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As a large percentage of faults in transmission lines are single-line-to-ground (SLG) and transient in nature, the use of adaptive single-phase auto-reclosing (ASPAR) schemes is essential. In this paper, a new scheme for ASPAR is proposed to improve the stability and reliability of power transmission lines. The proposed approach distinguishes between the transient and permanent faults by using the feature of Stationary Wavelet Transform (SWT) and Gate Recurrent Unit (GRU) deep artificial neural network. Also, by utilizing another GRU network, the proposed scheme predicts the extinction time of the secondary arc (SA) one power cycle before the complete extinction. This can increase the speed of the faulty phase reclosing in the case of a transient fault. Simulation results carried out on a 400 kV power transmission line in the EMTP-RV and MATLAB software environments illustrate that the F 1 score of the proposed ASPAR in classifying the faults is 99.55 %, and its average error in predicting the extinction time of the SA based on the root mean square error (RMSE) criterion is 0.0557. Also, the superiority and high-quality performance of the proposed protection scheme are demonstrated by comparing the results with several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

As a large percentage of faults in transmission lines are single-line-to-ground (SLG) and transient in nature, the use of adaptive single-phase auto-reclosing (ASPAR) schemes is essential. In this paper, a new scheme for ASPAR is proposed to improve the stability and reliability of power transmission lines. The proposed approach distinguishes between the transient and permanent faults by using the feature of Stationary Wavelet Transform (SWT) and Gate Recurrent Unit (GRU) deep artificial neural network. Also, by utilizing another GRU network, the proposed scheme predicts the extinction time of the secondary arc (SA) one power cycle before the complete extinction. This can increase the speed of the faulty phase reclosing in the case of a transient fault. Simulation results carried out on a 400 kV power transmission line in the EMTP-RV and MATLAB software environments illustrate that the F1 score of the proposed ASPAR in classifying the faults is 99.55 %, and its average error in predicting the extinction time of the SA based on the root mean square error (RMSE) criterion is 0.0557. Also, the superiority and high-quality performance of the proposed protection scheme are demonstrated by comparing the results with several state-of-the-art methods.

In information and security, the personal identification of individuals becomes much more important. For improving security, several biometric recognition techniques are implemented. However, in finger vein recognition, it faces the critical problem of fake finger vein images, security and less accuracy. To conquer this problem, Hybrid Feature Extraction with Linear Local Tangent Space Alignment-based dimension reduction and Support Vector Machine classifier (HFE–LLTSA–SVM) is proposed. In this hybrid, FE is considered as the combination of histogram of oriented gradients (HOG), grey-level co-occurrence matrix (GLCM), stationary wavelet transform (SWT), and local binary pattern (LBP) for extracting the hybrid feature. LLTSA perform dimension reduction in the outputs of HFE from HOG, GLCM, and LBP. Furthermore, SVM is used for classification which gives authentication based on error-correcting code. Finally, the performance parameters were calculated and the proposed method achieved better accuracy of 99.75%, when compared with existing methods. [ABSTRACT FROM AUTHOR]

Historical fine-scale information of archaeological landscapes is crucial in archaeological investigations. However, documenting such information using satellite sensor data prior to 2000 remains a daunting challenge. Images from the declassified archives of KH-9 HEXAGON (KH-9) cameras, including the panoramic camera system (PCS) and mapping camera system (MCS), offer fine-scale information about archaeological sites. However, noise, contrast distortion and the availability of only a single panchromatic band can limit their potential, particularly for identifying features in subtropical climates within heterogeneous landscape types. This paper focuses on developing a novel multifaceted analytical framework with two components: image pre-processing and feature identification. The image pre-processing component is divided into two steps. First, a trained stationary wavelet transform (SWT) based on the normalized sill (NS) is developed to not only de-noises the image, but also preserve its original image characteristics. Then, the contrast of the de-noised images is optimized by the multi-resolution Top-hat (MTH) using multi-scale information. In the feature identification component, the MCS image is analysed using spatial colour composite write function memory (SCCWFM) and spatial novelty detection (SND). An ultra-fine spatial three-dimensional colour composite (UFSTCC) image and ultra-fine spatial digital surface model (UFSDSM) are produced to aid interpretation of the KH-9 PCS images. The proposed processing pipelines were tested on KH-9 MCS and PCS images of the World Heritage site at Liangzhu Ancient City (LAC) in China, which is characterized by a subtropical climate and a heterogeneous landscape types. The proposed pre-processing pipeline improved considerably the appearance of these images across the LAC landscape while maintaining the original image information. The developed digital analytical approaches for KH-9 PCS and MCS images facilitated straightforward identification of archaeological features in the LAC. The proposed framework has the potential to increase exploitation of the available KH-9 images in archaeological applications. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak has spread rapidly worldwide since 2019. This pandemic has complicated and intricate human existence, and thousands have died from it. Due to the spread of coronavirus, people wear masks while going outside. Consequently, the system cannot identify their faces while wearing the masks. This issue can be overcome by introducing a system that recognizes masked faces of random people trained with 100 images taken from the Internet. This paper presents a novel deep learning-based double generator network to precisely identify the face behind the mask images. Initially, the gathered images are split into low- and high-frequency components using 2D-stationary wavelet transform (2D-SWT). Afterward, the Haar cascade classifier was implemented to capture the masked image biometrics to recognize the individual faces. The proposed double generator network involves two modules: edge generation and image reconstruction. The first modules consist of dilated convolutional for retrieving the relevant features from the masked face images created on the generated edges. The generated edges are reconstructed using the reflection of generated edges in the second module. Finally, the output images are reconstructed to identify the masked face. From the simulation results, the proposed framework showed effective performance based on the network parameters. The proposed network attains an accuracy of 97.08% for masked face recognition which demonstrates it achieves higher accuracy than the prior frameworks. [ABSTRACT FROM AUTHOR]

