Empirical Mode Decomposition Technique Research Articles

Application of the Empirical Mode Decomposition Method to Noise Reduction Using Seismic Event Data Recorded at Kirkuk Seismological Station.

This study aims to use the Empirical Mode Decomposition for reducing the noise of passive seismic signals recorded across seismic event recorded at Kirkuk station which is one of the Iraqi Meteorological Organization and Seismology data, where human activities in Iraq's populous cities significantly impact the seismic recordings at Iraqi Meteorological Organization and Seismology stations sites. In this paper, we present an algorithm based on the Empirical Mode Decomposition. It is a data driven method used as a propelling tool for analyzing and decomposing non-stationary and non-linear data without any previous assumptions. Empirical Mode Decomposition generates a finite and often small number of Intrinsic Mode Functions. The decomposition is based on the direct extraction of the energy related to various intrinsic time scales, an important parameter of the system. Empirical Mode Decomposition technique is applied to the available seismic records to reduce the noise level in the records, which helps in the interpretation of the records.

Capacity optimal allocation of hybrid energy storage in DC distribution network based on Ensemble Empirical Mode Decomposition

In response to fluctuations in the power levels within the link connecting the direct current transmission system to the upper-level power grid, we propose an optimization approach for determining the ability of a Hybrid Energy Storage System (HESS). Our first step involves calculating the power agreement for the link and the total power requirements for the HESS, taking into account the system’s net load power and the dynamic pricing of electricity based on time-of-use. Subsequently, we utilize the Ensemble Empirical Mode Decomposition (EEMD) technique to break down the total energy generated by the HESS into its constituent components. Incorporating instructions for both powering up and losing power of the HESS across varying filter orders, we solve the capacity allocation model for the HESS using a particle swarm algorithm, with rated power and rated capacity as decision variables. Finally, we identify the filter order that yields the best objective function value and the corresponding energy storage configuration scheme. An illustrative analysis, conducted on an enhanced IEEE-14 node DC distribution network, demonstrates that the proposed HESS configuration effectively mitigates tie-line fluctuations and enhances system stability.

ELM-based stroke classification using wavelet and empirical mode decomposition techniques

ABSTRACT Biomedical signal processing is crucial in many sectors that save lives. Artificial intelligence improvement in signal collection and conditioning boosted this application’s adaptability to varied bodily circumstances. In this study, a novel method is put forth for predicting the type of stroke in the human brain based on the observation of the Electroencephalography (EEG) signal. The signal is the first condition for removing undesirable frequencies by passing through a lowpass filter. To accurately extract the signal features, the signal is first transformed into a 1-second frame format and then normalised. Certain statistical and frequency domain aspects are highlighted to increase taxonomic accuracy. Under the wavelet packet transform, the empirical mode decomposition approach is utilised to recover the most information feasible from the signal. After training on extracted characteristics, the extreme learning machine is regarded to conduct classification. These work achieves 94.95 of Sensitivity, 84.95 of Specificity, 93.74 of Precision, 96.96 of Accuracy, 96.12 of F1 Score. Compared to the standard procedures, the proposed techniques have a greater accuracy rate of about 98%.

Differential absorption LIDAR signal denoising using empirical mode decomposition technique

Differential absorption LIDAR signal denoising using empirical mode decomposition technique

Improving speech communication in the age of face masks: A study on EMD denoising method by subjective speech comparison

The widespread use of face masks during the COVID-19 pandemic has posed challenges to effective communication. This study investigates the application of the Empirical Mode Decomposition (EMD) denoising method to enhance the quality of acoustic signals affected by face masks. The EMD technique utilizes a Fast Fourier transform (FFT) of the intrinsic mode function to distinguish noise from the signal and improve the acoustic signal. The EMD-enhanced speech signal is compared to the acquired speech signal using Comparison Mean Opinion Scores (CMOS), and the spectral subtraction speech enhancement signal is also evaluated using CMOS. The results from various experimental conditions consistently demonstrate the superior performance of EMD denoising in producing enhanced signals. Specifically, the EMD method outperforms the spectral subtraction method in improving speech signal quality affected by face masks. These findings underscore the effectiveness of the EMD denoising method in improving the quality of speech signals impacted by face masks, highlighting its potential for enhancing communication in challenging acoustic environments.

EEG-based clinical decision support system for Alzheimer's disorders diagnosis using EMD and deep learning techniques.

