Search

An infrared camera is affected by the photon effect, temperature changes, time drift, and other factors when operating in orbit, which makes the ground nonuniformity correction coefficient invalid, resulting in nonuniformity stripes in the infrared images and restricting their practicality in further analysis and applications. The existing destriping methods often suffer from the loss of image details and artifact generation. To solve this problem, we proposed a weighted guided filtering-based multidomain fusion destriping approach that leverages the structural, directional, and spectral characteristics of stripe noise. First, we addressed the issue of artifacts caused by Fourier-domain filtering through an adaptive filtering approach that employs a variable threshold to minimize filtering-induced artifacts and obtain clearer guided images. Furthermore, capitalizing on the directional properties of wavelet decomposition effectively separates image information from stripe information. To integrate the advantages of both approaches, we employed a weighted guided filter to seamlessly fuse the guided image with the wavelet decomposition image. In terms of quantitative metrics, the proposed method generally beats the other five comparative methods, with significant improvements in image PSNR, SSIM, NIQE, and mean relative deviation (MRD), particularly for complex images where the enhancements were more pronounced. These experimental results collectively demonstrate the significant progress achieved by the proposed method in effectively reducing stripe noise, better preserving the original structural details of the image, and suppressing the occurrence of artifacts.

Electroencephalography (EEG) artifacts are very common in clinical diagnosis and can heavily impact diagnosis. Manual screening of artifact events is labor-intensive with little benefit. Therefore, exploring algorithms for automatic detection and classification of EEG artifacts can significantly assist clinical diagnosis. In this paper, we propose a learnable and explainable wavelet neural network (WaveNet) for EEG artifact detection and classification. The model is powered by the wavelet decomposition block based on invertible neural network, which can extract signal features without information loss, and a tree generator for building wavelet tree structure automatically. They provide the model with good feature extraction capabilities and explainability. To evaluate the model’s performance more fairly, we introduce the base point level matching score (BASE) and the Event-Aligned Compensation Scoring (EACS) at the event level as two metrics for model performance evaluation. On the challenging Temple University EEG Artifact (TUAR) dataset, our model outperforms other baselines in terms of F1-score for both artifact detection and classification tasks. The case study also validates the model’s ability to offer explainability for predictions based on frequency band energy, suggesting potential applications in clinical diagnosis.

Abstract Introduction Undiagnosed and untreated lung pathologies are among the leading causes of neonatal deaths in developing countries. Lung Ultrasound (LUS) has been widely accepted as a diagnostic tool for neonatal lung pathologies due to its affordability, portability, and safety. However, healthcare institutions in developing countries lack well-trained clinicians to interpret LUS images, which limits the use of LUS, especially in remote areas. An automated point-of-care tool that could screen and capture LUS morphologies associated with neonatal lung pathologies could aid in rapid and accurate diagnosis. Methods We propose a framework for classifying the six most common neonatal lung pathologies using spatially localized line and texture patterns extracted via 2D dual-tree complex wavelet transform (DTCWT). We acquired 1550 LUS images from 42 neonates with varying numbers of lung pathologies. Furthermore, we balanced our data set to avoid bias towards a pathology class. Results Using DTCWT and clinical features as inputs to a linear discriminant analysis (LDA), our approach achieved a per-image cross-validated classification accuracy of 74.39% for the imbalanced data set. Our classification accuracy improved to 92.78% after balancing our data set. Moreover, our proposed framework achieved a maximum per-subject cross-validated classification accuracy of 64.97% with an imbalanced data set while using a balanced data set improves its classification accuracy up to 81.53%. Conclusion Our work could aid in automating the diagnosis of lung pathologies among neonates using LUS. Rapid and accurate diagnosis of lung pathologies could help to decrease neonatal deaths in healthcare institutions that lack well-trained clinicians, especially in developing countries.

With the increase of cable operating age, various latent defects gradually appear, so it is important to identify and locate the defects accurately for power operation and maintenance. The common methods for cable failure detection primarily work with cables in an offline state, which causes a certain degree of economic loss. In this research, SSTDR is used to achieve online defect detection, which can avoid further damage to the cable in a working state. This paper established the numerical model of the cable distribution characteristics and investigated how various incident signals affect the effect of defect localization. Based on the obtained results, the localization curve is wavelet decomposed in order to filter the operating frequency signal. The findings of the experiments and simulations demonstrate that SSTDR and its enhanced algorithm may achieve online cable defect detection and serve as an effective method for cable defect locating technology advancement.

The forecast of wind speed and the power produced from wind farms has been a challenge for a long time and continues to be so. This work introduces a method that we label as Wavelet Decomposition-Neural Networks (WDNN) that combines wavelet decomposition principles and deep learning. By merging the strengths of signal processing and machine learning, this approach aims to address the aforementioned challenge. Treating wind speed and power as signals, the wavelet decomposition part of the model transforms these inputs, as appropriate, into a set of features that the neural network part of the model can ingest to output accurate forecasts. WDNN is unconstrained by the shape, layout, or number of turbines in a wind farm. We test our WDNN methods using three large datasets, with multiple years of data and hundreds of turbines, and compare it against other state-of-the-art methods. It's very short-term forecast, like 1-h ahead, can outperform some deep learning models by as much as 30%. This shows that wavelet decomposition and neural network are a potent combination for advancing the quality of short-term wind forecasting. [ABSTRACT FROM AUTHOR]

