Discrete Wavelet Decomposition Research Articles

Seawater Temperature Outlier Detection Strategy Based on Wavelet Analysis for Solving the Migration Problem of Fishery Companies

As the global ocean temperature rises, many marine organisms begin to find new habitats. The problem of fish migration has a profound impact on the development of fisheries. This paper uses wavelet anomaly analysis method to find the abnormal value of seawater temperature, uses discrete wavelet decomposition and reconstruction to de-noise the time series, and predicts the age of the fishing company. Firstly, we conducted continuous wavelet analysis of seawater temperature data to obtain the relationship between seawater temperature change rate and time. We performed Fourier transform to obtain its transform function, and then reconstructed the sequence to obtain wavelet coefficients of seawater temperature transformation speed. After that, we used wavelet coefficients to conduct simulation screening to obtain all outliers. In this way, the time for bankruptcy under the best and worst conditions of the fishing company can be obtained. Although the model comprehensively considers many factors, in order to establish a universal model, idealizing many influencing factors has certain limitations. The predicted bankruptcy time may have a certain error from the actual.

Personal identity verification based ECG biometric using non-fiducial features

Biometrics was used as an automated and fast acceptable technology for human identification and it may be behavioral or physiological traits. Any biometric system based on identification or verification modes for human identity. The electrocardiogram (ECG) is considered as one of the physiological biometrics which impossible to mimic or stole. ECG feature extraction methods were performed using fiducial or non-fiducial approaches. This research presents an authentication ECG biometric system using non-fiducial features obtained by Discrete Wavelet Decomposition and the Euclidean Distance technique was used to implement the identity verification. From the obtained results, the proposed system accuracy is 96.66% also, using the verification system is preferred for a large number of individuals as it takes less time to get the decision.

Exchange Rate Interdependence in ASEAN Markets: A Wavelet Analysis

Severe swings among the financial markets’ linkages in the recent past motivate the research to examine the exchange rate co-movements. Therefore, this study examines the interdependence and contagion among the exchange rates of five important Asian emerging markets, namely Indonesia, Malaysia, Philippines, Singapore and Thailand exploring the multifarious nature of interdependence and lead lag association using discrete and continuous wavelet decomposition. The findings show the co-movement among many exchange rate pairs showing a substantial increase during the global financial crisis. There are moderate rises in co-movements, indicating regional market convergence in the long run. Singapore seems to hold driving position and Indonesia falls behind in all exchange rate pairs of the ASEAN-5. The findings provide policy implications for caring the interdependence in foreign exchange markets.

Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19.

Everywhere around the globe, the hot topic of discussion today is the ongoing and fast-spreading coronavirus disease (COVID-19), which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2). Earlier detected in Wuhan, Hubei province, in China in December 2019, the deadly virus engulfed China and some neighboring countries, which claimed thousands of lives in February 2020. The proposed hybrid methodology involves the application of discreet wavelet decomposition to the dataset of deaths due to COVID-19, which splits the input data into component series and then applying an appropriate econometric model to each of the component series for making predictions of death cases in future. ARIMA models are well known econometric forecasting models capable of generating accurate forecasts when applied on wavelet decomposed time series. The input dataset consists of daily death cases from most affected five countries by COVID-19, which is given to the hybrid model for validation and to make one month ahead prediction of death cases. These predictions are compared with that obtained from an ARIMA model to estimate the performance of prediction. The predictions indicate a sharp rise in death cases despite various precautionary measures taken by governments of these countries.

Optimal breast tumor diagnosis using discrete wavelet transform and deep belief network based on improved sunflower optimization method

Breast cancer is one of the most widespread types of cancer among women, but it does not necessarily mean pre-death, such that timely diagnosis of it can make the patient get to survive. Due to the significance of breast cancer, early diagnosis of abnormal areas in breast helps to cure this cancer in the initial steps. This study presents a new computer-aided diagnosis system for the early detection of breast cancer. The proposed method contains five important stages including noise reduction, image segmentation, mathematical morphology, feature extraction based on the combination of discrete wavelet decomposition and GLCM, and finally classification based on Deep Belief Network (DBN). To improve the DBN efficiency, it is optimized by an enhanced version of the sunflower optimization algorithm. Simulation results are applied to the MIAS database and the achievements have been compared with three different methods. Simulation results showed that the rate of accuracy, specificity, and sensitivity for the proposed model are achieved 91.5%, 72.4%, and 94.1%, respectively for the MIAS benchmark which gives better achievements toward the previous methods.