Despite the existence of numerous clinical techniques for identifying neurological brain disorders in their early stages, Electroencephalogram (EEG) data shows great promise as a means of detecting Alzheimer's disease (AD) at an early stage. The main goal of this research is to create a reliable and accurate clinical decision support system leveraging EEG signal processing to detect AD in its initial phases. The research utilized a dataset consisting of 35 neurotypical individuals, 31 patients with mild AD, and 22 patients with moderate AD. Data were collected while participants were at rest. To extract features from the EEG signals, a band-pass filter was applied to the dataset and the Empirical Mode Decomposition (EMD) technique was employed to decompose the filtered signals. The EMD technique was then leveraged to generate feature vectors by combining multiple signal features, thereby enhancing diagnostic performance. Various artificial intelligence approaches were also explored and compared to identify features of the extracted EEG signals distinguishing mild AD, moderate AD, and neurotypical cases. The performance of the classifiers was evaluated using k-fold cross-validation and leave-one-subject-out (LOSO) cross-validation methods. The results of this study provided valuable insights into potential avenues for the early diagnosis of AD. The performance of the various offered methodologies has been compared and evaluated by computing the overall diagnosis precision, recall, and accuracy. The proposed methodologies achieved a maximum classification accuracy of 99.9 and 94.8% for k-fold and LOSO cross-validation techniques, respectively. The study aims to assess and compare different proposed methodologies and determine the most effective combination approach for the early detection of AD. Our research findings strongly suggest that the proposed diagnostic support technique is a highly promising supplementary tool for discovering prospective diagnostic biomarkers that can greatly aid in the early clinical diagnosis of AD.

Exploring the dynamics of arrivals and prices volatility in onion (Allium cepa) using advanced time series techniques

Modeling the arrivals and prices of agricultural commodities is an essential requirement for farmers, consumers, and governmental organizations to make informed decisions. This is particularly important for perishable commodities such as vegetables, where spoilage can lead to significant losses for farmers and have a ripple effect on supply and demand dynamics. Volatility in the arrivals and prices of vegetables like onion is a serious issue affecting the common person in different ways. The study attempts to employ different time series models like the autoregressive integrated moving average (ARIMA), Artificial neural network (ANN), hybrid, and ensemble empirical mode decomposition (EEMD) techniques to analyze the pattern and trend of onions in Chandigarh and Delhi markets. From the results of the study, the amount of volatility in the data was found to range from medium to high among the markets. Decomposition techniques such as EEMD-ARIMA and EEMD-ANN performed better for the study data with the least mean absolute percentage error (MAPE) values, such as 17.74 and 6.78% for arrivals and 9.76 and 10.24% for prices at Chandigarh and Delhi markets, respectively. The EEMD techniques exceled in handling the non-linearity and non-stationarity by decomposing the data into different intrinsic modes and a residual, providing a better understanding of the fluctuation levels of data.

Ambient oscillatory mode assessment in power system using an advanced signal processing method

Estimation of electromechanical mode properties is crucial in modern power systems for providing the operators with an adequate indication of the stress in the system. Measurement-based approaches use signal processing algorithms for mode identification and parameter estimation. This paper presents a novel framework for the assessment of low-frequency oscillation modes using real-world synchrophasor data with minimum computational effort. A nonstationary approach known as Time-Varying Filter based Empirical Mode Decomposition (TVF-EMD) technique is used to identify the dominant low-frequency modes present in the ambient PMU data. The combination of TVF-EMD with Teager Kaiser Energy Operator (TKEO) precisely estimates the instantaneous mode parameters, such as frequency, amplitude, and damping ratio. The efficacy of the proposed approach is demonstrated by applying it in a synthetic signal, simulated data of a standard IEEE test system, and in real-world PMU data of the Indian power grid. The proposed method is compared with the existing methodologies and the observations reveal that the proposed method has robust performance in estimating the instantaneous mode features in the power system with less computational complexities.

