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The presented research paper proposes a novel integrated technique combining LeNet-5 with Continuous Wavelet Transform (CWT) along with Long Short-Term Memory (LSTM). The purpose of this integration is to improve the performance of mechanisms used for the detection of defects in rotatory machines across various operating conditions. The Convolutional Neural Networks (CNN) assists the presented CWT-LeNet-5-LSTM technique in finding the complex characteristics in the data, while LSTM learns the trends in the dataset and performs the necessary analysis of vibrations occurring in faulty machines. The developed model was examined for various loads and faults to extract results having accuracies of 99.6%, 96.9%, 92.5% and 96.6% for load conditions 3, 2, 1, and 0, respectively. These results demonstrate the ability of the proposed model to adapt according to varying load conditions while having the necessary levels of accuracy. This validates the model to perform precise fault detection and diagnosis, offering capabilities of predictive maintenance in industrial settings. more...

Fluid–structure interaction introduces critical failure modes due to varying stiffness and changing contact states in rotor-stator systems. This is further aggravated by stress fluctuations due to shaft impact with a fixed stator when the shaft rotates. In this paper, the investigation of imbalance and rotor-stator contact on a rotating shaft was carried out in viscous fluid. The shaft was modelled as a vertical elastic rotor system based on a vertically oriented elastic rotor operating in an incompressible medium. Implicit representation of the rotating system including the rotor-stator contact and the hydrodynamic resistance was formulated for the coupled system using the energy principle and the Navier–Stokes equations. Additionally, the monolithic approach included an implicit strategy of the rotor-stator fluid interaction interface conditions in the solution methodology. Advanced time-frequency methods, such as Hilbert transform, continuous wavelet transform, and estimated instantaneous frequency maps, were applied to extract the vibration features of the dynamic response of the faulted rotor. Time-varying stiffness due to friction is thought to be the main reason for the frequency fluctuation, as indicated by historical records of the vibration displacement, whirling orbit patterns of the centre shaft, and the amplitude–frequency curve. It has also been demonstrated that the augmented mass associated with the rotor and stator decreases the natural frequencies, while the amplitude signal remains relatively constant. This behaviour indicates a quasi-steady-state oscillatory condition, which minimises the energy fluctuations caused by viscous effects. more...

The automatic detection of arrhythmia is of primary importance due to the huge number of victims caused worldwide by cardiovascular diseases. To this aim, several deep learning approaches have been recently proposed to automatically classify heartbeats in a small number of classes. Most of these approaches use convolutional neural networks (CNNs), exploiting some bi-dimensional representation of the ECG signal, such as spectrograms, scalograms, or similar. However, by adopting such representations, state-of-the-art approaches usually rely on the magnitude information alone, while the important phase information is often neglected. Motivated by these considerations, the focus of this paper is aimed at investigating the effect of fusing the magnitude and phase of the continuous wavelet transform (CWT), known as the scalogram and phasogram, respectively. Scalograms and phasograms are fused in a simple CNN-based architecture by using several fusion strategies, which fuse the information in the input layer, some intermediate layers, or in the output layer. Numerical results evaluated on the PhysioNet MIT-BIH Arrhythmia database show the effectiveness of the proposed ideas. Although a simple architecture is used, their competitiveness is high compared to other state-of-the-art approaches, by obtaining an overall accuracy of about 98.5% and sensitivity and specificity of 98.5% and 95.6%, respectively. [ABSTRACT FROM AUTHOR] more...

Fluid–structure interaction introduces critical failure modes due to varying stiffness and changing contact states in rotor-stator systems. This is further aggravated by stress fluctuations due to shaft impact with a fixed stator when the shaft rotates. In this paper, the investigation of imbalance and rotor-stator contact on a rotating shaft was carried out in viscous fluid. The shaft was modelled as a vertical elastic rotor system based on a vertically oriented elastic rotor operating in an incompressible medium. Implicit representation of the rotating system including the rotor-stator contact and the hydrodynamic resistance was formulated for the coupled system using the energy principle and the Navier–Stokes equations. Additionally, the monolithic approach included an implicit strategy of the rotor-stator fluid interaction interface conditions in the solution methodology. Advanced time-frequency methods, such as Hilbert transform, continuous wavelet transform, and estimated instantaneous frequency maps, were applied to extract the vibration features of the dynamic response of the faulted rotor. Time-varying stiffness due to friction is thought to be the main reason for the frequency fluctuation, as indicated by historical records of the vibration displacement, whirling orbit patterns of the centre shaft, and the amplitude–frequency curve. It has also been demonstrated that the augmented mass associated with the rotor and stator decreases the natural frequencies, while the amplitude signal remains relatively constant. This behaviour indicates a quasi-steady-state oscillatory condition, which minimises the energy fluctuations caused by viscous effects. [ABSTRACT FROM AUTHOR] more...

The aerodynamic stability of a compressor has a crucial impact on the performance of modern aircraft power system. It is necessary to design an accurate and reliable rotating stall warning system to take active control measures to avoid compressor instability as much as possible. This paper proposes a compressor rotating stall warning system that combines continuous wavelet transform (CWT) and a vision transformer (ViT), called a CWT-ViT system. Specifically, the system transforms one-dimensional time-series dynamic pressure signal data into two-dimensional color time–frequency images using CWT, which serves as the input to train the ViT classifier. In response to sensor failure, a model ranking execution strategy was adopted to improve the reliability of the whole system. The feasibility and performance of the proposed system were evaluated in different operating modes and sensor failure conditions using compressor stall experiments. The results showed that the average classification accuracy of the proposed system in stall warning tasks was 97.66%, which was the highest among all methods. In addition, the proposed system can maintain an early warning time of over 160 m seven in the case of sensor faults, which was the best warning performance among all methods. [ABSTRACT FROM AUTHOR] more...

