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The primary objective of this research is to develop innovative methods for efficient condition monitoring of electro-motors. The study investigates the current signals of electro-motors in their early stages of failure, specifically identifying high-frequency fluctuations. These fluctuations, due to their low intensity and lack of modulation, elude detection through FFT analysis. As such, this study analyzes the quantitative and qualitative nature of such disturbances, including their frequency content and intensity in healthy and defective states, and identifies and isolates high-frequency disturbances from the current signal using the EMD and CEEMDAN methods. The research involved a comprehensive analysis of the FFT spectrum, envelope, and factors of main current signals and IMFs. The results indicate mode mixing only in the defective state. The EMD results demonstrate that the IMF associated with the original signal in the defective state exhibited a frequency content of less than 50, as evidenced by the FFT and the envelope spectrums. Additionally, the amplitude of the dominant frequencies in the FFT spectrum of the disturbances was significantly higher in the defective state. Additionally, the outcomes disclose that the original signal's RMS to the disturbances' RMS was significantly higher in healthy cases. Furthermore, the crest factor ratio in the healthy state was slightly higher than that of the defective state. The CEEMDAN findings reveal that the IMFs associated with the disturbance in a healthy state exhibit characteristics akin to white noise. Conversely, some IMFs demonstrate dominant frequencies of 50 and 150 Hz in the defective state. Furthermore, the RMS ratio of the original signal to the RMS of the disturbance is higher in the healthy state than in the defective state. Additionally, the shape factor of disturbances in the healthy state surpasses that of the faulty state. However, the ratio of shape factor notably exceeds in the healthy state. [ABSTRACT FROM AUTHOR]

Actual acquired air quality time series data are highly volatile and nonstationary, and accurately predicting nonlinear time series data containing complex noise is an ongoing challenge. This paper proposes an air quality prediction method based on empirical mode decomposition (EMD), a transformer and a bidirectional long short-term memory neural network (BiLSTM), which is good at addressing the ultrashort-term prediction of nonlinear time-series data and shows good performance for application to the air quality dataset of Patna, India (6:00 am on October 3, 2015–0:00 pm on July 1, 2020). The AQI sequence is first decomposed into intrinsic mode functions (IMFs) via EMD and subsequently predicted separately via the improved transformer algorithm based on BiLSTM, where linear prediction is performed for IMFs with simple trends. Finally, the predicted values of each IMF are integrated using BiLSTM to obtain the predicted AQI values. This paper predicts the AQI in Patna with a time window of 5 h, and the RMSE, MAE and MAPE are as low as 5.6853, 2.8230 and 2.23%, respectively. Moreover, the scalability of the proposed model is validated on air quality datasets from several other cities, and the results prove that the proposed hybrid model has high performance and broad application prospects in real-time air quality prediction. [ABSTRACT FROM AUTHOR]

Due to the lack of field monitoring points, the monitoring data is incomplete, making it difficult to obtain more complete characteristics of mining subsidence. To address this issue, a novel method for interpolating missing subsidence points is proposed. Firstly, the subsidence data are decomposed into multi-frequency domains utilizing the empirical mode decomposition (EMD) method. Subsequently, the missing subsidence data are reconstructed by combining the radial basis function (RBF) with phase space reconstruction technology. The interpolation accuracy is then analyzed under various conditions, including different mining degrees, sample numbers, and observation point locations. The results indicate that the interpolation accuracy increases with the mining degree coefficient. During the active stage of surface subsidence, a larger sample number correlates positively with higher interpolation accuracy. However, in the recession stage, the impact of sample number on interpolation accuracy is less pronounced compared to the timeliness of sample acquisition. The location of observation points has minimal effect on interpolation accuracy. By applying this method to the 833 working face in the Shanjiacun coal mine, the mean absolute percentage error (MAPE) is less than 20%, and the mean squared error (MSE) is below 0.5 mm, objectively demonstrating the method's effectiveness and engineering applicability. These research findings offer valuable technical support for mine safety production and ecological environment protection. [ABSTRACT FROM AUTHOR]

This paper extracts and separates seasonal term of GPS (Global Positioning System) time series based on empirical mode decomposition and wavelet transformation. Through time series analysis of 9 GPS continuous stations in Dali, Yunnan, it is found that the vertical (U), north–south (N) and east–west (E) components of the relative motion have distinct annual and semi-annual period components. In the vertical direction, the U component has the strongest seasonal deformation characteristics, on the annual period term, between each station the correlation coefficient reaches 0.98, this is consistent with the relevant research results of many researchers; In horizontal direction, seasonal deformation is also more significant, the N component annual and semi-annual period signals are more obvious and the correlation coefficient is high, but the E component 9 stations signal are relatively scattered and poorly correlated. On the semi-annual period, the N and U directions have a very obvious and consistent semi-annual periodicity, and their two correlation coefficient numbers are 0.95 and 0.94 on average, respectively, the N and E are negatively correlated with a correlation coefficient of -0.98. In time series trend term, 9 stations show southeast movement in horizontal direction, but have great differences in vertical movement trend. Among them, YNLJ, YNYS, YNSD and YNLC are linear uplift movement with good consistency. YNYA, YNCX and YNJD show overall uplift movement, but with subsidence fluctuation, the trend of the three stations is very similar, with an average correlation coefficient reaches 0.8; XIAG station shows uplift movement as a whole, the relative motion trend in the E direction is very different from other stations in phase and amplitude, probably because it is closer to the Erhai lake and more susceptible to the influence of water level changes; YNYL also has different motion changes at different times, but the overall performance is subsidence motion. The analysis suggests that the GPS time series contains rich information on the seasonal deformation of the Earth's crust, and precipitation has an important role in influencing the seasonal deformation of continuous stations. [ABSTRACT FROM AUTHOR]

