Wavelet Transform-based Approach Research Articles

Multi-field coupling vibration patterns of the multiphase sink vortex and distortion recognition method

Fluid-induced vibration sensing technology plays an essential role in the safety and stable operation of fluid control equipment. The sensing works oriented to the multiphase sink vortex-induced vibration (MSVIV) face significant challenges in state recognition, such as the aero-engine oil pipe, metallurgical casting ladle, and microfluidic equipment. This paper presents a multi-field coupling vibration modelling and solving strategy to investigate the MSVIV transition patterns. Based on the volume of fluid and level set method, an efficient volume-correction strategy is presented to consider the mass variation and keep velocity fields without divergence. Then, a fluid–structure-acoustic dynamic model is conducted to explore multiphase vortex transport regularities. Considering the solution singularity of the fluid–solid-acoustic coupling system, a residue theorem-based solution strategy is presented to obtain the MSVIV evolution behaviors. Finally, a multichannel sensing MSVIV water-model experiment (MSVIV-WME) platform is conducted to verify the above results, and a wavelet transform-based processing approach can be utilized to obtain mutation energy features. Research results illustrate that the proposed strategy has revealed the MSVIV transition behaviors. The MSVIV process in critical states dominates nonlinear vibration patterns. The self-development sensing platform can monitor the MSVIV evolution process in real-time, and the wavelet transform-based sensing attribute with the dw3 frequency band can identify distortion characteristics. Relevant results can offer useful references for MSVIV state recognition and offer technical solving strategies for microfluidics monitoring equipment.

An empirical wavelet transform-based approach for motion artifact removal in electroencephalogram signals

Motion artifacts reduce the quality of information in the electroencephalogram (EEG) signals. In this study, we have developed an effective approach to mitigate the motion artifacts in EEG signals by using empirical wavelet transform (EWT) technique. Firstly, we decompose EEG signals into narrowband signals called intrinsic mode functions (IMFs). These IMFs are further processed to suppress the artifacts. In our first approach, principal component analysis (PCA) is employed to suppress the noise from these decomposed IMFs. In the second approach, the IMFs with noisy components are identified using the variance measure, which are then removed to obtain the artifact-suppressed EEG signal. Our experiments are conducted on a publicly available Physionet dataset of EEG signals to demonstrate the effectiveness of our approach in suppressing motion artifacts. More importantly, the IMF-variance-based approach has provided significantly better performance than the EWT-PCA based approach. Also, the IMF-variance based approach is computationally more efficient than the EWT-PCA based approach. Our proposed IMF-variance based approach achieved an average signal to noise ratio (ΔSNR) of 28.26 dB and surpassed the existing methods developed for motion artifact removal.

Development of multi-scale indices of human mobility restriction during the COVID-19 based on air quality from local and global NO2 concentration

This study investigated whether variability in air quality, especially related to vehicular emissions, during the COVID-19 pandemic could indicate social distancing. Data from in situ measurements and satellite estimates were used. The study areas were São Paulo, Brazil, and Bologna, Italy. We focused our analysis on NO2, a combustion-derived pollutant, because of its availability in surface stations and satellite tracking, and because it has a short atmospheric lifetime. The analyses included graphical, statistical, and wavelet transform-based approaches to understand NO2 concentrations before and during the pandemic. After confirming the reduction in vehicular emissions during the pandemic, we created normalized indices to assess the social remoteness in 2020 in different locations, with a focus on São Paulo and Bologna. These indices were compared to existing indices based on cell phone mobility. The indices proposed in this study suffered high sensitivity to social distance compared to existing ones and helped to understand the actual application of social distance and contamination rates, considering the various dimensions of the problem.

Do oil and natural gas prices affect carbon efficiency? Daily evidence from China by wavelet transform-based approaches

This study analyzes the impacts of oil and natural gas prices on China's carbon efficiency using wavelet transform-based time series methods. To this end, the study applies the continuous wavelet transform (CWT), wavelet transform coherence (WTC), and a time-varying wavelet causality test (TVWCT) to daily data during the period from October 31, 2012 to November 3, 2022. The CWT results show that there are volatilities in oil prices due to the coronavirus 2019 (COVID-19) pandemic, in natural gas prices because of the Russia-Ukraine war, and in carbon efficiency resulting from the Paris Climate Agreement. The WTC results demonstrate that increasing oil prices promote carbon efficiency in the 2014–2016 and 2018–2020 periods, while natural gas prices have a weak impact on carbon efficiency in 2022. Moreover, the TVWCT results prove that there is a strong causality leading from oil and natural gas prices to carbon efficiency in the long run. These results highlight that China can use volatilities in oil and natural gas prices as a policy tool to enhance carbon efficiency.