Traditional methods have various problems such as poor robustness and low accuracy in the remaining service life of rolling bearings. In recent years, the development of deep learning has provided new ideas for solving these problems. In order to further improve the accuracy of predicting bearing life, a rolling bearing life prediction method based on ConvNeXt network, stacked bidirectional long shortterm memory network (SBiLSTM) and self-attention mechanism (Self-Attention) was proposed. Firstly, continuous wavelet transform (CWT) was used to construct the time-frequency map of the vibration signal, in order to better capture the time-domain and frequency-domain characteristics of the signal. Then, the obtained time-frequency map was input into the constructed ConvNeXt network, and key features of the time-frequency map were extracted through operations such as convolution, pooling, and layer normalization. Finally, the extracted features were input into the SBiLSTM-Self-Attention module for further extraction of temporal information and feature weight allocation. The PHM2012 challenge data set was used for experimental verification. The root means square error (RMSE) and mean absolute error (MAE) of the proposed method were experimentally analyzed. The results show that, comparing with existing technical methods, the average RMSE of this method is 0. 031. Comparing with the other three comparison methods, convolutional neural network (CNN), deep residual networkbidirectional gated recurrent unit ( DRN-BiGRU) and deep convolutional neural network-self attention-bidirectional gated recurrent unit (DCNN-Self Attention-BiGRU), its average RMSE values respectively decrease by 79%, 74% and 55%, the average MAE values respectively decrease by 78%, 73% and 53%. This method has achieved good performance in predicting the remaining life of rolling bearings. [ABSTRACT FROM AUTHOR] more...

BACKGROUND: Autism Spectrum Disorder (ASD) is a condition with social interaction, communication, and behavioral difficulties. Diagnostic methods mostly rely on subjective evaluations and can lack objectivity. In this research Machine learning (ML) and deep learning (DL) techniques are used to enhance ASD classification. OBJECTIVE: This study focuses on improving ASD and TD classification accuracy with a minimal number of EEG channels. ML and DL models are used with EEG data, including Mu Rhythm from the Sensory Motor Cortex (SMC) for classification. METHODS: Non-linear features in time and frequency domains are extracted and ML models are applied for classification. The EEG 1D data is transformed into images using Independent Component Analysis-Second Order Blind Identification (ICA-SOBI), Spectrogram, and Continuous Wavelet Transform (CWT). RESULTS: Stacking Classifier employed with non-linear features yields precision, recall, F1-score, and accuracy rates of 78%, 79%, 78%, and 78% respectively. Including entropy and fuzzy entropy features further improves accuracy to 81.4%. In addition, DL models, employing SOBI, CWT, and spectrogram plots, achieve precision, recall, F1-score, and accuracy of 75%, 75%, 74%, and 75% respectively. The hybrid model, which combined deep learning features from spectrogram and CWT with machine learning, exhibits prominent improvement, attained precision, recall, F1-score, and accuracy of 94%, 94%, 94%, and 94% respectively. Incorporating entropy and fuzzy entropy features further improved the accuracy to 96.9%. CONCLUSIONS: This study underscores the potential of ML and DL techniques in improving the classification of ASD and TD individuals, particularly when utilizing a minimal set of EEG channels. [ABSTRACT FROM AUTHOR] more...

The traditional five-face composite computerized numerical control (CNC) drilling surface roughness measurement is complicated, and there is a large human error in manual measurement. The traditional multiple regression and polynomial fitting methods only use rotational speed and feed speed parameters with low data utilization and high noise sensitivity; traditional machine learning can not effectively extract the deep and complex features of the signal. Aiming at the above problems, a classification and prediction method of drilling surface roughness based on ResNet model, fusion of spectrogram features and time-frequency graph features was proposed. Firstly, the process parameter variables of the CNC drilling processing experiment were determined according to the theory of CNC drilling processing and the actual CNC drilling experience of the enterprise. Secondly, a multi-source data acquisition system was developed based on SYNTEC CNC system, and the drilling process data were collected in real time. Then, the spectral and time-frequency characteristics of the three-axis vibration signals were analyzed, and the correlation between the vibration signals and the surface roughness category was verified. Then, the Kalman filtering was used for noise reduction of the three-axis vibration signals, and the fast Fourier transform (FFT) and the continuous wavelet transform (CWT) were used to convert the spectro-thermograms and time-frequency maps of the vibration signals, and matrix splicing was used to splice and merge the uniaxial time-frequency maps of the three-axis vibration signals to get the three-axis vibration time-frequency map. Finally, the fusion of spectral and time-frequency features was realized by convolving the spectral heat map and time-frequency map, and the comparison experiments between ResNet and other network models such as Densenet, Shufflenet and Mobilenet _ v3 _ small were carried out. The research results show that the correct rate of surface roughness classification based on the ResNet network model is improved by about 9% relative to the other network models mentioned above, and the correctness of the three-axis time-frequency feature fusion as well as the fusion method of spectral and time-frequency features is also verified. Due to the low cost of model training and fast training convergence, the method has a good prospect for industrial application in lightweight and low-cost prediction and classification of surface roughness of drilling on CNC machine tools. [ABSTRACT FROM AUTHOR] more...

