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Biometric-based systems for secure access to different services have gained a significant attention in recent years. To ensure the protection of biometric data from potential hackers, it is crucial to store them in the form of secure templates. Cancelable templates offer an effective solution through allowing template replacement in case of security breaches. In this paper, we propose a novel unimodal cancelable biometric system that works on bio-signals such as voiceprint, electroencephalography (EEG), and electrocardiography (ECG) signals. The key feature of our proposed system is the utilization of Empirical Mode Decomposition (EMD) to decompose the bio-signals into different Intrinsic Mode Functions (IMFs). Among these IMFs, the first IMF, which carries the majority of the signal energy and distinguishes the bio-signal, plays a pivotal role in our system. To ensure the security of the cancelable biometric template, an encryption algorithm is employed. We use the Double Random Phase Encoding (DRPE) algorithm along with its random phase masks to encrypt the first IMF after converting it into 2D format. The use of DRPE and its random masks ensures a non-invertible transformation, which enhances the security of the encrypted data. To generate the cancelable template, we replace the first IMF of a reference signal with the encrypted first IMF obtained from the bio-signal. The resulting template retains the essential distinguishing characteristics of the bio-signal, while safeguarding its security. The verification process in our system involves matching of the encrypted first IMF of the stored templates with the encrypted first IMF of a new input signal. Extensive simulation analysis has been conducted to evaluate the performance of the proposed system. Various metrics, including Equal Error Rate (EER) and Area under Receiver Operating Characteristic curve (AROC), have been considered. The results demonstrate the high performance and stability of our system, even in the presence of different levels of white Gaussian noise, with an EER close to 0 and an AROC close to 1. In conclusion, our work presents an efficient implementation of the DRPE and EMD for the development of a robust and secure cancelable biometric system. The proposed system shows promising results and holds great potential for enhancing the security and reliability of biometric-based access control. [ABSTRACT FROM AUTHOR]

Automatic seizure detection and prediction using clinical Electroencephalograms (EEGs) are challenging tasks due to factors such as low Signal-to-Noise Ratios (SNRs), high variance in epileptic seizures among patients, and limited clinical data constraints. To overcome these challenges, this paper presents two approaches for EEG signal classification. One of these approaches depends on Machine Learning (ML) tools. The used features are different types of entropy, higher-order statistics, and sub-band energies in the Hilbert Marginal Spectrum (HMS) domain. The classification is performed using Support Vector Machine (SVM), Logistic Regression (LR), and K-Nearest Neighbor (KNN) classifiers. Both seizure detection and prediction scenarios are considered. The second approach depends on spectrograms of EEG signal segments and a Convolutional Neural Network (CNN)-based residual learning model. We use 10000 spectrogram images for each class. In this approach, it is possible to perform both seizure detection and prediction in addition to a 3-state classification scenario. Both approaches are evaluated on the Children's Hospital Boston and the Massachusetts Institute of Technology (CHB-MIT) dataset, which contains 24 EEG recordings for 6 males and 18 females. The results obtained for the HMS-based model showed an accuracy of 100%. The CNN-based model achieved accuracies of 97.66%, 95.59%, and 94.51% for Seizure (S) versus Pre-Seizure (PS), Non-Seizure (NS) versus S, and NS versus S versus PS classes, respectively. These results demonstrate that the proposed approaches can be effectively used for seizure detection and prediction. They outperform the state-of-the-art techniques for automatic seizure detection and prediction. Block diagram of proposed epileptic seizure detection method using HMS with different classifiers. [ABSTRACT FROM AUTHOR]

Gearboxes are widely used in various industries such as aircrafts, automobiles, wind turbines, ship industries among others. Due its complex configuration, it is a challenging task to identify fault and failures patterns. Its internal components, such as bearings and gears, have different fault patterns, that can appear in one or in both components. The vibration signals were processed using the Empirical Mode Decomposition (EMD) and the Pearson Correlation Coefficient (PCC) to select the significant Intrinsic Mode Functions (IMFs) and then 18 features were extract from this IMFs. Four features ranking techniques [ReliefF, Chi-Square, Max Relevance Min Redundancy (mRMR) and Decision Tree] were used in a committee to select the best feature set, among the 10 with the highest rank, that appears at least in 3 of the 4 methods. The new feature set was used as an input to Support Vector Machine (SVM), Random Forest (RF) and Artificial Neural Networks (ANN) algorithms. The results showed that the use of the PCC value as a tool for selecting the significant IMFs, combined with the feature committee led to good results for this classification problem. In this case study, the ANN model outperformed the SVM and the RF algorithms, by using only 4 features to achieve 95.42% of accuracy and 6 features to achieve 100% of accuracy. [ABSTRACT FROM AUTHOR]

Significant water loss caused by pipeline leaks emphasizes the importance of effective pipeline leak detection and localization techniques to minimize water wastage. All of the state-of-the-art approaches use deep learning (DL) for leak detection and cross-correlation for leak localization. The existing methods' complexity is very high, as they detect and localize the leak using two different architectures. This paper aims to present an independent architecture with a single sensor for detecting and localizing leaks with enhanced performance. The proposed approach combines a novel EMD with an optimal mode selector, an MFCC, and a two-dimensional convolutional neural network (2DCNN). The suggested technique uses acousto-optic sensor data from a real-time water pipeline setup in UTAR, Malaysia. The collected data are noisy, redundant, and a one-dimensional time series. So, the data must be denoised and prepared before being fed to the 2DCNN for detection and localization. The proposed novel EMD with an optimal mode selector denoises the one-dimensional time series data and identifies the desired IMF. The desired IMF is passed to the MFCC and then to 2DCNN to detect and localize the leak. The assessment criteria employed in this study are prediction accuracy, precision, recall, F-score, and R-squared. The existing MFCC helps validate the proposed method's leak detection-only credibility. This paper also implements EMD variants to show the novel EMD's importance with the optimal mode selector algorithm. The reliability of the proposed novel EMD with an optimal mode selector, an MFCC, and a 2DCNN is cross-verified with cross-correlation. The findings demonstrate that the novel EMD with an optimal mode selector, an MFCC, and a 2DCNN surpasses the alternative leak detection-only methods and leak detection and localization methods. The proposed leak detection method gives 99.99% accuracy across all the metrics. The proposed leak detection and localization method's prediction accuracy is 99.54%, precision is 98.92%, recall is 98.86%, F-score is 98.89%, and R-square is 99.09%. [ABSTRACT FROM AUTHOR]