In the process of coal mining, one of the main reasons for the high labor intensity of workers and the frequent occurrence of casualties is the low level of intelligence of coal mining equipment. As the core equipment in the process of coal mining, the intelligence level of shearers directly affects the safety production and mining efficiency of coal mines. Coal and rock recognition technology is the core technology used to realize the intelligentization of shearers, which is an urgent technical problem to be solved in the field of coal mining. In this paper, coal seam images, rock stratum images, and coal–rock mixed-layer images of a coal mining area are taken as the research object, and key technologies such as the construction of a sample image library, classification and recognition, and semantic segmentation are studied by using the relevant theoretical knowledge of artificial neural network models. Firstly, the BP neural network is used to classify and identify coal seam images, rock stratum images, and coal–rock mixed-layer images, so as to distinguish which of the current mining targets of a shearer is the coal seam, rock stratum, or coal–rock mixed layer. Because different mining objectives will lead to different working modes of a shearer, it is necessary to maintain normal power to cut coal when encountering a coal seam, to stop working when encountering rock stratum, and to cut coal along the boundary between a coal seam and rock stratum when encountering a coal–rock mixed stratum. Secondly, the DeepLabv3+ model is used to perform semantic segmentation experiments on the coal–rock mixed-layer images. The purpose is to find out the distribution of coal and rocks in the coal–rock mixed layer in the coal mining area, so as to provide technical support for the automatic adjustment height of the shearer. Finally, the research in this paper achieved a 97.16% recognition rate in the classification and recognition experiment of the coal seam images, rock stratum images, and coal–rock mixed-layer images and a 91.2% accuracy in the semantic segmentation experiment of the coal–rock mixed-layer images. The research results of the two experiments provide key technical support for improving the intelligence level of shearers. [ABSTRACT FROM AUTHOR]

Power transformers are an essential part of the power grid. They have a relatively low rate of failure, but removing the consequences is costly when it occurs. One of the elements of power transformers that are often the reason for shutting down the unit is the on-load tap changer (OLTC). Many methods have been developed to assess the technical condition of OLTCs. However, they require the transformer to be taken out of service for the duration of the diagnostics, or they do not enable precise diagnostics. Acoustic emission (AE) signals are widely used in industrial diagnostics. The generated signals are difficult to interpret for complex systems, so artificial intelligence tools are becoming more widely used to simplify the diagnostic process. This article presents the results of research on the possibility of creating an online OLTC diagnostics method based on AE signals. An extensive measurement database containing many frequently occurring OLTC defects was created for this research. A method of feature extraction from AE signals based on wavelet decomposition was developed. Several machine learning models were created to select the most effective one for classifying OLTC defects. The presented method achieved 96% efficiency in OLTC defect classification. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) artifacts are very common in clinical diagnosis and can heavily impact diagnosis. Manual screening of artifact events is labor-intensive with little benefit. Therefore, exploring algorithms for automatic detection and classification of EEG artifacts can significantly assist clinical diagnosis. In this paper, we propose a learnable and explainable wavelet neural network (WaveNet) for EEG artifact detection and classification. The model is powered by the wavelet decomposition block based on invertible neural network, which can extract signal features without information loss, and a tree generator for building wavelet tree structure automatically. They provide the model with good feature extraction capabilities and explainability. To evaluate the model’s performance more fairly, we introduce the base point level matching score (BASE) and the Event-Aligned Compensation Scoring (EACS) at the event level as two metrics for model performance evaluation. On the challenging Temple University EEG Artifact (TUAR) dataset, our model outperforms other baselines in terms of F1-score for both artifact detection and classification tasks. The case study also validates the model’s ability to offer explainability for predictions based on frequency band energy, suggesting potential applications in clinical diagnosis. [ABSTRACT FROM AUTHOR]

This paper presents a phase retrieval algorithm that incorporates sparsity priors into total variation and framelet regularization. The proposed algorithm exploits the sparsity priors in both the gradient domain and the spatial distribution domain to impose desirable characteristics on the reconstructed image. We utilize structured illuminated patterns in holography, consisting of three light fields. The theoretical and numerical analyses demonstrate that when the illumination pattern parameters are non-integers, the three diffracted data sets are sufficient for image restoration. The proposed model is solved using the alternating direction multiplier method. The numerical experiments confirm the theoretical findings of the lighting mode settings, and the algorithm effectively recovers the object from Gaussian and salt–pepper noise. [ABSTRACT FROM AUTHOR]