A Deep Learning and Fast Wavelet Transform-Based Hybrid Approach for Denoising of PPG Signals

This letter presents a novel hybrid method that leverages deep learning to exploit the multi-resolution analysis capability of the wavelets, in order to denoise a photoplethysmography (PPG) signal. Under the proposed method, a noisy PPG sequence of length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> is first decomposed into <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L$</tex-math></inline-formula> detailed coefficients using the fast wavelet transform (FWT). Then, the clean PPG sequence is reconstructed with the help of a custom feedforward neural network (FFNN) that provides the binary weights for each of the wavelet sub-signals outputted by the inverse-FWT block. This way, all those sub-signals which correspond to noise or artefacts are discarded during reconstruction. The FFNN is trained on the Beth Israel Deaconess Medical Center (BIDMC) dataset and a custom video-PPG dataset, whereby we compute the mean squared-error (MSE) between the denoised sequence and the reference clean PPG signal, and compute the gradient of the MSE for the back-propagation. Simulation results reveal that our proposed method reduces the MSE of the PPG signal significantly (compared to the MSE of the original noisy PPG signal): by 56.40% for Gaussian noise, by 64.01% for Poisson noise, 46.02% for uniform noise, and by 72.36% for salt-and-pepper noise (with ‘db10’ mother wavelet).

WavelNet: A novel convolutional neural network architecture for arrhythmia classification from electrocardiograms

Background and objectiveAutomated detection of arrhythmias from electrocardiograms (ECGs) can be of considerable assistance to medical professionals in providing efficient treatment for patients with cardiovascular diseases. In recent times, convolutional neural network (CNN)-based arrhythmia classification models have been introduced, but their decision-making processes remain unclear and their performances are not reproducible. This paper proposes an accurate, interpretable, and reproducible end-to-end arrhythmia classification model based on a novel CNN architecture named WavelNet, which is interpretable and optimal for dealing with ECGs. MethodsInspired by SincNet, which is capable of band-pass filtering-based spectral analysis, WavelNet was devised to achieve wavelet transform-based spectral analysis. WavelNet was trained using a subject-oriented five-class ECG arrhythmia dataset generated from the MIT-BIH Arrhythmia Database while following a benchmark scheme. By adopting various mother wavelets, multiple WavelNet-based arrhythmia classification models were implemented. To investigate whether our wavelet transform-based approach outperforms original end-to-end and band-pass filtering-based approaches, our proposed models were compared with vanilla CNN- and SincNet-based models. Model implementation and evaluation processes were repeated ten times in a Google Colab Pro+ environment. Furthermore, our most successful model was compared with state-of-the-art arrhythmia classification models for performance evaluation. ResultsThe proposed WavelNet-based models showed excellent performance on classifying non-ectopic, supraventricular ectopic, and ventricular ectopic beats because of their ability to perform adaptive spectral analysis while preserving temporal ECG information compared with vanilla CNN- and SincNet-based models. In particular, a Symlet 4 wavelet-adopting WavelNet-based model achieved the best performance with nearly 90% overall accuracy as well as the highest levels of sensitivity in classifying each arrhythmia class: 91.4%, 49.3%, and 91.4% for non-ectopic, supraventricular ectopic, and ventricular ectopic beat classifications, respectively. These results were comparable to those of state-of-the-art models. In addition, the results are reproducible, which differentiates our study from previous studies. ConclusionsOur proposed WavelNet-based arrhythmia classification model achieved remarkable performance based on a reasonable decision-making process, in comparison with other models. As its noteworthy performance is clinically reasonable and reproducible, our proposed model can contribute toward implementing a real-world precision healthcare system for patients with cardiovascular diseases.

Discrete Wavelet Transform-based Approach of Real-time Wave Filtering for Dynamic Positioning of Marine Vessels

