Continuous Wavelet Research Articles

Re-Visiting the Co-movement between FinTech, Economic Policy Uncertainty, Governance Quality and Bank Variability in Pakistan: A Wavelet Coherence Approach

The recent literature about FinTech services in Pakistani commercial banks has grabbed much attention as these services have increased rapidly in the last few decades. This paper examines the co-movement between FinTech, economic policy uncertainty, bank- variability, and governance quality. The data was obtained from 2014-2020 on a monthly frequency. To address the relationship and co-movement, we have applied more advanced and sophisticated econometric models, autoregressive distributed lag model (ARDL), and continuous wavelet (CTW) to investigate the relationship between the variables. These approaches provide flexibility of assumptions for time-series data and provide robust results. The results indicate a short and long-run symmetric relationship with a significant co-movement between the variables.

End-to-End Speaker Profiling Using 1D CNN Architectures and Filter Bank Initialization

The automatic estimation of speaker characteristics, such as height, age, and gender, has various applications in forensics, surveillance, customer service, and many human-robot interaction applications. These applications are often required to produce a response promptly. This work proposes a novel approach to speaker profiling by combining filter bank initializations, such as continuous wavelets and gammatone filter banks, with one-dimensional (1D) convolutional neural networks (CNN) and residual blocks. The proposed end-to-end model goes from the raw waveform to an estimated height, age, and gender of the speaker by learning speaker representation directly from the audio signal without relying on handcrafted and pre-computed acoustic features. The conducted experiments on the TIMIT dataset show that the proposed approach outperforms many previous studies on speaker profiling with a mean absolute error (MAE) of 5.18 and 4.91 cm in height estimation and MAE of 5.36 and 6.07 years in age estimation for males and females, respectively, and achieving an accuracy of 99.98% in gender prediction.

Intelligent Machinery Fault Diagnosis Method Based on Adaptive Deep Convolutional Neural Network: Using Dental Milling Cutter Malfunction Classifications as an Example

Intelligent machinery fault diagnosis is one of the key technologies for the transformation and competitiveness of traditional factories. Complex production environments make it difficult to maintain good prediction performance using traditional methods. This paper proposes a deep convolutional neural network combined with an adaptive environmental noise method to achieve robust fault classification. The proposed method uses six-dimensional physical signals for data fusion and feature fusion, extracts obvious features and enhances subtle features, and uses continuous wavelets and Gramian angular fields to transform signals with different physical and frequency characteristics into time–frequency maps and two-dimensional images. The fusion technology of different signals can provide comprehensive features for fault prediction, improving upon the blind spots of traditional methods to extract features, and then perform prediction and classification through deep convolutional neural networks. In the experiment, the tool failure classification of the dental milling machine is used as a verification case. The results show that the prediction accuracy of the proposed method is nearly 100%, much better than other comparison methods. In addition, white noise was added in the experiment to verify the noise immunity of the model. The results show that the accuracy of the proposed method is 99%, which is better than other comparison methods in terms of accuracy and robustness, proving the effectiveness of the proposed method for fault diagnosis and classification.

Experimental mode decomposition investigation on 3-stage axial flow compressor stall phenomena using aeroacoustics measurements

Safely operating compressors is quite critical. Rotating stall is highly undesirable and affects negatively the performance and stability of aero-engine compressors. It is important to identify a precursor of such rotating stalling phenomena observed on the compressor. In this work, we conduct experimental measurements on a 3-stage axial flow compressor by using 8 acoustic pressure sensors. Emphasis is being placed on applying different mode decomposition methods such as Proper Orthogonal Decomposition (POD) and Empirical Mode Decomposition (EMD) on these acoustics data to shed lights on the stall phenomena. Comparison is then made between these methods and conventional means like continuous wavelet (CWA), and PSD (Power Spectrum Density) analyses to achieve a timely detection of stall inception and the energy distribution before and after stall occurred. It is found from POD that the dominant acoustic modes contributing to more than 90% of the total fluctuation energy are changed from 3 to 4, when the stall occurred. The EMD investigation shows that the compressor stall is associated with a few low-frequency IMFs (intrinsic mode function). Time evolutions of such IMFs are observed to grow from negligible amplitude disturbances into limit cycles. Additionally, applying CWA reveals that the stall cell is originated from the first stage and then propagates circumferentially and axially to the second and third stage. Finally, PSD analysis shows that the stall frequency related to the spike-type stall cell exists only on the first stage. This is different from the blade passing frequency as observed on all stages of the compressor. In summary, the present work offers alternative acoustic measurements to examine the dynamic instability of a compressor stall with advanced signal-processing techniques applied.