Introduction: Undiagnosed and untreated lung pathologies are among the leading causes of neonatal deaths in developing countries. Lung Ultrasound (LUS) has been widely accepted as a diagnostic tool for neonatal lung pathologies due to its affordability, portability, and safety. However, healthcare institutions in developing countries lack well-trained clinicians to interpret LUS images, which limits the use of LUS, especially in remote areas. An automated point-of-care tool that could screen and capture LUS morphologies associated with neonatal lung pathologies could aid in rapid and accurate diagnosis. Methods: We propose a framework for classifying the six most common neonatal lung pathologies using spatially localized line and texture patterns extracted via 2D dual-tree complex wavelet transform (DTCWT). We acquired 1550 LUS images from 42 neonates with varying numbers of lung pathologies. Furthermore, we balanced our data set to avoid bias towards a pathology class. Results: Using DTCWT and clinical features as inputs to a linear discriminant analysis (LDA), our approach achieved a per-image cross-validated classification accuracy of 74.39% for the imbalanced data set. Our classification accuracy improved to 92.78% after balancing our data set. Moreover, our proposed framework achieved a maximum per-subject cross-validated classification accuracy of 64.97% with an imbalanced data set while using a balanced data set improves its classification accuracy up to 81.53%. Conclusion: Our work could aid in automating the diagnosis of lung pathologies among neonates using LUS. Rapid and accurate diagnosis of lung pathologies could help to decrease neonatal deaths in healthcare institutions that lack well-trained clinicians, especially in developing countries. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid modelling approach for forecasting returns and volatilities of the stock market. The model, called ARFIMA-WLLWNN model, integrates the advantages of the ARFIMA model, the wavelet decomposition technique (namely, the discrete MODWT with Daubechies least asymmetric wavelet filter) and artificial neural network (namely, the LLWNN neural network). The model develops through a two-phase approach. In phase one, a wavelet decomposition improves the forecasting accuracy of the LLWNN neural network, resulting in the Wavelet Local Linear Wavelet Neural Network (WLLWNN) model. The Back Propagation and Particle Swarm Optimization (PSO) learning algorithms optimize the WLLWNN structure. In phase two, the residuals of an ARFIMA model of the conditional mean become the input to the WLLWNN model. The hybrid ARFIMA-WLLWNN model is evaluated using daily returns of the Dow Jones Industrial Average index over 01/05/2010 to 02/11/2020. The experimental results indicate that the PSO-optimized version of the hybrid ARFIMA-WLLWNN outperforms the LLWNN, WLLWNN, ARFIMA-LLWNN, and the ARFIMA-HYAPARCH models and provides more accurate out-of-sample forecasts over validation horizons of one, five and twenty-two days. [ABSTRACT FROM AUTHOR]

Copyright of Engineering Mechanics / Gongcheng Lixue is the property of Engineering Mechanics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Microcystis aeruginosa is the dominant species in the blooms of eutrophic lakes such as Taihu Lake in China. Chlorophyll-a is one of the most common indicators to characterize its biomass. The nonlinearity and unsteadiness of the chlorophyll-a sequence decrease the prediction accuracy. In this paper, a secondary decomposition prediction method based on the integration of wavelet decomposition, variational modal decomposition, and gated recurrent unit (WD–VMD–GRU) is proposed. First, the original sequence is decomposed once using wavelet decomposition (WD). Then, the components with higher sample entropy values are decomposed using variational modal decomposition (VMD). Finally, each component is predicted using a gated recurrent unit (GRU), and the final prediction results are obtained by reconstructing each component result. The decomposition effect is ranked as VMD > WD > CEEMDAN > EMD. The WD–VMD–GRU model has a significant advantage compared to the basic model, with an increase of over 6.5% in R2. The secondary decomposition reduces the difficulty of predicting GRU components and has better prediction performance. The RMSE, MAE, and R2 were 1.752, 1.450, 0.969 at 2-day prediction, and 3.169, 2.711, 0.908 at 6-day prediction. Therefore, the WD–VMD–GRU model is superior in accuracy to other methods and can provide a scientific basis for the growth prediction research of M. aeruginosa. HIGHLIGHTS Pearson correlation coefficient and spectrum diagram are used to analyze the decomposition effect of the four decomposition models. Experiments show that the results are reliable.; The use of secondary decomposition reduces the complexity of high sample entropy components in primary decomposition and improves the accuracy of GRU prediction.; The model is reliable in the multi-step prediction of chlorophyll-a concentration.;

The continuous observation data recorded by broadband vertical pendulum tiltmeter at Huangmei station during the typhoons “BAVI” and “IN-FA” were studied. Wavelet decomposition and Fourier transform were used to investigate the disturbance and spectrum characteristics of the microseism signal which caused by the typhoons. The results show that the disturbance amplitude has an increase-peak-decrease pattern during the process of typhoons, and has a good correlation with the wind speed of typhoons, the distance between the typhoon center and the station. The dominant frequency range of the microseism caused by the typhoons is from 0.13 to 0.3 Hz, with the corresponding period of 3.3 to 7.7 s.

When a multi-degree-of-freedom manipulator operates in underground coal mines, its communication signal is easily affected by external factors such as pulses and electromagnetic interference. It reduces the reliability of signal transmission and affects the flexibility and control precision of the multi-degree-of-freedom manipulator. To improve the communication signal quality of underground multi-degree-of-freedom manipulator, a filtering method for underground manipulator communication signal based on improved time-frequency peak filtering is proposed. The method extracts carrier features based on the carrier nodes of the manipulator communication signal, and extracts transmission period features based on Hilbert transform theory. The method uses wavelet decomposition to solve the wavelet transform coefficient of each level, obtains the disturbance frequency of communication signals, and completes the classification of communication signal features. The method introduces phase compensation factors to improve time-frequency peak filtering. The reduction threshold is calculated according to the scaling factor and reduction factor, so as so adjust the fixed window length. The improved time-frequency peak filtering is used to filter the noise in communication signals. Thus the noise suppression of the multi-degree-of-freedom manipulator communication signal is achieved. A 3-degree-of-freedom manipulator is taken as the experimental object. The pulse width is set to be 8 μs. The Baud rate is 120 bit/s, the communication signal transmission interval is 2 ms, and the signal amplitude is 1. 30 dB Gaussian white noise is added to the communication signal. The experimental results show that the output signal-to-noise ratio of the communication signal processed by the filtering method for underground manipulator communication signal based on improved time-frequency peak filtering is 94 dB. The bit error rate is basically stable at 0.6%, and the average frame loss rate is reduced from 2.9% before filtering to 0.8%. The method effectively improves the communication signal quality and transmission reliability of the multi-degree-of-freedom manipulator.