The power system is one of the most susceptible systems to failures, which are most frequently caused by transmission line faults. Transmission line failures account for 85% of all power system malfunctions. However, over the last decade, numerous fault detection methods have been developed to ensure the reliability and stability of power systems. A hybrid detection method based on the idea of redundancy property is presented in this paper. Because the continuous wavelet transform itself does not extract fault features for small defects effectively, the stationary wavelet transform approach is employed to assist in their detection. As a result of its ability to decompose the signal into high- and low-frequency components, undecimated reconstruction by using the algebraic summation operation (ASO) is used. This approach creates redundancy, which is useful for the feature extraction of small defects and makes faulty parts more evident. The numerical value of the redundancy ratio’s contribution to the original signal is approximately equal to 36%. Following this method for redundant signal reconstruction, a continuous wavelet transform is used to extract the fault characteristic significantly easier in the time-scale (frequency) domain. Finally, the suggested technique has been demonstrated to be an efficient fault detection and identification tool for use in power systems. In fact, using this advanced signal processing technique will help with early fault detection, which is mainly about predictive maintenance. This application provides more reliable operation conditions.

Neural oscillations are critical to understanding the synchronisation of neural activities and their relevance to neurological disorders. For instance, the amplitude of beta oscillations in the subthalamic nucleus has gained extensive attention, as it has been found to correlate with medication status and the therapeutic effects of continuous deep brain stimulation in people with Parkinson's disease. However, the frequency stability of subthalamic nucleus beta oscillations, which has been suggested to be associated with dopaminergic information in brain states, has not been well explored. Moreover, the administration of medicine can have inverse effects on changes in frequency and amplitude. In this study, we proposed a method based on the stationary wavelet transform to quantify the amplitude and frequency stability of subthalamic nucleus beta oscillations and evaluated the method using simulation and real data for Parkinson's disease patients. The results suggest that the amplitude and frequency stability quantification has enhanced sensitivity in distinguishing pathological conditions in Parkinson's disease patients. Our quantification shows the benefit of combining frequency stability information with amplitude and provides a new potential feedback signal for adaptive deep brain stimulation.

A novel enhancement method to improve resolution and contrast has been proposed to address the issues of blurring and distortion commonly encountered in traditional patterns. Initially, a discrete wavelet transform, a stationary wavelet transform, and an interpolation algorithm are used to obtain high-resolution images of traditional patterns. Subsequently, improved singular value matrix coefficients and reconstructed gamma function are used to enhance the image contrast to obtain high-resolution and contrast-enhanced patterns. Experimental results demonstrate the efficacy of this method, as evidenced by improved evaluation indexes, such as mean square error, peak signal-to-noise ratio, and structural similarity, in comparison to other existing methods. The proposed method effectively improves the quality of traditional patterns and offers significant contributions to research on the restoration and protection of traditional patterns. [ABSTRACT FROM AUTHOR]

The development of deep learning has greatly improved the image inpainting performance in the past decades. In fact, image inpainting for different tasks usually requires different models. For instance, the highly structured images need to be compensated the structural consistency, and the textured ones need to be reconstructed the local high-frequency details. However, it is still challenging to realize an effective algorithm taking account of the global structure and texture details separately. Herein, we proposed a two-stage inpainting method, combining the information of frequency and spatial domain. Stationary Wavelet Transform (SWT) with good time-frequency characteristics was applied to obtain the sub-band images as the basic inputs for frequency-domain inpainting. Contextual Attention Layer (CAL) modules were optionally introduced in the network to adapt to various inpainting tasks. We also tested and discussed the impacts of some commonly used loss functions, including normal L1 loss, normal GAN loss, weighted L1 loss, and WGAN-GP loss, on highly structured and textured images. [ABSTRACT FROM AUTHOR]