Quantized Information in Spectral Cyberspace.

The constant-Q Gabor atom is developed for spectral power, information, and uncertainty quantification from time-frequency representations. Stable multiresolution spectral entropy algorithms are constructed with continuous wavelet and Stockwell transforms. The recommended processing and scaling method will depend on the signature of interest, the desired information, and the acceptable levels of uncertainty of signal and noise features. Selected Lamb wave signatures and information spectra from the 2022 Tonga eruption are presented as representative case studies. Resilient transformations from physical to information metrics are provided for sensor-agnostic signal processing, pattern recognition, and machine learning applications.

Monitoring of ductile–brittle transition mechanisms in sapphire ultra-precision grinding used small grit size grinding wheel through force and acoustic emission signals

Progressive ultra-precision grinding experiments used small grit size grinding wheels were performed to investigate the ductile–brittle transition process of sapphire, force and AE signals were employed to monitor the material removal behavior. Precision dressing and measurement of circular arc grinding wheels were performed to meet the minimum grinding depth for ultra-precision grinding. A grinding force model was proposed to interpret the grinding forces in different materials removal modes. A continuous complete ductile–brittle transition process was observed and monitored, the progressive grinding was divided into three stages: ductile dominant, ductile–brittle transition and brittle eruption. The grinding force, surface profile, micro morphology and brittle fracture percentage were discussed. Brittle fracture will not entirely occur, but rather brittle fracture and ductile grooves were coexisted on the surface in the brittle eruption stage, which was attributed to the grinding characteristics of small grit wheels. In addition, raw AE signals and multiple transformation forms were utilized to monitor the material removal behavior and their respective roles were analyzed. The Fourier transform, discrete wavelet, continuous wavelet and wavelet coefficients in different stages can all establish a mapping relationship with material removal modes.

Intelligent fault diagnosis of rolling bearings based on continuous wavelet transform-multiscale feature fusion and improved channel attention mechanism

Accurate fault diagnosis is critical to operating rotating machinery safely and efficiently. Traditional fault information description methods rely on experts to extract statistical features, which inevitably leads to the problem of information loss. As a result, this paper proposes an intelligent fault diagnosis of rolling bearings based on a continuous wavelet transform(CWT)-multiscale feature fusion and an improved channel attention mechanism. Different from traditional CNNs, CWT can convert the 1-D signals into 2-D images, and extract the wavelet power spectrum, which is conducive to model recognition. In this case, the multiscale feature fusion was implemented by the parallel 2-D convolutional neural networks to accomplish deeper feature fusion. Meanwhile, the channel attention mechanism is improved by converting from compressed to extended ways in the excitation block to better obtain the evaluation score of the channel. The proposed model has been validated using two bearing datasets, and the results show that it has excellent accuracy compared to existing methods.

A continuous wavelet and fast fourier transform-based single-phase adaptive auto-reclosing scheme for ehv transmission lines

A continuous wavelet and fast fourier transform-based single-phase adaptive auto-reclosing scheme for ehv transmission lines

Dynamic linkages between Islamic equity indices, oil prices, gold prices, and news-based uncertainty: New insights from partial and multiple wavelet coherence

This study examines the dynamic relationships between Islamic stocks, oil prices, gold prices, and global policy uncertainty. We used monthly data for the Dow Jones world Islamic stock index, oil prices, and global policy uncertainty from 1996M1 to 2018M12. We employed a cointegration approach, a discrete wavelet, a wavelet-based Granger causality test, a continuous wavelet, wavelet coherence, partial wavelet coherence, and multiple wavelet coherence. The findings of the study exhibit strong bi-directional Granger causality for both the original and decomposed series among the variables considered. Wavelet coherence revealed positive coherence between gold prices and Islamic stocks, negative coherence between gold prices and Islamic stocks due to global policy uncertainty, negative medium-run coherence between gold prices and oil prices during crisis periods, and positive coherence between oil prices and Islamic stocks. By adding new series, MWC always has a stronger correlation than PWC in the short, medium, and long term.