Nowadays, a variety of studies have shown that a person's physiological indicators can be reliably extracted from a distance through an RGB camera. For this purpose, our work suggests an effective hybrid approach based on photoplethysmographic signal (PPG) analysis for the continuous monitoring of physiological signs, including heart rate and respiration rate. Our approach presents dependable methods for image and signal processing that combine moving average filtering and successive variable mode decomposition methods (w.r.t. MAF-SVMD). These two latter approaches will be applied to the PPG signal in order to overcome drawbacks, filter the disturbances in the signal, and decompose the PPG signal. Additionally, the suggested method, MAF-SVMD, has much lower computational complexity and is more robust to the initial values. Our findings were contrasted with those of traditional techniques, including EMD and VMD methods. Between the proposed approach and the VMD algorithm, the derived respiratory rates show an average delta error of 0 and a coefficient correlation of 0.99; however, between the suggested technique and the EMD method, the relative delta value is equal to 7.71 and a coefficient correlation of 0.69. While the retrieved heart rate from each of the three methods shown is generally convergent to the same value. [ABSTRACT FROM AUTHOR]

In response to the challenge of limited model availability for predicting the lifespan of super-high arch dams, a hybrid model named EMD-PSO-GPR (EPR) is proposed in this study. The EPR model leverages Empirical Mode Decomposition (EMD), Gaussian Process Regression (GPR), and Particle Swarm Optimization (PSO) to provide an effective solution for super-high arch dam stress prediction. This research focuses on three strategically selected measurement points within the dam, characterized by complex stress conditions. The predicted results from the EPR are compared with those from GPR, Long Short-Term Memory (LSTM), and Support Vector Regression (SVR), using actual stress data measured at research points within a super-high arch dam in Southwest China. The findings reveal that the proposed EPR model attains a maximum mean absolute error (MAE) of 0.02916 and a maximum root mean square error (RMSE) of 0.03055, surpassing the compared models. As a result, the EPR model introduces an innovative computational framework for stress prediction in super-high arch dams, excelling in handling stress data characterized by high vibration frequencies and providing more accurate predictions. [ABSTRACT FROM AUTHOR]

An algorithm based on EMD-LSTM (Empirical Mode Decision – Long Short Term Memory) is proposed for predicting short time series with uncertainty, rapid changes, and no following cycle. First, the algorithm eliminates the abnormal data; second, the processed time series are decomposed into basic modal components for different characteristic scales, which can be used for further prediction; finally, an LSTM neural network is used to predict each modal component, and the prediction results for each modal component are summed to determine a final prediction. Experiments are performed on the public datasets available at UCR and compared with a machine learning algorithm based on LSTMs and SVMs. Several experiments have shown that the proposed EMD-LSTM-based short-time series prediction algorithm performs better than LSTM and SVM prediction methods and provides a feasible method for predicting short-time series. [ABSTRACT FROM AUTHOR]

Abstract The detection and identification of epileptic seizures attracted considerable relevance for the neurophysiologists. In order to accomplish the detection of epileptic seizures or equivalently ictal EEG states, this paper proposes the use of nonlinear and chaos features not computed over the raw EEG signals as it was commonly experienced, but instead over intrinsic mode functions (IMFs) extracted subsequently to the application of newly time-frequency signal decomposition methods on the basis of empirical mode decomposition (EMD) and variational mode decomposition (VMD) methods. The first step within the proposed methodology is to excerpt the various components of the IMFs by EMD and VMD decomposition methods on time EEG segments. The Hjorth parameters, the Hurst exponent, the Recurrence Quantification Analysis (RQA), the detrended fluctuation analysis (DFA), the Largest Lyapunov Exponent (LLE), The Higuchi and Katz fractal dimensions (HFD and KFD), seven nonlinear and chaos features computed over the IMFs were investigated and their classification performances evaluated using the k-nearest neighbor (KNN) and the multilayer perceptron neural network (MLPNN) classifiers. Furthermore, the combination of the best nonlinear features has also been examined in terms of sensitivity, specificity and overall classification accuracy. The publicly available Bonn EEG dataset has been has been employed to validate the efficiency of the proposed method for detecting ictal EEG signals from normal or interictal EEG segments. Among the several experiments involved in the current study, the ultimate results establish that the overall classification accuracy can achieve 100%, 99.45%, 99.8%, 99.8%, 98.6% and 99.1% for six different epileptic seizure detection case problems studied, confirming the ability of the proposed methodology in helping the clinic practitioners in the epilepsy detection care units to classify seizure events with a great confidence. [ABSTRACT FROM AUTHOR]

Identifying and assessing Parkinson's disease in its early stages is critical to effectively monitoring the disease's progression. Methodologies based on machine learning enhanced speech analysis are gaining popularity as the potential of this field is revealed. Acoustic features, in particular, are used in a variety of algorithms for machine learning and could serve as indicators of the general health of subjects' voices. In this research paper, a novel method is introduced for the automated detection of Parkinson's disease through speech signal analysis, a support vector machines classifier (SVM) and an Artificial Neural Network (ANN) are used to evaluate and classify the data based on two acoustic features: Bark Frequency Cepstral Coefficients (BFCC) and Mel Frequency Cepstral Coefficients (MFCC). These features are extracted from the denoised signals using Empirical Mode Decomposition (EMD). The most relevant results obtained for a dataset of 38 participants are by the BFCC coefficients with an accuracy up to 92.10%. These results confirm that EMD-BFCC-SVM method can contribute to the detection of Parkinson's disease. [ABSTRACT FROM AUTHOR]

