Wavelet Multiresolution Analysis Research Articles

Cumulant analysis in wavelet space for studying effects of aging on electrical activity of the brain

Multiresolution wavelet analysis with thorough processing of decomposition coefficients using a set of cumulants is proposed as a way to improve the characterization of complex dynamics based on experimental data. The application of this approach for quantification the effects of aging in the responses of the electrical activity of the brain to fine motor tasks (clenching the fist) is considered. It is shown that young and elderly adults have significant differences in reactions to this type of movements carried out by the dominant and non-dominant hand. The characterization of inter-group distinctions using the skewness and kurtosis of the probability distribution of the wavelet decomposition coefficients outperforms the diagnostics of age-related differences based on standard deviation of this distribution.

Acquisition and Classification of Lung Sounds for Improving the Efficacy of Auscultation Diagnosis of Pulmonary Diseases.

PurposeLung diseases are the third leading cause of death worldwide. Stethoscope-based auscultation is the most commonly used, non-invasive, inexpensive, and primary diagnostic approach for assessing lung conditions. However, the manual auscultation-based diagnosis procedure is prone to error, and its accuracy is dependent on the physician’s experience and hearing capacity. Moreover, the stethoscope recording is vulnerable to different noises that can mask the important features of lung sounds which may lead to misdiagnosis. In this paper, a method for the acquisition of lung sound signals and classification of the top 7 lung diseases has been proposed for improving the efficacy of auscultation diagnosis of pulmonary disease.MethodsAn electronic stethoscope has been constructed for signal acquisition. Lung sound signals were then collected from people with COPD, upper respiratory tract infections (URTI), lower respiratory tract infections (LRTI), pneumonia, bronchiectasis, bronchiolitis, asthma, and healthy people. Lung sounds were analyzed using a wavelet multiresolution analysis. To choose the most relevant features, feature selection using one-way ANOVA was performed. The classification accuracy of various machine learning classifiers was compared, and the Fine Gaussian SVM was chosen for final classification due to its superior performance. Model optimization was accomplished through the application of Bayesian optimization techniques.ResultsA test classification accuracy of 99%, specificity of 99.2%, and sensitivity of 99.04%, have been achieved for the 7 lung diseases using the optimized Fine Gaussian SVM classifier.ConclusionOur experimental results demonstrate that the proposed method has the potential to be used as a decision support system for the classification of lung diseases, especially in those areas where the expertise and the means are limited.

WMR-DepthwiseNet: A Wavelet Multi-Resolution Depthwise Separable Convolutional Neural Network for COVID-19 Diagnosis.

Timely discovery of COVID-19 could aid in formulating a suitable treatment plan for disease mitigation and containment decisions. The widely used COVID-19 test necessitates a regular method and has a low sensitivity value. Computed tomography and chest X-ray are also other methods utilized by numerous studies for detecting COVID-19. In this article, we propose a CNN called depthwise separable convolution network with wavelet multiresolution analysis module (WMR-DepthwiseNet) that is robust to automatically learn details from both spatialwise and channelwise for COVID-19 identification with a limited radiograph dataset, which is critical due to the rapid growth of COVID-19. This model utilizes an effective strategy to prevent loss of spatial details, which is a prevalent issue in traditional convolutional neural network, and second, the depthwise separable connectivity framework ensures reusability of feature maps by directly connecting previous layer to all subsequent layers for extracting feature representations from few datasets. We evaluate the proposed model by utilizing a public domain dataset of COVID-19 confirmed case and other pneumonia illness. The proposed method achieves 98.63% accuracy, 98.46% sensitivity, 97.99% specificity, and 98.69% precision on chest X-ray dataset, whereas using the computed tomography dataset, the model achieves 96.83% accuracy, 97.78% sensitivity, 96.22% specificity, and 97.02% precision. According to the results of our experiments, our model achieves up-to-date accuracy with only a few training cases available, which is useful for COVID-19 screening. This latest paradigm is expected to contribute significantly in the battle against COVID-19 and other life-threatening diseases.