Purpose: In the winding process of chemical fiber production, the winding machine was responsible for winding the post-treated primary fiber. The winding machine clamp head was used as the direct winding part, and its running state would directly affect the quality and production efficiency of the chemical fiber cake. With the increasing complexity and precision of chemical fiber equipment, the traditional method of fault diagnosis relying on manual experience has been unable to meet the needs of intelligent production. The winding equipment also has some problems, such as few fault samples, strong background noise interference and large speed fluctuation. Therefore, the study of a better intelligent fault diagnosis method is of great significance to improve the winding reliability of the clamp head and ensure the quality and production efficiency of the chemical fiber cake. Methods: In this paper, a fault diagnosis method based on Empirical Mode Decomposition Order Spectrum of vibration signal and Dual-Channel Convolutional Neural Network (EMDOS-DCCNN) was proposed and the corresponding fault diagnosis model was designed. The trigger interval sampling scheme was designed according to the characteristics of the changing working conditions of the winder. The vibration data at the base of the winder's clamp head was collected by data acquisition equipment from Zhongli Winder in a chemical fiber production plant in Zhejiang Province as the real measured data for rolling bearing diagnosis. The strong background noise and the influence of the variable speed of the winder were removed by Empirical Mode Decomposition (EMD) reconstruction of the original signal and order spectrum analysis. The dual-channel convolutional neural network model was used for fault diagnosis, and the dual-channel data was fused to further improve the fault diagnosis accuracy. Order analysis and validation set confusion matrix were used to evaluate the diagnostic model proposed in this paper. Results and Conclusion: The model was trained with vibration data collected at three rotational speeds and verified on data sets collected at other rotational speeds. The results showed that the fault diagnosis model of rolling bearings based on EMDOS-DCCNN could effectively remove noise, and a single diagnosis model could be used to diagnose rolling bearings at different rotational speeds. In the model evaluation experiment, the accuracy of the dual-channel CNN model was stable at 98.6%, which was 3.1% higher than that of the single-channel model. It had high fault identification accuracy and adaptability at different speeds, and the model was suitable for variable working conditions and had better robustness. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

To predict the most suitable mode decomposition technique for machine learning in meteorological time series prediction, this study has been carried out. The best mode decomposition technique along with a suitable machine learning algorithm will help predict a time series accurately. For this empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), coupled ensemble EMD with adaptive noise (CEEMDAN), variational mode decomposition (VMD), singular spectrum analysis (SSA) and independent component analysis (ICA) have been taken for comparison. The long short-term memory (LSTM) network has been used for machine learning. The max–min temperature time series have been constructed from Kolkata's max–min temperature data. Those time series have been predicted individually by the LSTM network along with each of these techniques. It has been found that the predicted time series by LSTM decomposed by ICA mostly matches the original time series compared to other techniques. [ABSTRACT FROM AUTHOR]

Accurate prediction is a fundamental and leading work of the emergency medicine reserve management. Given that the emergency medicine reserve demand is affected by various factors during the public health events and thus the observed data are composed of different but hard-to-distinguish components, the traditional demand forecasting method is not competent for this case. To bridge this gap, this paper proposes the EMD-ELMAN-ARIMA (ELA) model which first utilizes Empirical Mode Decomposition (EMD) to decompose the original series into various components. The Elman neural network and ARIMA models are employed to forecast the identified components and the final forecast values are generated by integrating the individual component predictions. For the purpose of validation, an empirical study is carried out based on the influenza data of Beijing from 2014 to 2018. The results clearly show the superiority of the proposed ELA algorithm over its two rivals including the ARIMA and ELMAN models. [ABSTRACT FROM AUTHOR]

This work aims to investigate the significance of different Empirical Mode Decomposition (EMD) based statistical features for discrimination of speech and low frequency music signal (guitar signals) which mostly lie in the frequency range of 80–1200 Hz. Each of the speech/guitar audio samples is decomposed into 10 Intrinsic Function Mode (IMFs). These IMFs are further analyzed for discriminatory evidence using statistical features like Mean, Absolute Mean, Kurtosis, Variance and Skewness. These features are then fed to different classifiers and their performances were tabulated for varying tuning parameters of the classifiers. Initial experiments were conducted on isolated features to shortlist features with best discriminatory evidence. These shortlisted features were then used in different combinations and their performances were reported. An improvement of 19.13% is observed for hybrid features over isolated features. Speech samples were obtained from Scheirer and Slaney database and Guitar samples were generated from a continuous guitar monologue uploaded on YouTube. Feature selection technique using Fisher Method and F-ratio were also implemented and best feature vectors were reported for both the algorithm. Best overall accuracy of 82.16% is reported for Hybrid features with Radial Basis Function (RBF) kernel of SVM classifier when trained with top 38 feature vectors obtained using F-Ratio Method. Different experiments verified Absolute Mean and Variance as best performing features for our task. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the usefulness and effectiveness of four machine learning (ML) models for modelling cyanobacteria blue-green algae (CBGA) at two rivers located in the USA. The proposed modelling framework was based on establishing a link between five water quality variables and the concentration of CBGA. For this purpose, artificial neural network (ANN), extreme learning machine (ELM), random forest regression (RFR), and random vector functional link (RVFL) are developed. First, the four models were developed using only water quality variables. Second, based on the results of the first, a new modelling strategy was introduced based on preprocessing signal decomposition. Hence, the empirical mode decomposition (EMD), the variational mode decomposition (VMD), and the empirical wavelet transform (EWT) were used for decomposing the water quality variables into several subcomponents, and the obtained intrinsic mode functions (IMFs) and multiresolution analysis (MRA) components were used as new input variables for the ML models. Results of the present investigation show that (i) using single models, good predictive accuracy was obtained using the RFR model exhibiting an R and NSE values of ≈0.914 and ≈0.833 for the first station, and ≈0.944 and ≈0.884 for the second station, while the others models, i.e., ANN, RVFL, and ELM, have failed to provide a good estimation of the CBGA; (ii) the decomposition methods have contributed to a significant improvement of the individual models performances; (iii) among the thee decomposition methods, the EMD was found to be superior to the VMD and EWT; and (iv) the ANN and RFR were found to be more accurate compared to the ELM and RVFL models, exhibiting high numerical performances with R and NSE values of approximately ≈0.983, ≈0.967, and ≈0.989 and ≈0.976, respectively. [ABSTRACT FROM AUTHOR]

It is hard to forecast waiting time from mobile trajectory big data on the traditional centralized mining platform, and especially the taxi driving direction cannot be clearly distinguished by the GPS trajectories of taxicabs in intelligent transportation systems. To this end, we propose a direction identification method (named CA-D) combined with Coordinate Axis and GPS Direction to distinguish taxi driving direction and then establish a distributed model (named EMDN-GRU) on Spark to forecast passenger waiting time based on an Empirical Mode Decomposition (EMD) algorithm with Normalization and a Gated Recurrent Unit (GRU) model. Specifically, in the process of waiting time forecasting, the CA-D method is used to differentiate directions to reduce the data interference caused by different taxi driving directions. Furthermore, the EMD algorithm with normalization is utilized to process large-scale GPS trajectory data. Finally, the GRU model with adjusted parameters is employed to forecast the time-series data obtained in the previous step, and the forecasting results are denormalized and superimposed to produce waiting time for passengers. Compared with LSTM, GRU, EMD-LSTM, EMD-GRU, CNN, and BP, the experimental results from a case study indicate that EMDN-GRU is significantly superior to others. In particular, from three data sets of weekday, weekend, and one week, the MAPE values of EMDN-GRU are reduced by 92.48%, 95.01%, and 90.47% at most. [ABSTRACT FROM AUTHOR]