Novel phase difference extraction method of FPP system based on DWT and OMP algorithm

In this paper, to reduce the non-linear phase error caused by gamma effect of the digital projector, a novel phase difference extraction method is proposed. First, through multi-layer discrete wavelet decomposition (DWT), the compo-nents of the original phase difference at different frequencies are extracted. Subsequently, the effective components are taken by the orthogonal matching pursuit (OMP) algorithm, and the phase difference is reconstructed. According to the upper and lower envelopes of the original phase difference extracted by cubic spline interpolation, the residual threshold of OMP is set. According to the results of three-dimensional (3-D) reconstruction experiment on a standard sphere, the proposed method is capable of reducing the errors in the original phase difference and achieving a better imaging result.

Optimized Wavelet Decomposition and Gaussian interpolation based Satellite Image Enhancement

Satellite images (SI) play a vital role in various remote sensing applications like geoscience, geographical studies, observing the earth's atmosphere, monitoring natural disasters, etc. The SI are used in these applications require high-resolution. The performance of the wavelet transforms based resolution enhancement methods depends on the type of the mother wavelet used and it varies with image to image. The novel robust SI resolution enhancement technique including Optimized wavelet transform based image decomposition and Gaussian interpolation is proposed in this paper. Optimized wavelet decomposition is obtained using the Stochastic Diffusion Search algorithm and the Gaussian distribution function is used for interpolation. The proposed method is compared with the Discrete wavelet decomposition and Gaussian interpolation resolution enhancement method and proved that the proposed method gives the best results for any image.

Dynamic modeling and control of five phase SVPWM inverter fed induction motor drive with intelligent speed controller

Multi-phase induction motor (IM) drive is an emerging field of electric drive applications. This enables us to use the multi-phase AC machine than a three-phase machine. Precision speed control is important for industrial applications. Hence, direct torque control (DTC) with PI or PID regulators used, and these regulators are not flexible because of a constant gain in all working conditions. Therefore, robust speed control is essential for the precision speed control in industrial applications. The major objective of this study is to investigate a novel speed control for DTC of 5-ϕ VSI with space vector PWM (SVPWM) technique fed IM drive using wavelet fuzzy logic interface system. In precise, wavelet-fuzzy controller is an alternative for the PID controller used for DTC IM drive. The speed error signal comprises many short and extraordinary-frequency components. The scaled gains are generated for a DTC IM drive by the decomposition of discrete wavelet transform and the fuzzy logic controller (FLC) from high and low-frequency signals. The investigation of 2 hp 5-ϕ VSI fed IMD is carried out using the simulation model developed in MATLAB2016a software and examined under various working conditions. The simulation results are shown in this paper to confirm the suitability for industrial applications.

File Entropy Signal Analysis Combined With Wavelet Decomposition for Malware Classification

With the rapid development of the Internet, malware variants have increased exponentially, which poses a key threat to cyber security. Persistent efforts have been made to classify malware variants, but there are still many challenges, including the incapacity to deal with various malware variants belonging to similar families, the problem of time and resource consuming, etc. This paper proposes a novel method, called Malware Entropy Sequences Reflect the Family (MESRF), to improve the classification of malware based on the entropy sequences features. In prior research, entropy demonstrated good performance in many areas. First, the global features of the signals were extracted from the entropy sequences by some statistical methods. Next, some local features (i.e. structural entropy features) are extracted based on the discrete wavelet decomposition algorithm and vectorized by the Bag-of-words model, endowing it the high accuracy of malware classification. To evaluate our method, we conducted numerous experiments on the malware datasets with more than 20,000 samples. Through experiments, MESRF showed superiority comparing with other malware classification models, and the accuracy and ROC of the method even could reach 99.83% and 99.98% respectively on the malimg dataset.