A financial market is non-linear and chaotic in nature. So, the accurate prediction of foreign exchange rate is very difficult and challengeable task. Hence, many proposed techniques and new approaches are used for forecasting various countries' exchange rates with different parameters. This paper proposes EMD-ANFIS for foreign currency exchange rate prediction. In this research, we would like to propose a model which could develop multivariate exchange rates information and put these features to better use. The performance of the proposed system has been tested with European EURO against US Dollar (EUR/USD), British POUND against US Dollar (GBP/USD), US Dollar against Swiss FRANK (USD/CHF), US Dollar against Japanese YEN (USD/JPY) and used to predict one day exchange rate in advance. Empirical mode decomposition (EMD) and QPSO (Quantum Particle Swarm Optimization) are techniques that used here which generates optimal weight for the proposed model. The proposed approach has been found with the best prediction rate against previous studies. [ABSTRACT FROM AUTHOR]

In this paper, we combine two feature extraction algorithms, Empirical Mode Decomposition (EMD) and wavelet packet transform, to analyze the EGG signals of students exposed to vocal music. We extract features from these signals by determining the instantaneous frequency and node signals based on the mean value of the envelope. The EGG signals were cut into short-time smooth signals. The average sample entropy value of the processed EGG signals was calculated to reflect the EEG activities of students under vocal stimulation. Then the EGG signals were used to reflect the changes in the mental health status of college students. In the vocal stimulation experiment, it was found that the students’ psychological comprehensive relaxation reached 85.47% on average, α and the amplitude of the EEG signals was able to reach about ±20. The total score of the psychological health scale of students in the experimental class after the implementation of integrated teaching reached 99.63, which was much higher than that of the control class, which was 91.26, and the difference was significant (P=0.000

Abstract Background A manual evaluation of the CI electrode position from CT and DVT scans may be affected by diagnostic errors due to cognitive biases. The aim of this study was to compare the CI electrode localization using an automated method (image-guided cochlear implant programming, IGCIP) with the clinically established manual method. Methods This prospective experimental study was conducted on a dataset comprising N=50 subjects undergoing cochlear implantation with a Nucleus® CI532 or CI632 Slim Modiolar electrode. Scalar localization, electrode-to-modiolar axis distances (EMD) and angular insertion depth (aDOI) were compared between the automated IGCIP tool and the manual method. Two raters made the manual measurements, and the interrater reliability (±1.96·SD) was determined as the reference for the method comparison. The method comparison was performed using a correlation analysis and a Bland-Altman analysis. Results Concerning the scalar localization, all electrodes were localized both manually and automatically in the scala tympani. The interrater differences ranged between ±0.2 mm (EMD) and ±10° (aDOI). There was a bias between the automatic and manual method in measuring both localization parameters, which on the one hand was smaller than the interrater variations. On the other hand, this bias depended on the magnitude of the EMD respectively aDOI. A post-hoc analysis revealed that the deviations between the methods were likely due to a different selection of mid-modiolar axis. Conclusions The IGCIP is a promising tool for automated processing of CT and DVT scans and has useful functionality such as being able to segment the cochlear using post-operative scans. When measuring EMD, the IGCIP tool is superior to the manual method because the smallest possible distance to the axis is determined depending on the cochlear turn, whereas the manual method selects the helicotrema as the reference point rigidly. Functionality to deal with motion artifacts and measurements of aDOI according to the consensus approach are necessary, otherwise the IGCIP is not unrestrictedly ready for clinical use.

Abstract The wind power forecasting (WPF) technology can reduce the adverse impact of wind power grid connection. Based on the characteristics of wind power data, an algorithm based on improved variational mode decomposition (IVMD) and long short-term memory (LSTM) Network is proposed to predict the wind power, and hyper parameter optimization search of LSTM using Whale Swarm Algorithm with Iterative Counter (WSA-IC). Firstly, through correlation analysis, the characteristics of 10 different wind power data are screened, and two kinds of data with large correlation with wind power are determined as input of the mode. Secondly, IVMD is used to calculate the maximum envelope kurtosis, determine the best decomposition parameters of the variational mode decomposition (VMD), and the original wind power and wind speed sequences are decomposed to obtain the IMF with different time scales. Finally, to address the problems of difficult optimization of hyper parameter and difficulty in obtaining optimal solutions for LSTM neural network modes, the WSA-IC algorithm is proposed to optimize its key hyper parameter, and the IVMD-WSA-IC-LSTM forecasting mode is established to obtain the short-term forecasting results of wind power. The algorithm is tested with the data of China Longyuan Power Group Corporation Limited. Compared with other common forecasting approaches using same data, the mean absolute error (MAE) of the forecasting approach is reduced to 0.007859, the mean square error (MSE) is reduced to 0.00011, and the determination coefficient is improved to 0.998828, which has higher forecasting accuracy.

Background: The search for effective antimicrobial agents to mitigate peri-implant infections remains a crucial aspect of implant dentistry. This study aimed to evaluate and compare the antimicrobial efficacy of i-PRF, A-PRF+, and enamel matrix derivative (EMD) on decontaminated rough and smooth titanium (Ti) discs. Materials and Methods: Rough and smooth Ti discs were coated with multispecies biofilm and thoroughly debrided using a chitosan-bristled brush. Subsequently, i-PRF, A-PRF+, and EMD were applied. Untreated discs served as control. Residual adherent bacteria present on the treated Ti discs were visualized by SEM and quantified using culture technique, and colony-forming units (CFUs) were measured after 48 h and 7 days. Results: i-PRF demonstrated better antimicrobial effectiveness on both smooth and rough implant surfaces as compared to A-PRF+ and EMD (p < 0.001). In all the experimental groups, smooth Ti discs displayed a greater reduction in microbes compared to rough Ti discs when treated with the biologics. The major reduction in CFU values was determined after seven days. Conclusions: i-PRF as a regenerative material may also be suitable for decontaminating implant surfaces, which could influence tissue healing and regenerative outcomes positively.

Protein-DNA complex interactivity plays a crucial role in biological activities such as gene expression, modification, replication and transcription. Understanding the physiological significance of protein-DNA binding interfacial hot spots, as well as the development of computational biology, depends on the precise identification of these regions. In this paper, a hot spot prediction method called EC-PDH is proposed. First, we extracted features of these hot spots’ solid solvent-accessible surface area (ASA) and secondary structure, and then the mean, variance, energy and autocorrelation function values of the first three intrinsic modal components (IMFs) of these conventional features were extracted as new features via the empirical modal decomposition algorithm (EMD). A total of 218 dimensional features were obtained. For feature selection, we used the maximum correlation minimum redundancy sequence forward selection method (mRMR-SFS) to obtain an optimal 11-dimensional-feature subset. To address the issue of data imbalance, we used the SMOTE-Tomek algorithm to balance positive and negative samples and finally used cat gradient boosting (CatBoost) to construct our hot spot prediction model for protein-DNA binding interfaces. Our method performs well on the test set, with AUC, MCC and F1 score values of 0.847, 0.543 and 0.772, respectively. After a comparative evaluation, EC-PDH outperforms the existing state-of-the-art methods in identifying hot spots.