The ongoing development of deep-sea resources has contributed toward the widespread use of dynamic positioning (DP) systems that can operate in arbitrary sea areas without the limitation of operating water depth compared to mooring systems. The second-order slowly varying forces can be compensated by DP feedback control. Wave filtering techniques are crucial for designing the DP controller, which can improve the positioning performance and service of multiple actuators. The objective of wave filtering is to separate the motion induced by the first-order high-frequency oscillatory waves from the motion caused by the second-order low-frequency (LF) slowly varying disturbances. This paper proposes a novel real-time wave filtering approach for DP systems on the basis of the discrete wavelet transform without relying on the previous knowledge of dynamics of the vessel. Meanwhile, to suppress the end effects of wavelet filtering and achieve real-time application, symmetric boundary extension and the sliding window method are investigated in order to capture the slow time-varying LF motion of DP vessels. The proposed real-time wavelet filtering approach can significantly improve the quality of the separated components by selecting the optimal decomposition level, wavelet function parameters, and threshold denoising method. The real-time and filtering performance of the proposed approach is verified using a scaled model of an offshore supply vessel as well as full-scale experiments on the EXPLOERE I scientific investigation vessel under the action of winds, currents, and waves. The experimental results confirm that the proposed wave filtering algorithm is efficient and that the LF component waveform can be effectively recovered after wavelet filtering.

A Novel Method for Real Time Protection of DC Microgrid Using Cumulative Summation and Wavelet Transform

DC microgrid is a compact framework comprising interconnected nearby sources and loads. The renewable energy source used in DC microgrids being intermittent leads to the change in the power availability as well as the fault current levels. In such situations, detecting and clearing the faults is very important to protect the DC microgrid without compromising on fault clearing time and interruption of the load. This paper proposes a hybrid Cumulative Sum (CumSum) and Wavelet transform-based approach to detect the fault. The CumSum value raises the amplitude by averaging the fault current. Wavelet transforms obtain important fault current features by decomposing the current signal. The hybrid method of CumSum and Wavelet analysis proposed here enables the detection of the fault and differentiates the fault condition from sudden load variation. Additionally, it helps to recognize the location of the fault by the wavelet energy difference. The proposed scheme is tested with a developed ring-type low voltage DC (LVDC) microgrid hardware model under various fault conditions. The scheme is implemented using TMS320F28069 digital signal processors (DSP) of Texas Instruments. The hardware results are validated using MATLAB simulation. The proposed method performance is also compared with the existing methods used for DC microgrid protection. The outcome shows that the proposed method has a high accuracy of 98.72%, selectivity of 96.08%, and reliability of 99.01%. The execution time required by the proposed method is also less.

Melanoma Skin Cancer Detection Using Wavelet Transform and Local Ternary Pattern

Melanoma is the most serious form of skin cancer that affects millions of people globally. Through image analytics, early identification of skin cancer is enabled, resulting in more effective treatment and a lower mortality rate. The ph2 and human against machine datasets were used to collect images. After preprocessing the image with a weighted median filter, segmentation is investigated using a number of common techniques, with the best result generated by combining watershed transform and maximum similarity region merging. U-net architecture is explored for segmentation. Segmentation efficiency is calculated by dice loss and Jaccard coefficient. Segmentation architecture outperform the conventional method. Additionally, a novel wavelet transform-based approach is used to extract features, followed by local ternary pattern analysis. The intersection of the histograms, the Bhattacharya distance, the Chi-square distance, and the Pearson correlation coefficients are all computed. This inquiry makes use of only the Histogram intersection and Chi-square distance characteristics. Additional categorization is examined through the use of a range of machine learning algorithms, including the k-nearest neighbour approach, Bayesian classification, decision trees, and Support Vector Machines (SVM). When a Radial Basis Function (RBF) kernel based SVM is applied, the classification accuracy is maximised. This work is entirely devoted to binary categorization. As evidenced by the data, they outperform other state-of-the-art approaches reported in the literature. SVM classifies data with an accuracy of 98.6 percent. Weighted median filter, Watershed transform, Merging regions with the highest degree of similarity, Wavelet transform, Local Ternary Pattern, Histogram intersection Pearson correlation coefficient, chi-square distance Distance between Bhattacharya and support vector machine.

Extraction of Weak Spectroscopic Signals with High Fidelity: Examples from ESR.

Noise impedes experimental studies by reducing signal resolution and/or suppressing weak signals. Signal averaging and filtering are the primary methods used to reduce noise, but they have limited effectiveness and lack capabilities to recover signals at low signal-to-noise ratios (SNRs). We utilize a wavelet transform-based approach to effectively remove noise from spectroscopic data. The wavelet denoising method we use is a significant improvement on standard wavelet denoising approaches. We demonstrate its power in extracting signals from noisy spectra on a variety of signal types ranging from hyperfine lines to overlapped peaks to weak peaks overlaid on strong ones, drawn from electron-spin-resonance spectroscopy. The results show that one can accurately extract details of complex spectra, including retrieval of very weak ones. It accurately recovers signals at an SNR of ∼1 and improves the SNR by about 3 orders of magnitude with high fidelity. Our examples show that one is now able to address weaker SNR signals much better than by previous methods. This new wavelet approach can be successfully applied to other spectroscopic signals.