Volatility spillover effects between oil and GCC stock markets: a wavelet-based asymmetric dynamic conditional correlation approach

Purpose This study aims to examine the spillover effects of the mean and volatility between oil prices and stock indices of six Gulf Cooperation Council (GCC) countries (UAE, Kuwait, Saudi Arabia, Qatar, Oman and Bahrain). Design/methodology/approach Over the period 2008–2019, a bivariate VARMA-GARCH-ADCC model was combined with the maximal overlap discrete wavelet transform technique filter to shed light on a wide range of possible spillover effects in the mean and variances of level prices at various time horizons. Findings The authors find that the spillover effects between oil prices and the GCC stock markets are time-varying and spread across various time horizons. Besides, oil prices and stock market indices are directly impacted by their own shocks and variations and indirectly influenced by other price volatilities and wavelet scales. The linkages in volatility spillovers between oil prices and the GCC stock markets occur in the short-term, midterm and long-term horizons. More specifically, the results also show that the asymmetric estimates are statistically significant for the associations between oil prices and each stock market in the GCC countries. This implies that negative shocks play a more vital role than positive shocks in driving the dynamic condition correlations between oil and stock markets under study. Practical implications The significant interrelatedness between oil prices and each stock market in the GCC countries has important implications for investors, portfolio managers, and other market participants. They can use the findings of this research to create the best oil-GCC stock portfolios and predict more precisely the volatility spillover patterns in constructing their hedging strategies. Originality/value In several ways, this study differs from previous research. First, while previous empirical studies of the dynamic link between oil prices and stock markets have focused primarily on developed or emerging markets, the focus of this is on six GCC countries. Second, the linkage between oil prices and stock markets is typically studied at the original data level in the time domain in relevant literature, while frequency information is overlooked. Therefore, the current study examines this relationship from a multiscale perspective. Third, in this paper, to capture a wide range of possible spillover effects in the mean and variance of level prices at multiple wavelet scales, the authors use a VARMA-GARCH-ADCC model in conjunction with wavelet multiresolution analysis. Additionally, this article also applies wavelet hedge ratio and wavelet hedge portfolio analysis at various time horizons.

Identification of the planetary magnetosphere boundaries with the wavelet multi-resolution analysis

The study of the properties and variations of planetary boundaries such as magnetosheaths and bow shocks is an important subject for magnetospheric dynamics and interaction with solar wind. The identification of these boundaries is important for those studies. Thus, the Haar wavelet decomposition technique is used to detect the planetary magnetosphere boundaries and discontinuities. We use the magnetometer data from the CASSINI and MESSENGER spacecraft to identify the abrupt changes in the magnetic field when the spacecraft crossed the magnetospheric bow shocks and magnetopauses of Saturn and Mercury, respectively. The methodology based on variance obtained by scale and edge identifications was shown to be a simple tool to perform this task. The results confirm that the Haar transform can efficiently identify the planetary magnetosphere boundaries characterized by the abrupt magnetic field changes. Due to this wavelet function to be a discrete function it promotes the abrupt and sharp identification of the boundaries. It is suggested that this technique can be applied to detect the planetary boundaries as well as the discontinuities such as the shock waves in the interplanetary space.

COVID-19 Pneumonia Classification Based on NeuroWavelet Capsule Network.