Computed tomography (CT) is one of the fundamental imaging modalities used in medicine, allowing for the acquisition of accurate cross-sectional images of internal body tissues. However, during the acquisition and reconstruction process, various artifacts can arise, and one of them is ring artifacts. These artifacts result from the inherent limitations of CT scanner components and the properties of the scanned material, such as detector defects, non-uniform distribution of radiation from the source, or the presence of metallic elements within the scanning region. The purpose of this study was to identify and reduce ring artifacts in tomographic images using image decomposition and average filtering methods. In this study, tests were conducted on the effectiveness of identifying ring artifacts using wavelet decomposition methods for images. The test was performed on a Shepp–Logan phantom with implemented artifacts of different intensity levels. The analysis was performed using different wavelet families, and linear approximation methods were used to filter the image in the identified areas. Additional filtering was performed using moving average methods and empirical mode decomposition (EMD) techniques. Image comparison methods, i.e., RMSE, SSIM and MS-SSIM, were used to evaluate performance. The results of this study showed a significant improvement in the quality of tomographic phantom images. The authors obtained more than 50% improvement in image quality with reference to the image without any filtration. The different wavelet families had different efficiencies with relation to the identification of the induction regions of ring artifacts. The Haar wavelet and Coiflet 1 showed the best performance in identifying artifact induction regions, with comparative RMSE values for these wavelets of 0.1477 for Haar and 0.1469 for Coiflet 1. The applied additional moving average filtering and EMD permitted us to improve image quality, which is confirmed by the results of the image comparison. The obtained results allow us to assess how the used methods affect the reduction in ring artifacts in phantom images with induced artifacts.

The forecast of wind speed and the power produced from wind farms has been a challenge for a long time and continues to be so. This work introduces a method that we label as Wavelet Decomposition-Neural Networks (WDNN) that combines wavelet decomposition principles and deep learning. By merging the strengths of signal processing and machine learning, this approach aims to address the aforementioned challenge. Treating wind speed and power as signals, the wavelet decomposition part of the model transforms these inputs, as appropriate, into a set of features that the neural network part of the model can ingest to output accurate forecasts. WDNN is unconstrained by the shape, layout, or number of turbines in a wind farm. We test our WDNN methods using three large datasets, with multiple years of data and hundreds of turbines, and compare it against other state-of-the-art methods. It’s very short-term forecast, like 1-h ahead, can outperform some deep learning models by as much as 30%. This shows that wavelet decomposition and neural network are a potent combination for advancing the quality of short-term wind forecasting.

The echo signals of the radar in complex backgrounds are often very unstable and thus require effective noise cancellation. In this paper, according to the characteristics of continuous wavelet variation and discrete wavelet variation, the decomposition effect of multi-resolution analysis and orthogonal Mallat algorithm on low-frequency and high-frequency non-smooth signals is studied, and the selection method of wavelet bases is explored. Then, the noise characteristics affecting the pulsed LIDAR system are analyzed, and the LIDAR pulse signal is simulated by MATLAB, while Gaussian white noise is introduced to obtain the noise-added echo signal, and then multiple wavelet threshold denoising methods are applied to denoise the echo signal. For the input signal-to-noise ratio of −10.57 dB, the output signal-to-noise ratios of db8, db9, db10, and bior3.5 wavelet bases under forced thresholding are −1.971, −2.178, −2.173, and −1.032, respectively. For different input signal-to-noise ratios, the average root mean square error of db8, db9, db10, and bior3.5 wavelet bases under default thresholding is 1.51. The denoising methods for radar signals using the properties of wavelet decomposition have obvious superiority compared to traditional filters, and the wavelet transforms threshold denoising methods have wide adaptability.

Electricity consumption prediction is an important part of power planning and the basis of power dispatch planning. The SVM and GBDT algorithms that were optimized by the PSO algorithm are used to build the machine learning-based electricity consumption prediction model in this paper. The decomposed and reconstructed high-frequency signals and low-frequency signals are optimized by the particle swarm algorithms SVM and GBDT for power consumption prediction, respectively. Improve the clustering performance of the traditional fuzzy C-mean algorithm for unbalanced data. The performance of the algorithms is analyzed in two different application scenarios, namely, artificial dataset and power users' real measurement dataset. Power consumption prediction in the manufacturing industry is performed. It is found that the ARI, FMI, and AMI index values of the improved algorithms proposed in this paper are 0.9543, 0.9347, and 0.9344, respectively, on the grid user-measured dataset, while the traditional DPC and K-means clustering algorithms are less effective. The machine learning algorithm optimized after wavelet decomposition increased R² by 8.98%, MAPE decreased by 19.78%, and RMSE decreased by 11.53% compared to PSO-GBDT, and the predictive evaluation indexes were all improved, and the two machine learning algorithms in this paper optimized by wavelet decomposition combined with PSO have good predictive effect R² increased from 0.801 to 0.842, and the two machine learning algorithms designed in this paper based on wavelet decomposition have good predictive effect R² increased from 0.801 to 0.842. The designed machine learning model for electricity consumption prediction based on wavelet decomposition and PSO has excellent performance, and the design expectation has been fulfilled. This paper makes a useful exploration and proposes an effective method for accurate prediction of electricity consumption in the manufacturing industry.