Monolithic zirconia (MZ) crowns are widely utilized in dental restorations, particularly for substantial tooth structure loss. Inspection, tactile, and radiographic examinations can be time-consuming and error-prone, which may delay diagnosis. Consequently, an objective, automatic, and reliable process is required for identifying dental crown defects. This study aimed to explore the potential of transforming acoustic emission (AE) signals to continuous wavelet transform (CWT), combined with Conventional Neural Network (CNN) to assist in crack detection. A new CNN image segmentation model, based on multi-class semantic segmentation using Inception-ResNet-v2, was developed. Real-time detection of AE signals under loads, which induce cracking, provided significant insights into crack formation in MZ crowns. Pencil lead breaking (PLB) was used to simulate crack propagation. The CWT and CNN models were used to automate the crack classification process. The Inception-ResNet-v2 architecture with transfer learning categorized the cracks in MZ crowns into five groups: labial, palatal, incisal, left, and right. After 2000 epochs, with a learning rate of 0.0001, the model achieved an accuracy of 99.4667%, demonstrating that deep learning significantly improved the localization of cracks in MZ crowns. This development can potentially aid dentists in clinical decision-making by facilitating the early detection and prevention of crack failures. [ABSTRACT FROM AUTHOR] more...

In this research, five systems were developed to classify four distinct motor functions—forward hand movement (FW), grasp (GP), release (RL), and reverse hand movement (RV)—from EEG signals, using the WAY-EEG-GAL dataset where participants performed a sequence of hand movements. During preprocessing, band-pass filtering was applied to remove artifacts and focus on the mu and beta frequency bands. The initial system, a preliminary study model, explored the overall framework of EEG signal processing and classification, utilizing time-domain features such as variance and frequency-domain features such as alpha and beta power, with a KNN model for classification. Insights from this study informed the development of a baseline system, which innovatively combined the common spatial patterns (CSP) method with continuous wavelet transform (CWT) for feature extraction and employed a GoogLeNet classifier with transfer learning. This system classified six unique pairs of events derived from the four motor functions, achieving remarkable accuracy, with the highest being 99.73% for the GP–RV pair and the lowest 80.87% for the FW–GP pair in intersubject classification. Building on this success, three additional systems were developed for four-way classification. The final model, ML-CSP-OVR, demonstrated the highest intersubject classification accuracy of 78.08% using all combined data and 76.39% for leave-one-out intersubject classification. This proposed model, featuring a novel combination of CSP-OVR, CWT, and GoogLeNet, represents a significant advancement in the field, showcasing strong potential as a general system for motor imagery (MI) tasks that is not dependent on the subject. This work highlights the prominence of the research contribution by demonstrating the effectiveness and robustness of the proposed approach in achieving high classification accuracy across different motor functions and subjects. [ABSTRACT FROM AUTHOR] more...

The issue of failed weaning is a critical concern in the intensive care unit (ICU) setting. This scenario occurs when a patient experiences difficulty maintaining spontaneous breathing and ensuring a patent airway within the first 48 hours after the withdrawal of mechanical ventilation. Approximately 20% of ICU patients experience this phenomenon, which has severe repercussions on their health. It also has a substantial impact on clinical evolution and mortality, which can increase by 25% to 50%. To address this issue, we propose a medical support system that uses a convolutional neural network (CNN) to assess a patient's suitability for disconnection from a mechanical ventilator after a spontaneous breathing test (SBT). During SBT, respiratory flow and electrocardiographic activity were recorded and after processed using time-frequency analysis (TFA) techniques. Two CNN architectures were evaluated in this study: one based on ResNet50, with parameters tuned using a Bayesian optimization algorithm, and another CNN designed from scratch, with its structure also adapted using a Bayesian optimization algorithm. The WEANDB database was used to train and evaluate both models. The results showed remarkable performance, with an average accuracy 98 ± 1.8% when using CNN from scratch. This model has significant implications for the ICU because it provides a reliable tool to enhance patient care by assisting clinicians in making timely and accurate decisions regarding weaning. This can potentially reduce the adverse outcomes associated with failed weaning events. [ABSTRACT FROM AUTHOR] more...

Disease detection in agricultural crops, such as Solanum tuberosum L. (potato), is of utmost importance to ensure crop health and maximize yield. Traditional methods for disease detection in potatoes rely on manual inspection, which can be time-consuming and prone to human error . Image processing and machine learning techniques have shown promise in automating disease detection processes. This study proposes a novel approach for disease detection in Solanum tuberosum L. by leveraging image fusion techniques. The proposed method involves the fusion of multiple images of potato plants, acquired using different sensors or imaging modalities, to create a comprehensive and informative representation of the crop. Image fusion methods, such as discrete wavelet transform and continuous wavelet transform, are employed to combine the spectral and spatial information from the images effectively. The different image fusion rule is applied to the input images and resultant fused images, where relevant features are extracted to distinguish between healthy and diseased potato plants. The training dataset comprises diverse samples of both healthy and diseased potato plants, captured under various environmental conditions and disease stages. The performance of the proposed disease detection system is evaluated using standard metrics such as entropy. The results demonstrate the effectiveness of the image fusion approach in accurately identifying diseased potato plants, achieving high detection accuracy and generalization capabilities. The potential benefits of this paper include providing farmers and agricultural experts with an efficient and reliable tool for early disease detection in potato crops. Early detection can lead to timely intervention, minimizing crop losses and optimizing agricultural practices. The proposed methodology also lays the groundwork for future research in using advanced image processing techniques and machine learning algorithms for disease detection in other agricultural crops, contributing to the overall improvement of crop management and food security. [ABSTRACT FROM AUTHOR] more...