ObjectiveTo construct the 3D point cloud model of green plants a large number of clear images are needed. Due to the limitation of the depth of field of the lens, part of the image would be out of focus when the green plant image with a large depth of field is collected, resulting in problems such as edge blurring and texture detail loss, which greatly affects the accuracy of the 3D point cloud model. However, the existing processing algorithms are difficult to take into account both processing quality and processing speed, and the actual effect is not ideal. The purpose of this research is to improve the quality of the fused image while taking into account the processing speed.MethodsA plant image fusion method based on non-subsampled shearlet transform (NSST) based parameter-adaptive dual channel pulse-coupled neural network (PADC-PCNN) and stationary wavelet transform (SWT) was proposed. Firstly, the RGB image of the plant was separated into three color channels, and the G channel with many features such as texture details was decomposed by NSST in four decomposition layers and 16 directions, which was divided into one group of low frequency subbands and 64 groups of high frequency subbands. The low frequency subband used the gradient energy fusion rule, and the high frequency subband used the PADC-PCNN fusion rule. In addition, the weighting of the eight-neighborhood modified Laplacian operator was used as the link strength of the high-frequency fusion part, which enhanced the fusion effect of the detailed features. At the same time, for the R and B channels with more contour information and background information, a SWT with fast speed and translation invariance was used to suppress the pseudo-Gibbs effect. Through the high-precision and high-stability multi-focal length plant image acquisition system, 480 images of 8 experimental groups were collected. The 8 groups of data were divided into an indoor light group, natural light group, strong light group, distant view group, close view group, overlooking group, red group, and yellow group. Meanwhile, to study the application range of the algorithm, the focus length of the collected clear plant image was used as the reference (18 mm), and the image acquisition was adjusted four times before and after the step of 1.5 mm, forming the multi-focus experimental group. Subjective evaluation and objective evaluation were carried out for each experimental group to verify the performance of the algorithm. Subjective evaluation was analyzed through human eye observation, detail comparison, and other forms, mainly based on the human visual effect. The image fusion effect of the algorithm was evaluated using four commonly used objective indicators, including average gradient (AG), spatial frequency (SF), entropy (EN), and standard deviation (SD).Results and DiscussionsThe proposed PADC-PCNN-SWT algorithm and other five algorithms of common fast guided filtering algorithm (FGF), random walk algorithm (RW), non-subsampled shearlet transform based PCNN (NSST-PCNN) algorithm, SWT algorithm and non-subsampled shearlet transform based parameter-adaptive dual-channel pulse-coupled neural network (NSST-PADC) and were compared. In the objective evaluation data except for the red group and the yellow group, each index of the PADC-PCNN-SWT algorithm was second only to the NSST-PADC algorithm, but the processing speed was 200.0% higher than that of the NSST-PADC algorithm on average. At the same time, compared with the FDF, RW, NSST-PCNN, and SWT algorithms, the PADC-PCN -SWT algorithm improved the clarity index by 5.6%, 8.1%, 6.1%, and 17.6%, respectively, and improved the spatial frequency index by 2.9%, 4.8%, 7.1%, and 15.9%, respectively. However, the difference between the two indicators of information entropy and standard deviation was less than 1%, and the influence was ignored. In the yellow group and the red group, the fusion quality of the non-green part of the algorithm based on PADC-PCNN-SWT was seriously degraded. Compared with other algorithms, the sharpness index of the algorithm based on PADC-PCNN-SWT decreased by an average of 1.1%, and the spatial frequency decreased by an average of 5.1%. However, the indicators of the green part of the fused image were basically consistent with the previous several groups of experiments, and the fusion effect was good. Therefore, the algorithm based on PADC-PCNN-SWT only had a good fusion effect on green plants. Finally, by comparing the quality of four groups of fused images with different focal length ranges, the results showed that the algorithm based on PADC-PCNN-SWT had a better contour and color restoration effect for out-of-focus images in the range of 15-21 mm, and the focusing range based on PADC-PCNN-SWT was about 6 mm.ConclusionsThe multi-focal length image fusion algorithm based on PADC-PCNN-SWT achieved better detail fusion performance and higher image fusion efficiency while ensuring fusion quality, providing high-quality data, and saving a lot of time for building 3D point cloud model of green plants.

For decades, many conventional signal processing methods have been applied in the image fusion field to extract image features, such as discrete wavelet transform (DWT), contourlet transform, shiftinvariant shearlet transform and quaternion wavelet transform etc. For the infrared and visible image fusion task. But these methods may introduce artifacts into the fused image. To overcome these problems optimization based fusion schemes are proposed. These methods take multiple iterations to find the optimal solution (fused image). These optimization methods may over smooth the fused image because of multiple iterations. This work focused on hardware-oriented implementation of VLSI based implementation of VI-IR image fusion mechanism. Initially, images are read using Matlab environment. Then, stationary wavelet transform is applied to decompose the VI, and IR images into multiple bands. The low-low bands are converted into text files. Further, the band fusion rule is applied on low-low bands, where multiplexer-based adder is used to combine both images. Finally, Matlab environment is used to reconstruct the fused image from the obtained text files. The simulation results show that the proposed method resulted in better performance. [ABSTRACT FROM AUTHOR]

One method for securing multimedia data, and images in particular, from malicious attacks is watermarking, which involves adding a signature to the images. Traditional watermarking techniques, however, have drawbacks when used on sensitive images like those used in medicine. A reliable and blind watermarking method is suggested in this work to secure medical image exchanged in telemedicine. Medical image watermarking enables accurate patient identification, prevents scan confusion, and minimizes the risk of diagnostic mistakes that might have detrimental effects. In this method, three distinct transformations are used to get the frequency content of the picture. The low frequency subbands are subsequently subjected to Schur decomposition. In order to incorporate the watermark bits, the resultant upper triangular matrix values are modified. The suggested approaches effectively retain a considerable quality of watermarked images, according to experimental results on imperceptibility and robustness, and are resistant against various common attacks. [ABSTRACT FROM AUTHOR]