Effect of moisture content on propagation characteristics of acoustic emission signal of Pinus massoniana Lamb

The propagation velocity and energy of acoustic emission (AE) signals at different moisture contents (MC) of wood are studied, providing a theoretical basis for studying the law of wood stress and strain with the change in MC. The AE source was simulated by lead core fracture on the surface of Pinus massoniana Lamb. Signal de-noising was performed by discrete wavelet decomposition method. On this basis, the propagation velocity and energy of AE signals at different MCs were studied according to the signal cross-correlation analysis and time difference localization method, and the law of velocity and energy change with MC was further determined by fitting. The fitting results show that the propagation velocity of AE signals decreased exponentially with the increase in MC. Moreover, the propagation velocity decreased rapidly when the MC was below fiber saturation point (FSP), and decreased relatively gently when the MC was above FSP. In addition, the energy attenuation of the AE signal was obvious during the propagation process, and the energy attenuation rate of the AE signal presented a linear relationship with the increase in MC. The change in MC has a significant effect on the propagation velocity and energy of AE signals of wood.

Active control of multiscale features in wall-bounded turbulence

This study experimentally investigates the impact of a single piezoelectric (PZT) actuator on a turbulent boundary layer from a statistical viewpoint. The working conditions of the actuator include a range of frequencies and amplitudes. The streamwise velocity signals in the turbulent boundary layer flow are measured downstream of the actuator using a hot-wire anemometer. The mean velocity profiles and other basic parameters are reported. Spectra results obtained by discrete wavelet decomposition indicate that the PZT vibration primarily influences the near-wall region. The turbulent intensities at different scales suggest that the actuator redistributes the near-wall turbulent energy. The skewness and flatness distributions show that the actuator effectively alters the sweep events and reduces intermittency at smaller scales. Moreover, under the impact of the PZT actuator, the symmetry of vibration scales’ velocity signals is promoted and the structural composition appears in an orderly manner. Probability distribution function results indicate that perturbation causes the fluctuations in vibration scales and smaller scales with high intensity and low intermittency. Based on the flatness factor, the bursting process is also detected. The vibrations reduce the relative intensities of the burst events, indicating that the streamwise vortices in the buffer layer experience direct interference due to the PZT control.

Enhancement of Fault Feature Extraction from Displacement Signals by Suppressing Severe End Distortions via Sinusoidal Wave Reduction

Displacement signals, acquired by eddy current sensors, are extensively used in condition monitoring and health prognosis of electromechanical equipment. Owing to its sensitivity to low frequency components, the displacement signal often contains sinusoidal waves of high amplitudes. If the digitization of the sinusoidal wave does not satisfy the condition of full period sampling, an effect of severe end distortion (SED), in the form of impulsive features, is likely to occur because of boundary extensions in discrete wavelet decompositions. The SED effect will complicate the extraction of weak fault features if it is left untreated. In this paper, we investigate the mechanism of the SED effect using theories based on Fourier analysis and wavelet analysis. To enhance feature extraction performance from displacement signals in the presence of strong sinusoidal waves, a novel method, based on the Fourier basis and a compound wavelet dictionary, is proposed. In the procedure, ratio-based spectrum correction methods, using the rectangle window as well as the Hanning window, are employed to obtain an optimized reduction of strong sinusoidal waves. The residual signal is further decomposed by the compound wavelet dictionary which consists of dyadic wavelet packets and implicit wavelet packets. It was verified through numerical simulations that the reconstructed signal in each wavelet subspace can avoid severe end distortions. The proposed method was applied to case studies of an experimental test with rub impact fault and an engineering test with blade crack fault. The analysis results demonstrate the proposed method can effectively suppress the SED effect in displacement signal analysis, and therefore enhance the performance of wavelet analysis in extracting weak fault features.

