirs, condensate is created around wellbore areas, when the reservoir pressure falls below the dew point pressure. Consequently, the distribution of this condensate bank makes fluid flow in the reservoir even more complicated by changing rock-fluid properties (e.g., relative permeability). This alteration can be assumed as a new heterogeneity, called fluid heterogeneity. The separation of fluid heterogeneity from rock heterogeneity is a challenging task. The main idea of this study is to investigate the transient pressure responses of a gas condensate reservoir to separate rock and fluid heterogeneities. Different homogeneous and heterogeneous reservoir models of a reservoir were constructed by the geostatistical approach. A commercial reservoir simulator was used to simulate the behavior of different drawdown and buildup scenarios. The fluid is a lean gas condensate selected from one of the Middle East formations. The wavelet transform (WT) approach was implemented to characterize the behavior of condensate banks for both homogeneous and heterogeneous models. Analyzing the wavelet approximate and detail coefficients of the pressure-transient responses enabled us to distinguish the rock and fluid heterogeneities. The results showed that the wavelet detail coefficient could be a good indicator of reservoir heterogeneity. Moreover, the WT could be successfully used to distinguish different regions of a condensate bank inside the reservoir under study. The implemented approach showed promising results in analyzing the pressure-transient responses of gas condensate reservoirs.