Constant-frequency time cells in the vortex-shedding from a square cylinder in accelerating flows

An extensive experimental campaign has been carried out in a multiple-fan wind tunnel to study the effects of flow acceleration on a sectional model of a sharp-edged square cylinder. Different levels of positive and negative acceleration are reproduced, which are compatible with those induced by full-scale thunderstorm outflows. Various initial and final conditions are also explored and, in all conditions, multiple test repeats are carried out in order to obtain satisfactory ensemble averages. Particular attention is devoted to the acquisition of signals associated with vortex-shedding, for which tailored time-frequency analyses, based on the continuous wavelet and Hilbert transforms, are introduced. Sensitivity analyses are carried out on a selection of the relevant parameters that better allow the tracing of the temporal variation of the shedding frequency. Time intervals in which the shedding frequency is constant, separated by discontinuities, are found during the transients. The number and extent of such constant-frequency time cells and discontinuities seem to be connected with the flow acceleration, but are not strictly repetitive. For higher levels of acceleration the ensemble mean of the Strouhal number is found to be comparable with or moderately lower than the steady-flow value corresponding to the instantaneous velocity.

Bridge modal property identification based on asynchronous mobile sensing data

There is a growing attention in real-time bridge condition assessment using data from drive-by vehicles as a potentially scalable approach. Most system identification methods are based on synchronized vibration data collection for this purpose. This study presents an approach for bridge modal identification that estimates high-resolution absolute value of the operational mode shapes using asynchronous mobile data. With each trip of a vehicular sensor, the spatio-temporal response of the bridge is sampled, along with various sources of noise, e.g. vehicle dynamics, environmental effects, road profile, etc. The crowdsourced modal identification using continuous wavelet (CMICW) method is proposed that gradually magnifies the bridge dynamical signatures and mitigates noise over the spatio-temporal map. The performance of the CMICW method is validated in an experimental setting. The method successfully identifies natural frequencies and absolute value of the operational mode shapes of a bridge with high resolution and accuracy. Notably, by including data collected from various bridge lanes, the method can reconstruct 3D representation of the mode shapes. The influence of the speed of the mobile sensors on the accuracy of the estimated modal properties is investigated as well. Using a hybrid simulation framework, the effect of vehicle dynamic is included in the mobile sensing data. The study shows that the CMICW method is successful in discounting the effect of vehicle dynamics, thereby strengthening the bridge modal information. Finally, a blind source separation technique is implemented to separate the effects of road irregularities, which further improves the accuracy of modal property estimates. This study contributes to the growing body of knowledge on mobile crowdsensing for physical properties of transportation infrastructure.

Estimation of earthquake frequency content and its effect on dynamic analysis using continuous and discrete wavelet transform

Estimation of earthquake frequency content and its effect on dynamic analysis using continuous and discrete wavelet transform

Bearing fault diagnosis based on wavelet sparse convolutional network and acoustic emission compression signals.

A bearing is an important and easily damaged component of mechanical equipment. For early fault diagnosis of ball bearings, acoustic emission signals are more sensitive and less affected by mechanical background noise. To cope with the large amount of data brought by the high sampling frequency and high sampling points of acoustic emission signals, a compressed sensing processing framework is introduced to research data compression and feature extraction, and a wavelet sparse convolutional network is proposed for resolved diagnosis and evaluation. The main research objective of this paper is to maximize the compression rate of the signal under the constraint of ensuring the reconstruction error of the acoustic emission signal, which can reduce the data volume of the acoustic emission signal and reduce the pressure of data analysis for subsequent fault diagnosis. At the same time, a wide convolution kernel based on a continuous wavelet is introduced when designing the neural network, and the energy information of different frequency bands of the signal is extracted by the wavelet convolution kernel to characterize the fault characteristics of the equipment. The energy pooling layer is designed to enhance the deep mining ability of compressed features, and the regularized loss function is introduced to improve the diagnostic accuracy and robustness through feature sparseness. The experimental results show that the method can effectively extract the fault characteristics of the bearing acoustic emission signal, improve the analysis efficiency and accurately classify the bearing faults.