Optical fiber sensors are extensively employed for their unique merits, such as small size, being lightweight, and having strong robustness to electronic interference. The above-mentioned sensors apply to more applications, especially the detection and monitoring of vital signs in medical or clinical. However, it is inconvenient for daily long-term human vital sign monitoring with conventional monitoring methods under the uncomfortable feelings generated since the skin and devices come into direct contact. This study introduces a non-invasive surveillance system that employs an optical fiber sensor and advanced deep-learning methodologies for precise vital sign readings. This system integrates a monitor based on the MZI (Mach–Zehnder interferometer) with LSTM networks, surpassing conventional approaches and providing potential uses in medical diagnostics. This could be potentially utilized in non-invasive health surveillance, evaluation, and intelligent health care.

Parkinson's disease is a recognizable clinical syndrome with a variety of causes and clinical presentations; it represents a rapidly growing neurodegenerative disorder. Since about 90 percent of Parkinson's disease sufferers have some form of early speech impairment, recent studies on tele diagnosis of Parkinson's disease have focused on the recognition of voice impairments from vowel phonations or the subjects' discourse. This paper presents a new approach for Parkinson's disease detection from speech sounds that are based on CNN and LSTM and uses two categories of characteristics. These are Mel Frequency Cepstral Coefficients (MFCC) and Gammatone Cepstral Coefficients (GTCC) obtained from noise-removed speech signals with comparative EMD-DWT and DWT-EMD analysis. The proposed model is divided into three stages. In the first step, noise is removed from the signals using the EMD-DWT and DWT-EMD methods. In the second step, the GTCC and MFCC are extracted from the enhanced audio signals. The classification process is carried out in the third step by feeding these features into the LSTM and CNN models, which are designed to define sequential information from the extracted features. The experiments are performed using PC-GITA and Sakar datasets and 10-fold cross validation method, the highest classification accuracy for the Sakar dataset reached 100% for both EMD-DWT-GTCC-CNN and DWT-EMD-GTCC-CNN, and for the PC-GITA dataset, the accuracy is reached 100% for EMD-DWT-GTCC-CNN and 96.55% for DWT-EMD-GTCC-CNN. The results of this study indicate that the characteristics of GTCC are more appropriate and accurate for the assessment of PD than MFCC.

Analysis of heart sound signals for automatic segmentation and classification has revealed in recent decades that it has the potential to detect pathology accurately in clinical applications. Various audio signal processing techniques have been used to reduce the subjectivity of heart sound analysis. This study aims to classify normal and abnormal heart sound signals. The feature extraction process was optimized by EMD and calculated using five first-order statistical parameters: mean, variance, kurtosis, skewness, and entropy. The classification system is optimized with a mutual information algorithm to select traits that can significantly improve system performance. In addition, the selection of the optimal system configuration also includes the k-fold cross-validation and kNN methods with k values ​​and the proper distance type. Based on the test results, the highest accuracy of 98.2% was obtained when the value of k = 1 and the type of cosine distance on kNN with a five-fold cross-validation system evaluation model.

Background: A manual evaluation of the CI electrode position from CT and DVT scans may be affected by diagnostic errors due to cognitive biases. The aim of this study was to compare the CI electrode localization using an automated method (image-guided cochlear implant programming, IGCIP) with the clinically established manual method. Methods: This prospective experimental study was conducted on a dataset comprising N=50 subjects undergoing cochlear implantation with a Nucleus® CI532 or CI632 Slim Modiolar electrode. Scalar localization, electrode-to-modiolar axis distances (EMD) and angular insertion depth (aDOI) were compared between the automated IGCIP tool and the manual method. Two raters made the manual measurements, and the interrater reliability (±1.96·SD) was determined as the reference for the method comparison. The method comparison was performed using a correlation analysis and a Bland-Altman analysis. Results: Concerning the scalar localization, all electrodes were localized both manually and automatically in the scala tympani. The interrater differences ranged between ±0.2 mm (EMD) and ±10° (aDOI). There was a bias between the automatic and manual method in measuring both localization parameters, which on the one hand was smaller than the interrater variations. On the other hand, this bias depended on the magnitude of the EMD respectively aDOI. A post-hoc analysis revealed that the deviations between the methods were likely due to a different selection of mid-modiolar axis. Conclusions: The IGCIP is a promising tool for automated processing of CT and DVT scans and has useful functionality such as being able to segment the cochlear using post-operative scans. When measuring EMD, the IGCIP tool is superior to the manual method because the smallest possible distance to the axis is determined depending on the cochlear turn, whereas the manual method selects the helicotrema as the reference point rigidly. Functionality to deal with motion artifacts and measurements of aDOI according to the consensus approach are necessary, otherwise the IGCIP is not unrestrictedly ready for clinical use. [ABSTRACT FROM AUTHOR]

High-speed railways in the Beijing–Tianjin–Hebei (BTH) Plain are gradually becoming more widespread, covering a greater area. The operational safety of high-speed railways is influenced by the continuous development of land subsidence. It is necessary to predict the subsidence along the high-speed railways; thus, this work is of critical importance to the safety of high-speed railway operation. In this study, we processed Sentinel-1A data using the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique to acquire the land subsidence in the typical BTH area. Then, we combined the Empirical Mode Decomposition (EMD) and Gradient Boosting Decision Tree (GBDT) methods (EMD-GBDT) to forecast land subsidence along high-speed railways. The results revealed that some parts of the high-speed railways in the BTH plain had passed through or approached the land subsidence area; the maximum cumulative subsidence of the Beijing–Shanghai, Tianjin–Baoding and Shijiazhuang–Jinan high-speed railways reached 326 mm, 384 mm and 350 mm, respectively. The forecasting accuracy for land subsidence along high-speed railways was enhanced by the EMD-GBDT model. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were 0.38 mm to 0.56 mm and 0.23 mm to 0.38 mm, respectively. [ABSTRACT FROM AUTHOR]