An electrocardiogram (ECG) can detect heart abnormalities through signals from the rhythm of the human heartbeat. One of them is arrhythmia disease, which is caused by an improper heartbeat and causes failure of blood pumping. In reading ECG signals, a common problem encountered is the uncertainty of the prediction results. An accurate and efficient heart defect classification system is needed to help patients and healthcare providers carry out appropriate therapy or treatment. Several studies have developed algorithms that are more effective in Machine Learning (ML) in automatically providing initial screening of patients' heart conditions. This study proposed the Long Short-Term Memory (LSTM) method as a classifier of heart conditions experienced by humans and Continuous Wavelet Transform (CWT) as a feature extractor to eliminate noise during data collection. CWT and LSTM methods are believed to perform well in feature extraction and classification of ECG signals. The dataset used in this study was taken from the MIT-BIH Arrhythmia Database. The results of this study successfully extracted ECG signals using CWT, thus improving the understanding of ECG characteristics. This research also succeeded in classifying ECG signals using the LSTM method, which obtained an accuracy training value of 98.4% and an accuracy testing value of 94.42 %. [ABSTRACT FROM AUTHOR] more...

Polygonal wheels are one of the most common defects in train wheels, causing a reduction in comfort levels for passengers and a higher degradation of vehicle and track components. With the aim of contributing to the safety and reliability of railway transport, this paper presents the development of an innovative methodology for classifying polygonal wheels based on a wayside system. To achieve that, a numerical train-track interaction model was adopted to simulate the passage of a freight train over a virtual wayside monitoring system composed of a set of accelerometers installed on the rails. Then, the acquired acceleration time series was transformed to a frequency domain using a Fast Fourier transform (FFT), and on this data, damage-sensitive features were extracted. The features based on Principal Component Analysis (PCA) showed great sensitivity to the harmonic order, while the ones based on Continuous Wavelet Transform (CWT) model showed great sensitivity to the defect amplitude. One step further, all features are merged using the Mahalanobis distance in order to obtain a damage index strongly correlated with the polygonal defect. Finally, a cluster analysis allowed the automatic classification of polygonal wheels, according to the harmonic order (harmonic-based) and defect amplitude (amplitude-based). The proposed methodology demonstrated high efficiency in identifying different types of polygonal wheels using a minimum layout of two sensors. [ABSTRACT FROM AUTHOR] more...

In this study, we highlighted the growing need for automated electrocardiogram (ECG) signal classification using deep learning to overcome the limitations of traditional ECG interpretation algorithms that can lead to misdiagnosis and inefficiency. Convolutional neural networks (CNN) application to ECG signals is gaining significant attention owing to their exceptional image-classification capabilities. However, we addressed the lack of standardized methods for converting 1D ECG signals into 2D-CNN-compatible input images by using time-frequency methods and selecting hyperparameters associated with these methods, particularly the choice of function. Furthermore, we investigated the effects of fine-tuned training, a technique where pre-trained weights are adapted to a specific dataset, on 2D-CNNs for ECG classification. We conducted the experiments using the MIT-BIH Arrhythmia Database, focusing on classifying premature ventricular contractions (PVCs) and abnormal heartbeats originating from ventricles. We employed several CNN architectures pre-trained on ImageNet and fine-tuned using the proposed ECG datasets. We found that using the Ricker Wavelet function outperformed other feature extraction methods with an accuracy of 96.17%. We provided crucial insights into CNNs for ECG classification, underscoring the significance of fine-tuning and hyperparameter selection in image transformation methods. The findings provide valuable guidance for researchers and practitioners, improving the accuracy and efficiency of ECG analysis using 2D-CNNs. Future research avenues may include advanced visualization techniques and extending CNNs to multiclass classification, expanding their utility in medical diagnosis. [ABSTRACT FROM AUTHOR] more...

This research presents a novel Customised Load Adaptive Framework (CLAF) for fault classification in Induction Motors (IMs), utilising the Machinery Fault Prevention Technology (MFPT) bearing dataset. CLAF represents a pioneering approach that extends traditional fault classification methodologies by accounting for load variations and dataset customisation. Through a meticulous two-phase process, it unveils load-dependent fault subclasses that have not been readily identified in traditional approaches. Additionally, new classes are created to accommodate the dataset's unique characteristics. Phase 1 involves exploring load-dependent patterns in time and frequency domain features using one-way Analysis of Variance (ANOVA) ranking and validation via bagged tree classifiers. In Phase 2, CLAF is applied to identify mild, moderate, and severe load-dependent fault subclasses through optimal Continuous Wavelet Transform (CWT) selection through Wavelet Singular Entropy (WSE) and CWT energy analysis. The results are compelling, with a 96.3% classification accuracy achieved when employing a Wide Neural Network to classify proposed load-dependent fault subclasses. This underscores the practical value of CLAF in enhancing fault diagnosis in IMs and its future potential in advancing IM condition monitoring. [ABSTRACT FROM AUTHOR] more...

The automatic monitoring of activities in construction sites through the proper use of acoustic signals is a recent field of research that is currently in continuous evolution. In particular, the use of techniques based on Convolutional Neural Networks (CNNs) working on the spectrogram of the signal or its mel-scale variants was demonstrated to be quite successful. Nevertheless, the spectrogram has some limitations, which are due to the intrinsic trade-off between temporal and spectral resolutions. In order to overcome these limitations, in this paper, we propose employing the scalogramas a proper time–frequency representation of the audio signal. The scalogram is defined as the square modulus of the Continuous Wavelet Transform (CWT) and is known as a powerful tool for analyzing real-world signals. Experimental results, obtained on real-world sounds recorded in construction sites, have demonstrated the effectiveness of the proposed approach, which is able to clearly outperform most state-of-the-art solutions. [ABSTRACT FROM AUTHOR] more...