Economic policy uncertainty and tourism nexus: time and frequency approaches

ABSTRACT This paper provides the relationship between Economic Policy Uncertainty (EPU) and international tourism activities in the BRIC countries (i.e. Brazil, Russia, India and China). The researchers used continuous wavelets, wavelet coherence and wavelet phase-difference based on Granger causality analysis to investigate the relationship between EPU and tourism activities using the annual data. The findings show that there is a unidirectional causal influence of EPU on international tourism arrivals (ITA) in the short run and mid run for Brazil. In addition, there is a bi-directional causal influence of EPU on ITA in China and Russia in the long run.

Geopolitical-Risk and Economic Policy—Uncertainty Impacts on Tourist Flows from Neighboring Countries: A Wavelet Analysis

This paper investigates the nexus of geopolitical risks (GPRs), economic policy uncertainty (EPU), and tourist arrivals in South Korea. Specifically, this research examines whether arrivals from neighboring tourism source countries (i.e., China and Japan) are influenced by geopolitical events and economic volatilities in South Korea. To establish the research purpose, we investigated the relationships among GPRs, EPU, and tourism demand by using monthly data from January 2003 to November 2019. Additionally, innovative techniques (continuous wavelets, wavelet coherency, and wavelet phase difference) were employed, which allow the decomposition of time series considering different time and frequency components. The results demonstrate inconsistent and heterogeneous co-movements between variables that are localized across different time periods and frequencies. In addition, we detected several significant coherencies that prove the important role of GPR and EPU in explaining changes in the numbers of tourists arriving in South Korea from China and Japan. In terms of time domain, negative and positive correlations in tourism demand were detected, meaning that economic and geopolitical shocks may not always lead to negative consequences. From the frequency domain, the causal effects of GPR mostly appear to have short- to mid-run implications, with almost no relationship in the low-frequency band, whereas EPU holds a heterogeneous influence varying short-term to long-term, including higher to lower frequencies. Results show the resilience of the tourism industry against the transient effects of economic and geopolitical shocks. Tourists become adversely affected by external events such as geopolitical risks and economic uncertainties, but the impact is not consistent over time for tourists from countries neighboring Korea. The findings provide a deeper understanding of how crisis events, including political instability and economic fluctuations, can affect inbound tourism in geographically and historically interrelated countries. Therefore, to minimize the negative effect on tourism demand, it is important for practitioners to consider potential external threats when making forecasts.

Sinusoidal Noise Removal in PD Measurement Based on Synchrosqueezing Transform and Singular Spectrum Analysis

In electrical engineering, partial discharge (PD) measurement has been widely used for inspecting and judging insulation conditions of high voltage (HV) apparatus. However, on-site PD measurement easily becomes contaminated by noises. Particularly, sinusoidal noise makes it difficult to recognize real PD signal, thus leading to the misjudgment of insulation conditions. Therefore, sinusoidal noise removal is necessary. In this paper, instantaneous frequency (IF) is introduced, and the synchrosqueezing transform (SST) as well as singular spectrum analysis (SSA) is proposed for sinusoidal noise removal. A continuous analytic wavelet transform is firstly applied to the noisy PD signal and then the time frequency representation (TFR) is reassigned by SST. Narrow-band sinusoidal noise has fixed IF, while PD signal has much larger frequency range and time-varying IF. Due to the difference, the reassigned TFR enables the sinusoidal noise to be distinguished from PD signal. After synthesizing the signal with the recognized IF, SSA is further applied to signal refinement. At last, a numerical simulation is carried out to verify the effectiveness of the proposed method, and its robustness to white noise is also validated. After the implementation of the proposed method, wavelet thresholding can be further applied for white noise reduction.

