RF Fingerprint Extraction Method Based on CEEMDAN and Multidomain Joint Entropy

Radio frequency (RF) fingerprinting technology has been developed as a unique method for maintaining security based on physical layer characteristics. In this paper, we propose the RF fingerprinting by extracting the parameter characteristics such as information dimension, constellation feature, and phase noise spectrum in the transmitted information when applied to the universal software radio peripheral (USRP) software-defined radio (SDR) platform. To achieve a great performance improvement of classification, not only the traditional support vector machine (SVM) classifier, but also the machine-based integrated classifier bagged tree and the adaptive weighting algorithm weighted k-nearest neighbor (KNN) are both discussed. It is demonstrated that the proposed method achieves good classification performance under different signal-to-noise ratios (SNR).

Spatiotemporal Characteristics of Precipitation and Drought Variability in Taiwan Using Multi-dimensional Complementary Ensemble Empirical Mode Decomposition AbstractMost time series observed in natural systems are nonlinear and nonstationary, particularly under the influence of climate change. Taiwan has experienced increasingly frequent drought events in recent decades. Droughts are characterized by their gradual development, cumulative impacts, and lack of clear early warning signals, which makes their detection and analysis challenging. To address these issues, this study applies Multi-dimensional Complementary Ensemble Empirical Mode Decomposition (MCEEMD) to analyze long-term temperature and precipitation data in Taiwan from 1960 to 2023. MCEEMD is an effective time–frequency analysis method designed for nonlinear and nonstationary time series. It enables the decomposition of multi-dimensional signals into a set of Intrinsic Mode Functions (IMFs), allowing both spatial and temporal characteristics of climate variables to be examined. Through these IMFs, meaningful instantaneous frequencies and long-term trends in the signals can be identified. This study considers both stochastic and deterministic influences by reconstructing the IMFs into two components based on their autocorrelation coefficients. The relationships between temperature, precipitation variability, and drought-related characteristics are then examined, providing insights into the spatiotemporal behavior of drought events in Taiwan. Key word: Multi-dimensional Complementary Ensemble Empirical Mode Decomposition(MCEEMD ),Intrinsic Mode Functions (IMFs)

Frequency-modulated continuous-wave (FMCW) radar is used to extract range and velocity information from the beat signal. However, the traditional joint range-velocity estimation algorithms often experience significant performances degradation under low signal-to-noise ratio (SNR) conditions. To address this issue, this paper proposes a novel approach utilizing the complementary ensemble empirical mode decomposition (CEEMD) combined with singular value decomposition (SVD) to reconstruct the beat signal prior to applying the FFT-Root-MUSIC algorithm for joint range and velocity estimation. This results in a novel joint range-velocity estimation algorithm termed as the CEEMD-SVD-FFT-Root-MUSIC (CEEMD-SVD-FRM) algorithm. First, the beat signal contaminated with additive white Gaussian noise is decomposed using CEEMD, and an appropriate autocorrelation coefficient threshold is determined to select the highly correlated intrinsic mode functions (IMFs). Then, the SVD is applied to the selected highly correlated IMFs for denoising the beat signal. Subsequently, the denoised IMFs and signal residuals are combined to reconstruct the beat signal. Finally, the FFT-Root-MUSIC algorithm is applied to the reconstructed beat signal to estimate both the range and Doppler frequencies, which are then used to calculate the range and velocity estimates of the targets. The proposed CEEMD-SVD-FRM algorithm is validated though simulations and experiments, demonstrating significant improvement in the robustness and accuracy of range and velocity estimates for the FMCW radar due to the effective denoising of the reconstructed beat signal. Moreover, it substantially outperforms the traditional methods in low SNR environments.

The magnetic flux leakage (MFL) evaluation is often used for the overhauling of oil extracting operation in the oil field to realize the real-time damage assessment of the pipeline. Since the MFL signal is affected by various noise sources in the field, this paper introduces the complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN). On the basis of this, a particle swarm optimization wavelet threshold (PSO-WT) method is proposed, and the signal reconstruction option is improved to extract the leakage magnetic flux signal of tubing defects. First, CEEMDAN is used to add pairs of positive and negative white noise to the MFL signal, and then the signal is decomposed into several intrinsic mode functions (IMFs). Second, the correlation coefficient selection limit is defined. Taking into account the characteristics of the decomposed signal, the useless IMFs and useful IMFs are selected from the IMF components, where some of the useful IMF components contain less noise. Third, the PSO-WT algorithm is combined to further filter the noisy and useful IMF components. Finally, the filtered IMF components and the pure useful IMF components are selected to reconstruct the signal. In the experiment, the ensemble empirical mode decomposition (EEMD) method and CEEMDAN are used to decompose the noisy MFL signals ensemble in the field. The MFL signal is reconstructed under the correlation coefficient selection. It can be seen from the comparison of EEMD that the MFL signal is reconstructed under the same conditions after CEEMDAN decomposition, and its signal-to-noise ratio is increased by 8%. At the same time, after CEEMDAN decomposition, the selected noisy useful IMFs are further filtered by the wavelet threshold (WT) method and the PSO-WT method. Also, it indicates that the reconstructed signal processed by PSO-WT is 17% higher than the reconstructed signal after WT processing.