Psychotic disorders are mental disorders that negatively affect human life. Diagnosis of psychotic patients is usually done in consultation with the patient, and this is a time-consuming process. In this study, a Computer Aided Diagnosis (CAD) system that will support expert opinion with automatic diagnosis of schizophrenia (SZ) disease, which is the leading psychotic disorder, is presented. In this study, Hilbert Huang Transform (HHT) method was used to analyze the non-stationary and non-periodic structure of EEG (Electroencephalograph) signals in the best way. The data set we used in our study includes 19-channel EEG signals from 28 (14 SZ and 14 healthy controls) participants, and the second data set includes 16-channel EEG signals from 84 (45 SZ and 39 healthy controls) participants. First of all, HS (Hilbert Spectrum) images of the first four Intrinsic Mode Functions (IMF) components obtained by applying Empirical Mode Decomposition (EMD) to EEG signals were created. These images were then classified with the VGG16 pre-trained Convolutional Neural Network (CNN) network. With our proposed method, the highest classification performance was obtained as 98.2% for Dataset I and 96.02% for Dataset II, respectively, by training the HS images obtained from the IMF 1 component with the VGG16 pre-trained CNN network. In the next step, classification performances were tested with VGG16, XCeption, DenseNet121, ResNet152 and Inception V3 pre-trained CNN networks. The high classification success achieved by the proposed method in our study demonstrates the accuracy of the model in distinguishing between SZ and healthy control. [ABSTRACT FROM AUTHOR]

In this paper, the security of an optical cryptosystem based on interference and equal modulus decomposition (EMD) has been evaluated. The security strength of the asymmetric cryptosystem has been enhanced by combining interference and EMD to generate four phase-only masks (POMs). Compared to the previous interference- or EMD-based cryptosystems where only one POM is the private key, the number of private keys in this system increases (i.e., four POMs) consequently increasing the key space of the cryptosystem. Moreover, due to additional private keys as unknown parameters in iterative procedures, this cryptosystem with silhouette removal is immune to special attacks which the EMD-based cryptosystems are vulnerable to. It seems that the security strength of the optical cryptosystem based on interference and EMD has been improved; however, we have noticed that the two POMs used as private key are still related to the ciphertexts and can be potentially utilized to attack the cryptosystem. According to the POMs generation principle, a relationship between private keys and ciphertexts could be derived and used as additional constraints to investigate the iterative attacks. Based on this finding, new iterative ciphertext-only attacks with different constraints have been proposed to crack the cryptosystem. In addition, we found that the POMs P 2 (u , v) and P 4 (u , v) which are used as private keys have low key sensitivity, which may lead to information leak. Numerical simulations have been carried out to validate the feasibility and effectiveness of the proposed attacks. [ABSTRACT FROM AUTHOR]

On average, five to six storms occur in the Qiongzhou Strait every year, causing significant damage to coastal geomorphology and several property losses. Tropical Storm Bebinca is the most unusual and complex storm event that has occurred in this region over the last 10 years. To detect the high-frequency beachface responses to the storm, a pressure sensor was deployed in the surf zone to record the free sea surface height, and the heights of grid pile points on the beachface were measured manually to determine beach elevation changes during this storm. Empirical Mode Decomposition and related analysis techniques were used to analyze the high-frequency topography and wave data. The results showed that: (1) the beachface response process occurred in three stages. The first stage was the rapid response stage, wherein the spring tide berm began to erode significantly, and the front edge of the beach berm reacted closely. The two beach sections resisted the harmful energy of the main storm. In the second stage, the beach slope increased after a large sediment loss on the beach berm and its front edge. To adapt to the storm energy, the beach at the low tide line began to erode, and the beach slope decreased. In the third stage, after the storm turned, the wave energy was significantly attenuated, and the beach berm eroded to resist the residual wave energy. The beachface began to oscillate and recover. (2) The main wave surface was the superimposed product of a few internal mode functions. Similar results were observed in beachface changes. High-frequency driving factors determine the local characteristics of beach evolution, and low-frequency driving factors determine the beach evolution trend. (3) The response of sediment to the storm was not a single sea-transportation, but a single- or two-way conversion driven by factors such as wave energy, swash flow, and secondary wave breaking. (4) The Ω-RTR model is not completely applicable to beach states that undergo rapid changes during storms. Therefore, it is necessary to carry out further research on beach state identification during storms. [ABSTRACT FROM AUTHOR]

Autonomous underwater vehicles (AUVs) are essential assets for ocean exploration requiring reliable underwater positioning technology. Aiming to improve the latter technology in a low SNR and reverberation environment, the Chan–Taylor hybrid positioning algorithm establishes a long-baseline system (LBL) based on the time difference of arrival (TDOA) by reducing the number of sensors required while preserving the positioning accuracy. However, the traditional LBL algorithm's accuracy is reduced due to the critical time delay estimation under such environmental conditions. Hence, this paper suggests a new LBL positioning technology relying on an empirical mode decomposition (EMD) to construct a filter function combined with the maximum likelihood (ML) estimation method. MATLAB/Simulink is applied to establish the simulation environment of LBL localization system, simulating the AUV motion under 5–30 dB SNR. This paper analyzes the accuracy of TDOA by generalizing the cross-correlation method (GCC), phase transform (PHAT), ML, and EMD–ML. Based on the TDOA value obtained by the EMD–ML filtering algorithm, the positioning errors of the Chan–Taylor hybrid positioning algorithm and the Chan algorithm are compared. The results show that comparative synthetic evaluations against the traditional GCC demonstrate that the proposed method has a higher time delay estimation accuracy within a reverberation environment with SNR less than 15 dB. The Chan–Taylor hybrid positioning algorithm limits the errors of the CHAN algorithm and improves the overall positioning system accuracy. [ABSTRACT FROM AUTHOR]

Acoustic Emission (AE) has emerged as a popular damage detection and localization tool due to its high performance in identifying minor damage or crack. Due to the high sampling rate, AE sensors result in massive data during long-term monitoring of large-scale civil structures. Analyzing such big data and associated AE parameters (e.g., rise time, amplitude, counts, etc.) becomes time-consuming using traditional feature extraction methods. This paper proposes a 2D convolutional neural network (2D CNN)-based Artificial Intelligence (AI) algorithm combined with time–frequency decomposition techniques to extract the damage information from the measured AE data without using standalone AE parameters. In this paper, Empirical Mode Decomposition (EMD) is employed to extract the intrinsic mode functions (IMFs) from noisy raw AE measurements, where the IMFs serve as the key AE components of the data. Continuous Wavelet Transform (CWT) is then used to obtain the spectrograms of the AE components, serving as the "artificial images" to an AI network. These spectrograms are fed into 2D CNN algorithm to detect and identify the potential location of the damage. The proposed approach is validated using a suite of numerical and experimental studies. [ABSTRACT FROM AUTHOR]