The article’s subject matter is to classify Electroencephalography (EEG) signals in Autism Spectrum Disorder (ASD) sufferers. The goal is to develop a classification model using Machine Learning (ML) algorithms that are often implemented in Brain-Computer Interfaces (BCI) technology. The tasks to be solved are as follows: pre-processing the EEG dataset signal to separate the source signal from the noise/artifact signal to produce an observation signal that is free of noise/artifact; obtaining an effective feature comparison to be used as an attribute at the classification stage; and developing a more optimal classification method for detecting people with ASD through EEG signals. The methods used are: one of the wavelet techniques, namely the Continuous Wavelet Transform (CWT), which is a technique for decomposing time-frequency signals. CWT began to be used in EEG signals because it can describe signals in great detail in the time-frequency domain. EEG signals are classified into two scenarios: classification of CWT coefficients and classification of statistical features (mean, standard deviation, skewness, and kurtosis) of CWT. The method used for classifying this research uses ML, which is currently very developed in signal processing. One of the best ML methods is Support Vector Machine (SVM). SVM is an effective super-vised learning method to separate data into different classes by finding the hyper-plane with the largest margin among the observed data. The following results were obtained: the application of CWT and SVM resulted in the best classification based on CWT coefficients and obtained an accuracy of 95% higher than the statistical feature-based classification of CWT, which obtained an accuracy of 65%. Conclusions. The scientific contributions of the results obtained are as follows: 1) EEG signal processing is performed in ASD children using feature extraction with CWT and classification with SVM; 2) the combination of these signal classification methods can improve system performance in ASD EEG signal classification; 3) the implementation of this research can later assist in detecting ASD EEG signals based on brain wave characteristics. more...

In this research, five systems were developed to classify four distinct motor functions—forward hand movement (FW), grasp (GP), release (RL), and reverse hand movement (RV)—from EEG signals, using the WAY-EEG-GAL dataset where participants performed a sequence of hand movements. During preprocessing, band-pass filtering was applied to remove artifacts and focus on the mu and beta frequency bands. The initial system, a preliminary study model, explored the overall framework of EEG signal processing and classification, utilizing time-domain features such as variance and frequency-domain features such as alpha and beta power, with a KNN model for classification. Insights from this study informed the development of a baseline system, which innovatively combined the common spatial patterns (CSP) method with continuous wavelet transform (CWT) for feature extraction and employed a GoogLeNet classifier with transfer learning. This system classified six unique pairs of events derived from the four motor functions, achieving remarkable accuracy, with the highest being 99.73% for the GP–RV pair and the lowest 80.87% for the FW–GP pair in intersubject classification. Building on this success, three additional systems were developed for four-way classification. The final model, ML-CSP-OVR, demonstrated the highest intersubject classification accuracy of 78.08% using all combined data and 76.39% for leave-one-out intersubject classification. This proposed model, featuring a novel combination of CSP-OVR, CWT, and GoogLeNet, represents a significant advancement in the field, showcasing strong potential as a general system for motor imagery (MI) tasks that is not dependent on the subject. This work highlights the prominence of the research contribution by demonstrating the effectiveness and robustness of the proposed approach in achieving high classification accuracy across different motor functions and subjects. more...

In this paper, we explored the use of deep learning for the prediction of aortic flow metrics obtained using 4-dimensional (4D) flow magnetic resonance imaging (MRI) using wearable seismocardiography (SCG) devices. 4D flow MRI provides a comprehensive assessment of cardiovascular hemodynamics, but it is costly and time-consuming. We hypothesized that deep learning could be used to identify pathological changes in blood flow, such as elevated peak systolic velocity ( V max ) in patients with heart valve diseases, from SCG signals. We also investigated the ability of this deep learning technique to differentiate between patients diagnosed with aortic valve stenosis (AS), non-AS patients with a bicuspid aortic valve (BAV), non-AS patients with a mechanical aortic valve (MAV), and healthy subjects with a normal tricuspid aortic valve (TAV). In a study of 77 subjects who underwent same-day 4D flow MRI and SCG, we found that the V max values obtained using deep learning and SCGs were in good agreement with those obtained by 4D flow MRI. Additionally, subjects with non-AS TAV, non-AS BAV, non-AS MAV, and AS could be classified with ROC-AUC (area under the receiver operating characteristic curves) values of 92%, 95%, 81%, and 83%, respectively. This suggests that SCG obtained using low-cost wearable electronics may be used as a supplement to 4D flow MRI exams or as a screening tool for aortic valve disease. [ABSTRACT FROM AUTHOR] more...

Extreme hydrological events such as tsunamis, high tides, or storm surges seriously threaten coastal communities. These events result in flooding, property damage, loss of life, and long-term economic and social impacts. Therefore, monitoring and detecting extreme hydrological events significantly affect coastal areas in disaster response efforts. However, the cost of installing and maintaining these stations can be a significant challenge for developing countries. The objective of this study is to use a low-cost GNSS receiver to monitor tides and detect extreme coastal hydrological phenomena by analyzing changes in water level, using analysis of the signal-to-noise ratio (SNR) data. Data used in this study were collected from a GNSS station located in the Tam Giang Lagoon area, Thua Thien Hue, Vietnam, from September to October 2022. The water level based on GNSS-R is compared with the sensor's measured water level, with the Pearson correlation coefficient reaching 0.96 and RMSE of 0.08m. Continuous Wavelet Transform analysis demonstrated the relationship between water levels and extreme hydrological events. The results show that distinct signatures in the data correspond to the Noru typhoon from September 27-29, 2022, and the inundation from October 14-19, 2022, in Thua Thien Hue. This information is the basis for forecasting and early warning of extreme events and informing disaster response and management efforts. [ABSTRACT FROM AUTHOR] more...

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. [ABSTRACT FROM AUTHOR] more...