[Objective] To construct the 3D point cloud model of green plants a large number of clear images are needed. Due to the limitation of the depth of field of the lens, part of the image would be out of focus when the green plant image with a large depth of field is collect‐ ed, resulting in problems such as edge blurring and texture detail loss, which greatly affects the accuracy of the 3D point cloud model. However, the existing processing algorithms are difficult to take into account both processing quality and processing speed, and the ac‐ tual effect is not ideal. The purpose of this research is to improve the quality of the fused image while taking into account the process‐ ing speed. [Methods] A plant image fusion method based on non-subsampled shearlet transform (NSST) based parameter-adaptive dual channel pulse-coupled neural network (PADC-PCNN) and stationary wavelet transform (SWT) was proposed. Firstly, the RGB image of the plant was separated into three color channels, and the G channel with many features such as texture details was decomposed by NSST in four decomposition layers and 16 directions, which was divided into one group of low frequency subbands and 64 groups of high frequency subbands. The low frequency subband used the gradient energy fusion rule, and the high frequency subband used the PADCPCNN fusion rule. In addition, the weighting of the eight-neighborhood modified Laplacian operator was used as the link strength of the high-frequency fusion part, which enhanced the fusion effect of the detailed features. At the same time, for the R and B channels with more contour information and background information, a SWT with fast speed and translation invariance was used to suppress the pseudo-Gibbs effect. Through the high-precision and high-stability multi-focal length plant image acquisition system, 480 images of 8 experimental groups were collected. The 8 groups of data were divided into an indoor light group, natural light group, strong light group, distant view group, close view group, overlooking group, red group, and yellow group. Meanwhile, to study the application range of the algorithm, the focus length of the collected clear plant image was used as the reference (18 mm), and the image acquisi‐ tion was adjusted four times before and after the step of 1.5 mm, forming the multi-focus experimental group. Subjective evaluation and objective evaluation were carried out for each experimental group to verify the performance of the algorithm. Subjective evalua‐ tion was analyzed through human eye observation, detail comparison, and other forms, mainly based on the human visual effect. The image fusion effect of the algorithm was evaluated using four commonly used objective indicators, including average gradient (AG), spatial frequency (SF), entropy (EN), and standard deviation (SD). [Results and Discussions] The proposed PADC-PCNN-SWT algorithm and other five algorithms of common fast guided filtering al‐ gorithm (FGF), random walk algorithm (RW), non-subsampled shearlet transform based PCNN (NSST-PCNN) algorithm, SWT algo‐ rithm and non-subsampled shearlet transform based parameter-adaptive dual-channel pulse-coupled neural network (NSST-PADC) and were compared. In the objective evaluation data except for the red group and the yellow group, each index of the PADC-PCNNSWT algorithm was second only to the NSST-PADC algorithm, but the processing speed was 200.0% higher than that of the NSSTPADC algorithm on average. At the same time, compared with the FDF, RW, NSST-PCNN, and SWT algorithms, the PADC-PCN -SWT algorithm improved the clarity index by 5.6%, 8.1%, 6.1%, and 17.6%, respectively, and improved the spatial frequency index by 2.9%, 4.8%, 7.1%, and 15.9%, respectively. However, the difference between the two indicators of information entropy and stan‐ dard deviation was less than 1%, and the influence was ignored. In the yellow group and the red group, the fusion quality of the nongreen part of the algorithm based on PADC-PCNN-SWT was seriously degraded. Compared with other algorithms, the sharpness in‐ dex of the algorithm based on PADC-PCNN-SWT decreased by an average of 1.1%, and the spatial frequency decreased by an aver‐ age of 5.1%. However, the indicators of the green part of the fused image were basically consistent with the previous several groups of experiments, and the fusion effect was good. Therefore, the algorithm based on PADC-PCNN-SWT only had a good fusion effect on green plants. Finally, by comparing the quality of four groups of fused images with different focal length ranges, the results showed that the algorithm based on PADC-PCNN-SWT had a better contour and color restoration effect for out-of-focus images in the range of 15-21 mm, and the focusing range based on PADC-PCNN-SWT was about 6 mm. [Conclusions] The multi-focal length image fusion algorithm based on PADC-PCNN-SWT achieved better detail fusion performance and higher image fusion efficiency while ensuring fusion quality, providing high-quality data, and saving a lot of time for building 3D point cloud model of green plants. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR), which is seen in approximately one-third of diabetes patients worldwide, leads to irreversible vision loss and even blindness if not diagnosed and treated in time. It is vital to limit the progression of DR disease in order to prevent the loss of vision in diabetic patients. It is therefore essential that DR disease is diagnosed at an early phase. Thanks to retinal screening at least twice a year, DR disease can be diagnosed in its early phases. However, due to the variations and complexity of DR, it is really difficult to determine the phase of DR disease in current clinical diagnoses. This paper presents a robust artificial intelligence (AI)-based model that can overcome nonlinear dynamics with low computational complexity and high classification accuracy using fundus images to determine the phase of DR disease. The proposed model consists of four stages, excluding the preprocessing stage. In the preprocessing stage, fractal analysis is performed to reveal the presence of chaos in the dataset consisting of 12,500 color fundus images. In the first stage, two-dimensional stationary wavelet transform (2D-SWT) is applied to the dataset consisting of color fundus images in order to prevent information loss in the images and to reveal their characteristic features. In the second stage, 96 features are extracted by applying statistical- and entropy-based feature functions to approximate, horizontal, vertical, and diagonal matrices of 2D-SWT. In the third stage, the features that keep the classifier performance high are selected by a chaotic-based wrapper approach consisting of the k-nearest neighbor (kNN) and chaotic particle swarm optimization algorithms (CPSO) to cope with both chaoticity and computational complexity in the fundus images. At the last stage, an AI-based classification model is created with the recurrent neural network-long short-term memory (RNN-LSTM) architecture by selecting the lowest number of feature sets that can keep the classification performance high. The performance of the DR disease classification model was tested on 2500 color fundus image data, which included five classes: no DR, mild non-proliferative DR (NPDR), moderate NPDR, severe NPDR, and proliferative DR (PDR). The robustness of the DR disease classification model was confirmed by the 10-fold cross-validation. In addition, the classification performance of the proposed model is compared with the support vector machine (SVM), which is one of the machine learning techniques. The results obtained show that the proposed model can overcome nonlinear dynamics in color fundus images with low computational complexity and is very effective and successful in precisely diagnosing all phases of DR disease. [ABSTRACT FROM AUTHOR]