Purpose: Literature is conflicted on whether electromechanical delay durations decrease following resistance training programs. Therefore, the aim of this study is to examine the contributions and durations of the electrochemical (EMDE-M) and mechanical (EMDM-F) components to the overall electromechanical delay (EMDE-F) during step isometric muscle actions following 4-weeks of structured, multi-joint, lower-body variable resistance training (VRT) program. Methods: Twelve men performed 4-weeks of VRT leg press training utilizing combination of steel plates (80% total load) and elastic bands (20% total load). Training consisted of 3 sets of 10 repetitions at a 10 repetition maximum load, 3 day week−1 for 4-weeks. EMDE-M, EMDM-F, and EMDE-F was measured at Baseline, Week-2, and Week-4 during voluntary step isometric muscle actions (20, 40, 60, 80, and 100% of maximal voluntary isometric contraction) from the vastus lateralis using electromyographic, mechanomyographic, and force signals. Results: The EMDE-M, EMDM-F, and EMDE-F exhibited decreases in duration following 4-weeks of VRT. In addition, EMDE-M contributed significantly less (42–47%) than EMDM-F (53–58%) to the total duration of EMDE-F across the 4-weeks of VRT. Conclusions: These findings indicated that a structured, VRT program utilizing multi-joint exercise was sufficient to induce decreases in the electrochemical and mechanical processes associated with step isometric muscle contractions. In addition, the utilization of the electromyographic, mechanomyographic, and force signals were capable of quantifying electrochemical and mechanical component changes associated with voluntary muscle contraction. Thus, EMDE-M, EMDM-F, and EMDE-F can be useful in quantifying physiological changes in athletic, clinical, and applied research interventions. [ABSTRACT FROM AUTHOR]

Contemporary fault diagnosis algorithms constitute advanced signal processing techniques integrated with the data-driven feature classification algorithms which make an effective fault diagnosis scheme for rotating machinery such as gearboxes and motors. Feature extraction is a prevalent task which is intended to assist the fault diagnosis process by eliciting a set of condition indicators (features) from the input raw signal. In actual scenario, the gearboxes may have multiple stages and are rather operating under fluctuating speeds. The feature extraction technique employed at medium and high ranges of operating speed may not be adequate during low operating speeds. In this present study, the feature extraction abilities of discrete wavelet transform (DWT) and empirical mode decomposition (EMD) in terms of their relative effectiveness while ascertaining the local gear tooth defects of a multistage gearbox are compared. Two local gear tooth defects, namely root crack and tooth chip with three severity levels, are seeded artificially. The experiments are carried out on a three-stage spur gearbox experiencing fluctuating operating speeds. Vibration analysis is performed, and the recorded raw vibration signatures are decomposed using DWT and EMD analyses separately. Mother wavelet selection is done using the criteria of energy-to-Shannon entropy ratio. The identification of intrinsic mode functions (IMFs) is made by examining the Pearson correlation coefficient. Various descriptive statistics are obtained from the wavelet coefficients and IMFs and the potential indices among them are chosen by implementing the decision tree algorithm. Finally, support vector machine (SVM) algorithm is executed to distinguish among the various defect severity levels. It has been observed that the SVM in conjunction with DWT has resulted in better classification than SVM in conjunction with EMD. [ABSTRACT FROM AUTHOR]

The monitoring data of landslide deformation are characterized by non-smooth, nonlinear and random changes, and the cumulative changes of the monitored objects have both monotonous growth trends and short-term fluctuations. The GM(1,1) model can get better results only when the data series are monotonous. Due to the limitations of the model, the prediction accuracy of the GM(1,1) model is limited to a certain extent. An improved algorithm based on the GM(1,1) model and the empirical mode decomposition (EMD-GM(1,1) model) for deformation prediction was presented to improve the forecast accuracy in this paper. Firstly, EMD was used to effectively separate the nonlinear high-frequency and low-frequency components hidden in the deformation sequence; then the moving average method was applied to build a prediction model for high-frequency component, and the GM(1,1) was applied to build the prediction model for low-frequency one according to the characteristics of each component; finally, the predicted value of each component was superimposed. The experimental results indicate that the optimized EMD-GM(1,1) model combines the advantages of the two models to separate effectively the different frequency components of the deformation sequence, which has higher prediction accuracy. Compared with the conventional GM(1,1) model, DGM(2,1) model and the Linear fitting model, the proposed model could satisfactorily describe the landslide deformation prediction practically. [ABSTRACT FROM AUTHOR]

The nondestructive testing technology of generated acoustic emission (AE) signals for wood is of great significance for the evaluation of internal damages of wood. To achieve more accurate and adaptive evaluation, an AE signals classification method combining the empirical mode decomposition (EMD), discrete wavelet transform (DWT), and linear discriminant analysis (LDA) classifier is proposed. Five features (entropy, crest factor, pulse factor, margin factor, waveform factor) are selected for classification because they are more sensitive to the uncertainty, complexity, and non-linearity of AE signals generated during wood fracture. The three-point bending load damage experiment was implemented on sample wood of beech and Pinus sylvestris to generate original AE signals. Evaluation indexes (precision, accuracy, recall, F1-score) were adopted to assess the classification model. The results show that the ensemble classification accuracies of two tree species reach 94.58% and 90.58%, respectively. Moreover, compared with the results of the original AE signal, the accuracy of the AE signal processed by the methods proposed is increased by 27.68%. It indicates that the EMD and DWT signal processing methods and selected features improve the classification accuracy, and this automatic classification model has good AE signal recognition performance. [ABSTRACT FROM AUTHOR]

Efficient resource demand prediction and management are two main challenges for cloud service providers in order to control dynamic autoscaling and power consumption in recent years. The behavior of cloud workload time-series at subminute scale is highly chaotic and volatile; therefore, traditional machine learning-based time-series analysis approaches fail to obtain accurate predictions. In recent years, deep learning-based schemes are suggested to predict highly nonlinear cloud workloads, but sometimes they fail to obtain excellent prediction results. Hence, demands for more accurate prediction algorithm exist. In this paper, we address this issue by proposing a hybrid E2LG algorithm, which decomposes the cloud workload time-series into its constituent components in different frequency bands using empirical mode decomposition method which reduces the complexity and nonlinearity of prediction model in each frequency band. Also, a new state-of-the-art ensemble GAN/LSTM deep learning architecture is proposed to predict each sub band workload time-series individually, based on its degree of complexity and volatility. Our novel ensemble GAN/LSTM architecture, which employs stacked LSTM blocks as its generator and 1D ConvNets as discriminator, can exploit the long-term nonlinear dependencies of cloud workload time-series effectively specially in high-frequency, noise-like components. By validating our approach using extensive set of experiments with standard real cloud workload traces, we confirm that E2LG provides significant improvements in cloud workload prediction accuracy with respect to the mean absolute and standard deviation of the prediction error and outperforming traditional and state-of-the-art deep learning approaches. It improves the prediction accuracy at least 5% and 12% in average compared to the main contemporary approaches in recent papers such as hybrid methods which employs CNN, LSTM or SVR. [ABSTRACT FROM AUTHOR]