Speech Enhancement Using Sliding Window Empirical Mode Decomposition and Hurst-based Technique

The most challenging in speech enhancement technique is tracking non-stationary noises for long speech segments and low Signal-to-Noise Ratio (SNR). Different speech enhancement techniques have been proposed but, those techniques were inaccurate in tracking highly non-stationary noises. As a result, Empirical Mode Decomposition and Hurst-based (EMDH) approach is proposed to enhance the signals corrupted by non-stationary acoustic noises. Hurst exponent statistics was adopted for identifying and selecting the set of Intrinsic Mode Functions (IMF) that are most affected by the noise components. Moreover, the speech signal was reconstructed by considering the least corrupted IMF. Though it increases SNR, the time and resource consumption were high. Also, it requires a significant improvement under nonstationary noise scenario. Hence, in this article, EMDH approach is enhanced by using Sliding Window (SW) technique. In this SWEMDH approach, the computation of EMD is performed based on the small and sliding window along with the time axis. The sliding window depends on the signal frequency band. The possible discontinuities in IMF between windows are prevented by the total number of modes and the number of sifting iterations that should be set a priori. For each module, the number of sifting iterations is determined by decomposition of many signal windows by standard algorithm and calculating the average number of sifting steps for each module. Based on this approach, the time complexity is reduced significantly with suitable quality of decomposition. Finally, the experimental results show the considerable improvements in speech enhancement under non-stationary noise environments.

Unbalance Bearing Fault Identification Using Highly Accurate Hilbert–Huang Transform Approach

Abstract The dynamic characteristics of rolling element bearings are strongly related to their geometric and operating parameters, most importantly the bearing unbalance. Modern condition monitoring necessitates the use of intrinsic mode functions (IMFs) to diagnose unbalance bearing failure. This paper presents a Hilbert–Huang transform (HHT) method to diagnose the unbalanced rolling bearing faults of rotating machinery. To initially reduce the noise levels with slight signal distortion, the noises of the sample in normal and unbalanced fault states are measured and denoised using the wavelet threshold approach. The complex vibration signatures are decomposed into finite IMFs with ensemble empirical mode decomposition technique. Fast Fourier techniques are employed to extract the vibration responses of bearings that are artificially damaged using electrochemical machining on a newly established test setup for rotor disc bearings. The similarities between the information-contained marginal Hilbert spectra can be used to diagnose rotating machinery bearing faults. The data marginal Hilbert spectra of Mahalanobis and cosine index are compared to determine the fault indicator index’s similarity score. The HHT model’s simplicity enhanced the precision of diagnosis correlated to the results of the experiments with weak fault characteristic signals. The effectiveness of the proposed approach is evaluated with several theoretical models from the literature. The HHT approach is experimentally proven with unbalance diagnosis and capable of classifying marginal Hilbert spectra distribution. Because of its superior time-frequency characteristics and pattern identification of marginal Hilbert spectra and fault indicator indices, the newly stated HHT can process nonlinear, non-stationary, and even transient signals. The findings demonstrate that the suggested method is superior in terms of unbalance fault identification accuracy for monitoring the dynamic stability of industrial rotating machinery.

HISET: Hybrid interpretable strategies with ensemble techniques for respiratory sound classification

The human respiratory systems can be affected by several diseases and it is associated with distinctive sounds. For advanced biomedical signal processing, one of the most complex issues is automated respiratory sound classification. In this research, five Hybrid Interpretable Strategies with Ensemble Techniques (HISET) which are quite interesting and robust are proposed for the purpose of respiratory sounds classification. The first approach is termed as an Ensemble GSSR technique which utilizes L2 Granger Analysis and the proposed Supportive Ensemble Empirical Mode Decomposition (SEEMD) technique and then Support Vector Machine based Recursive Feature Elimination (SVM-RFE) is used for feature selection and followed by classification with Machine Learning (ML) classifiers. The second approach proposed is the implementation of a novel Realm Revamping Sparse Representation Classification (RR-SRC) technique and third approach proposed is a Distance Metric dependent Variational Mode Decomposition (DM-VMD) with Extreme Learning Machine (ELM) classification process. The fourth approach proposed is with the usage of Harris Hawks Optimization (HHO) with a Scaling Factor based Pliable Differential Evolution (SFPDE) algorithm termed as HHO-SFPDE and it is classified with ML classifiers. The fifth or the final approach proposed analyzes the application of dimensionality reduction techniques with the proposed Gray Wolf Optimization based Support Vector Classification (GWO-SVC) and another parallel approach utilizes a similar kind of analysis with the Grasshopper Optimization Algorithm (GOA) based Sparse Autoencoder. The results are examined for ICBHI dataset and the best results are shown for the 2-class classification when the analysis is carried out with Manhattan distance-based VMD-ELM reporting an accuracy of 95.39%, and for 3-class classification Euclidean distance-based VMD-ELM reported an accuracy of 90.61% and for 4-class classification, Manhattan distance-based VMD-ELM reported an accuracy of 89.27%.