This paper proposes a wavelet domain-based method for multispectral image super-resolution. The stationary wavelet transform is proposed to decompose the multispectral image into directional wavelet components and for each wavelet component, a joint dictionary learning algorithm is proposed. Using sparse and redundant representations, the proposed approach helps capture intrinsic multispectral features using wavelet domain learning utilizing the up-sampling property of (SWT). The proposed method can learn and recover those image features more accurately. In order to validate the proposed method, we conducted comprehensive experiments. Moreover, we present a comparison of our proposed method with state-of-the-art algorithms over PSNR and SSIM evaluation parameters. The results of the experiments indicate that the proposed method outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The use of the web is developing for surreptitious and non-surreptitious communication with the expansion of brilliant innovations. In this circumstance, there is a need to improve the security level of the covert information. Our motive is to propose a unique algorithm by using steganography along with cryptography which provides a better solution for secret communication and improves security with more levels. In this paper, the two-level stationary wavelet transform technique is applied to cover image to acquire HH2 sub-band alongside the remaining sub-bands. The data are implanted into the HH2 sub-band of the cover image by utilizing the random pixel embedded strategy to accomplish the stego image. To improve the security of the stego image, the surreptitious text data are changed into cipher form with a novel cryptography algorithm that incorporates unique data conversion and gray-code techniques. The resultant stego image undergoes to split-logical algorithm to create numerous imperceptible stego images. The main purpose of imperceptible stego images is to provide better security to covert information and also it increases the payload capacity. The concept of generating imperceptible stego images is, each of them will be treated as a stego image for hackers. But original stego image is reconstructed by combining all generated imperceptible stego images. These imperceptible stego images are ready to oppose the diverse steganalysis assaults like visual, noise, rotational assaults, and so forth. [ABSTRACT FROM AUTHOR]

Breast cancer is very prevalent and because of its death rate is taken into deliberation to be the second dangerous disease in the world. There is a relentless effort to create more effective techniques for an early and reliable diagnosis. Classical approaches require oncologists to investigate breast lesions to detect and classify different cancer stages. Such manual attempts in many cases are time-consuming and inefficient. Hence there is a requirement for effective methods to diagnose cancer cells with high accuracy without human involvement. A "Moon Phase Wavelet Chain Rule Model" has been proposed in this research we introduced Moon Light Dimming Illumination Technique and Smart Recon Techniques. Thus, it overcomes the dense mass accumulation by providing a clear view of fat density, heterogeneous density, tumour size and thus it reduces the beam hardening problems. Our work has initiated Modified Segmented Stationary Wavelet Transform and Multivariable Chain Rule-Based Back Propagation Neural Network, to improvised the features extraction and classifying the phases of breast cancer by avoiding the under and overfitting problems. The proposed model reduces the dense mass accumulation, beam hardening, and obtains a segmented feature image for feature extraction.The Accuracy, Sensitivity, Specificity, Recall, Precision, prevalence performances of 98.62%, 98.25%, 97.52%, 98.25%, 97.25%, and 25.03% respectively. Hence, the outcome of the proposed model has been showing that our system is a promising and robust method for detecting breast cancer. [ABSTRACT FROM AUTHOR]

This work proposes a signal-based fault location method combining both time and frequency-domain features. The method is based on the Traveling Wave (TW) detection and analysis, and only employs 100 microseconds of data around the TW time-of-arrival to the measuring location. Time-domain features are extracted using Mathematical Morphology, while frequency-domain features are obtained using the Stationary Wavelet Transform. The IEEE 34 nodes system is selected for demonstration. TW detection is performed using a method based on Dynamic Mode Decomposition. This work exhaustively analyzes the performance of the proposed method in both fault location classification and regression tasks. Combining both time and frequency-domain features is proven to be more effective than using each type of feature independently. In addition, the sensitivity to noisy measurements and lesser amount of training data are analyzed as well. The performance of this method is compared to other works in the same test system, showing better accuracy than other signal-based methods. Furthermore, the proposed method has a low fault location estimation error rate across the entire length of the feeder and shows a similar performance to slower phasor-based fault location methods.