In this work, empirical mode decomposition (EMD)-based optimized added kernel least square support vector machine (OAKLSSVM) hybridized model is proposed for automatic identification of epileptic electroencephalogram (EEG) signals where the kernel parameters are being optimized using water cycle algorithm (WCA). The proposed model with EMD decomposition and WCA optimization together is known as EMD-OAKLSSVM-WCA. Here, two kernel functions i.e., radial basis function and wavelet kernel functions are deployed together to form the added kernel framework. From EMD, intrinsic mode functions (IMFs) are obtained where Hilbert transform (HT) is used to obtain analytic form of IMFs. For classifying seizure and non-seizure EEG signals, the frequency modulation bandwidth and amplitude modulation bandwidth parameters are obtained from the analytical IMFs and are used as features for the OAKLSSVM model. The experimental results validate the efficiency of the proposed model which provides better classification accuracy (99.33%) as compared to different promising classifiers and some state-of-the-art models. [ABSTRACT FROM AUTHOR]

Purpose: Knee flexor electromechanical delay (EMD) has been proposed as a contributing factor to non-contact anterior cruciate ligament (ACL) injury risk and the semitendinosus (ST) autograft technique has been shown to impair knee flexor torque at large angles of knee flexion. The purpose of this study was to analyse the effects of ACL reconstruction (ACLR) using the ST tendon autograft technique on knee flexor EMD across the knee flexion range of motion, in athletes who had returned to competition.Methods: Athletes with ACLR (n = 8 females, n = 3 males, 1.7 ± 0.5 years post-surgery) and non-injured control athletes (n = 6 females, n = 4 males) performed rapid maximal voluntary contractions of isometric knee flexion and extension at 30°, 50°, 70°, 90°,and 105° of knee flexion. Electrical activity of the ST, biceps femoris (BF), vastus lateralis, and vastus medialis was recorded using surface electromyography.Results: No change in EMD for the knee flexors or extensors was observed across joint angles. Greater EMD was found only for the BF in the ACLR limb of injured athletes compared to the contralateral limb (P < 0.05). In post-hoc analysis, evidence of ST tendon regrowth was noted for only 2/11 athletes.Conclusion: While the EMD-joint angle relationship appeared to be unaffected by ST tendon harvest for ACLR, the absence of ST tendon regrowth should be considered. Despite return to competition, greater BF EMD was found, which may impair knee joint stabilization capacity by delaying the transfer time of muscle tension to the tibia after ST autograft. [ABSTRACT FROM AUTHOR]

The existence of fractures and vugs in igneous formation is a key factor to determine the productivity of oil and gas reservoirs. Fracture–vug plane porosity and porosity spectrum (fracture–vug parameters) are important parameters to evaluate the development of fractures and vugs. In the process of drilling, the bit forms shallow holes and scratches on the borehole wall which is characterized by pitting, strip and block noise in the electrical imaging logging static image. The background noise affects the identification of fractures and vugs and the extraction of parameters. It is found that the background noise mainly exists in the high-frequency conductivity data. In order to suppress the background noise, empirical mode decomposition is applied to conductivity data of electrical imaging logging, and the wavelet hard threshold de-noising is applied to high-frequency intrinsic mode function components. The de-noising fracture–vug parameters have a good correspondence with the electrical imaging logging static image, and have a better linear relationship with the core porosity. These illustrate that the application of the de-noising method in the electrical imaging logging is reasonable and effective. The de-noising porosity spectrum becomes narrower in the reservoir with poor fractures and vugs, which can reveal the development of secondary pores more clearly. In reservoir interpretation, the de-noising fracture–vug plane porosity and porosity spectrum have good consistency with conventional and acoustic logging data, which can effectively evaluate the fractures and vugs in reservoirs. [ABSTRACT FROM AUTHOR]

Both equilibrium and nonequilibrium molecular dynamics (EMD and NEMD, respectively) methods have been used to predict the thermal conductivity of nanofluids. However, there are considerable discrepancies among the results of these two methods. In this study, by estimating the effects of different mechanisms including the Brownian motion of nanoparticles, the micro-convection in the base fluid, the nanolayers around the nanoparticles, and the thermal boundary resistance at the surface of nanoparticle, we determine upper and lower physical limits for the thermal conductivity of a nanofluid with spherical nanoparticles. The prediction of the NEMD simulations is in the acceptable range, while the result of the EMD simulations is higher than the upper bound. Since the prediction of the EMD method is not physically justifiable, we conclude the inadequacy of the traditional EMD method in calculating the thermal conductivity of nanofluids. Consequently, we recommend the researchers to use a modified version of the EMD method or the NEMD method for new studies in this field. We also apply the NEMD method to investigate the effects of the shape of nanoparticles and the formation of percolation networks in enhancing the thermal conductivity of nanofluids. The interference of the effects of nanolayer and thermal boundary resistance on the thermal conductivity of nanofluids is a new phenomenon we introduce in this study. [ABSTRACT FROM AUTHOR]