The influence of the control strategy of the power electronic converter obscures the fault characteristics of DC distribution networks. The existence of arc faults over an extended period of time poses a grave threat to the security of power grids and may result in electric shock, fire, and other catastrophes. In recent years, the method of fault localization based on the traveling wave method has been a popular topic of research in the field of DC distribution system protection. In this paper, the fault localization principle of the traveling wave method is described in depth, and the propagation characteristics of the traveling wave of fault current in the online mode network are deduced. We present a method for wave head calibration that combines empirical mode decomposition (EMD) and singular value decomposition (VMD). After the fault-traveling current signal has been subjected to EMD, the first eigenmode function is extracted and subjected to singular value decomposition (SVD). After SVD, the detail component can reflect the singularity of the signal. The point of the maximum value of the detail component signal corresponds to the moment when the faulty traveling wave head reaches the monitoring point. Finally, the DC distribution system is modeled based on the PSCAD/EMTDC simulation environment, and the fault location method is verified. The simulation results show that the method can effectively realize fault localization. [ABSTRACT FROM AUTHOR]

The wind power forecasting (WPF) technology can reduce the adverse impact of wind power grid connection. Based on the characteristics of wind power data, an algorithm based on improved variational mode decomposition (IVMD) and long short-term memory (LSTM) Network is proposed to predict the wind power, and hyper parameter optimization search of LSTM using Whale Swarm Algorithm with Iterative Counter (WSA-IC). Firstly, through correlation analysis, the characteristics of 10 different wind power data are screened, and two kinds of data with large correlation with wind power are determined as input of the mode. Secondly, IVMD is used to calculate the maximum envelope kurtosis, determine the best decomposition parameters of the variational mode decomposition (VMD), and the original wind power and wind speed sequences are decomposed to obtain the IMF with different time scales. Finally, to address the problems of difficult optimization of hyper parameter and difficulty in obtaining optimal solutions for LSTM neural network modes, the WSA-IC algorithm is proposed to optimize its key hyper parameter, and the IVMD-WSA-IC-LSTM forecasting mode is established to obtain the short-term forecasting results of wind power. The algorithm is tested with the data of China Longyuan Power Group Corporation Limited. Compared with other common forecasting approaches using same data, the mean absolute error (MAE) of the forecasting approach is reduced to 0.007859, the mean square error (MSE) is reduced to 0.00011, and the determination coefficient is improved to 0.998828, which has higher forecasting accuracy. [ABSTRACT FROM AUTHOR]

In recent years, limited works on EOG (electrooculography)-based biometric authentication systems have been carried out with eye movements or eye blinking activities in the current literature. EOGs have permanent and unique traits that can separate one individual from another. In this work, we have investigated FSST (Fourier Synchrosqueezing Transform)-ICA (Independent Component Analysis)-EMD (Empirical Mode Decomposition) robust framework-based EOG-biometric authentication (one-versus-others verification) performances using ensembled RNN (Recurrent Neural Network) deep models voluntary eye blinkings movements. FSST is implemented to provide accurate and dense temporal-spatial properties of EOGs on the state-of-the-art time-frequency matrix. ICA is a powerful statistical tool to decompose multiple recording electrodes. Finally, EMD is deployed to isolate EOG signals from the EEGs collected from the scalp. As our best knowledge, this is the first research attempt to explore the success of the FSST-ICA-EMD framework on EOG-biometric authentication generated via voluntary eye blinking activities in the limited EOG-related biometric literature. According to the promising results, improved and high recognition accuracies (ACC/Accuracy: ≥99.99% and AUC/Area under the Curve: 0.99) have been achieved in addition to the high TAR (true acceptance rate) scores (≥98%) and low FAR (false acceptance rate) scores (≤3.33%) in seven individuals. On the other hand, authentication and monitoring for online users/students are becoming essential and important tasks due to the increase of the digital world (e-learning, e-banking, or e-government systems) and the COVID-19 pandemic. Especially in order to ensure reliable access, a highly scalable and affordable approach for authenticating the examinee without cheating or monitoring high-data-size video streaming is required in e-learning platforms and online education strategies. Hence, this work may present an approach that offers a sustainable, continuous, and reliable EOG-biometric authentication of digital applications, including e-learning platforms for users/students. [ABSTRACT FROM AUTHOR]

Microtubules (MTs) are essential cytoskeletal polymers of eukaryote cells implicated in various cell functions, including cell division, cargo transfer, and cell signaling. MTs also are highly charged polymers that generate electrical oscillations that may underlie their ability to act as nonlinear transmission lines. However, the oscillatory composition and time–frequency differences of the MT electrical oscillations have not been identified. Here, we applied the Empirical Mode Decomposition (EMD) to bovine brain MT sheet recordings to determine the number and fundamental frequencies of the Intrinsic Modes Functions (IMF) and evaluate their energetic contribution to the electrical signal. As previously reported, raw signals were obtained from cow brain MTs (Cantero et al. Sci Rep 6:27143, 2016), sampled, filtered, and subjected to signal decomposition from representative experiments. Filtered signals (200 Hz) allowed us to identify either six or seven IMFs. The reconstructed tracings faithfully resembled the original signals, with identifiable frequency peaks. To extend the analysis to obtain time–frequency information and the energy implicated in each IMF, we applied the Hilbert–Huang Transform (HHT) and the Continuous Wavelet Transform (CWT) to the same samples. The analyses disclosed the presence of more fundamental frequency peaks than initially reported and evidenced the advantages and disadvantages of each transform. The study indicates that the EMD is a robust approach to quantifying signal decomposition of brain MT oscillations and suggests novel similarities with human brain wave electroencephalogram (EEG) recordings. The evidence points to the potentially fundamental role of MT oscillations in brain electrical activity. [ABSTRACT FROM AUTHOR]