The EEG signals were used in many medical and technological applications such as diagnosis of diseases, rehabilitation of disabled peoples, preventive healthcare, BCI (brain computer interface) systems. EEG signal is prone to the physiological and non-physiological artifacts which severely affect them and lead to its misinterpretation. An automatic method and/or algorithm; for handling EEG artifacts; is proposed. The proposed method is based on three statistical parameters (entropy, kurtosis and skewness), fuzzy inference system (FIS) and stationary wavelet transform (SWT). Each incoming EEG epoch is described using these three statistical parameters. Based on the extracted statistical parameters, the designed FIS decides if an epoch is artifactual or not. Then SWT is used to decompose the EEG epoch into detail and approximation coefficients. To reduce the effect of artifact removal, we propose to use other fuzzy inference systems, which allow to select the contaminated wavelet coefficients. The universal thresholding method is then applied to the corrupted coefficients. Finally, the inverse SWT applies to the thresholded and non-corrupted coefficients to restore the cleaned EEG signal. The performance of the proposed method in terms of amount of artifact removal and signal distortion is evaluated in three scenarios: fully simulated, semi-simulated, and real artifactual EEG data. The comparison of our method with some existing state-of-the-art methods shows the superiority of our method over others in terms of performance and computational time.

The increase in energy demand due to rapid population growth, industrialization, depleting fossil energy resources, distribution network congestion, and environmental concerns of fossil fuels used to generate electricity are some of the reasons behind the integration of renewable Distributed Generation (DG) units into the existing power grid. Integrating DGs into the existing distribution network makes the system more flexible and increases power availability for the load demand increment. Although there are significant technical benefits of integrating DGs into the power grid, protection philosophy has been affected by these network topologies. This paper proposes a hybrid fault protection scheme in a power-integrated system. The proposed technique aims at minimizing the impact of the fault in the design and appropriately locating the position of the fault. The proposed approach comprises a signal processing and feature extraction segment using the Wavelet Packet Transform (WPT), a fault classification and detection segment using the Support Vector Machine (SVM), and a fault location segment using the Gaussian Process Regression (GPR). Subsequently, these techniques are combined to design a hybrid protection scheme. The scheme is tested by using a modified Eskom power system network and modeled in Power Factory Software. The classification and the regression functions are implemented by using the MATLAB platform. The proposed technique's main contribution is the scheme's ability to classify, detect, and locate fault using a ½ cycle of the fault signal window. The results show that the proposed system has achieved a 99.8% classification accuracy and a minimum fault estimation error. [ABSTRACT FROM AUTHOR]

Most of the biomedical signals gathered from various electronic devices contain a certain amount of noise based on the external atmosphere, the working condition of the devices and the activity of the clinician handling the observation section. Analysing such signals may result in an ambiguous prediction of the patients' diagnosis. Thus, the eradication of noises in the biosignal waves gets a tremendous attraction in the medical field. Hence, a specific aim of this study is to handle two perfect and precise methods of clearing the defectiveness in the electromyography (EMG) signals which are termed stationary wavelet transform (SWT) and fractional linear prediction (FLP). Once we get the filtered signal from these two techniques, we can calculate the fractal dimension of the denoised EMG signals for obtaining the accurate way for offering medication. In this regard, the present study primarily reveals the comparison between fractal dimensions that are gained through the following specified methods, Higuchi, Katz, rescaled-range analysis, relative dispersion and power spectral density. Ultimately, these dimensions are compared to show a clear-cut result by considering both the filtering mechanisms. For the comparison of statistical data of an undertaken sample, mathematics provides an estimating procedure called the ANOVA test. This paper also deals with some of the statistical measures for comparing the denoising methods and results to attain a better one. As a consequence, we have concluded that the FLP filtering method is more reliable than the SWT technique, based on the fractal dimension forecasted via the five mechanisms. [ABSTRACT FROM AUTHOR]

BACKGROUND: Because clinically used 12-lead electrocardiography (ECG) devices have high falsepositive errors in automatic interpretations of atrial fibrillation (AF), they require substantial improvements before use. OBJECTIVE: A clinical 12-lead ECG pre-processing method with a parallel convolutional neural network (CNN) model for 12-lead ECG automatic AF recognition is introduced. METHODS: Raw AF diagnosis data from a 12-lead ECG device were collected and analyzed by two cardiologists to differentiate between true- and false-positives. Using a stationary wavelet transform (SWT) and independent component analysis (ICA) noise reduction was conducted and baseline wandering was corrected for the raw signals. AF patterns were learned and predicted using a parallel CNN deep learning (DL) model. (1) The proposed method alleviates the decreased ECG QRS amplitude enhances the signal-to-noise ratio and clearly shows atrial and ventricular activities. (2) After training, the CNNbased AF detector significantly reduced false-positive errors. The precision of AF diagnosis increased from 77.3% to 94.0 ± 1.5% as compared to ECG device interpretation. For AF screening, the model showed an average sensitivity of 96.8 ± 2.2%, specificity of 79.0 ± 5.8%, precision of 94.0 ± 1.5%, F1-measure of 95.2 ± 1.0%, and overall accuracy of 92.7 ± 1.5%. CONCLUSIONS: The method can bridge the gap between the research and clinical practice The ECG signal pre-processing and DL-based AF interpretation can be rapidly implemented clinically. [ABSTRACT FROM AUTHOR]