This paper proposes an efficiency quantum image steganography protocol based on improved exploiting modification direction algorithm (QIS-IEMD). The new methods of expanding modification range and dynamically sharing between subgroups are introduced in the new protocol. Compared with the quantum image steganography protocol based on EMD algorithm (QIS-EMD), secret information can be embedded on multiple bit planes after expanding pixel's modification range, and the carrier pixel groups can be dynamically shared to achieve higher embedding efficiency, embedding rate and larger capacity. In addition, in order to reflect the feasibility and practicability of QIS-IEMD, dedicated quantum circuits for embedding and extracting secret information are designed for it. Finally, the experimental results and detailed mathematical analysis prove that, compared with QIS-EMD, QIS-IEMD not only performs well in imperceptibility, but also significantly increases embedding efficiency, embedding rate and capacity. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to verify the effectiveness of EMD-liquid in combination with a synthetic bone substitute in a rabbit calvarial model. Material and methods: Four 7-mm outer diameter circular slits were created in the calvaria of 10 New Zealand white rabbits, and polycarbonate cylinders were inserted into the slits. Two experimental groups were established: (1) EMD-liquid + bone substitute (Osteon III®; biphasic calcium phosphate (BCP), β-TCP/HA = 40:60) and (2) saline + bone substitute (Osteon III®; BCP). The cylinders were filled with saturated graft materials and covered with polycarbonate caps. Micro-CT and histomorphometric evaluation were conducted. Results: In the histomorphometric analysis, new bone formation was significantly higher in the bone substitute (BS) + EMD-liquid group than in the BS only group at both 5 and 10 weeks (p < 0.01). There were statistically significant differences in the material area between the bone substitute and bone substitute + EMD-liquid groups at only 5 weeks (p < 0.05). The BS + EMD-liquid group demonstrated reduced material area to a greater extent. In micro-CT analysis, the BS + EMD-liquid group (27.04 ± 8.06 at 5 weeks, 28.49 ± 9.22 at 10 weeks) showed a significantly higher percentage of mineralized tissue volume at both 5 and 10 weeks (p < 0.05) than the BS only group. Conclusion: EMD-liquid enhances new bone formation when combined with BCP bone substitute in an animal model. Moreover, the EMD-liquid + BS has significantly lesser material area than BS alone, indicating accelerated graft degradation. Further studies on types of graft materials are required to verify the effect of EMD-liquid and to optimize its regenerative potential. Clinical relevance: This study suggests that EMD-liquid may have beneficial effect on bone regeneration with synthetic bone substitute. [ABSTRACT FROM AUTHOR]

A new signal reconstruction is proposed for damage detection on a simply supported beam using multiple measurements of displacement induced by a moving sprung mass. The new signal is constructed from the difference between the spatially integrated deflection for the intact (baseline) and damaged beams under quasi-static loading. To that end, it is shown that the static component of displacement from the dynamic moving mass experiment may be extracted very effectively using a robust smoothing technique and that this outperforms some comparable techniques. It is shown that by measuring displacement at a modest number of points on the beam the new reconstructed signal is able to detect the location of the damage more accurately than methods that use only a single-point data. In particular, the technique is able to detect damage present simultaneously at multiple locations and can do so with a highly variable moving mass velocity. In order to construct an a posteriori baseline, the strain data from the same traverse could be used to recover the displacement-time history of the intact beam, which could enhance the method by enabling the baseline to be determined from the same experiment, further eliminating effects of experimental conditions if required. However, a Monte Carlo simulation is run to consider the effect of signal noise, showing that the proposed damage detection strategy locates damage even in the presence of noise of 50% in the measured signals ( SNR = 7 dB ). [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a name for a group of neurodevelopmental conditions that are characterized by some degree of impairment in social interaction, verbal and non-verbal communication, and difficulty in symbolic capacity and repetitive behaviors. The only protocol followed currently for ASD diagnosis is the qualitative behavioral assessment by experts through internationally established descriptive scaling standards. The assessment can, therefore, be affected by the degree of the evaluator experience as well as by the level of the descriptive standard robustness. This paper presents an EEG-based quantitative approach intended for automatic discrimination between children with typical neurodevelopment and children with ASD. The suggested work relies on second-order difference plot (SODP) area as a discriminative feature: First, every EEG channel in a 64 electrode cap—for every volunteer—is decomposed into intrinsic mode functions (IMFs) by empirical mode decomposition (EMD). Next, the second-order difference plot for the first ten intrinsic mode functions—of every channel—is sketched. Third, the value of the elliptical area —for every plot—is calculated. The 95% confidence ellipse area is used as the discriminative feature. Fourth, paired t-student test is applied to the vectors consisting of discriminative feature values for counterpart channels/IMFs (e.g., channel FPz/IMF7 in autistic and neurotypical) for all volunteers. Finally, principal component analysis (PCA) and neural network (NN) are applied to the SODP area feature matrix for two-class classification (ASD and neurotypical). Moreover, the 3D mapping of EEG SODP area values was implemented and analyzed. The obtained results show that the conducted t-student tests yield values of less than 0.05, and that the NN two-class classification based on SODP features leads to a 94.4% accuracy, which indicates significant differences between SODP area values of children with neurotypical development and those diagnosed with ASD. The obtained results have also been emphasized by the analysis of the findings of the performed 3D mapping. [ABSTRACT FROM AUTHOR]

The prediction of a time series such as climate indices and the sunspot number (SSN) with long-term oscillatory behaviors has been a challenging task due to the complex combination of oscillations. Frequency extraction algorithms have been developed to separate a time series into different oscillation components according to frequency, such as empirical model decomposition (EMD) and wavelet analysis. In the current study, the deep learning long short-term memory (LSTM) model was employed to predict the oscillation components extracted using EMD. The SSN series was modeled with the hybrid EMD-LSTM model. The simulation study results indicate that the LSTM model reproduces the smooth cyclic pattern of the sine function, and only a few hidden units are needed to model it. The EMD-LSTM model achieves better performance than does the LSTM model for mid-range SSN predictions while the LSTM achieves better performance within the first few time lags. However, the cyclic prediction of the SSN requires mid-range lags; thus, the superior performance of the EMD-LSTM model for these lags cannot be ignored. Furthermore, the remaining components from the significant EMD signals can be modeled to reveal the variability (or uncertainty) in the prediction. The summed residual is fitted by k-nearest neighbor resampling. The final SSN prediction results show that the EMD-LSTM model predicts a later and larger SSN for Solar Cycle 25 than does the LSTM model. Overall, the results lead to the conclusion that the EMD-LSTM model might be a suitable alternative for modeling complex sunspot time series with cyclic patterns. [ABSTRACT FROM AUTHOR]

This paper presents two pre-processing methods that can be implemented for noise reduction in speaker recognition systems. These methods are adaptive noise canceller (ANC) and Savitzky-Golay (SG) filter. Also, discrete cosine transform (DCT), discrete wavelet transform (DWT) and discrete sine transform (DST) are considered for consistent feature extraction from noisy speech signals. A neural network with only one hidden layer is used as a classifier. The performances of the proposed noise reduction methods are compared with those of a hybrid method that comprises empirical mode decomposition (EMD) and spectral subtraction and also with spectral subtraction method only. Recognition rate is taken as a performance metric to evaluate the behavior of the system with these enhancement strategies. Simulation results prove that the DCT is the optimum transform with the suggested methods, while the DWT is the best one with the hybrid method and the spectral subtraction method. [ABSTRACT FROM AUTHOR]

