Showing papers on "Wavelet published in 2021"

WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis

Abstract: Convolutional neural network (CNN), with the ability of feature learning and nonlinear mapping, has demonstrated its effectiveness in prognostics and health management (PHM). However, an explanation on the physical meaning of a CNN architecture has rarely been studied. In this article, a novel wavelet-driven deep neural network, termed as WaveletKernelNet (WKN), is presented, where a continuous wavelet convolutional (CWConv) layer is designed to replace the first convolutional layer of the standard CNN. This enables the first CWConv layer to discover more meaningful kernels. Furthermore, only the scale parameter and translation parameter are directly learned from raw data at this CWConv layer. This provides a very effective way to obtain a customized kernel bank, specifically tuned for extracting defect-related impact component embedded in the vibration signal. In addition, three experimental studies using data from laboratory environment are carried out to verify the effectiveness of the proposed method for mechanical fault diagnosis. The experimental results show that the accuracy of the WKNs is higher than CNN by more than 10%, which indicate the importance of the designed CWConv layer. Besides, through theoretical analysis and feature map visualization, it is found that the WKNs are interpretable, have fewer parameters, and have the ability to converge faster within the same training epochs.

Wavelet analysis of impact of renewable energy consumption and technological innovation on CO2 emissions: evidence from Portugal

Abstract: This paper uncover a new perception of the dynamic interconnection between CO2 emission and economic growth, renewable energy use, trade openness, and technological innovation in the Portuguese economy utilizing innovative Morlet wavelet analysis. The research applied continuous wavelet transform, wavelet correlation, the multiple and partial wavelet coherence, and frequency domain causality analyses are applied on variables of investigation using dataset between 1980 and 2019. The result of these analyses disclosed that the interconnection among the indicators progresses over time and frequency. The present analysis finds notable wavelet coherence and significant lead and lag interconnections in the frequency domain, while conflicting relationships among the variables are found in the time domain. The wavelet analysis according to economic viewpoint affirms that renewable energy consumption helps to curb CO2 while trade openness, technological innovation, and economic growth contribute to CO2. The outcomes also proposed that renewable energy consumption decreases CO2 in medium and long run in Portugal. Therefore, policymakers in Portugal should stimulate investment in renewable energy sources, establish restrictive laws, and enhance energy innovation.

Accurate Magnetic Resonance Image Super-Resolution Using Deep Networks and Gaussian Filtering in the Stationary Wavelet Domain

Abstract: In this correspondence, we present an accurate Magnetic Resonance (MR) image Super-Resolution (SR) method that uses a Very Deep Residual network (VDR-net) in the training phase. By applying 2D Stationary Wavelet Transform (SWT), we decompose each Low Resolution (LR)-High Resolution (HR) example image pair into its low-frequency and high-frequency subbands. These LR-HR subbands are used to train the VDR-net through the input and output channels. The trained parameters are then used to generate residual subbands of a given LR test image. The obtained residuals are added with their LR subbands to produce the SR subbands. Finally, we attempt to maintain the intrinsic structure of images by implementing the Gaussian edge-preservation step on the SR subbands. Our extensive experimental results show that the proposed MR-SR method outperforms the existing methods in terms of four different objective metrics and subjective quality.

Modified Stacked Auto-encoder Using Adaptive Morlet Wavelet for Intelligent Fault Diagnosis of Rotating Machinery

Abstract: Intelligent fault diagnosis techniques play an important role in improving the abilities of automated monitoring, inference, and decision-making for the repair and maintenance of machinery and processes In this paper, a modified stacked auto-encoder (MSAE) that uses adaptive Morlet wavelet is proposed to automatically diagnose various fault types and severities of rotating machinery Firstly, the Morlet wavelet activation function is utilized to construct an MSAE to establish an accurate nonlinear mapping between the raw nonstationary vibration data and different fault states Then, the nonnegative constraint is applied to enhance the cost function to improve sparsity performance and reconstruction quality Finally, the fruit fly optimization algorithm (FOA) is used to determine the adjustable parameters of the Morlet wavelet to flexibly match the characteristics of the analyzed data The proposed method is used to analyze the raw vibration data collected from a sun gear unit and a roller bearing unit Experimental results show that the proposed method is superior to other state-of-the-art methods