In order to realize real time monitoring of foundation displacement of railway cable bridge based on satellite observation system, reasonable data sources of satellite monitoring and data analysis duration are necessary, and the influence of various errors should be eliminated in the process of satellite signal acquisition. In this paper, the validity of foundation settlement monitoring of bridge tower by satellite is verified through accuracy and stability tests. For eliminating the multipath error and random noise of GNSS signal, a new method is presented by combining stationary wavelet transform and empirical mode decomposition in this study. For improving the observation accuracy of the satellite monitoring system, combing the measured data, the GNSS data are corrected by establishing non-linear mapping between the GNSS data and precise leveling data by BP neural network. Based on that, the accuracy of the presented method is verified by the foundation settlement data of a new railway cable bridge tower. [ABSTRACT FROM AUTHOR]

This study proposes an edge-preserving image deraining network using a wavelet feature aggregation method. Wavelet subbands re correlated with each other, and the high-frequency subband in the horizontal direction is the least affected by rain streak contamination. On this basis, we introduce a single image deraining network that cumulatively aggregates wavelet subband features according to their importance. The network architecture primarily comprises a wavelet feature aggregation block and a residue channel guide block. The aggregation of features with the cumulative wavelet feature aggregation block moves downward and upward, and a long short-term memory-based multiscale attentive rain streak removal block is developed to serve as the backbone for rain streak removal. We use a residual channel map based on the low-frequency subband to construct guide features that assist in rain streak removal. A repetitive image restoration framework that incorporates two proposed blocks is used to iteratively improve rainy images. We test the proposed network on various image datasets and compare the deraining performance with those of existing methods The experimental results demonstrate that the performance of the proposed scheme is superior to that of other tested deraining methods. https://github.com/SYChoi98/CWFANet/ • This paper presents a single image deraining network that cumulatively aggregates wavelet subband features. • The wavelet feature aggregation block integrates long short-term memory-based multiscale attentive rain streak removal blocks. • The fusion block is used to incorporate residual channel prior knowledge into the wavelet-transformed image features. • The simulation results confirm that the proposed image deraining network outperforms other state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In clinic, the acquisition of airflow with nasal prongs, masks, thermistor to monitor respiratory function is more uncomfortable and inconvenience than surface diaphragm electromyography (EMGdi) using electrode pads. The EMGdi with strong electrocardiograph (ECG) interference affect the extraction of its characteristic information. In this work, surface EMGdi and airflow signals of 20 subjects were collected under 5 incremental inspiratory threshold loading protocols from quiet breathing to maximum forced breathing. First, we filtered out the ECG interference in EMGdi based on the combination of stationary wavelet transform and the positioning of ECG to obtain pure EMGdi (EMGdip). Second, the Spearman’s rank correlation coefficients between EMGdi and EMGdip quantified by time series fixed sample entropy (fSampEn), root mean square (RMS), and envelope were compared to verify the robustness of the fSampEn to ECG. A comparative analysis of correlation between fSampEn of EMGdi and inspiratory airflow and the correlation between envelope of EMGdip (EMGdie) and inspiratory airflow found that there was no significant difference between the two, indicating the feasibility of using fSampEn to predict airflow. Moreover, fSampEn of EMGdi was used as characteristic parameter to build a quantitative relationship with the airflow by polynomial regression analysis. Mean coefficient of determination of all subjects in any breathing state is greater than 0.88. Finally, nonlinear programming method was used to solve a universal fitting coefficient between fSampEn of EMGdi and airflow for each subject to further evaluate the possibility of using surface EMGdi to monitor and control respiratory activity.

This paper introduces a machine learning based copy-move forgery (CMF) localization method. The basic convolutional neural network cannot be applied to CMF detection because CMF frequently involves rotation transformation. Therefore, we propose a rotation-invariant feature based on the root-mean squared energy using high-frequency wavelet coefficients. Instead of using three color image channels, two-scale energy features and low-frequency subband image are fed into the conventional VGG16 network. A correlation module is used by employing small feature patches generated by the VGG16 network to obtain the possible copied and moved patch pairs. The all-to-all similarity score is computed using the correlation module. To generate the final binary localization map, a simplified mask decoder module is introduced, which is composed of two simple bilinear upsampling and two batch-normalized-inception-based mask deconvolution followed by bilinear upsampling. We perform experiments on four test datasets and compare the proposed method with state-of-the-art tampering localization methods. The results demonstrate that the proposed scheme outperforms the existing approaches.

In this paper, we present a new method for forecasting power consumption. Household power consumption prediction is essential to manage and plan energy utilization. This study proposes a new technique using machine learning models based on the stationary wavelet transform (SWT) and transformers to forecast household power consumption in different resolutions. This approach works by leveraging self-attention mechanisms to learn complex patterns and dynamics from household power consumption data. The SWT and its inverse are used to decompose and reconstruct the actual and the forecasted household power consumption data, respectively, and deep transformers are used to forecast the SWT subbands. Experimental findings show that our hybrid approach achieves superior prediction performance compared to the existing power consumption prediction methods.