Since it was first reported, coronavirus disease 2019, also known as COVID-19, has spread expeditiously around the globe. COVID-19 must be diagnosed as soon as possible in order to control the disease and provide proper care to patients. The chest X-ray (CXR) has been identified as a useful diagnostic tool, but the disease outbreak has put a lot of pressure on radiologists to read the scans, which could give rise to fatigue-related misdiagnosis. Automatic classification algorithms that are reliable can be extremely beneficial; however, they typically depend upon a large amount of COVID-19 data for training, which are troublesome to obtain in the nick of time. Therefore, we propose a novel method for the classification of COVID-19. Concretely, a novel neurowavelet capsule network is proposed for COVID-19 classification. To be more precise, first, we introduce a multi-resolution analysis of a discrete wavelet transform to filter noisy and inconsistent information from the CXR data in order to improve the feature extraction robustness of the network. Secondly, the discrete wavelet transform of the multi-resolution analysis also performs a sub-sampling operation in order to minimize the loss of spatial details, thereby enhancing the overall classification performance. We examined the proposed model on a public-sourced dataset of pneumonia-related illnesses, including COVID-19 confirmed cases and healthy CXR images. The proposed method achieves an accuracy of 99.6%, sensitivity of 99.2%, specificity of 99.1% and precision of 99.7%. Our approach achieves an up-to-date performance that is useful for COVID-19 screening according to the experimental results. This latest paradigm will contribute significantly in the battle against COVID-19 and other diseases.

Analysis of Y-component of Geomagnetic Field and SYM-H Index Using Wavelet Multiresolution Analysis

Analysis of Y-component of Geomagnetic Field and SYM-H Index Using Wavelet Multiresolution Analysis

Intelligent identification of two-phase flow patterns in a long pipeline-riser system

Accurate and rapid identification of multiphase flow patterns in long-distance pipelines is an important means for flow assurance. In this paper, an experiment of air-water two-phase flow has been carried out on a pipeline-S-shaped riser system with a length of 1687 m. Based on the amplitude of riser differential pressure, the working conditions are categorised into three typical flow patterns, severe slugging flow, oscillatory flow, and stable flow. Only two difference pressure signals that are most practical near the offshore platforms are used. The data of liquid phase accumulation of severe slugging is adopted as samples, and the signal features are extracted by wavelet multiresolution analysis. The parameters of six common classifiers are optimized, and the effects of different classifiers with the optimal hyperparameters on flow pattern recognition are compared and analyzed. For neural networks, decision trees, support vector machines and k-Nearest Neighbors classifier with the optimal hyperparameters, the recognition rate of severe slugging is higher than 95.8% and the average recognition rate of the three flow patterns is higher than 94.2% when the sample duration is only 6.2 s. On the premise of achieving the highest recognition rate, the number of features is substantially reduced to 15.6% of the original number by principal component analysis.

The Evolution of Preseismic Patterns Related to the Central Crete (Mw6.0) Strong Earthquake on 27 September 2021 Revealed by Multiresolution Wavelets and Natural Time Analysis

On 27 September 2021, a shallow earthquake with focal depth of 10 km and moment magnitude Mw6.0 occurred onshore in central Crete (Greece). The evolution of possible preseismic patterns in the area of central Crete before the Mw6.0 event was investigated by applying the method of multiresolution wavelet analysis (MRWA), along with that of natural time (NT). The monitoring of preseismic patterns by critical parameters defined by NT analysis, integrated with the results of MRWA as the initiation point for the NT analysis, forms a promising framework that may lead to new universal principles that describe the evolution patterns before strong earthquakes. Initially, we apply MRWA to the interevent time series of the successive regional earthquakes in order to investigate the approach of the regional seismicity towards critical stages and to define the starting point of the natural time domain. Then, using the results of MRWA, we apply the NT analysis, showing that the regional seismicity approached criticality for a prolonged period of ~40 days before the occurrence of the Mw6.0 earthquake, when the κ1 natural time parameter reached the critical value of κ1 = 0.070, as suggested by the NT method.