BayesWave analysis pipeline in the era of gravitational wave observations

Abstract: We describe updates and improvements to the BayesWave gravitational wave transient analysis pipeline, and provide examples of how the algorithm is used to analyze data from ground-based gravitational wave detectors. BayesWave models gravitational wave signals in a morphology-independent manner through a sum of frame functions, such as Morlet-Gabor wavelets or chirplets. BayesWave models the instrument noise using a combination of a parametrized Gaussian noise component and nonstationary and non-Gaussian noise transients. Both the signal model and noise model employ trans-dimensional sampling, with the complexity of the model adapting to the requirements of the data. The flexibility of the algorithm makes it suitable for a variety of analyses, including reconstructing generic unmodeled signals; cross-checks against modeled analyses for compact binaries; as well as separating coherent signals from incoherent instrumental noise transients (glitches). The BayesWave model has been extended to account for gravitational wave signals with generic polarization content and the simultaneous presence of signals and glitches in the data. We describe updates in the BayesWave prior distributions, sampling proposals, and burn-in stage that provide significantly improved sampling efficiency. We present standard review checks indicating the robustness and convergence of the BayesWave trans-dimensional sampler.

To what extent does COVID-19 drive stock market volatility? A comparison between the U.S. and China

Abstract: This paper presents a novel wavelet-based quantile-on-quantile method for comparing the impact of COVID-19 on stock market volatility between the U.S. and China. Wavelet decomposition shows that th...

Time-frequency super-resolution with superlets.

Abstract: Due to the Heisenberg–Gabor uncertainty principle, finite oscillation transients are difficult to localize simultaneously in both time and frequency. Classical estimators, like the short-time Fourier transform or the continuous-wavelet transform optimize either temporal or frequency resolution, or find a suboptimal tradeoff. Here, we introduce a spectral estimator enabling time-frequency super-resolution, called superlet, that uses sets of wavelets with increasingly constrained bandwidth. These are combined geometrically in order to maintain the good temporal resolution of single wavelets and gain frequency resolution in upper bands. The normalization of wavelets in the set facilitates exploration of data with scale-free, fractal nature, containing oscillation packets that are self-similar across frequencies. Superlets perform well on synthetic data and brain signals recorded in humans and rodents, resolving high frequency bursts with excellent precision. Importantly, they can reveal fast transient oscillation events in single trials that may be hidden in the averaged time-frequency spectrum by other methods. Identifying the frequency, temporal location, duration, and amplitude of finite oscillation packets in neurophysiological signals with high precision is challenging. The authors present a method based on multiple wavelets to improve the detection of localized time-frequency packets.

The Imperativeness of Environmental Quality in China Amidst Renewable Energy Consumption and Trade Openness

Abstract: It is widely accepted that CO2 emissions are the primary cause of climate change and environmental destruction. China, the world’s biggest carbon emitter, is the subject of this research. Utilizing the wavelet tools (wavelet correlation, wavelet coherence, multiple wavelet coherence, and partial wavelet coherence), the present study intends to capture the time-frequency dependence between CO2 emissions and renewable energy, economic growth, trade openness, and energy usage in China between 1965 and 2019. The advantage of the wavelet tools is that they can differentiate between short, medium, and long-run dynamics over the period of study. Furthermore, the study utilized the gradual shift causality test to capture the causal interconnection between CO2 emissions and the regressors. The findings from Bayer and Hanck showed a long-run relationship among the variables of interest. Furthermore, the findings from the wavelet coherence test revealed a positive relationship between CO2 emissions and economic growth and energy usage at all frequencies. Although there is a weak negative relationship between renewable energy and CO2 emissions in the short run, there is no significant co-movement between CO2 emissions and trade openness. The outcomes of the partial and multiple wavelet coherence also give credence to the outcomes of the wavelet coherence test. Lastly, the gradual shift causality test revealed a one-way causality from energy usage and economic growth to CO2 emissions. Based on the findings, suitable policy suggestions were proposed.