In this study; time series electromyography (EMG) data have been classified according to hand movements using wavelet analysis and deep learning. A pre-trained deep CNN (Convolitonal Neural Network-GoogLeNet) has been used in the classification process performed with signal processing, by this way the results can be obtained by continuous wavelet transform and classification methods. The dataset used has been taken from the Machine Learning Repository at the University of California. In the data set; EMG data of 5 healthy individuals, 2 males and 3 females, of the same age (~20-22 years) are available. Data; It consists of grasping spherical objects (Spher), grasping small objects with fingertips (Tip), grasping objects with palms (Palm), grasping thin/flat objects (Lat), grasping cylindrical objects (Cyl) and holding heavy objects (Hook). It is desired to perform 6 hand movements at the same time. While these movements are necessary, speed and power depend on one's will. People perform each movement for 6 seconds and repeat each movement (action) 30 times. The CWT (Continuous Wavelet Transform) method was used to transform the signal into an image. The scalogram image of the signal was created using the CWT method and the generated images were collected in a data set folder. The collected scalogram images have been classified using GoogLeNet, a deep learning network model. With GoogLeNet, results with 97.22% and 88.89% accuracy rates were obtained by classifying the scalogram images of the signals received separately from channel 1 and channel 2 in the data set. The applied model can be used to classify EMG signals in EMG data with high success rate. In this study, 80% of data was used for educational purposes and 20% for validation purposes. In the study, the results of the classification processes have been evaluated separately for first and second channel data. more...

Among many agricultural practices proposed to cut carbon emissions in the next 30 years is the deposition of carbon in soils as plant matter. Adding rooting traits as part of a sequestration strategy would result in significantly increased carbon sequestration. Integrating these traits into production agriculture requires a belowground phenotyping method compatible with high-throughput breeding (i.e., rapid, inexpensive, reliable, and non-destructive). However, methods that fulfill these criteria currently do not exist. We hypothesized that ground-penetrating radar (GPR) could fill this need as a phenotypic selection tool. In this study, we employed a prototype GPR antenna array to scan and discriminate the root and rhizome mass of the perennial sorghum hybrid PSH09TX15. B-scan level time/discrete frequency analyses using continuous wavelet transform were utilized to extract features of interest that could be correlated to the biomass of the subsurface roots and rhizome. Time frequency analysis yielded strong correlations between radar features and belowground biomass (max R −0.91 for roots and −0.78 rhizomes, respectively) These results demonstrate that continued refinement of GPR data analysis workflows should yield an applicable phenotyping tool for breeding efforts in contexts where selection is otherwise impractical. [ABSTRACT FROM AUTHOR] more...

Aiming at the problems that the traditional fault diagnosis methods had poor diagnosis effect and poor generalization ability in the wind turbine gearbox under variable operating conditions and mixed fault conditions, a wind turbine gearbox fault diagnosis method (TL-RN-ELM) based on deep residual network (ResNet) -extreme learning machine (ELM) and transfer learning (TL) was proposed. Firstly, the principles of continuous wavelet transform (CWT), convolutional neural network (CNN), ResNet, TL and ELM were introduced. Then, the TL-RN-ELM fault diagnosis model of wind turbine gearbox was established. Finally, the bearing data set and gearbox data set were used to validate the proposed method. Data acquisition and processing were carried out from the CWRU bearing data set and the SEU gearbox data set. The original one-dimensional vibration signal was converted into two-dimensional wavelet time-frequency image using CWT, and the ResNet18 model was trained using CWRU bearing data set to generate a pre-training model. The data in the pre-training model was migrated to the SEU gearbox dataset, the module was fine-tuned, features were extracted and input into the ELM classifier, and then the classification results were compared with the other three types of models. The experiment results show that the average accuracy of TL-RN-ELM can reach 98.79% for small sample migration fault diagnosis from bearing to bearing, bearing to gear and mixed fault. Comparing with other methods, the average accuracy rate is increased by 4.73%～9.6%. The research results show that this method has good diagnostic effect and generalization ability. [ABSTRACT FROM AUTHOR] more...

Surface wave tomography is performed by first estimating surface wave dispersion curves and then inverting them. The objective of this letter is the efficient and reliable estimation of the group velocity dispersion curves of surface waves. In this letter, group velocity dispersion curves of surface waves recorded at a single station with a known location of seismic source or between a pair of stations are estimated using continuous wavelet transform (CWT). The advantage of CWT is its multiresolution property and flexible choice of analyzing function. In the proposed method, the number of CWT filters used is almost half of the total number of filters used in the conventional frequency–time analysis (FTAN) method. The CWT coefficients of the seismogram are the functions of time and scale (analogs to wave period). The arrival time to reach the peak of the envelope function of CWT coefficients is estimated to calculate the group velocity of the surface waves. The group velocity dispersion curve of the surface waves is the plot of the change of group velocity with the period. Synthetic test data and ambient noise recordings of MesoAmerica seismic experiment (MASE) stations are acquired to investigate the performance of the proposed method. It is observed that the proposed method effectively retrieves the weaker surface waves and results in the broader band group-velocity dispersion curve when compared to the conventional FTAN method. The proposed method is also found to be computationally efficient. [ABSTRACT FROM AUTHOR] more...