Wavelet Multi Resolution Analysis based Data Hiding with Scanned Secrete Images

Wavelet Multi Resolution Analysis (MRA) based data hiding with scanned secrete images is proposed for improvement of invisibility of the secrete images. Daubechies (biorthogonal basis was adopted as the wavelet, but it was demonstrated that the key image (or secret image data) information can be restored with the biorthogonal wavelet. Also, the information of what to adopt as the biorthogonal wavelet is hidden. Key image information can also be protected by doing so, that the horizontal biorthogonal wavelet of the image does not have to be the same as the vertical biorthogonal wavelet, and the insertion position of the secret image data can be freely selected. It is also possible to divide the bit string of the secret image data and insert it into an arbitrary high frequency component, that the information hiding capability changes depending on the number of bit strings (information amount) of the secret image data, and the secret image in the public image data. Random scanning is effective for improving the visibility of data, selection of scanning method type, random number initial value It was shown that sharing only among parties is useful for improving confidentiality, resistance to noise, resistance to data compression, and resistance to tampering with data.

Isolating Orbital Error From Multitemporal InSAR Derived Tectonic Deformation Based on Wavelet and Independent Component Analysis

Isolating the orbital error from the interferometric synthetic aperture radar (InSAR) observations is a great challenge, especially in the presence of tectonic deformation due to their similar spatial patterns. The influence of orbital error is systematic, which can reduce the reliability of deformation monitoring. In this letter, we propose a method to isolate the orbital error from the multitemporal InSAR (MTInSAR) derived tectonic deformation based on the wavelet multiresolution analysis and independent component analysis (ICA). Starting from the sequential interferometric phase of unwrapping, the tectonic deformation and orbital error are firstly extracted from the interferometric phase by wavelet analysis based on their longwavelength spatial patterns, and ICA is then used to isolate the orbital error from the tectonic deformation according to the different temporal characteristics of the two types of signals. In the simulation experiment, the root-mean-square error (RMSE) of the isolated orbital error is 2.6 mm. Experiments with real data in Southern California show that the proposed method can successfully separate the orbital error from the tectonic deformation, and the InSAR deformation rates are in good agreement with the GPS observations.

A substructural and wavelet multiresolution approach for identifying time-varying physical parameters by partial measurements

Currently, most wavelet multiresolution based methods for the identification of time-varying structural physical parameters request the full measurements of all structural responses. All physical parameters including time-varying and time-invariant parameters are expanded by wavelet multiresolution, so it is only applicable to structures with a few degree-of-freedoms. In this paper, based on the substructural identification technique, a novel two-step approach is proposed to identify the time-varying physical parameters of linear structures with more degree-of-freedoms under unknown excitations using only partially measured responses. The whole structure is divided into several substructures. For a substructure concerned, the unknown interaction forces from neighboring structures are treated as ‘additional unknown inputs’ to the substructure, so the identification of complete structure can be transformed to the identification of each substructure with parallel computing in two steps. In the first step, the fading-factor generalized extended Kalman filter under unknown input algorithm is proposed to locate the time-varying physical parameters. In the second step, a synthesized method is developed for the quantitative identification of time-varying physical parameters based on the integration of wavelet multiresolution analysis and the generalized Kalman filter under unknown input. The time-varying structural physical parameters distinguished in the first step are expanded by wavelet multiresolution analysis. Then, the time-invariant physical parameters and the scale coefficients of time-varying physical parameters are identified by performing a nonlinear optimization with an objective function established by the extended Kalman filter under unknown input. Finally, the estimated scale coefficients are used to reconstruct the original time-varying structural physical parameters. Several numerical examples are used to demonstrate the effectiveness of the proposed approach.

Image Reconstruction from Multiscale Singular Points Based on the Dual-Tree Complex Wavelet Transform

The representation of an image with several multiscale singular points has been the main concern in image processing. Based on the dual-tree complex wavelet transform (DT-CWT), a new image reconstruction (IR) algorithm from multiscale singular points is proposed. First, the image was transformed by DT-CWT, which provided multiresolution wavelet analysis. Then, accurate multiscale singular points for IR were detected in the DT-CWT domain due to the shift invariance and directional selectivity properties of DT-CWT. Finally, the images were reconstructed from the phases and magnitudes of the multiscale singular points by alternating orthogonal projections between the CT-DWT space and its affine space. Theoretical analysis and experimental results show that the proposed IR algorithm is feasible, efficient, and offers a certain degree of denoising. Furthermore, the proposed IR algorithm outperforms other classical IR algorithms in terms of performance metrics such as peak signal-to-noise ratio, mean squared error, and structural similarity.