A novel material removal prediction method based on acoustic sensing and ensemble XGBoost learning algorithm for robotic belt grinding of Inconel 718

High-performance component manufacturing has increasing needs of robotic grinding process that can achieve accurate material removal. This article proposes a novel material removal model for robotic belt grinding of Inconel 718 based on acoustic sensing and machine learning. The sound signal is collected online by an audio sensor during the grinding process. A novel method to identify the idle running period and eliminate noise is developed using discrete wavelet decomposition (DWD) and fast Fourier transformation (FFT). Statistical features are extracted from each clean acoustic signal segment to better represent and quantify grinding process. A new k-fold eXtreme Gradient Boosting (k-fold-XGBoost) algorithm after training and optimization is integrated into the material removal (MR) model. The test results indicate that the values forecasted by the model are consistent with the measured values. The mean absolute percentage error (MAPE) of material removal evaluated by the model is 4.373%, which shows a better performance than the reported results which are in the range of 6.4 to 8.72%. In comparison with other prediction models, such as optimally pruned extreme learning machine and random forest and support vector regression, k-fold-XGBoost model shows superior results for the same datasets. It can be concluded that the proposed method based on acoustic signal and the ensemble learning model is effective in predicting the material removal despite the complicated grinding environment.

An Approach to Detect Image Forgery by Discrete Wavelet Decomposition

Image forgery or manipulation by using the multimedia technology is becoming a challenging issue. The most common type of image forgery is copy-move forgery where some part of one image is copied and spliced in the other image. In this article, first the images in RGB color space is converted into YCbCr color space and the four-level discrete wavelet transform (DWT) is implemented to detect image forgery. The output of the DWT is further processed by using the image gradient technique for the edge detection of spliced objects. Morphological operation and Wiener filtering are applied for locating the tempered region in the forged image. Sensitivity, specificity and accuracy calculated for spliced images of CASIA datasets are obtained 89%, 86% and 88% respectively.

Global Soil Moisture‐Air Temperature Coupling Based on GRACE‐Derived Terrestrial Water Storage

AbstractUnderstanding global soil moisture‐air temperature (θ‐Ta) coupling is needed to improve the representation of land‐atmosphere interactions in Earth system models. Most studies on θ‐Ta coupling have focused on hot extremes, where precipitation‐related indices and model‐derived soil moisture products are commonly used. In this study, global θ‐Ta coupling is examined based on monthly air temperature anomalies and the Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS). A discrete wavelet decomposition is used to partition the TWS into different components. The results show that TWS is useful in revealing the spatial patterns of θ‐Ta coupling. Decomposed GRACE TWS shows improved skill compared to raw TWS in explaining temporal variability of monthly air temperature, which likely reflects different roles of soil moisture at different depths in the θ‐Ta coupling. The explanatory power improves further by using a combination of decomposed GRACE TWS and precipitation. Such improvement is observed particularly in places where vegetation tends to have a deeper rooting system, such as eastern region of South America, the southern tip of Africa, and north of the Tropic of Capricorn in Australia. The occurrence of θ‐Ta coupling is mainly constrained by the coupling of root zone moisture and land surface temperature. In addition to deeper rooting systems, clear wet and dry season alternation is another favorable factor for developing significant monthly θ‐Ta coupling.

The application of fractional wavelet transform in image enhancement

The fractional wavelet transform is a generalized extension of the wavelet multi-resolution analysis in time-fractional frequency domain. It can characterize signal features in time domain and fractional frequency domain. Based on the multi-resolution analysis of fractional wavelet, the process of decomposition and reconstruction of discrete fractional wavelet transform is derived, and it is extended to two dimensions. According to the characteristic that the subband energy of fractional wavelet transform varies with the order P, a novel image enhancement method is proposed. All the fractional wavelet coefficients at large scale and low resolution are retained, and a nonlinear adaptive gain method is used for the high-resolution subband coefficients. The experimental results show that the performance of this method is better than the traditional wavelet enhancement method both in vision effect and contrast improvement index.