Beamforming-based algorithms for recovering information from fetal electrocardiographic sensors

We deal with the extraction of the fetal electrocardiography (ECG) signal from the raw ECG signals of the mother by the beamforming- based algorithms. The foetal ECG sensors bring out signals containing information from the pregnant mother and the infant. Detailed and separate signals are already provided by the foetal ECG instruments; but for some specific studies related to the infant conditions, it is necessary to improve the quality of the signal with a dedicated processing. In this paper, four techniques, with some enhancements, are proposed to perform the processing; we have applied the following techniques: Least Mean Square (LMS) with adaptive noise cancellation technique, Discrete Wavelet Transform (DWT)-based technique, Empirical Wavelet Transform (EWT) technique, and Multiple Signal Classification (MUSIC). The LMS and the MUSIC pertain to beamforming approach. The techniques were used to decompose and identify the different elements constituting the source signal (mother's signal) and noise cancellation by Multivariate Empirical Mode Decomposition (MEMD) technique. The signal was adaptively decomposed by LMS, DWT and MUSIC according to optimised parameters to extract some hidden components of the source signal, such as the foetal features, QRS, heartbeat etc. The results have showed that LMS, with enhancements, is more effective in identifying and removing useless noise. The techniques were applied to the ECG signal of a 30-year-old healthy pregnant woman, which allowed to verify their applicability. The present research leads to the below main contributions among others: separation of the ECG signal of the foetus from the mother, highlighting the functional state of the foetal heart rhythm (heart rate and heartbeat,) and this can show us if the foetal ECG has malfunctions.

A new hybrid PM[Formula: see text] volatility forecasting model based on EMD and machine learning algorithms.

In recent years, the frequent occurrence of air pollution incidents has seriously affected people's health and life. Therefore, PM[Formula: see text], as the main pollutant, is an important research object of air pollution at present. Effectively improving the prediction accuracy of PM[Formula: see text] volatility makes the PM[Formula: see text] prediction content perfect, which is an important aspect of PM[Formula: see text] concentration research. The volatility series has an inherent complex function law, which drives the volatility movement. When machine learning algorithms such as LSTM (Long Short-Term Memory Network) and SVM (Support Vector Machine) are used for volatility analysis, a high-order nonlinear form is used to fit the functional law of the volatility series, but the time-frequency information of the volatility has not been utilized. Based on EMD (Empirical Mode Decomposition) technique, GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) model and machine learning algorithms, a new hybrid PM[Formula: see text] volatility prediction model is proposed in this study. This model realizes time-frequency characteristic extraction of volatility series through EMD technology, and integrates residual and historical volatility information through GARCH model. The simulation results of the proposed model are verified by comparing the samples of 54 cities in North China with the benchmark models. The experimental results in Beijing showed that MAE (mean absolute deviation) of hybrid-LSTM decreased from 0.00875 to 0.00718 compared with LSTM, and hybrid-SVM based on the basic model SVM also significantly improved generalization ability, and its IA (index of agreement) improved from 0.846707 to 0.96595, showing the best performance. The experimental results show that the hybrid model is superior to other considered models in terms of prediction accuracy and stability, which verifies that the hybrid system modeling method is suitable for PM[Formula: see text] volatility analysis.

Short-Term Electricity Price Forecasting Using EEMD and GRU-NN

A vital necessity for the reliable operation of the power system as well as for the financial well-being of the consumers is an accurate forecast of the price of electricity. The price of electricity is highly volatile, nonlinear, and subject to seasonal fluctuations. The price series is also affected by electrical market uncertainty and demand, which is strongly reliant on weather and electricity usage time. This study presents a short-term electricity price forecasting method that follows the similar days approach comprising Ensemble Empirical Mode Decomposition technique. The suggested method utilizes a feature selection methodology that combines the Random Forest Regressor and the Gradient Boosting Regressor to determine which features are most important for the machine learning model. To forecast the electricity price, a Gated Recurrent Unit Neural Network (GRU-NN) is employed as the machine learning model. The GRU-NN has the capability of accurately capturing complicated temporal relationships in electricity price time series, which enables it to make correct predictions. To evaluate the validity of the proposed method, data from the PJM electricity market have been used. The simulation results demonstrate that the suggested method is superior to the existing technique, with significantly improved values achieved for both the mean absolute percentage error (MAPE) and the root mean square error (RMSE).