Polygonal wheels are one of the most common defects in train wheels, causing a reduction in comfort levels for passengers and a higher degradation of vehicle and track components. With the aim of contributing to the safety and reliability of railway transport, this paper presents the development of an innovative methodology for classifying polygonal wheels based on a wayside system. To achieve that, a numerical train-track interaction model was adopted to simulate the passage of a freight train over a virtual wayside monitoring system composed of a set of accelerometers installed on the rails. Then, the acquired acceleration time series was transformed to a frequency domain using a Fast Fourier transform (FFT), and on this data, damage-sensitive features were extracted. The features based on Principal Component Analysis (PCA) showed great sensitivity to the harmonic order, while the ones based on Continuous Wavelet Transform (CWT) model showed great sensitivity to the defect amplitude. One step further, all features are merged using the Mahalanobis distance in order to obtain a damage index strongly correlated with the polygonal defect. Finally, a cluster analysis allowed the automatic classification of polygonal wheels, according to the harmonic order (harmonic-based) and defect amplitude (amplitude-based). The proposed methodology demonstrated high efficiency in identifying different types of polygonal wheels using a minimum layout of two sensors. more...

One of the main reasons for human death is diseases caused by the heart. Detecting heart diseases in the early stage can stop heart failure or any damage related to the heart muscle. One of the main signals that can be beneficial in the diagnosis of diseases of the heart is the electrocardiogram (ECG). This paper concentrates on the diagnosis of four types of ECG records such as myocardial infarction (MYC), normal (N), variances in the ST-segment (ST), and supraventricular arrhythmia (SV). The methodology captures the data from six main datasets, and then the ECG records are filtered using a pre-processing chain. Afterward, a proposed 1D CNN model is applied to extract features from the ECG records. Then, two different classifiers are applied to test the extracted features’ performance and obtain a robust diagnosis accuracy. The two classifiers are the softmax and random forest (RF) classifiers. An experiment is applied to diagnose the four types of ECG records. Finally, the highest performance was achieved using the RF classifier, reaching an accuracy of 98.3%. The comparison with other related works showed that the proposed methodology could be applied as a medical application for the early detection of heart diseases. more...

This paper presents a hybrid fault type identification technique based on continuous wavelet transform (CWT) and fast fourier transform (FFT) algorithm for an adaptive single-phase auto-reclosing scheme. Fast fault type identification i.e., identification between temporary and permanent fault is essential in protection algorithms. Integration of HVDC transmission links have made the protection system more complex. The HVDC converters cannot withstand high fault current for long time and disconnect for self-protection; consequently, the entire HVDC grid/link can be lost resulting a large blackout. Therefore, false detection of fault type on the AC side may challenge the reliability of the entire AC/DC power system. Hence, an adaptive auto-reclosing scheme based on CWT with the FFT algorithm (CWTFT) is presented in this paper. The proposed algorithm recognizes the fault type and arc extermination moment in least dead time. The temporary and permanent faults are identified based on the energies of CWTFT coefficients; the energies higher than the threshold level indicate that the fault is temporary. Subsequently, in case of a temporary fault, the arc extermination instant is estimated by the total harmonic distortion values; the values lower than the threshold level indicate that the arc is completely exterminated and it's safe to initiate reclosing. The performance of the algorithm is verified on Cassie and Kizilcay arc models with both transmission line types i.e., (compensated and uncompensated) under a diversity of fault conditions. The proposed technique is tested on a model developed in MATLAB using practical parameters. The results indorse the effectiveness of the proposed scheme in single-phase auto-reclosing applications by achieving minimum dead time. [ABSTRACT FROM AUTHOR] more...

The electrocardiogram (ECG) signal consists of vital information that can be used in detecting various heart diseases. R-peaks in ECG signal play a major role in the diagnosis of the heart disorder. While numerous methods exist for the purpose, this research work aims at improving the efficiency of R-peak detection through a novel pattern-adapted wavelet designed to reduce the rate of false positives and the detection error. The experimental results show that the proposed pattern-adapted wavelet method achieves better performance when compared with the Symlet4 and other published methods. The new wavelet was designed using the least square optimisation method such that it not only approximates the given R-peak pattern of the ECG signal but also is admissible according to the constraints prescribed by Continuous Wavelet Transform (CWT). The algorithm uses the wavelet-specific property that CWT coefficients of a given signal are computed where local maximum and minimum pair appear around the signal peak location. When applied to the signals available through the standard MIT-BIH (Massachusetts Institute of Technology, Beth Israel Hospital) Arrhythmia database, Symlet4 detects R-peaks with an average of 98.73% accuracy, 99.99% sensitivity, 98.74% positive predictive value, 1.3336% error rate and overall F-score of 0.9937, while the proposed pattern-adapted wavelet detects the same with an average of 99.83% accuracy, 99.91% sensitivity, 99.92% positive predictive value, 0.16% error rate and overall F-score of 0.999. [ABSTRACT FROM AUTHOR] more...

Nowadays, the number of sudden deaths due to heart disease is increasing with the coronavirus pandemic. Therefore, automatic classification of electrocardiogram (ECG) signals is crucial for diagnosis and treatment. Thanks to deep learning algorithms, classification can be performed without manual feature extraction. In this study, we propose a novel convolutional neural networks (CNN) architecture to detect ECG types. In addition, the proposed CNN can automatically extract features from images. Here, we classify a real ECG dataset using our proposed CNN which includes 34 layers. While this dataset is one-dimensional signals, these are transformed into images (scalograms) using continuous wavelet transform (CWT). In addition, the proposed CNN is compared to known architectures: AlexNet and SqueezeNet for classifying ECG images, and we find it more effective than others. This study, which not only performed CWT but also implemented short-time Fourier transform, examines the success in recognizing ECG types for the proposed CNN. Besides, different split methods: training and testing, and cross-validation are applied in this study. Eventually, CWT and cross-validation are the best pre-processing and split methods for the proposed CNN, respectively. Although the results are quite good, we benefit from support vector machines (SVM) to obtain the best algorithm and for detecting ECG types. Essentially, the main aim of the study increases classification results. In this way, the proposed CNN is utilized as deep feature extractor and combined with SVM. As a conclusion of this study, we achieve the highest accuracy of 99.21% from the proposed CNN–SVM when using CWT. Therefore, we can express that this framework can be used as an aid to clinicians for ECG-type identification. [ABSTRACT FROM AUTHOR] more...