A novel method based on empirical model decomposition (EMD), cyclostationary, spectral kurtosis (SK) and two-phase test sample sparse representation (TPTSR), called ECK-TPTSR is proposed for fault diagnosis in this paper. In the ECK-TPTSR method, the vibration signal is decomposed into several components by EMD. Then each component can be modelled as cyclostationary for noise reduction. Next, the proposed method computes the kurtosis of the unbiased autocorrelation on the squared envelope of each component, and extracts the component with the highest kurtosis. Finally, the extracted component is used to construct training samples and test samples, which are input into the TPTSR classifier to fulfill fault classification accurately. Moreover, the experimental results indicate that the ECK-TPTSR method can effectively achieve fault diagnosis of motor bearing and obtain higher classification accuracy. [ABSTRACT FROM AUTHOR]

In the context of data embedding in audio for communications, Spread Spectrum (SS)-based techniques, combined with auditory models, are efficient in terms of robustness and perceptual quality of the modified host audio. However, their main drawback is the limited embedding capacity due to the strong interference caused by the host audio. In this work, we combine under-determined Blind Source Separation with a generic SS receiver -made of a Wiener equalizer and a correlation-based demodulator- to efficiently reduce the effect of the host interference. Two blocks are added to the generic receiver: an Under-determined Independent Subspace Analysis (UISA) block is placed after the Wiener equalizer in order to separate the components of the equalized output, and a cooperative detection block is applied to the UISA outputs in order to extract the relevant information from all available components. The UISA block uses Empirical Mode Decomposition to obtain multiple observations of the modified host signal. The performance of the proposed system in terms of bit error rate vs. bit-rate is significantly improved: average error rate is null for bit-rates below 500 bps and smaller than 0.1% for bit-rates reaching 1 kbps. The objective evaluation of the perceptual quality of the modified audio confirms the imperceptibility of the embedded data. The proposed system also exhibits robustness against common signal processing operations such as gain modification, noise, MPEG compression and re-quantization. [ABSTRACT FROM AUTHOR]

Time—frequency electromagnetic data contain frequency and transient electromagnetic information and can be used to determine the apparent resistivity both in the frequency and time domains. The observation data contains three types of noise: the harmonics interference at 50 Hz, high-frequency random noise, and low-frequency noise. We use frequency-domain bandstop filtering to remove the harmonics interference noise, segmentation and extension median filtering, and fitting of fixed extremes in empirical mode decomposition to remove the high-frequency and low-frequency noise, respectively; furthermore, we base the selection of median filtering window size on the variance and skewness coefficient of the data. We first remove the harmonics interference at 50 Hz, then the high-frequency noise, and finally the low-frequency noise. We test the proposed methodology by using theory and experiments, and we find that the three types of noises are removed, the phase and amplitude information of the signal are maintained, and high-quality waveforms are obtained in the time domain. [ABSTRACT FROM AUTHOR]

In this paper, a new method for fault detection of parallel shaft gearbox based on the Empirical Mode Decomposition (EMD) and Multipoint Optimal Minimum Entropy Deconvolution (MOMEDA) is proposed. MOMEDA can overcome the shortcomings of minimum entropy deconvolution (MED) and Maximum Correlated Kurtosis Deconvolution (MCKD), and it is introduced to extract the fault cycle of gearbox signals. The vibration signals of gearbox are complex, including fault signals, noise signals and deterministic signals such as gear meshing component. Fault signal is often buried in these other components, which increases the difficulty of gearbox fault detection. Thus the EMD is proposed to decompose the signal and extract the fault impact components from the signal. The parallel shaft gearbox preset fault experiment is carried out to verify the effectiveness of method. In addition, some traditional methods, such as Fourier transform, cepstrum analysis, MED and MCKD, are used to compare with the proposed methods. Experimental results show that the effectiveness of the proposed method is better than that of traditional methods. [ABSTRACT FROM AUTHOR]

Pan-sharpening is one of the most important tasks performed on satellite images as it enhances spectral and spatial information of the images. Empirical mode decomposition (EMD) is one of the powerful methods for pan-sharpening. It first decomposes the image into a set of intrinsic mode functions and a residual component. These panchromatic and multi-spectral components are then fused to create an enhanced pan-sharpened image. This paper presents two efficient hybrid methods to enhance pan-sharpening of multi-spectral images. The new proposals combine the EMD with the two pan-sharpening methods: high-pass filtering and discrete wavelet transform to maximize the pan-sharpened image quality. The two methods are evaluated using satellite images of Cairo and Suez Canal region, Egypt, captured by Spot-4 and Landsat-8 satellites, respectively. The results imply that the proposed hybrid methods provide better qualitative and quantitative results compared to the individual and the common pan-sharpening methods. [ABSTRACT FROM AUTHOR]

A series of layered Li[Li0.2Mn0.54Ni0.13Co0.13]O2 cathode materials, of which manganese source was electrolytic manganese dioxide, with different contents of SO42− were successfully synthesized via ball-milling process and solid-state sintering method. The obtained materials were characterized by XRD, XPS, SEM-EDS, C-S, ICP, and HR-TEM. All the obtained materials presented well-ordered layered structure. When the content of SO42− was below 1.30 wt%, the electrochemical properties and structural stabilities at low rate for the layered materials with SO42− were not changed dramatically, while when the content of SO42− increased to 5.85 wt%, the initial discharge capacities decreased dramatically from 248.24 to 209.23 mAh g−1 at 10 mA g−1. And the pristine sample shows excellent cyclic property and rate capability. It delivered the discharge capacity of 175.25 mAh g−1 after 100 cycles with the highest capacity retention of 90.67% at 200 mA g−1. Particularly, the treated Li-rich Mn-based materials with the highest amount of SO42− exhibited the best cyclic stability and it delivers the highest capacity retention of 95.17% after 100 cycles at 200 mA g−1. However, its discharge capacities were much lower than the pristine material. As a result, the addition of SO42− could promote side reactions between electrode and electrolyte and deep-degree corrosion of electrode materials to affect the electrochemical properties and structural stabilities of the Li-rich Mn-based materials. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel ECG signal enhancement method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Higher Order Statistics (HOS). In our scheme, the noisy ECG signal is first decomposed adaptively into oscillatory components called intrinsic mode functions (IMFs) by using Empirical Mode Decomposition (EMD) or its variants. Therefore, the obtained modes are separated into two groups of noisy signal modes and one group of useful signal modes, by using a novel criterion derived from the HOS namely the fourth order cumulant or kurtosis. After that, a modified shrinkage scheme based on Interval Thresholding technique is adaptively applied to each selected IMF from the noise-dominant groups in order to reduce the noise and to preserve the QRS complex. The overall filtered ECG signal is then reconstructed by combining the thresholded IMFs and the retained unprocessed lower frequency relevant IMFs. Various tests and simulations are investigated to evaluate the performance of our proposed approach in combination with the EMD, Ensemble EMD (EEMD) and CEEMDAN algorithms. The simulation results carried on MIT-BIH Arrhythmia database, show that CEEMDAN method gives better performance than the two other methods, and outperforms some state-of-the-art methods in terms of Signal to Noise Ratio (SNR) and Root Mean Square Error (RMSE). [ABSTRACT FROM AUTHOR]