Timely preterm labor prediction plays an important role for increasing the chance of neonate survival, the mother's mental health, and reducing financial burdens imposed on the family. The objective of this study is to propose a method for the reliable prediction of preterm labor from the electrohysterogram (EHG) signals based on different pregnancy weeks. In this paper, EHG signals recorded from 300 subjects were split into 2 groups: (I) those with preterm and term labor EHG data that were recorded prior to the 26th week of pregnancy (referred to as the PE-TE group), and (II) those with preterm and term labor EHG data that were recorded after the 26th week of pregnancy (referred to as the PL-TL group). After decomposing each EHG signal into four intrinsic mode functions (IMFs) by empirical mode decomposition (EMD), several linear and nonlinear features were extracted. Then, a self-adaptive synthetic over-sampling method was used to balance the feature vector for each group. Finally, a feature selection method was performed and the prominent ones were fed to different classifiers for discriminating between term and preterm labor. For both groups, the AdaBoost classifier achieved the best results with a mean accuracy, sensitivity, specificity, and area under the curve (AUC) of 95%, 92%, 97%, and 0.99 for the PE-TE group and a mean accuracy, sensitivity, specificity, and AUC of 93%, 90%, 94%, and 0.98 for the PL-TL group. The similarity between the obtained results indicates the feasibility of the proposed method for the prediction of preterm labor based on different pregnancy weeks. [ABSTRACT FROM AUTHOR]

To more accurately evaluate the health state of rolling bearings, this paper proposes a health status evaluation method based on empirical pattern decomposition, genetic algorithm and BP neural network. Firstly, the vibration signal is decomposed by empirical mode decomposition (EMD) and the time domain features of each intrinsic mode function (IMF) component are extracted, and the signal‐to‐noise ratio (Snr) of the signal is improved effectively. Then, the initial threshold and weight of BP neural network are optimized by genetic algorithm, which effectively improves the Snr of the signal. Finally, the extracted features are input into the optimized BP neural network to realize the identification of different states of the bearing. The effectiveness of the method has been effectively verified in the bearing data of Case Western Reserve University bearing dataset and it has higher accuracy and robustness than other common evaluation methods. [ABSTRACT FROM AUTHOR]

Introduction: Apical migration of gingival margin i.e. gingival recession is one of the most common periodontal esthetic issues. Successful minimally invasive management of gingival tissue recession is still a concern in esthetic dentistry. Material & Method: 50 sites in 50 patients with Miller' class I/II recessions were treated with tunnel and pouch technique (TPT) and platelet rich fibrin (PRF) with (Test group, n=25) or without (Control group, n=25) enamel matrix derivative (EMD) application. Subjects were followed for 12 months. Gingival recession depth (RD), Clinical attachment level (CAL), percentage of root coverage, Gingival Index, Plaque Index were measured at baseline and at 12 month. Data obtained were fed to IBM SPSS 22.0. Wilcoxon Sign Rank Test for Intragroup statistical analysis and Student't' test for intergroup statistical analysis were performed. The level of significance was fixed at p≤ 0.05. Results: CAL in control group was 2.60±0.91mm and in test group it was 3.33±1.05mm at 12th month. The mean difference of CAL gain was 0.73±0.14mm, which was statistically significant (p=0.025). At 12th month the mean amount of RD coverage in control group was 2.13±0.74mm and in the test group it was 2.67±0.72mm. The mean RD difference of 0.54±0.02mm was found to be statistically highly significant (p=0.012). Conclusion: TPT technique when combined with PRF and root bio modification with EMD provides convincing results in miller's class I or II gingival soft tissue recession cases. [ABSTRACT FROM AUTHOR]

Speech signal enhancement is a subject of study in which a large number of researchers are working to improve the quality and perceptibility of speech signals. In the existing Kalman Filter method, the short-time magnitude or power spectrum due to random variations of noise was a serious problem and the signal-to-noise ratio was very low. This issue severely reduced the perceived qualityand intelligibility of enhanced speech. Thus, this paper intent to develop an improved speech enhancement model and it includes"training phase and testing phase". In the training phase, the input noise corrupted signal is initially fed as input to both STFT-based noise estimation and NMF-based spectrum estimation forestimating the noise spectrum and signal spectrum, respectively. The obtained noise spectrum and the signal spectrum are fed as input to the Wiener filter and these filtered signals are subjected to Empirical Mean Decomposition (EMD).Since, tuning factor η plays a key role in Wiener filter, it has to be determined for each signal and from the denoised signal the bark frequency is evaluated. The computed bark frequency is fed as input to the learning algorithm referred as Fuzzy Wavelet Neural Network (FW-NN)for detecting the suited tuning factor η for the entire input signal in Wiener filter.An Adaptive Randomized Grey Wolf Optimization (AR-GWO) is proposed for proper tuning of the tuning factor η referred as tuned tuning factor (ηtuned). The proposed AR-GWO is the improved version of standard Grey wolf optimization (GWO). In the testing phase, the training is accomplished initially and from which the tuning factor is gathered for each of the relevant input signal. Then, the properly tuned tuning factor (ηtuned) from FW-NN is fed as input to EMD via adaptive wiener filter for decomposing the spectral signal and the output of EMD is denoised enhanced speech signal. At last, the performance of the adopted approach is evaluated to the existing approaches in terms of various metrics. In particular, the computation time of the adopted AR-GWO model is 34.07%, 43.57%, 28.86%, 38.88%, and 16.03% better than the existing GA, ABC, PSO, FF, and GWO approaches respectively. [ABSTRACT FROM AUTHOR]

Implementing intelligent identification of faults in hydroelectric units helps in the timely detection of faults and taking measures to minimize economic losses. Therefore, improving the accuracy of fault signal recognition has always been a research focus. This study is based on the improved empirical mode decomposition (EMD) theory to study the denoising and feature extraction of vibration signals of hydroelectric units and uses the backpropagation neural network (BPNN) to establish corresponding connections between signal features and vibration fault states. The improved EMD in this study can improve the performance of noise reduction processing and contribute to the accurate identification of vibration faults. The vibration fault identification criteria can adopt three dimensionless feature parameters: peak skewness coefficient, valley skewness coefficient, and kurtosis coefficient of the second- and third-order components of the signal, with recognition rates and accuracy reaching 90.6% and 96.2%, respectively. This paper's area under the curve (AUC) values were 0.7365, 0.7335, 0.9232, and 0.9141 for abnormal sound detection of the fan, water pump, slide, and valve, respectively, with an average AUC value of 0.8268. This paper's accuracy is 90.1%, and the loss function value is 0.27. The validation results demonstrate that this paper's method has high intelligent fault analysis capabilities. The experimental results confirm that this method can effectively detect vibration signals in hydroelectric units and perform effective noise reduction processing, thereby improving the diagnostic accuracy of fault signals. Therefore, this method can be effectively applied to the detection of vibration faults in hydroelectric units. [ABSTRACT FROM AUTHOR]