Due to the complexity of the crustal medium and stratigraphic boundaries, seismic waves typically exhibit attenuation and dispersion after propagation through the medium. By studying the regularity of seismic wave dispersion, soil layer information can be extracted. Current spectral decomposition techniques take less account of signal filtering processing of seismic waves, and the complexity of the stratigraphy tends to reduce the accuracy of dispersion analysis. Based on the above, a new method for calculating the dispersion characteristics of a site from surface and downhole ground vibration recordings is proposed, which is simpler than the spectra analysis of surface waves (SASW) method and eliminates the need for multiple pickups and additional equipment. This method is based on wavelet decomposition and seismic wave filtering, and a numerical simulation example is established to verify the effectiveness of the method. Then, the records of the Tohoku earthquake on March 11, 2011 are used as actual strong earthquake examples for filtering and dispersion analysis. Finally, it is concluded that the seismic wave dispersion analysis method based on wavelet decomposition can effectively filter and analyze seismic waves in specific frequency bands. [ABSTRACT FROM AUTHOR]

Air quality issues are critical to daily life and public health. However, air quality data are characterized by complexity and nonlinearity due to multiple factors. Coupled with the exponentially growing data volume, this provides both opportunities and challenges for utilizing deep learning techniques to reveal complex relationships in massive knowledge from multiple sources for correct air quality prediction. This paper proposes a prediction hybrid framework for air quality integrated with W-BiLSTM(PSO)-GRU and XGBoost methods. Exploiting the potential of wavelet decomposition and PSO parameter optimization, the prediction accuracy, stability and robustness was improved. The results indicate that the R2 values of PM2.5, PM10, SO2, CO, NO2, and O3 predictions exceeded 0.94, and the MAE and RMSE values were lower than 0.02 and 0.03, respectively. By integrating the state-of-the-art XGBoost algorithm, meteorological data from neighboring monitoring stations were taken into account to predict air quality trends, resulting in a wider range of forecasts. This strategic merger not only enhanced the prediction accuracy, but also effectively solved the problem of sudden interruption of monitoring. Rigorous analysis and careful experiments showed that the proposed method is effective and has high application value in air quality prediction, building a solid framework for informed decision-making and sustainable development policy formulation. [ABSTRACT FROM AUTHOR]

Frame duplication forgery is the most common inter-frame video forgery type to alter the contents of digital video sequences. It can be used for removing or duplicating some events within the same video sequences. Most of the existing frame duplication forgery detection methods fail to detect highly similar frames in the surveillance videos. In this paper, we propose a frame duplication forgery detection method based on textural feature analysis of video frames for digital video sequences. Firstly, we compute the single-level 2-D wavelet decomposition for each frame in the forged video sequences. Secondly, textural features of each frame are extracted using the Gray Level of the Co-Occurrence Matrix (GLCM). Four second-order statistical descriptors, Contrast, Correlation, Energy, and Homogeneity, are computed for the extracted textural features of GLCM. Furthermore, we calculate four statistical features from each frame (standard deviation, entropy, Root-Mean-Square RMS, and variance). Finally, the combination of GLCM's parameters and the other statistical features are then used to detect and localize the duplicated frames in the video sequences using the correlation between features. Experimental results demonstrate that the proposed approach outperforms other state-of-the-art (SOTA) methods in terms of P r e c i s i o n , R e c a l l , and F 1 S c o r e rates. Furthermore, the use of statistical features combined with GLCM features improves the performance of frame duplication forgery detection. [ABSTRACT FROM AUTHOR]

To reduce the influence of the random fluctuation on wind power prediction, a new ultra-short-term wind power prediction model, based on wavelet decomposition (WD), variational mode decomposition (VMD), and least-squares support vector machine (LSSVM), is proposed in this paper. The method is based on the double decomposition and LSSVM, where the wind power sequence is decomposed by WD into low- and high-frequency components, which are further decomposed by VMD to obtain many modal components with tendency and periodicity. Multiple LSSVM prediction models are then established with historical wind power data and weather data as the inputs to obtain the predicted values of the multiple modal components. The final predicted values of wind power are achieved by data fusion of outputs of these LSSVM models. The experimental results show that the MAPE (mean absolute percentage error) of the combined prediction model is 4.66%, which is the best compared with nine benchmark models. This demonstrates the high performance of the proposed WD-VMD-LSSVM model for short-term prediction of wind power. [ABSTRACT FROM AUTHOR]

Nonlinear diffusion has been proved to be an indispensable approach for the removal of noise in image processing. In this paper, we employ nonlinear diffusion for the purpose of denoising audio signals in order to have this approach also recognized as a powerful tool for audio signal processing. We apply nonlinear diffusion to wavelet coefficients obtained from different filters associated with orthogonal and biorthogonal wavelets. We use wavelet decomposition to keep signal components well-localized in time. We compare denoising results using nonlinear diffusion with wavelet shrinkage for different wavelet filters. Our experiments and results show that the denoising is much improved by using the nonlinear diffusion process. [ABSTRACT FROM AUTHOR]

To overcome the limitations of the hybrid tractor bumping tests, which include extended cycle times, high costs, and impracticality for single-part reliability verification, this study focuses on the exhaust system mounting bracket of a hybrid tractor. A novel approach that combines multi-objective particle swarm optimization (MOPSO) and wavelet decomposition algorithms was employed to enhance the reconstruction of shock vibration signals. This approach aims to enable the efficient acquisition of input signals for subsequent shaker table testing. The methodology involves a systematic evaluation of the spectral correlation between the original signal and the reconstructed signal at the stent's response position, along with signal compression time. These parameters collectively constitute the objective function. The multi-objective particle swarm optimization algorithm is then deployed to explore a range of crucial parameters, including wavelet basic functions, the number of wavelet decomposition layers, and the selection of wavelet components. This exhaustive exploration identifies an optimized signal reconstruction method that accurately represents shock vibration loads. Upon rigorous screening based on our defined objectives, the optimal solution vector was determined, which includes the utilization of the dB10 wavelet basic function, employing a 12-layer wavelet decomposition, and selecting wavelet components a12 and d3~d11. This specific configuration enables the retention of 95% of the damage coefficients while significantly compressing the test time to just 46% of the original signal duration. The implications of our findings are substantial as the reconstructed signal obtained through our optimized approach can be readily applied to shaker excitation. This innovation results in a notable reduction in test cycle time and associated costs, making it particularly valuable for engineering applications, especially in tractor design and testing. [ABSTRACT FROM AUTHOR]