Wavelet transform-based multipitch estimation in polyphonic music

In this paper, a Wavelet transform-based approach for estimation of multipitch in music signal has been proposed. Among the Morlet Wavelet (MW), Mexican hat, and Shannon wavelet that belong to the widely used wavelets in different applications, the Morlet wavelet performs well for estimation of pitch in polyphonic music signals. This is why a method involving modification of the Morlet wavelet has been proposed for achieving better accuracy in estimation of multiple pitches in polyphonic music. Performance of the Modified Morlet Wavelet (MMW) based Multipitch Estimation (MPE) scheme has been compared with that of a method based on Fast Fourier Transform and another based on the original Morlet Wavelet, in terms of percentage Gross Pitch Error (GPE). Piano chord data base and Standard music IOWA data base have been used for performance evaluation of the proposed scheme. Simulation results show that percentage error in pitch (described by the fundamental frequency) is minimum for the proposed i.e. MMW-based method.

An empirical wavelet transform-based approach for cross-terms-free Wigner–Ville distribution

This paper presents an efficient methodology based on empirical wavelet transform (EWT) to remove cross-terms from the Wigner–Ville distribution (WVD). An EWT-based filter bank method is suggested to remove the cross-terms that occur due to nonlinearity in modulation. The mean-square error-based filter bank bandwidth selection is done which has been applied for the boundaries selection in EWT. In this way, a signal-dependent adaptive boundary selection is performed. Thereafter, energy-based segmentation is applied in time domain to eliminate inter-cross-terms generated between components. Moreover, the WVD of all the components is added together to produce a complete cross-terms-free time–frequency distribution. The proposed method is compared with other existing methods, and normalized Renyi entropy measure is also computed for validating the performance.

A wavelet transform-based capacity curve estimation approach using seismic response data

This paper presents an automated method for derivation of capacity curve from measured structural acceleration response. Based on the decomposition by wavelet transform method, the structural behavior is directly evaluated in the form of a capacity curve of the predominant response, as a relationship between the spectral force and spectral displacement. It enables the user to judge the performance of the building quickly after an earthquake. The feasibility of the proposed method is demonstrated through numerical simulations, full-scale shaking-table tests, and actual response of a steel tower during 2016 Fukushima earthquake.

On the diagnosis of idiopathic Parkinson’s disease using continuous wavelet transform complex plot

Gait analysis provides valuable motor deficit quantitative information about Parkinson’s disease patients. Detection of gait abnormalities is key to preserving healthy mobility. The goal of this paper is to propose a novel gait analysis and continuous wavelet transform-based approach to diagnose idiopathic Parkinson’s disease. First, we eliminate the noise resulting from orientation changes of test subjects by filtering the continuous wavelet transform output below 0.8 Hz. Next, we analyze the complex plot output above 0.8 Hz, which takes an ellipse, and calculate the area using $$95\%$$ confidence level. We found out that this ellipse area, along with the mean continuous wavelet transform output value, and the peak of the temporal signal are excellent features for classification. Experiments using Artificial Neural Networks on the Physionet database produced an accuracy of $$97.6\%$$ . Furthermore, we have shown an association between the Parkinson’s disease severity stage and the ellipse complex plot area with a 97.8% overall accuracy. Based on the results, we could effectively recognize the gait patterns and distinguish apart Parkinson’s disease patients with varying severity from healthy individuals.

A Study on Efficacy of Wavelet Transform for Damage Identification in Reinforced Concrete Buildings