Fault Type Classification of 150 kV Transmission Line using Wavelet Multi-Resolution Analysis Method

To identify the fault, wavelet method is used in solving complex protection problems. This study uses a new approach, namely the wavelet multi resolution analysis method with its application where multi resolution analysis works to analyze signals at different frequencies with the same resolution. In this study, the classification of fault types that occur in the 150 kV transmission line quickly and accurately is carried out using the wavelet multi resolution analysis method. This research is included in applied research and was designed using computer simulation software, namely ATP and MATLAB. The data transmission system used is the Maninjau Hydroelectric Power Plant transmission line to Pauh Limo Substation. The modeled transmission system is given 1-phase to ground, 2-phase to ground, 2- phase, 3-phase and lightning faults. To determine the accuracy of this classification, the fault is varied according to the distance and impedance of the disturbance. From the analysis of the simulation results and calculations, based on the wavelet multi resolution analysis method used in fault classifying, the average value of the approximation coefficient used to classify the type of fault is obtained. Based on the results of the study, it can be said that all types of fault analyzed in this study have met the classification requirements using the wavelet multi resolution analysis method

Fault Type Classification of 150 kV Transmission Line using Wavelet Multi-Resolution Analysis Method

To identify the fault, wavelet method is used in solving complex protection problems. This study uses a new approach, namely the wavelet multi resolution analysis method with its application where multi resolution analysis works to analyze signals at different frequencies with the same resolution. In this study, the classification of fault types that occur in the 150 kV transmission line quickly and accurately is carried out using the wavelet multi resolution analysis method. This research is included in applied research and was designed using computer simulation software, namely ATP and MATLAB. The data transmission system used is the Maninjau Hydroelectric Power Plant transmission line to Pauh Limo Substation. The modeled transmission system is given 1-phase to ground, 2-phase to ground, 2- phase, 3-phase and lightning faults. To determine the accuracy of this classification, the fault is varied according to the distance and impedance of the disturbance. From the analysis of the simulation results and calculations, based on the wavelet multi resolution analysis method used in fault classifying, the average value of the approximation coefficient used to classify the type of fault is obtained. Based on the results of the study, it can be said that all types of fault analyzed in this study have met the classification requirements using the wavelet multi resolution analysis method

Volatility spillover effect between internet finance and banks

Cooperative relationships of internet finance and traditional banks opens up channels of volatility spillover between them. We study the direction of volatility spillover between internet finance and banks using Diebold-Yilmaz volatility spillover index model based on wavelet multi-resolution analysis and variable structure points, considering that volatility has different spillover characteristics on various time scales and time domains. The results show that on the whole, internet finance is the net exporter of volatility spillovers. In the early development stage of internet finance, the spillover direction of returns is from the bank to internet finance on the short and medium scales, and the volatility spillover direction is the opposite. In the period of stable development of internet finance, the spillover direction of returns in the long term is from internet finance to banks, and the volatility spillover is in the opposite direction.

Diffuse terahertz spectroscopy in turbid media using a wavelet-based bimodality spectral analysis

Current terahertz (THz) spectroscopy techniques only use the coherent light beam for spectral imaging. In the presence of electromagnetic scattering, however, the scattering-mitigated incoherent beams allow for flexible emitter-detector geometries, which enable applications such as seeing through turbid media. Despite this potential, THz spectroscopy using diffuse waves has not been demonstrated. The main obstacles are the very poor signal to noise ratios of the diffused fields and the resonance-like spectral artifacts due to multiple Mie scattering events that obscure the material absorption signatures. In this work, we demonstrate diffuse THz spectroscopy of a heterogeneous sample through turbid media using a novel technique based on the wavelet multiresolution analysis and the bimodality coefficient spectrum, which we define here for the first time using the skewness and kurtosis of the spectral images. The proposed method yields broadband and simultaneous material characterization at detection angles as high as 90° with respect to the incident beam. We determined the accuracy of the wavelet-based diffuse spectroscopy at oblique detection angles, by evaluating the area under the receiver operating characteristic curves, to be higher than 95%. This technique is agnostic to any a priori information on the spectral signatures of the sample materials or the characteristics of the scattering medium, and can be expanded for other broadband spectroscopic modalities.