LSTM Recurrent Neural Network Classifier for High Impedance Fault Detection in Solar PV Integrated Power System

Abstract: This paper presents the detection of High Impedance Fault (HIF) in solar Photovoltaic (PV) integrated power system using recurrent neural network-based Long Short-Term Memory (LSTM) approach. For study this, an IEEE 13-bus system was modeled in MATLAB/Simulink environment to integrate 300 kW solar PV systems for analysis. Initially, the three-phase current signal during non-faulty (regular operation, capacitor switching, load switching, transformer inrush current) and faulty (HIF, symmetrical and unsymmetrical fault) conditions were used for extraction of features. The signal processing technique of Discrete Wavelet Transform with db4 mother wavelet was applied to extract each phase’s energy value features for training and testing the classifiers. The proposed LSTM classifier gives the overall classification accuracy of 91.21% with a success rate of 92.42 % in identifying HIF in PV integrated power network. The prediction results obtained from the proffered method are compared with other well-known classifiers of K-Nearest neighbor’s network, Support vector machine, J48 based decision tree, and Naive Bayes approach. Further, the classifier’s robustness is validated by evaluating the performance indices (PI) of kappa statistic, precision, recall, and F-measure. The results obtained reveal that the proposed LSTM network significantly outperforms all PI compared to other techniques.

Object-Based Image Retrieval Using the U-Net-Based Neural Network.

Abstract: Day by day, all the research communities have been focusing on digital image retrieval due to more internet and social media uses. In this paper, a U-Net-based neural network is proposed for the segmentation process and Haar DWT and lifting wavelet schemes are used for feature extraction in content-based image retrieval (CBIR). Haar wavelet is preferred as it is easy to understand, very simple to compute, and the fastest. The U-Net-based neural network (CNN) gives more accurate results than the existing methodology because deep learning techniques extract low-level and high-level features from the input image. For the evaluation process, two benchmark datasets are used, and the accuracy of the proposed method is 93.01% and 88.39% on Corel 1K and Corel 5K. U-Net is used for the segmentation purpose, and it reduces the dimension of the feature vector and feature extraction time by 5 seconds compared to the existing methods. According to the performance analysis, the proposed work has proven that U-Net improves image retrieval performance in terms of accuracy, precision, and recall on both the benchmark datasets.

Automated accurate speech emotion recognition system using twine shuffle pattern and iterative neighborhood component analysis techniques

Abstract: Speech emotion recognition is one of the challenging research issues in the knowledge-based system and various methods have been recommended to reach high classification capability. In order to achieve high classification performance in speech emotion recognition, a nonlinear multi-level feature generation model is presented by using cryptographic structure. The novelty of this work is the use of cryptographic structure called shuffle box for feature generation and iterative neighborhood component analysis to select the features. The proposed method has three main stages: (i) multi-level feature generation using Tunable Q wavelet transform (TQWT), (ii) twine shuffle pattern (twine-shuf-pat) for feature generation, and (iii) discriminative features are selected using iterative neighborhood component analysis (INCA) and classified. The TQWT is a multi-level wavelet transformation method used to generate high-level, medium-level, and low-level wavelet coefficients. The proposed twine-shuf-pat technique is used to extract the features from the decomposed wavelet coefficients. INCA feature selector is employed to select the clinically significant features. The performance of the obtained model is validated using four speech emotion public databases (RAVDESS Speech, Emo-DB (Berlin), SAVEE, and EMOVO). Our developed twine-shuf-pat and INCA based method yielded 87.43%, 90.09%, 84.79%, and 79.08% classification accuracies using RAVDESS, Emo-DB (Berlin), SAVEE and EMOVO corpora respectively with 10-fold cross-validation strategy. A mixed database is created from four public speech emotion databases which yielded 80.05% classification accuracy. Our obtained speech emotion model is ready to be tested with huge database and can be used in healthcare applications.