We present Wavelet Monte Carlo (WMC), a new method for generating independent samples from complex target distributions. The methodology is based on wavelet decomposition of the difference between the target density and a user-specified initial density, and exploits both wavelet theory and survival analysis. In practice, WMC can process only a finite range of wavelet scales. We prove that the resulting L 1 approximation error converges to zero geometrically as the scale range tends to (- ∞ , + ∞) . This provides a principled approach to trading off accuracy against computational efficiency. We offer practical suggestions for addressing some issues of implementation, but further development is needed for a computationally efficient methodology. We illustrate the methodology in one- and two-dimensional examples, and discuss challenges and opportunities for application in higher dimensions. [ABSTRACT FROM AUTHOR]

Methods that make it possible to evaluate the heterogeneity of the distribution of local strains and stresses indirectly through the values of the temperature distribution in thermograms are presented. In this case, methods of fractal analysis in the form of multifractal spectra, image clustering, and multiscale analysis of discrete wavelet images decomposition are used. It is shown that, with increasing damage to the material, the width of the multifractal spectrum increases and local strains are redistributed along the plate length during loading. The use of the multiple-scale wavelet decomposition makes it possible to establish scale elements that have the greatest strain changes. [ABSTRACT FROM AUTHOR]

The lithospheric structure of the Tibetan Plateau and its adjacent area is a hot topic in geodynamic research. It is important to reveal the mechanism of crustal deformation and tectonic evolution of the study area. In this study, the techniques of wavelet multiscale decomposition and field edge detection were used to study the discontinuities of the lithosphere revealed by multilevel Bouguer gravity anomalies. Specifically, we evaluated the depth characteristics of the major active faults in the study area and identified 15 deep major faults that cut through the lithosphere. They are Chaman fault, Shyok suture zone, Altyn-Tagh fault, Karakash fault, Karakoram fault, Talas-Fergana fault, Kashgarr-Yeshgar transfer system, Rushan-Pshart suture zone, Sangri-Nacuo fault, Main Frontal thrust, Burmese fold belt, Yadong-Gulu fault, Gaoligong fault, Sagaing fault and Nujiang fault. We have also elucidated the tectonic mechanisms of two famous geodynamic phenomena in the Pamir Plateau. The first is the intense intermediate depth seismicity beneath Pamir-Hindukush. It cannot simply be described as the rupture of a subducted residual plate, which could be divided into two distinct tectonic units. One belongs to the Indian plate, the other to the Eurasian plate. Secondly, the mechanism of intense seismicity confined to the western upper crust of the Pamir Plateau could be explained as significant fragmentation of crustal material. Finally, and most importantly, we summarized the coupling mechanism between deep geodynamics and horizontal deformation as observed by modern geodetic techniques. In the upper mantle, the leading edge of the subducting Indian plate reached the SW boundary of Tarim basin and forms a closed structure in western Himalaya. Then, the Tibetan Plateau underwent a tectonic escape towards the east under the continuous compression between the Indian and Eurasian plates. During the process of tectonic escape, the role of the N–S direction normal faults in the Himalayan tectonic zone is limited. [ABSTRACT FROM AUTHOR]

Lithium battery health state estimation can help optimize battery usage and management strategies. In response to the challenges faced by traditional battery management systems in accurately estimating the State of Health of lithium-ion batteries and addressing issues such as capacity recovery and noise interference, this paper proposes a method based on wavelet decomposition and an improved whale optimization algorithm optimized deep extreme learning machine for estimating the SOH of lithium-ion batteries. Firstly, the lithium-ion battery capacity degradation sequence is extracted, and the wavelet decomposition method is used to decompose the battery capacity into global and local degradation trends. Next, the non-linear convergence factor and the whale optimization algorithm with adaptive weights are employed to optimize the deep extreme learning machine for predicting each trend component. Finally, the prediction results are effectively integrated to obtain the lithium-ion battery SOH. This experimental method is validated using NASA and CALCE datasets, and the results indicate that the root mean square error and mean absolute percentage error are both below 0.95%, with relative accuracy and absolute correlation coefficients exceeding 98%. This demonstrates the method's excellent accuracy and robustness. [ABSTRACT FROM AUTHOR]

Stroke is one of the leading causes of death in Indonesia. From 2013 to 2018, the prevalence of stroke increased from 7% to 10.9%. There are two types of strokes, namely Hemorrhagic and Acutte Ischemic Stroke (AIS) with the majority of it being AIS. Early detection and diagnosis are essential in stroke as it is a life-threatening disease, and the stroke treatment is based on its type. Currently, the gold imaging standards in stroke diagnosis are Computed Tomography (CT) scan and Magnetic Resonance Imaging (MRI). However, the mentioned services for stroke diagnosis are primarily available in hospitals classified as “class A” (general hospitals with extensive facilities and medical services). Compared to CT scans and MRI, electroencephalography (EEG) is a cost-friendly, non-invasive device studied for various brain-related diseases. This study aimed to determine the optimal epoch length to classify four stroke classes (healthy, minor, moderate, and severe) during resting condition for a machine learning-based AIS computer-aided diagnostics system. 32-channel EEG, CT scan, and NIHSS Scores were the obtained data. The features were delta-theta to alpha-beta ratio (DTABR), delta to alpha ratio (DAR), relative power ratio (RPR), and asymmetry, which were extracted using wavelet decomposition technique. The epoch length was varied by 1s, 2s, 10s, 30s, 60s, and 120s. The severity of stroke were classified using a feedforward neural network. The best performance was obtained at the 60-second epoch length with 89% accuracy using 15 hidden layers. This EEG-based diagnostic system would be expected to be implemented in “class C” hospitals, where only essential medical services and facilities are available, usually in rural areas.