Harmful exposure to erythemally-effective ultraviolet radiation (UVR) poses high health risks such as malignant keratinocyte cancers and eye-related diseases. Delivering short-term forecasts of the solar ultraviolet index (UVI) is an effective way to advise UVR exposure information to the public at risk. This research reports on a novel framework built to forecast UVI, integrating antecedent lagged memory of cloud statistical properties and the solar zenith angle (SZA). To produce the forecasts at multi-step horizon we design a 3-phase hybrid convolutional long short-term memory network (W-O-convLSTM) model, validated with Queensland-based datasets in near real-time (i.e., 10-minute, 20-minute, 30-minute and 1 hour forecast horizon). Our approach in optimizing the performance also entails a robust selective filtering method using the BorutaShap algorithm, data decomposition with stationary wavelet transformation and hyperparameter optimization using the Optuna algorithm. We assess the performance of the proposed W-O-convLSTM model alongside the baseline and benchmark models. The captured results, through statistical metrics and visual infographics, elucidate the superior performance of the objective model in short-term UVI forecasting. For instance, at a 10-minute forecast horizon, our objective model yields a relatively high correlation coefficient of ~0.961 in the autumn, 0.909 in the summer, 0.926 in the spring and 0.936 in the winter season. Overall, the proposed O-convLSTM model outperforms its competing counterpart models for all forecast horizons with the lowest absolute forecast error. The robustness of our newly proposed model avers its practical utility in delivering sun-protection behavior recommendations that can mitigate UV-exposure-related public health risk. We also recommend that future integration of aerosol and ozone effects with cloud cover data can enhance our forecasting framework for wider applications in solar energy or skin health monitoring systems.

[Display omitted] The EEG signals were used in many medical and technological applications such as diagnosis of diseases, rehabilitation of disabled peoples, preventive healthcare, BCI (brain computer interface) systems. EEG signal is prone to the physiological and non-physiological artifacts which severely affect them and lead to its misinterpretation. An automatic method and/or algorithm; for handling EEG artifacts; is proposed. The proposed method is based on three statistical parameters (entropy, kurtosis and skewness), fuzzy inference system (FIS) and stationary wavelet transform (SWT). Each incoming EEG epoch is described using these three statistical parameters. Based on the extracted statistical parameters, the designed FIS decides if an epoch is artifactual or not. Then SWT is used to decompose the EEG epoch into detail and approximation coefficients. To reduce the effect of artifact removal, we propose to use other fuzzy inference systems, which allow to select the contaminated wavelet coefficients. The universal thresholding method is then applied to the corrupted coefficients. Finally, the inverse SWT applies to the thresholded and non-corrupted coefficients to restore the cleaned EEG signal. The performance of the proposed method in terms of amount of artifact removal and signal distortion is evaluated in three scenarios: fully simulated, semi-simulated, and real artifactual EEG data. The comparison of our method with some existing state-of-the-art methods shows the superiority of our method over others in terms of performance and computational time. [ABSTRACT FROM AUTHOR]

This paper puts forward a denoising algorithm for low-dose computed tomography (LDCT) image based on optimal wavelet basis and morphological component analysis, which aims to solve the problem of severe noise and artifacts in LDCT imaging. First, the high-frequency (HF) component coefficients in the horizontal, vertical, and diagonal directions of LDCT after the stationary wavelet transform (SWT) are weighted to obtain the wavelet basis selection coefficients, and the wavelet basis with the smallest wavelet select coefficient is selected as the optimal wavelet basis. Second, the artifacts are processed using the MCA algorithm based on online dictionary learning (ODL) for the HF component. Third, the improved LDCT images are obtained using the inverse stationary wavelet transform (ISWT), which uses the low-frequency (LF) components and the denoised HF component. The extensive experiments on simulated and real data demonstrated the images denoised using the optimal wavelet basis algorithm showed the highest objective evaluation index, followed by the other wavelet-based algorithms. Additionally, our proposed method outperformed several classical denoising methods on both quantitative and qualitative assessments. It was therefore verified that the validity of wavelet selection and the feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Mental task categorization using single/limited channel(s) electroencephalogram (EEG) signals is crucial for designing portable brain–computer interface and neurofeedback systems. However, EEG signals are corrupted with ocular artifacts and muscle artifacts, which degrade the robustness of existing features resulting inaccurate categorization of mental tasks. Therefore, in this paper, we propose a real-time mental task categorization method using stationary wavelet transform (SWT)-based novel discriminant features and different classifiers. It consists of four major stages: EEG signal decomposition and reconstruction using SWT into subbands; computation of proposed relative subband energy features (i.e., total energy of all subbands( E t ), relative energy of each subband k ( E r (k) ) and sum of relative energy pair ratio and difference); significant feature selection; classification using seven classifiers. The robustness of the proposed method is evaluated using two publicly available datasets and in-house dataset recorded using single-channel EEG headset. Performance evaluation results show that the proposed method with support vector machine classifier achieves the highest average subject-dependent accuracy of 98% for classification of mental/mental and baseline/mental tasks. Extensive comparative performance analysis depicts the superiority of the proposed method as compared to existing techniques in terms of accurate categorization of mental tasks, computational complexity, EEG processing length and robustness under artifact-contaminated EEG signals. [ABSTRACT FROM AUTHOR]

Medical images like X-ray, computed tomography, ultrasound, and magnetic resonance imaging (MRI) are produced using different techniques; during this process, noise is added that decreases the image quality and image analysis. Image denoising is an important task in image processing; use of wavelet transform improves the quality of an image and reduces noise level. We propose in this research, a denoising approach basing on discrete wavelet transform (DWT) using Hybrid Thresholding (bayesShrink) with Wiener filter technique for enhancing the quality image. This technique improved a better balance between smoothness and accuracy than the traditional wavelet DWT and are less redundant than stationary wavelet transform (SWT). In addition, the Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) were used to analyse the denoised images quality. [ABSTRACT FROM AUTHOR]