A denoising method for ship radiated noise based on Spearman variational mode decomposition, spatial-dependence recurrence sample entropy, improved wavelet threshold denoising, and Savitzky-Golay filter

Abstract: Ship radiated noise denoising is the basis and premise of underwater acoustic signal processing. To obtain better denoising effect, a new denoising method for ship radiated noise based on Spearman variational mode decomposition (SVMD), spatial-dependence recurrence sample entropy (SdrSampEn), improved wavelet threshold denoising (IWTD) and Savitzky-Golay filter (SG) is proposed. Firstly, SVMD is proposed, ship radiated noise is decomposed a series of intrinsic mode functions (IMFs) by SVMD, and the SdrSampEn value of every IMF is counted. Then, according to the SdrSampEn value, these IMFs are divided into noise-dominated IMFs and real signal-dominated IMFs. Noise-dominated IMFs are denoised by IWTD, and real signal-dominated IMFs are denoised by SG. Finally, the processed IMFs are reconstructed, and the noise-reduced signal is acquired. The proposed method has three main advantages: (i) compared with empirical mode decomposition (EMD), variational mode decomposition (VMD) as a new non-recursive decomposition algorithm, overcomes the defect of mode mixing; (ii) the proposed SVMD method overcomes the problem that VMD needs to preset the number of decomposition levels K; (iii) real signal-dominated IMFs have also been denoised and the method improves signal-to-noise ratio (SNR) by 2 dB to 4 dB. The denoising experiments with the Lorenz signal and the Chen signal show that the proposed method can improve the SNR by 8 dB to 13 dB. Applying the proposed method to denoise ship radiated noise from the official website of National Park Administration ( https://www.nps.gov/glba/learn/nature/soundclips.htm ), the results show that the proposed method makes chaotic attractor phase waveform clearer and smoother, and can effective restrain marine environmental noise in ship radiated noise.

The optimization efficient energy cooperative communication image transmission over WSN

Abstract: We propose energy efficiency and quality-aware multi-hop one-way cooperative image transmission framework based on image pre-processing technique, wavelet-based two-dimensional discrete wavelet transform (2D-DWT) methodology, and decode-and-forward (DF) algorithm at relay nodes. The different cooperative communication methods that demonstrated their viability in various ways were reviewed. However, there are a few more issues that should be tended to while managing superb image transmission in WSNs, for example, extreme vitality utilization while preparing to proceed with image transmit, to achieve the broadcast between picture quality, and intensity of image transmitted. Before presenting the proposed model, this presents the review of recent and conventional techniques for cooperative image transmission.

Single Image Depth Prediction with Wavelet Decomposition

Abstract: We present a novel method for predicting accurate depths from monocular images with high efficiency. This optimal efficiency is achieved by exploiting wavelet decomposition, which is integrated in a fully differentiable encoder-decoder architecture. We demonstrate that we can reconstruct high-fidelity depth maps by predicting sparse wavelet coefficients.In contrast with previous works, we show that wavelet coefficients can be learned without direct supervision on coefficients. Instead we supervise only the final depth image that is reconstructed through the inverse wavelet transform. We additionally show that wavelet coefficients can be learned in fully self-supervised scenarios, without access to ground-truth depth. Finally, we apply our method to different state-of-the-art monocular depth estimation models, in each case giving similar or better results compared to the original model, while requiring less than half the multiply-adds in the decoder network.