Multi-modal IoT-based medical data processing for disease diagnosis using Heuristic-derived deep learning

Nowadays, the quick development of the Internet of Things (IoT) has changed our lifestyle. However, disease diagnosis is a difficult task owing to managing an enormous volume of data. Thus, in this research task, the latest IoT-assisted disease detection model for healthcare applications is developed. Initially, the patient data are taken from the IoT sensor devices in image, text, and signal data. The gathered patient data is further forward to the pre-processing phase. Accordingly, the images are gathered from patients’ data using IoT devices and forward to the feature extraction process. Feature extraction is done based on the IoT-collected images using VGG16 and Inception network. Moreover, the signals are garnered from the patient’s data and fed to the decomposition procedure. It is done by Empirical Mode Decomposition (EMD) technique. Then, the optimal feature selection of data, images, and signals is carried out by a hybrid algorithm termed Mating Probability-based Hybrid Strider Glowworm Swarm Optimization (MP-HSGSO). Finally, the Radial Basis Recurrent Neural Network (RBRNN) using MP-HSGSO is suggested to get the detected outcomes. Here, various brain diseases have been detected to classify the disease. This designed RBRNN method is utilized for efficient treatment, and also it provides the appropriate decision-making through processing various kinds of multimodal data. Throughout the simulation outcomes, the accuracy and precision of the offered method achieve 97 % and 96 %. Consequently, the simulation outcome of the offered method is proved that the designed model using a deep learning model shows enriched performance in clinical trial applications.

Hybrid EMD-RF Model for Predicting Annual Rainfall in Kerala, India

Rainfall forecasting is critical for the economy, but it has proven difficult due to the uncertainties, complexities, and interdependencies that exist in climatic systems. An efficient rainfall forecasting model will be beneficial in implementing suitable measures against natural disasters such as floods and landslides. In this paper, a novel hybrid model of empirical mode decomposition (EMD) and random forest (RF) was developed to enhance the accuracy of annual rainfall prediction. The EMD technique was utilized to decompose the rainfall signal into six intrinsic mode functions (IMFs) to extract underlying patterns, while the RF algorithm was employed to make predictions based on the IMFs. The hybrid RF–IMF model was trained and tested using a dataset of annual rainfall in Kerala from 1871 to 2020, and its performance was compared to traditional models such as RF regression and the autoregressive moving average (ARMA) model. Mean absolute error (MAE), mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination or R-squared (R2) were used to compare the performances of these three models. Model evaluation metrics show that the RF–IMF model outperformed both the RF model and ARMA model.

Study of nonlinear characteristics in hollow cathode discharge in unmagnetized plasmas

AbstractParametric study and nonlinear dynamical analysis of the discharge parameter variations are presented here for a hollow cathode source in the glow discharge regime and later in the radio frequency (RF) discharge regime. A plasma source consisting of a semi‐transparent hollow cathode leads to the generation of nonthermal energetic charged particles and the formation of multiple potential regimes in the plasma column during both the glow discharge as well as the RF discharge regimes. The hollow cathode discharge obtained using this single plasma source exhibits significant mixed‐mode oscillations (MMOs) during the glow discharge regime. Various regular and irregular sequences of MMOs evolved during the progressive change of discharge voltage in the glow discharge regime. During the RF power application across the hollow cathode source, the charged particles oscillate and appear in bursts in the time series. The present study investigates energy distribution among the coherent modes of discharge through the application of the Empirical Mode Decomposition technique. Multiple coherent modes are identified during both discharge regimes that carry most of the discharge energy.

Combined empirical mode decomposition and phase space reconstruction based psychologically stressed and non-stressed state classification from cardiac sound signals

The psychological stress and associated mental health conditions causes high socioeconomic impacts on society and the onset of pandemic worsened the situation making it imperative to timely detect psychological stress. This paper presents a novel framework for using a combination of empirical mode decomposition (EMD) and phase space reconstruction (PSR) analysis to capture non-linear, non-stationary dynamics of cardiac sound signals acquired from fifty-four healthy male adults for detecting psychological stress. The time interval among successive S1 peaks of acquired cardiac sound signals is extracted to obtain Interbeat Interval signal used for decomposition to Intrinsic Mode Functions (IMFs) using EMD technique. Thereafter, the feature vectors namely- largest singular value, smallest singular value from two-dimensional PSRs of IMFs and the mean value of Euclidean distance, standard deviation of Euclidean distance from three-dimensional PSRs are extracted to detect psychologically stressed state. The non-parametric Kruskal-Wallis statistical test is applied to select statistically significant features that are fed to Decision Tree, Naïve Bayes and Support Vector Machine classifiers for classifying stressed and non-stressed state with fivefold cross-validation to make it a reliable system. The average accuracy, sensitivity and specificity achieved is 97.14%, 99.8% and 94% respectively using SVM and Radial Basis Function kernel function. The proposed framework performed better on the dataset in comparison to ratio of low-frequency to high-frequency (LF/HF) power parameter of Electrocardiography signal. The use of easy to acquire, cost-effective cardiac sound signals for detecting psychological stress makes proposed framework feasible for rural healthcare centres of developing economies, homecare and telemedicine.