This article focuses on the Assembled Structure (AS) state recognition method based on vibration data. The difficulty of AS state recognition is mainly the extraction of effective classification features and pattern classification. This paper presents an integrated method based on Convolutional Neural Networks (CNNs) and data fusion for AS state recognition. The method takes the wavelet transform time-frequency images of the denoised vibration signal as input, uses CNNs to supervise and learn the data, extracts the deep data structure layer by layer, and improves the classification results through data fusion technology. The method is tested on an assembly concrete shear wall using shake-table testing, and the results show that it has a good overall identification accuracy (IA) of 94.7%, indicating that it is robust and capable of accurately recognizing very small changes in AS state recognition. [ABSTRACT FROM AUTHOR] more...

The non-stationary nature of musical signals presents challenges for conventional signal analysis methods. Wavelet transforms offer a powerful tool for capturing both temporal and frequency information simultaneously. This study introduces a novel approach to enhance wavelet analysis in music processing by utilizing matched wavelets optimized through evolutionary algorithms, specifically tailored for musical signals within the context of Indian Classical Music (ICM). Various evolutionary algorithms including Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE) were investigated. The proposed method optimizes wavelet parameters to match the characteristics of a given signal resulting in a customized CWT filter bank. The scalogram accurately highlights the fundamental frequency and its harmonic components. The efficacy of this approach is validated through comparisons with established techniques such as Short-Time Fourier Transform (STFT) and S-Transform. The designed wavelets achieve a high correlation coefficient in signal reconstruction, outperforming standard continuous wavelets. The customized wavelets not only facilitate the detailed analysis of signal components but also ensure robust signal reconstruction. The use of matched wavelets in feature extraction has shown promising results in tasks such as swara recognition and instrument identification in monophonic music. [ABSTRACT FROM AUTHOR] more...

[Display omitted] • A method of estimating the measuring and numerical uncertainty of the CWT of beam deflections measured with the DIC technique has been proposed. • Beam bending stiffness reduction may be clearly localized using CWT extremes only when considering both the measuring and numerical uncertainty. • The proposed method of detecting material defects can be used for the prefabricated timber beam quality control. The article presents a method for detecting damage in timber I-beams based on the continuous wavelet transform (CWT) of their static deflection, measured using the ARAMIS digital image correlation (DIC) system. So far, no detailed uncertainty analysis for this type of identification has been carried out. Filling this gap was the main motivation for the authors to conduct this research. For this purpose, analytical and experimental static deflection CWT analyses were performed for the timber I-beams with softwood flanges and oriented strand board webs, in which local cross-sectional weakenings were artificially introduced for verification purposes. A detailed analysis of the CWT uncertainty resulting from measurement errors and numerical data processing allowed its expression as a function of the beam axis position and the scale parameter. Based on the research, a new methodology was formulated for the diagnostic defect identification in timber beams related to bending stiffness reduction. [ABSTRACT FROM AUTHOR] more...

This research presents a novel Customised Load Adaptive Framework (CLAF) for fault classification in Induction Motors (IMs), utilising the Machinery Fault Prevention Technology (MFPT) bearing dataset. CLAF represents a pioneering approach that extends traditional fault classification methodologies by accounting for load variations and dataset customisation. Through a meticulous two-phase process, it unveils load-dependent fault subclasses that have not been readily identified in traditional approaches. Additionally, new classes are created to accommodate the dataset’s unique characteristics. Phase 1 involves exploring load-dependent patterns in time and frequency domain features using one-way Analysis of Variance (ANOVA) ranking and validation via bagged tree classifiers. In Phase 2, CLAF is applied to identify mild, moderate, and severe load-dependent fault subclasses through optimal Continuous Wavelet Transform (CWT) selection through Wavelet Singular Entropy (WSE) and CWT energy analysis. The results are compelling, with a 96.3% classification accuracy achieved when employing a Wide Neural Network to classify proposed load-dependent fault subclasses. This underscores the practical value of CLAF in enhancing fault diagnosis in IMs and its future potential in advancing IM condition monitoring. more...

Identification of thin interbedded non-coal bands and coal seams with varying carbon contents within a coal seam is of paramount interest in coal exploration due to its banded nature. The manual interpretation and conventional modelling based on Fourier/Walsh transform techniques fail to derive such information accurately from geophysical well log data due to its non-stationary nature. The present study proposes a combined principal component analysis (PCA) and continuous wavelet transform (CWT) algorithm for automatic lithological modelling of geophysical well log data. In the first step of well log lithology modelling, a median filter is applied on well log data to preserve the thinner beds and other valuable geological signatures of coal seams. In the second step, the filtered log data is subjected to PCA, and the variance level of PC scores is determined to study the physical relationship of input parameters. The third step is to apply CWT on the selected PC scores and determine lithological discontinuities from the modulus maxima lines drawn on the wavelet scalogram. For filling the lithology skeleton with the proper interpretation, a database is also created by correlating the selected PC score values with input parameters. We have applied the proposed algorithm to gamma ray, density, and resistivity logs of two boreholes located in the Bisrampur and Jharia coalfields of eastern India. The results of the proposed PCA-CWT based modelling match well with core data and manual interpretations of the boreholes. At a few depth ranges, the proposed algorithm also reveals some additional lithological discontinuities that were not mapped in the core data. The study further conveys that PCA-CWT-based lithological modelling of geophysical logs is helpful to pace up the exploration work in coal blocks with poor core recovery. [ABSTRACT FROM AUTHOR] more...