Automatic Eyeblink Artifact Removal From EEG Signal Using Wavelet Transform With Heuristically Optimized Threshold

Abstract: This paper proposes an automatic eyeblink artifacts removal method from corrupted-EEG signals using discrete wavelet transform (DWT) and meta-heuristically optimized threshold. The novel idea of thresholding approximation-coefficients (ACs) instead of detail-coefficients (DCs) of DWT of EEG in a backward manner is proposed for the first time for the removal of eyeblink artifacts. EEG is very sensitive and easily gets affected by eyeblink artifacts. First, the eyeblink corrupted EEG signals are identified using support vector machine (SVM) as a classifier. Then the corrupted EEG signal is decomposed using DWT up to the sixth level. Both the mother wavelet and the level of decomposition are selected using appropriate techniques. Then the ACs are thresholded in backward manner using the optimum threshold values followed by inverse DWT operation to reconstruct the original EEG signal. The AC at level 6 is thresholded and is used in IDWT with DC to get back the AC at level 5. Likewise, the backward thresholding of the ACs followed by IDWT is continued till the artifact free EEG signal is reconstructed at level 1. The optimum values of the thresholds of the ACs at different levels are optimized using two meta-heuristic algorithms, particle swarm optimization (PSO) and grey wolf optimization (GWO) for comparison. The results reveal that the proposed methodology is superior to the recently reported methods in terms of average correlation coefficient (CC) which states that the proposed method is better in terms of the quality of reconstruction in addition to being fully automatic.

A Machine Learning-Based Big EEG Data Artifact Detection and Wavelet-Based Removal: An Empirical Approach

Abstract: The electroencephalogram (EEG) signals are a big data which are frequently corrupted by motion artifacts. As human neural diseases, diagnosis and analysis need a robust neurological signal. Consequently, the EEG artifacts’ eradication is a vital step. In this research paper, the primary motion artifact is detected from a single-channel EEG signal using support vector machine (SVM) and preceded with further artifacts’ suppression. The signal features’ abstraction and further detection are done through ensemble empirical mode decomposition (EEMD) algorithm. Moreover, canonical correlation analysis (CCA) filtering approach is applied for motion artifact removal. Finally, leftover motion artifacts’ unpredictability is removed by applying wavelet transform (WT) algorithm. Finally, results are optimized by using Harris hawks optimization (HHO) algorithm. The results of the assessment confirm that the algorithm recommended is superior to the algorithms currently in use.

The relationship between the Covid-19 media coverage and the Environmental, Social and Governance leaders equity volatility: a time-frequency wavelet analysis

Abstract: We apply wavelet analyses to study how the social media coverage of the Covid-19 pandemic influenced the volatility of ESG (Environmental, Social and Governance) leaders indices encompassing World,...

A Secured and Robust Medical Image Watermarking Approach for Protecting Integrity of Medical Images

Abstract: In this paper, a novel medical image watermarking (MIW) technique for tele ‐ medicine applications is proposed. In this approach homomorphic transform (HT), redundant discrete wavelet tran...

A Survey on Change Detection and Time Series Analysis with Applications

Abstract: With the advent of the digital computer, time series analysis has gained wide attention and is being applied to many fields of science. This paper reviews many traditional and recent techniques for time series analysis and change detection, including spectral and wavelet analyses with their advantages and weaknesses. First, Fourier and least-squares-based spectral analysis methods and spectral leakage attenuation methods are reviewed. Second, several time-frequency decomposition methods are described in detail. Third, several change or breakpoints detection methods are briefly reviewed. Finally, some of the applications of the methods in various fields, such as geodesy, geophysics, remote sensing, astronomy, hydrology, finance, and medicine, are listed in a table. The main focus of this paper is reviewing the most recent methods for analyzing non-stationary time series that may not be sampled at equally spaced time intervals without the need for any interpolation prior to the analysis. Understanding the methods presented herein is worthwhile to further develop and apply them for unraveling our universe.