Development of a TVF-EMD-based multi-decomposition technique integrated with Encoder-Decoder-Bidirectional-LSTM for monthly rainfall forecasting

Accurate forecasting of rainfall is extremely important due to its complex nature and enormous impacts on hydrology, floods, droughts, agriculture, and monitoring of pollutant concentration levels. In this study, a new multi-decomposition deep learning-based technique was proposed to forecast monthly rainfall in Himalayan region of India (i.e., Haridwar and Nainital). In the first stage, the original rainfall signals as the individual accessible datasets were decomposed into intrinsic mode decomposition functions (IMFs) through the time-varying filter-based empirical mode decomposition (TVF-EMD) technique, and then the significant lagged values were computed from the decomposed sub-sequences (i.e., IMFs) using the partial autocorrelation function (PACF). In the second stage, the PACF-based decomposed IMFs signals were again decomposed by the Singular Valued Decomposition (SVD) approach to reduce the dimensionality and enhance the forecasting accuracy. The machine learning approaches including the bidirectional long-short term memory reinforced with the Encoder-Decoder Bidirectional (EDBi-LSTM), Adaptive Boosting Regression (Adaboost), Generalized Regression Neural Network (GRNN), and Random Forest (RF) were used to construct the hybrid forecasting models. Also, several statistical metrics i.e., correlation coefficient (R), root mean square error (RMSE), and Nash-Sutcliffe efficiency (NSE) and graphical interpretation tools were employed to evaluate the hybrid (TVF-EMD-SVD-RF, TVF-EMD-SVD-EDBi-LSTM, TVF-EMD-SVD-Adaboost, and TVF-EMD-SVD-GRNN) and standalone counterpart (EDBi-LSTM, Adaboost, RF, and GRNN) models. The outcomes of monthly rainfall forecasting ascertain that the TVF-EMD-SVD-EDBi-LSTM in the Haridwar (R = 0.5870, RMSE = 118.4782 mm, and NSE = 0.3116) and Nainital (R = 0.9698, RMSE = 44.3963 mm, NSE = 0.9388) outperformed the benchmarking models.

A novel time-of-flight auto-diagnosis model for ultrasonic signal using a sliding window technique based on empirical mode decomposition

Ultrasonic signal analysis is an essential technique used in various fields, including non-destructive testing (NDT), structural health monitoring, and medical diagnosis. Accurate auto-diagnosis of Time-of-Flight (ToF) for ultrasonic signal is crucial in defects locating, ultrasound imaging, etc. However, requisite signal extraction for ToF estimation was interfered with noise in time and frequency domain, and there is a tendency to be interrupted by spikes in ultrasound signal envelope for wave crest auto-location. In this study, a novel auto-diagnosis model for ultrasonic signals is proposed based on the empirical mode decomposition (EMD) technique and sliding window approach. The model utilizes EMD method to extract intrinsic mode functions (IMFs) and obtain time-frequency representations of the signal. Then the ultrasound signal envelope is extracted by Hilbert transform. The sliding window technique is employed to monitor the time-varying characteristics of the ultrasonic signal and capture the changes in ToF. Spline interpolation algorithm was embedded in the sliding window technique for reducing ToF error and computer burden. The proposed model can automatically estimate ToF for the ultrasonic signal without professional assistance. The performance of the proposed model is evaluated using steel plates with a thickness of 10mm and pipelines with a wall thickness of 7mm. The results demonstrate high accuracy in detecting signal abnormalities, with maximum errors of 0.25% and 1.25% for steel plates and pipelines under constant pressure, respectively. The proposed approach holds promising potential for fully automated applications in various fields in the future, such as structural health monitoring and medical diagnosis.