Structural damage and its extent can be detected by vibration-based techniques to avoid failure or to minimize maintenance. Among different damage identification techniques, modal curvature approaches are widely researched and applied one. On the contrary, wavelet transformation (WT) methods are gaining popularity in damage identification of real life buildings because of their suitability for non-stationary signals and non-dependency on baseline data. This paper presents a novel approach utilizing complex continuous wavelet to effectively locate change in physical properties of reinforced concrete (RC) buildings by virtue of variation in frequency and mode shapes due to small change in mass and stiffness. In this paper, the effect of variation of mass and stiffness of a building on the modal parameters is established analytically using theequation of motion for a multi-degree freedom system under forced vibration condition. A 3-D finite element model was developed for predicting the modal frequencies and mode shapes of the scaled down six storey RC building and the effect of addition of mass on a particular level of structure on the modal parameters was studied. Further, acceleration time histories were recorded with variation in mass on 3rd story of building using wireless tri-axial accelerometers and the time histories were processed to arrive at Curvature Damage Factor and wavelet coefficients for identification of the additional load on the particular floor. Vibration responses from all floors of RC building in ambient and loaded conditions were analyzed for frequency response spectra (FRS). Mode shapes were drawn for unloaded case and loaded cases. It was observed that the modal frequency of building decreases with the increase in mass at floors. It is observed that CDF approach could detect the change in mass in both numerical and experimental results. However, CDF algorithm could not detect the addition of load in case 1, 2 and 3, i.e. when load was less than 25 kg, i.e. only 2.6% of floor mass (960 kg). The acquired data for the above stated load cases were analyzed using complex Gaussian ‘cgau5’ wavelet in MATLAB toolbox to determine the singularity in the signal in terms of wavelet coefficient modulus. It is observed that the WT approach is able to precisely locate the change in physical parameters of the RC model building. However, it is seen that additional load could not be detected in case where only 9 kg, i.e. 0.93% of the total floor mass, was placed on 3rd floor. From the research work, it is observed that CDF technique is inefficient in damage detection and always demand prior baseline information, which is usually difficult to obtain in practice. However, the wavelet transform-based approach more accurately detects the location of change without relying on intact state vibration data.

Wavelet transform-based approach for processing ultrasonic B-scan images

Wavelet transform-based approach for processing ultrasonic B-scan images

Wavelet Transform-Based &lt;sc&gt;EMI&lt;/sc&gt; Noise Mitigation in Power Converter Topologies

This paper proposes a novel wavelet transform-based approach for electromagnetic interference (EMI) noise mitigation in power electronics topologies. The switching action of power semiconductor devices generates EMI in power converter modules which exceeds the noise limit set by the regulation of CISPR11. In order to cancel the EMI noise, the wavelet transform-based time-frequency localization approach has been used for generating a noise cancellation signal. The effectiveness of the proposed method has been evaluated in the terms of signal-to-noise ratio (SNR). Further, the effect of variance of the noise amplitude has been taken into consideration on the SNR. A relationship between the standard deviation, which is proportional to square root of the variance of the signal, and the SNR has been established. The proposed method has been implemented on the different power electronics topologies such as buck converter, boost converter, three-phase voltage source inverter, and Z-source inverter-based ac drive. The proposed method achieves the reduction of noise level below the safe limit set by CISPR11.

A Wavelet Transform-Based Approach for Detecting Postural Transitions in Patients Following Anterior Cruciate Ligament Reconstruction1

A Wavelet Transform-Based Approach for Detecting Postural Transitions in Patients Following Anterior Cruciate Ligament Reconstruction1

Modular continuous wavelet processing of biosignals: extracting heart rate and oxygen saturation from a video signal.

A novel method of extracting heart rate and oxygen saturation from a video-based biosignal is described. The method comprises a novel modular continuous wavelet transform approach which includes: performing the transform, undertaking running wavelet archetyping to enhance the pulse information, extraction of the pulse ridge time–frequency information [and thus a heart rate (HRvid) signal], creation of a wavelet ratio surface, projection of the pulse ridge onto the ratio surface to determine the ratio of ratios from which a saturation trending signal is derived, and calibrating this signal to provide an absolute saturation signal (SvidO2). The method is illustrated through its application to a video photoplethysmogram acquired during a porcine model of acute desaturation. The modular continuous wavelet transform-based approach is advocated by the author as a powerful methodology to deal with noisy, non-stationary biosignals in general.

Wavelet Transform-Based Approach to Defect Identification in Railway Carbon Contact Strips

Pantographs of electric rail vehicles are fitted with carbon contact strips, which slide along the contact wire of catenary to provide constant electrical contact. Contact strips are exposed to wear and damages. Using damaged contact strips significantly increases the risk of catenary rupture. Therefore, their technical condition has to be inspected frequently. In previous work a 3D laser scanning system was proposed for recording contact strips surface shape when vehicles pass an inspection point on a railway line. The system was complimented with an effective automatic wear estimation algorithm. However, this algorithm is insensitive to some shallow defects which are considered hazardous. This paper presents defects identification approach based on differentiation of contact strips profiles. It is assumed that the hazard of catenary rupture corresponds to the peak value in the differentiated profile. A wavelet transform was used to allow for robust differentiation of profiles featured by low signal-to-noise ratio. A method for choosing well-balanced dilatation parameter was proposed, in order to obtain good accuracy of differentiation of both smooth and narrow-edged contact strip defects. Results of simulations implemented in Matlab confirm that the proposed approach can be applied to an algorithm aiming for automatic defects identification.DOI: http://dx.doi.org/10.5755/j01.eee.21.6.13755