The detection of audio tampering plays a crucial role in ensuring the authenticity and integrity of multimedia files. This paper presents a novel approach to identifying tampered audio files by leveraging the unique Electric Network Frequency (ENF) signal, which is inherent to the power grid and serves as a reliable indicator of authenticity. The study begins by establishing a comprehensive Chinese ENF database containing diverse ENF signals extracted from audio files. The proposed methodology involves extracting the ENF signal, applying wavelet decomposition, and utilizing the autoregressive model to train effective classification models. Subsequently, the framework is employed to detect audio tampering and assess the influence of various environmental conditions and recording devices on the ENF signal. Experimental evaluations conducted on our Chinese ENF database demonstrate the efficacy of the proposed method, achieving impressive accuracy rates ranging from 91% to 93%. The results emphasize the significance of ENF-based approaches in enhancing audio file forensics and reaffirm the necessity of adopting reliable tamper detection techniques in multimedia authentication. [ABSTRACT FROM AUTHOR]

In recent years, lithologic oil and gas reservoirs have become an important target in continental hydrocarbon-bearing basins. Geophysical prospecting technology using seismic data is an indispensable tool for oil and gas exploration. However, while previous work has paid much attention to the seismic responses of reservoirs (sandstones), the seismic responses of depositional sequences composed of sandstone–mudstone cycles are not well understood in reservoir prediction. This problem seriously restricts efficient oil–gas exploration and development. The Cretaceous Baxigai Formation in the Yingmaili area, west of the Tabei Uplift, is an important exploration target for lithologic oil and gas reservoirs in the Tarim Basin. The Baxigai Formation is deeply buried with thin thickness. The Baxigai Formation in the study area is divided into a lower sandstone section and an upper mudstone section. Braided river delta sand bodies are developed in the lower sandstone section, and braided river delta sand bodies and beach bar sand bodies are developed in the upper mudstone section. According to the difference in the depositional sequences in different zones, five types of the vertical combination style of sandstone and mudstone were identified. Through seismic forward modeling, the seismic response variance of the five kinds of sequence models was established. Then, the amplitude attributes were extracted via wavelet decomposition to reflect the distribution of sandstone–mudstone in different zones. This could help predict the vertical and horizontal distributions of different depositional sequences and the sandstones in these sequences. During the sedimentary period of the upper mudstone section of the Baxigai Formation, the beach bar sand bodies were distributed along the northeast coast. The thin sand bodies pinched out along the up-dip direction to form favorable lithologic traps, which has important significance for lithologic reservoir exploration. [ABSTRACT FROM AUTHOR]

The magnetic resonance images focus on soft tissues, and it is often necessary for healthcare professionals to reach the final conclusion in clinical diagnosis. However, these images are often affected by random noise, which decreases the visual quality and reliability of the images. This paper presents an improved unbiased non-local mean (NLM) filter to solve the de-noising issue in the MRI images. Local statistics of the noise is combined with the NLM filter to design an unbiased NLM filter. First of all, the Gaussian noise information is extracted from the noisy image by performing the wavelet decomposition, statistically modeling the diagonal sub-band wavelet coefficients, and estimating the noise variance by applying the median absolute deviation (MAD) estimator. Next, the Rician noise is removed by applying a NLM filter which averages the noisy pixels by a Gaussian weight factor. Finally, the NLM filtered output pixels are unbiased by applying the noise bias subtraction method for recovering the original pixel values. Our experiments on real MRI and synthetic images demonstrate that promising results that can be obtained much superior than results estimated using existing non-local mean filtering schemes. [ABSTRACT FROM AUTHOR]

This study investigates the influence of wavelet decomposition on standalone adaptive neurofuzzy inference system (ANFIS) and its hybrid with evolutionary algorithms [genetic algorithm (GA) and particle swarm optimization (PSO)] at a medium-term temporal scale. Time series data of global horizontal irradiance used as inputs was decomposed according to 5-level wavelet sub-signals and modeled independently. Statistical metrics were used to evaluate the models, and these results were compared for all models. From our findings, the wavelet-based models incurred more computational time and lesser accuracy compared to the ANFIS and its hybrid models. ANFIS-GA and its wavelet hybrid had a high computational time of 7 s and 15.2 s, respectively., Among the six models, the standalone ANFIS model outperformed its hybrids with a root mean square error (RMSE) of 13.07, normalized root mean square error (NRMSE) of 0.11%, mean absolute percentage error (MAPE) of 4.57%, variance accounted for (VAF) of 92.03%, relative coefficient of variation (RCoV) of 0.24, and computational time (CT) of 3 s. The integration of wavelet decomposition reduced the performance of the standalone ANFIS and its hybrids with wavelet-based PSO-ANFIS recording the least model accuracy (RMSE = 31.02, NRMSE = 0.19, MAPE = 15.31, VAF = 55.89, RCoV = 0.21, CT = 8.4 s). Overall, standalone machine learning models could be more efficient, computationally less intensive, and computationally less costly than their hybrids if their hyperparameters are appropriately tuned. [ABSTRACT FROM AUTHOR]