Spatiotemporal traffic matrix prediction: A deep learning approach with wavelet multiscale analysis

AbstractNetwork traffic analysis has always been a key technique for operating and managing a network. However, due to some (non) technical issues, it is not trivial to directly obtain network‐wide traffic data. Although a large number of traffic matrix (TM) prediction methods have been used to obtain future network‐wide traffic, they achieve somewhat limited accuracy due to neglecting spatiotemporal evolution features of TM series at different time scales. In order to improve the performance of TM prediction, we propose a novel end‐to‐end deep neural network based on wavelet multiscale analysis, called WSTNet. In this network, the original TM series is first decomposed into multilevel time‐frequency TM subseries at different time scales by using discrete wavelet decomposition, and then the convolutional neural network without pooling is used to extract the spatial patterns among traffic flows, and finally, the long short‐term memory neural network with a self‐attention mechanism by relating different positions of input sequences across entire time steps is employed to explore the temporal evolution features within TM series. To investigate the performance of our proposed model, extensive experiments are conducted on two real network traffic data sets from the Abilene and GÉANT backbone networks. The results show that WSTNet is significantly better than the other four state‐of‐the‐art deep learning methods.

Material defects localization in concrete plate-like structures – Experimental and numerical study

Summary In this paper, the hybrid algorithm for localization of damage and defects is implemented on the concrete plate-like structures for localizing the clay and gypsum inclusions. The hybrid approach employs fast discrete wavelet decomposition of sensor output signals, as well as energy and time of flight criteria. The applied localization algorithm is verified both experimentally and numerically on the concrete plates with one and two inclusions. The experiment is conducted in controlled laboratory conditions, using a piezoelectric actuator for excitation of the wave propagation in the structure, while the ultrasonic laser is used for measuring vibrations at the sensor locations. Numerical simulation of wave propagation is done using the explicit finite element method on 3D models. The numerically obtained results are in full correspondence with the experimental results. The images of material defects positions obtained by the hybrid approach show a good agreement with the actual positions, which indicates a good potential of the used approach in localization of various types of material defects in plate-like concrete structures.

Clamping diodes failure identification based on the discrete wavelet decomposition of the magnetic near‐field

Diagnosis of static power converters for distributed power generation systems and electric machines is nowadays a common concern of researchers interested in the reliability of power electronic systems. The conventional diagnosis approaches such as those based on voltage, current, and flux analysis have already been well developed in recent decades. Recently, it was shown that the magnetic near-field measured above the power converter may carry substantial information about the health state of power components. This study proposes, therefore, a novel non-invasive diagnosis method based on the discrete wavelet decomposition of the near-field metered above a static power converter. It is proved that each faulty component can be identified by its appropriate electromagnetic signature. Accordingly, all faulty cases are easily distinguishable, which makes fault classification possible using very simple tools such as envelope detection. The proposed method is validated through experimental tests carried out on a three-phase three-level neutral point clamped inverter operating with faulty clamping diodes. Its advantages compared to previous diagnosis methods are also discussed.

Removal of ‘strip noise’ in radio-echo sounding data using combined wavelet and 2-D DFT filtering

ABSTRACTRadio-echo sounding (RES) can be used to understand ice-sheet processes, englacial flow structures and bed properties, making it one of the most popular tools in glaciological exploration. However, RES data are often subject to ‘strip noise’, caused by internal instrument noise and interference, and/or external environmental interference, which can hamper measurement and interpretation. For example, strip noise can result in reduced power from the bed, affecting the quality of ice thickness measurements and the characterization of subglacial conditions. Here, we present a method for removing strip noise based on combined wavelet and two-dimensional (2-D) Fourier filtering. First, we implement discrete wavelet decomposition on RES data to obtain multi-scale wavelet components. Then, 2-D discrete Fourier transform (DFT) spectral analysis is performed on components containing the noise. In the Fourier domain, the 2-D DFT spectrum of strip noise keeps its linear features and can be removed with a ‘targeted masking’ operation. Finally, inverse wavelet transforms are performed on all wavelet components, including strip-removed components, to restore the data with enhanced fidelity. Model tests and field-data processing demonstrate the method removes strip noise well and, incidentally, can remove the strong first reflector from the ice surface, thus improving the general quality of radar data.