Aiming at the problem of gear fault feature extraction and fault classification under different load excitation, we present a new fault diagnosis method that combines three methods, including empirical mode decomposition (EMD), particle swarm optimization support vector machine (PSO-SVM) and fractal box dimension. First, the non-stationary original vibration signal of gear fault is decomposed into several intrinsic mode functions (IMF) by EMD method. Then, the time, frequency, energy characteristic parameters and box dimension are calculated separately from the time domain, frequency domain, energy domain and fractal domain. And then the gear fault characteristics under different load excitation are obtained. Finally, the extracted feature parameters are input into the PSO-SVM model for gear fault classification. The experimental results show that the proposed method can effectively identify gear failure types under different load excitation. [ABSTRACT FROM AUTHOR]

Condition monitoring has been recognized as an effective and low-cost method to enhance the reliability and improve the maintainability of power electronic converters. In power electronic converters, high-frequency oscillation occurs during the switching transients of power transistors, which is known as ringing. The ringing frequency mainly depends on the values of the parasitic capacitance and stray inductance in the oscillation loop. Although circuit stray inductance is an important factor that leads to the ringing, it does not change with transistor aging. A shift in either the inside inductance or junction capacitance is an important failure precursor for power transistors. Therefore, ringing frequency can be used to monitor the health of power transistors. However, the switching actions of power transistors usually result in a dynamic behavior that can generate oscillation signals mixed with background noise, which makes it hard to directly extract the ringing frequency. A frequency extraction method based on empirical mode decomposition (EMD) and Fast Fourier transformation (FFT) is proposed in this paper. The proposed method is simple and has a high precision. Simulation results are given to verify the ringing analysis and experimental results are given to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

To better maintain accurate data signal and to more easily select the threshold parameter in a traditional empirical mode decomposition denoising algorithm, this paper proposes a threshold denoising algorithm based on variance estimation. The variance of true noise in the data is obtained from the energy of each component and the variance value is used as the criterion of completed denoising. Finally, nonlinear data are used for simulation and verification of the proposed method. It is confirmed that the algorithm is not constrained to the selection of the threshold parameter and that the denoising effect is better than the traditional denoising algorithm. [ABSTRACT FROM AUTHOR]

Signal processing is a widely used tool in the field of monitoring and diagnosis of rolling bearing faults. The vibration signals of rolling bearing contain important information which can be used for early detection and diagnosis of faults. These signals are usually noisy and masked by other sources and therefore the information about the fault can be lost. In this work, we propose an enhancement of rolling bearing fault diagnosis based on the improvement of empirical mode decomposition (EMD) denoising method. This method is made to extract the useful fault signal in order to use the detection indicators such as the kurtosis and the envelope spectrum. Firstly, EMD is applied to the vibration signals to obtain a series of functions called the intrinsic mode functions (IMFs). Secondly, we present an approach based on the energy content of each mode to determine the trip point which allows selecting the relevant modes. The singular selected IMFs are determined by comparing the average energy of all the unselected IMFs with the energy of each selected IMFs; then, an optimized thresholding operation is performed to denoise these IMFs. Finally, the kurtosis and spectral envelope analysis were investigated for early detection and localization of the fault position. Different experimental data are used to validate the effectiveness of the proposed method. The obtained results showed that the proposed method is more efficient and more sensitive to the early detection and diagnosis of rolling bearing faults than the conventional denoising method. [ABSTRACT FROM AUTHOR]

Understanding the characteristics of the dynamic relationship between the onshore Renminbi (CNY) and the offshore Renminbi (CNH) exchange rates considering the impact of some extreme events is very important and it has wide implications in several areas such as hedging. For better estimating the dynamic relationship between CNY and CNH, the Granger-causality test and Bry-Boschan Business Cycle Dating Algorithm were employed in this paper. Due to the intrinsic complexity of the lead-lag relationships between CNY and CNH, the empirical mode decomposition (EMD) algorithm is used to decompose those time series data into several intrinsic mode function (IMF) components and a residual sequence, from high to low frequency. Based on the frequencies, the IMFs and a residual sequence are combined into three components, identified as short-term composition caused by some market activities, medium-term composition caused by some extreme events and the long-term trend. The empirical results indicate that when it only matters the short-term market activities, CNH always leads CNY; while the medium-term impact caused by those extreme events may alternate the lead-lag relationships between CNY and CNH. [ABSTRACT FROM AUTHOR]

Hiding data on grayscale images has been drawing much attention in recent years. High capacity and better image quality are the two foremost research contents of data hiding. The traditional EMD which concealed an equal amount of data into each pixel and cause equal degree of distortion. Meanwhile due to being only an approximation, an interpolated image will always lose some quality each time interpolation is performed. In view of the problems existing in the two above method, we propose a novel data hiding scheme that employs human vision sensitivity to hide a large amount of secret bits into a original image with a high imperceptibility and low computational complexity. Experimental results reveal that the proposed method performs better than several state-of-the-art methods. It can resist the RS steganalysis attack and it is statistically invisible for the attack of PVD histogram attack. [ABSTRACT FROM AUTHOR]

Sleep staging is a significant process to diagnose sleep disorders. Like other stages, several parameters are required for the determination of N-REM2 stage. Sleep spindles (SSs) are among them. In this study, a methodology was presented to automatically determine starting and ending positions of SSs. To accomplish this, short-time Fourier transform-artificial neural networks (STFT-ANN), empirical mode decomposition (EMD) and discrete wavelet transform (DWT) methods were used. Two considerable methods which were determination envelope and thresholding of the decomposed signals by EMD and DWT were also presented in this study. A database from the EEG signals of nine healthy subjects—which consisted of 100 epochs including 172 SSs in total—was prepared. According to the test results, the highest sensitivity rate was obtained as 100 and 99.42 % for EMD and DWT methods. However, the sensitivity rate for the STFT-ANN method was recorded as 55.93 %. The results indicated that the EMD method could be confidently used in the automatic determination of SSs. Thanks to this study, the sleep experts will be able to reliably find out the epochs with SSs and also know the places of SSs in these epochs, automatically. Another important point of the study was that the sleep staging process—tiring, time-consuming and high fallibility for the experts—could be performed in less time and at higher accuracy rates. [ABSTRACT FROM AUTHOR]