Automatic Classification of Cardiac Arrhythmias based on ECG Signals Using Transferred Deep Learning Convolution Neural Network

In the current article, an automatic classification of cardiac arrhythmias is presented using a transfer deep learning approach with the help of electrocardiography (ECG) signal analysis. Now a days, an ECG waveform serves as a powerful tool used for the analysis of cardiac arrhythmias (irregularities). The goal of the present work is to implement an algorithm based on deep learning for classification of different cardiac arrhythmias. Initially, the one dimensional (1-D) ECG signals are transformed to two dimensional (2-D) scalogram images with the help of Continuous Wavelet(CWT). Four different categories of ECG waveform were selected from four PhysioNet MIT-BIH databases, namely arrhythmia database, Normal Sinus Rhythm database, Malignant Ventricular Ectopy database and BIDMC Congestive heart failure database to examine the proposed technique. The major interest of the present study is to develop a transferred deep learning algorithm for automatic categorization of the mentioned four different heart diseases. Final results proved that the 2-D scalogram images trained with a deep convolutional neural network CNN with transfer learning technique (AlexNet) pepped up with a prominent accuracy of 95.67%. Hence, it is worthwhile to say the above stated algorithm demonstrates as an effective automated heart disease detection tool

Optimum ECG Signal Filtering Based on Wavelet Transformation

The development of digital signal processing and microprocessor technology creates conditions for improving methods for diagnosing the functional state of organs. Wavelet analysis is a modern and promising method of information processing. In order to determine the effective optimal filtering of the electrocardiography signal based on the wavelet transform, wavelet filtering was performed using wavelets of different families, the efficiency of using different levels of decomposition, me¬thods for calculating the threshold and types of the threshold function was investigated. Aim. Determination of effective optimal filtering of electrocardiography signal based on wavelet transform. Materials and methods. Cardiograms were taken for analysis. Then they were digitized and entered into a computer for processing. A program was written in the Matlab environment that implements continuous and discrete wavelet transform. Results. As a result of the research, 56 combinations of noise reduction parameters were tested for three noise levels. It was found that the maximum degree of signal purification from noise was obtained using the Coiflets 5 wavelet using a rigid thresholding method, with a heuristic method for calculating the threshold value. Wavelet Simlet 8 has lower correlation coefficient values than Coiflets 5, at 35 dB the best result is 97%, the noise level is 40 dB the best result is 98.7%, the noise level is 45 dB the best result is 99.3%, which is generally negligible differs from the correlation coefficients of the wavelet Coiflets 5. Conclusion. As a result of the study, the first and the present work, the following conclusions were made: the optimal level of the wavelet decomposition of the ECG signal N = 2; the maximum degree of signal cleaning from noise was obtained using the Coiflets 5 wavelet using a rigid thresholding method, with a heuristic method for calculating the threshold value; Simlet 8 wavelet using a soft thresholding method with a minimax thresholding method also shows noteworthy results, slightly inferior to Coiflets 5 wavelet results.

Wavelet Based and Statistical EEG Analysis in Patients with Schizophrenia

Schizophrenia, which is considered a serious mental disorder and a psychological illness, is quite common in society today. Schizophrenia manifests itself with disordered thought development, hallucinations, and different behaviours and reactions. In this study, EEG data were collected and analyzed on a total of 84 subjects diagnosed with normal and schizophrenia. EEG data used as a diagnostic tool with low-resolution level has been used to distinguish between schizophrenic and normal individuals. First of all, statistical methods were used in the analyzes, and also frequency properties of the data were extracted by Wavelet analysis. As a result of the analysis, statistical findings include characteristics that distinguish between diagnosed schizophrenia and normal individuals. In addition, the findings obtained as a result of the Wavelet analysis were determined to distinguish between normal and schizophrenic individuals. While the mean used in statistical analysis takes the value 1.6 for normal individuals, it takes the value 2.9 for individuals diagnosed with schizophrenia. Also, in the results of Continuous Wavelet (CW) Analysis, very important findings were obtained in terms of detection in scale 16 and 64 bands.

A note on continuous fractional wavelet transform in ℝn

In this paper, we study the continuous fractional wavelet transform (CFrWT) in [Formula: see text]-dimensional Euclidean space [Formula: see text] with scaling parameter [Formula: see text] such that [Formula: see text]. We obtain inner product relation and reconstruction formula for the CFrWT depending on two wavelets along with the reproducing kernel function, involving two wavelets, for the image space of CFrWT. We obtain Heisenberg’s uncertainty inequality and Local uncertainty inequality for the CFrWT. Finally, we prove the boundedness of CFrWT on the Morrey space [Formula: see text] and estimate [Formula: see text]-distance of the CFrWT of two argument functions with respect to different wavelets.