A direct drive wave power generation system (DDWPGS) has the advantages of a simple structure and easy deployment, and is the first choice to provide electricity for islands and operation platforms in the deep sea. However, due to the off-grid, the source and load cannot be matched, so accommodation is an important issue. Hydrogen storage is the optimal choice for offshore wave energy accommodation. Therefore, aiming at the source-load mismatch problem of the DDWPGS, an electric-hydrogen hybrid energy storage system (HESS) for the DDWPGS is designed in this paper. Based on the characteristics of the devices in the electric-hydrogen HESS, a new dynamic power allocation strategy and its control strategy are proposed. Firstly, empirical mode decomposition (EMD) is utilized to allocate the power fluctuations that need to be stabilized. Secondly, with the state of charge (SOC) of the battery and the operating characteristics of the alkaline electrolyzer being considered, the power assignments of the battery and the electrolyzer are determined using the rule-based method. In addition, model predictive control (MPC) with good tracking performance is used to adjust the output power of the battery and electrolyzer. Finally, the supercapacitor (SC) is controlled to maintain the DC bus voltage while also balancing the system's power. A simulation was established to verify the feasibility of the designed system. The results show that the electric-hydrogen HESS can stabilize the power fluctuations dynamically when the DDWPGS captures instantaneous power. Moreover, its control strategy can not only reduce the start-stop times of the alkaline electrolyzer but also help the energy storage devices to maintain a good state and extend the service life. [ABSTRACT FROM AUTHOR]

River discharge is affected by many factors, such as water level, rainfall, and precipitation. This study proposes a new hybrid framework named LAES (LASSO-ANN-EMD-SVM) to model the relationship of daily river discharge with meteorological variables. This hybrid framework is a composite of the least absolute shrinkage and selection operator (LASSO), an artificial neural network (ANN), and an error correction method. In the first stage, LASSO identifies meteorological variables that have a significant influence on the generation of river discharge. Next, the ANN model is used to predict river discharge using meteorological variables selected by LASSO, and the error series is determined. The error series is decomposed into intrinsic mode functions and residuals using empirical mode decomposition (EMD). The EMD components are modeled using the support vector machine (SVM) model, and the error predictions are aggregated. In the last stage, the LASSO-ANN predictions and the predicted error series are aggregated as the final discharge prediction. The proposed hybrid framework is illustrated on the Kabul River of Pakistan. The performance of the proposed hybrid framework is compared with six models using various performance measures and the Diebold-Mariano test. These models include multiple linear regression (MLR), SVM, ANN, LASSO-MLR, LASSO-SVM, and LASSO-ANN models. The findings reveal that the proposed hybrid model outperforms all other models considered in the study. In the testing phase, the root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) of the proposed LAES hybrid model are 337.143 m3/s, 32.354%, and 218.353 m3/s which are smaller than all other models compared in the study. Our proposed hybrid system is an efficient model for river discharge prediction that will be helpful in water management and protection against floods. Long-term prediction can help to identify the major effects of climate change and to make evidence-based environmental policies. [ABSTRACT FROM AUTHOR]

Our work's primary goal is to process electrocardiogram data utilizing the Fission-Fusion method of the Hilbert spectrum. One of the most effective techniques for the analysis of nonlinear and non-stationary signals is based on the Hilbert Huang Transform (HHT). We have processed Electrocardiograms (ECGs) by applying EMD after the Wavelet Packet Transform since our method is better appropriate for nonlinear and nonstationary signals (WPT). In order to produce monocomponent, breakdown the ECG signal into a number of narrow band signals, and remove unnecessary IMF, WPT is applied. In order to identify human emotions in our work, we employed the Fi-Fu algorithm to analyze electrocardiogram (ECG) signals for the detection of significant parameters such instantaneous frequency, amplitude, mean frequency, and second order difference plot. The EMD procedure has the ability to handle noise seen in ECG signals. Wavelet Packet Transform (WPT) is a more practical alternative to wavelet transform in realworld scenarios including signal analysis and denoising. WPT possesses unusual localization and enhanced discerning capabilities in the high frequency domain. WPT separates the high-frequency and low-frequency components of the frequency information of the signals to be studied. [ABSTRACT FROM AUTHOR]

Earth’s ionosphere is an important medium for navigation, communication, and radio wave transmission. Total Electron Content (TEC) is a descriptive quantify for ionospheric research. However, the traditional empirical model could not fully consider the changes of TEC time series, the prediction accuracy level of TEC data performed not high. In this study, an improved Extreme Learning Machine (ELM) model is proposed for ionospheric TEC prediction. Improvements involved the use of Empirical Mode Decomposition (EMD) and a Fuzzy C-Means (FCM) clustering algorithm to pre-process data used as input to the ELM model. The proposed model fully uses the TEC data characteristics and expected to perform better prediction accuracy. TEC measurements provided by the Centre for Orbit Determination in Europe (CODE) were used to evaluate the performance of the improved ELM model in terms of prediction accuracy, applicable latitude, and the number of required training samples. Experimental results produced a Mean Relative Error (MRE) and a Root Mean Square Error (RMSE) of 8.5% and 1.39 TECU, respectively, outperforming the ELM algorithm (RMSE=2.33 TECU and MRE=17.1%). The improved ELM model exhibited particularly high prediction accuracy in mid-latitude regions, with a mean relative error of 7.6%. This value improved further as the number of available training data increased and when 20-doys data were trained, achieving a mean relative error of 4.9%. These results suggest the proposed model offers higher prediction accuracy than conventional algorithms.