Copyright of Transportation Science & Technolgy is the property of Transportation Science & Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Many kinds of electrical equipment are used in civil and building engineering. The motor is one of the main power components of this electrical equipment, which can provide stable power output. During the long-term use of motors, various motor faults may occur, which affects the normal use of electrical equipment and even causes accidents. It is significant to apply fault diagnosis for the motors at the construction site. Aiming at the problem that signal data of faulty motor lack diversity, this research designs a multi-layer perceptron Wasserstein generative adversarial network, which is used to enhance training data through distribution fusion. A discrete wavelet decomposition algorithm is employed to extract the low-frequency wavelet coefficients from the original motor current signals. These are used to train the multi-layer perceptron Wasserstein generative adversarial model. Then, the trained model is applied to generate fake current wavelet coefficients with the fused distribution. A motor fault classification model consisting of a feature extractor and pattern recognizer is built based on perceptron. The data augmentation experiment shows that the fake dataset has a larger distribution than the real dataset. The classification model trained on a real dataset, fake dataset and combined dataset achieves 21.5%, 87.2%, and 90.1% prediction accuracy on the unseen real data, respectively. The results indicate that the proposed data augmentation method can effectively generate fake data with the fused distribution. The motor fault classification model trained on a fake dataset has better generalization performance than that trained on a real dataset. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Remarkable achievements have been obtained with binary neural networks (BNN) in real-time and energy-efficient single-image super-resolution (SISR) methods. However, existing approaches often adopt the Sign function to quantize image features while ignoring the influence of image spatial frequency. We argue that we can minimize the quantization error by considering different spatial frequency components. To achieve this, we propose a frequency-aware binarized network (FABNet) for single image super-resolution. First, we leverage the wavelet transformation to decompose the features into low-frequency and high-frequency components and then employ a “divide-and-conquer” strategy to separately process them with well-designed binary network structures. Additionally, we introduce a dynamic binarization process that incorporates learned-threshold binarization during forward propagation and dynamic approximation during backward propagation, effectively addressing the diverse spatial frequency information. Compared to existing methods, our approach is effective in reducing quantization error and recovering image textures. Extensive experiments conducted on four benchmark datasets demonstrate that the proposed methods could surpass state-of-the-art approaches in terms of PSNR and visual quality with significantly reduced computational costs. Our codes are available at https://github.com/xrjiang527/FABNet-PyTorch. [ABSTRACT FROM AUTHOR]

Based on the barometric data recorded by the seismic monitoring network on the Chinese mainland, Lamb wave periods and relative arrival times of 462 stations are obtained with the aid of Meyer's wavelet decomposition, Welch's periodogram spectrum estimation and waveform cross-correlation. By extracting the seismic Rayleigh waves of two seismic stations in the South Pacific and comparing them with the synthetic seismograms, the occurrence time of the first two large volcanic eruptions and the largest volcanic eruption are credibly deduced, and then the travel times and propagation speeds of the Lamb waves are obtained. In order to further explain the attributes of the pressure disturbance associated with the complex acoustic gravity wave (AGW) phases on pressure, a series of numerical simulations with the QSSP program are applied to yield highly similar waves corresponding to the barometric records, which indicates that the eruption source may contain more than 10 sub-events within an hour. According to our detailed analyses, major conclusions are obtained as follows: (1) Although the primary pressure disturbance of the Tonga volcanic eruption appears to be a simple bulge, it is in fact a complex wave composed of multiple eruptions. The largest eruption occurred about 13 min after the first large eruption. (2) In addition to the propagation of the traditional Lamb mode, the Pekeris mode phase that propagates with a lower speed is also observed in some stations, and its amplitude is about a fifth of the Lamb wave. The Pekeris waves may have been more significantly delayed as they traveled against the westerly wind due to their much shorter periods and lower velocities. (3) The group velocity of the Lamb wave is about 308 m/s. Its average period is 70 min, and the wavelength is about 1300 km. The arrival time deviation at each station is negatively correlated with the difference in the near-surface air temperature between North and South China. However, accurately estimating the parameters of the Pekeris waves or the antipodal Lamb waves is challenging due to their low signal-to-noise ratio (SNR), even though the horizontal propagation speed of the Pekeris wave toward China is estimated to be approximately 225 m/s. (4) Much shorter periods and later arrivals at the stations within 200 km of Beijing may be related to the significant cooling (i.e. 12 °C temperature drop), which occurred from Ulantoba, Outer Mongolia, to Beijing beginning at noon on January 15 (Beijing time). [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Progress in Earthquake Sciences is the property of China Earthquake Administration, Institute of Geophysics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we examine price dynamics, cycles and lead-lag relationships between private and public commercial real estate markets. We utilize wavelet technology to capture both the frequency and the time variations of a time series. We find that the long-run trend of prices in public commercial real estate markets is steeper than that of private commercial real estate markets. In addition, both short-term and long-term cycles are longer in the public market than the private market. We also find that the private market led the public market up until the recession of early 1990s and the public market has led the private market since then. Finally, we offer the first evidence of contagion between the two markets. We find that there is an increase in excess (high frequency) contagion between the two markets during periods of crisis, but not beyond the crises periods. An understanding of co-movements of real estate prices across these two markets is of crucial importance policy makers and for maximizing portfolio diversification benefits and managing risk. [ABSTRACT FROM AUTHOR]