Auto-detection of Spectral Notches of Head-related Transfer Function using Wavelet Multi-resolution Analysis

The spectral features of pressure characterized by the human outer ear, head, and torso are vital for sound localization in humans, especially for sound above 5 kHz. Many studies have shown that spectral notches contribute significantly as cues for this purpose. These spectral notches and their center frequencies depend on the shape and geometry of each individual, so their characteristics are different between individuals. We used wavelet multi-resolution analysis to automatically detect the center frequencies of spectral notches in head-related transfer functions (HRTFs). Auto-detection of these notches at certain locations for an individual helps in using suitable, complete, 3D HRTFs from HRTF datasets that have similar notch characteristics for an individual. Symlet and Daubechies wavelets were successfully used at different decomposition levels for this purpose. Symlet2 gives the best performance in terms of the auto-detection sensitivity.

Noninvasive Methods for Fault Detection and Isolation in Internal Combustion Engines Based on Chaos Analysis.

The classic monitoring methods for detecting faults in automotive vehicles based on on-board diagnostics (OBD) are insufficient when diagnosing several mechanical failures. Other sensing techniques present drawbacks such as high invasiveness and limited physical range. The present work presents a fully noninvasive system for fault detection and isolation in internal combustion engines through sound signals processing. An acquisition system was developed, whose data are transmitted to a smartphone in which the signal is processed, and the user has access to the information. A study of the chaotic behavior of the vehicle was carried out, and the feasibility of using fractal dimensions as a tool to diagnose engine misfire and problems in the alternator belt was verified. An artificial neural network was used for fault classification using the fractal dimension data extracted from the sound of the engine. For comparison purposes, a strategy based on wavelet multiresolution analysis was also implemented. The proposed solution allows a diagnosis without having any contact with the vehicle, with low computational cost, without the need for installing sensors, and in real time. The system and method were validated through experimental tests, with a success rate of 99% for the faults under consideration.

Comparative study between objective and subjective methods for identifying the gravity of single and multiple gear defects in case of noisy signals

This paper aims to compare two approaches for the identification of gear defects in a very noisy environment. The first one, called subjective, is based on sound perception using paired comparison test; the second, objective, is based on vibration analysis using three advanced signal processing methods. The first part of the paper determines a correlation between the sound perception obtained by the listeners and the scalar indicators calculated for different gravities of gear defects (simple and/or double). The perception test was carried out on sounds obtained directly by converting the measured vibratory signals. The study is conducted in a two-dimensional proximity space. The results obtained allowed the determination of mathematical models that correlate between the objective and subjective aspects and highlight the important relationship between the vibratory indicators and the distances between the sounds in the perceptual space. The correlation ratios obtained for the first dimension (DIM1) prove to be very significant. In the second part of the paper, the same signals are processed by three methods, known to be powerful tools in vibratory signal processing, namely Optimized Wavelet Multi-Resolution Analysis (WMRA), cyclostationary analysis, and a hybrid approach based on an improved version of the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and multivariate denoising by wavelets and PCA (Principal Component Analysis). For all three methods considered, a demodulation approach is performed to obtain an envelope spectrum as a final result. In the case of very noisy signals, the subjective method allows a better assessment of the evolution of the severity of the gear defects compared to the objective methods used. In our opinion, this is due to the high sensitivity of the human ear to identify the existence of a defect and to assess its degree of severity, far better than the most sophisticated objective signal processing methods, which remain limited in the case of very noisy signals.