Optimal behind-the-meter energy management often requires a day-ahead electric load forecast capable of learning non-linear and non-stationary patterns, due to the spatial disaggregation of loads and concept drift associated with time-varying physics and behavior. There are many promising machine learning techniques in the literature, but black box models lack explainability and therefore confidence in the models’ robustness can’t be achieved without thorough testing on data sets with varying and representative statistical properties. Therefore this work adopts and builds on some of the highest-performing load forecasting tools in the literature, which are Long Short-Term Memory recurrent networks, Empirical Mode Decomposition for feature engineering, and k-means clustering for outlier detection, and tests a combined methodology on seven different load data sets from six different load sectors. Forecast test set results are benchmarked against a seasonal naive model and SARIMA. The resultant skill scores range from −6.3% to 73%, indicating that the methodology adopted is often but not exclusively effective relative to the benchmarks.

Seat suspension plays a vital role in improving riding comfort and protecting drivers’ health. This paper develops semi-active seat suspension that equips a controllable electromagnetic damper (EMD) and proposes a prescribed performance control-based semi-active vibration controller with experimental validation. The semi-active EMD mainly consists of a permanent magnet synchronous motor, a ball screw, a three-phase rectifier, and a controllable external resistor, which can vary its damping from 90 to 800 N·s/m by tuning the controllable external resistor in real-time. The EMD is applied to seat suspension, and a semi-active controller is proposed for the EMD seat suspension. In order to control the seat suspension vibration, a prescribed performance method is applied to obtain a desired control force and then a force-tracking strategy is designed to make the EMD track the desired control force. Finally, the semi-active seat suspension with the proposed controller is tested in experiments with different vibration conditions. The semi-active seat suspension performs excellently for the bump, sine wave and random vibration. The root mean square (RMS) acceleration, the frequency-weighted RMS acceleration and the acceleration’s fourth power vibration dose value were reduced by 17.5%, 39.9%, and 25.4%, respectively, in the random vibration, compared with a passive system.

In the context of motor imagery (MI)-based brain-computer interface (BCI) systems, a great amount of research has been studied for attaining higher classification performance by extracting discriminative features from MI-based electroencephalogram (EEG) signals. In this study, we propose an innovative approach for classifying multi-class MI-EEG signals, which consists of a signal processing technique based on empirical mode decomposition (EMD) and multi-class common spatial patterns (MCCSP). Specifically, after applying the EMD, we propose selecting the best intrinsic mode functions (IMF) as the substitution to the original EEG signal for the next stage of processing. The metric we used for the selection is based on the cross-correlation of each decomposed IMF with the original signal. Next, we extend the CSP algorithm to the MCCSP to be utilized as the feature extractor. We applied our technique to the BCI competition IV (2a). Results revealed that the proposed technique improved classification accuracy significantly compared to the original case when applying MCCSP directly to the original EEG channel data. Moreover, the K-nearest neighbor (KNN) achieved the highest mean classification accuracy rate of 91.28%. Our findings suggest that a promising elevated classification accuracy of 96.71% can be achieved by raising the feature dimension through MCCSP. Compared to state-of-the-art algorithms, the performance of the proposed method is highly convincing and motivating for future studies.

Cardiotocography (CTG) is a clinical procedure that is used to track and gauge the severity of fetal distress. Although CTG is the most often used equipment to monitor and assess the health of the fetus, the high rate of false positive results due to visual interpretation significantly contributes to needless surgical delivery or delayed intervention. In this study, a novel approach is introduced where both printing CTG paper is digitized and a machine learning approach is employed to detect the abnormality in the digitized CTG signal. Image processing-based preprocessing steps are employed to make the printing of CTG paper more convenient to extract the CTG signal. Various signal-processing techniques are used to calibrate the extracted CTG signal. Then, Empirical Mode Decomposition (EMD) is used to decompose the CTG signal into its frequency components and instantaneous frequency and spectral entropy features are extracted. After feature normalization and feature selection with ReliefF algorithm, support vector machines (SVM) is used for the classification of the normal and abnormal classes. A novel dataset is used in the experimental works and various performance evaluation metrics are used for the evaluation of the achievement of the proposed method. 10-fold cross-validation-based experiments show that the proposed method is quite efficient in abnormality detection in printing CTG papers where an average accuracy score of around 90.0% is produced.

SAR in THz band is very important and valuable in the field of radar signal processing, and it is much sensitive to the high-frequency vibration of the platform due to the short wavelength. In this article, the high-frequency vibration is characterized as a multicomponent SFM signal, and the parameters estimation method based on the linear chirplet transform and empirical mode decomposition is proposed to compensate for the high-frequency vibration errors. This method can extract the instantaneous frequency of the received signal with high precision, and the focused SAR image can be obtained consequently. Results of simulated and real measured data are provided to illustrate the effectiveness of the novel algorithm proposed in this article.

The regenerative capacity of well-preserved blood clots may be enhanced by biologics like enamel matrix derivative (EMD). This retrospective analysis compares outcomes reported by three centers using different heterografts. Center 1 (C1) treated intrabony defects combining cross-linked high-molecular-weight hyaluronic acid (xHyA) with a xenograft; center 2 (C2) used EMD with an allograft combination to graft a residual pocket. Center 3 (C3) combined xHyA with the placement of a resorbable polymer membrane for defect cover. Clinical parameters, BoP reduction, and radiographically observed defect fill at 12-month examination are reported. The 12-month evaluation yielded significant improvements in PPD and CAL at each center (p < 0.001, respectively). Analyses of Covariance revealed significant improvements in all parameters, and a significantly greater CAL gain was revealed for C2 vs. C1 (p = 0.006). Radiographic defect fill presented significantly higher scores for C2 and C3 vs. C1 (p = 0.003 and = 0.014; C2 vs. C3 p = 1.00). Gingival recession increased in C1 and C3 (p = 1.00), while C2 reported no GR after 12 months (C2:C1 p = 0.002; C2:C3 p = 0.005). BoP tendency and pocket closure rate shared similar rates. Within the limitations of the study, a data comparison indicated that xHyA showed a similar capacity to enhance the regenerative response, as known for EMD. Radiographic follow-up underlined xHyA’s unique role in new attachment formation.