In order to solve the noise reduction problem of acoustic emission signals with cracks, a method combining Complementary Ensemble Empirical Mode Decomposition (CEEMD) and wavelet packet (WPT) is proposed and named CEEMD-WPT. Firstly, the single Empirical Mode Decomposition (EMD) used in the traditional CEEMD is improved into the WPT-EMD with a more stable noise reduction effect. Secondly, after decomposition, the threshold value of the correlation coefficient is determined for the Intrinsic Mode Function (IMF), and the low correlation component is further processed by WPT. In addition, in order to solve the problem that it is difficult to quantify the real signal noise reduction effect, a new quantization index “principal interval coefficient (PIC)” is designed in this paper, and its reliability is verified through simulation experiments. Finally, noise reduction experiments are carried out on the real crack acoustic emission dataset consisting of tensile, shear, and mixed signals. The results show that CEEMD-WPT has the highest number of signals with a principal interval coefficient of 0–0.2, which has a better noise reduction effect compared with traditional CEEMD and Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). Moreover, the statistical variance of CEEMD-WPT is evidently one order of magnitude smaller than that of CEEMD, so it has stronger stability.

Aiming at the difficulty of accurate prediction due to the randomness and nonstationary nature of blood glucose concentration series, a blood glucose concentration prediction model based on complementary ensemble empirical mode decomposition (CEEMD) and least squares support vector machine (LSSVM) is proposed. Firstly, CEEMD is used to convert the blood glucose concentration sequence into a series of intrinsic mode functions (IMFs) to reduce the impact of randomness and nonstationary signals on prediction performance. Then, a LSSVM prediction model is established for each mode IMF. The comprehensive learning particle swarm optimization (CLPSO) algorithm is used to optimize the kernel parameters of LSSVM. Finally, the prediction results of all IMFs are superimposed to yield the final blood glucose concentration prediction value. The experimental results show that the proposed prediction model has higher prediction accuracy in short-term blood glucose concentration values.

Obtaining the instantaneous frequency of seismic data by Hilbert transform is widely used in reservoir prediction and fluid identification. In order to obtain high accuracy and clear physical significance instantaneous frequency, it is usually required that the analyzed signal is stationary and narrow-band. While practical seismic data is a nonstationary and bandwidth signal, directly applying Hilbert transform to seismic data to obtain instantaneous frequency will lack physical significance or even distort. Complementary Ensemble Empirical Mode Decomposition (CEEMD) can adaptively decompose a complex signal into a limited number of Intrinsic Mode Function (IMF), which is stationary, narrow-band and contains the local characteristics information of the original signal. Therefore, applying Hilbert transform to IMF can obtain high accuracy and clear physical significance instantaneous frequency. The amplitude and frequency attributes of seismic data will change abnormally after the reservoir is oil or gas bearing. Extracting the corresponding abnormal change can be used as an effective indicator to identify hydrocarbon information. In this article, the Hilbert Huang transform based on CEEMD is applied to hydrocarbon detection. First, the seismic data is processed by CEEMD to obtain IMF components. Then the IMF component which can reflect the local anomaly changes caused by hydrocarbon is selected. Finally, the instantaneous frequency of the IMF component related to hydrocarbon is obtained by Hilbert transform, and take it as the sign of hydrocarbon detection. Note: This paper was accepted into the Technical Program but was not presented at IMAGE 2021 in Denver, Colorado.

Rotating devices such as gears often generate fault vibration signals during eration. Under the interference of strong external noise, it is often difficult to extract these weak fault feature. To this end, a method based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Singular Spectrum Analysis (SSA) is proposed to reduce the noise of the gear signals. Firstly, original vibration signals are decomposed into a series of intrinsic mode functions (IMFs) by CEEMD, and the reconstructed signals are obtained after the some IMFs are removed according to evaluation coefficients. Then, the reconstructed signals are further denoised using SSA. After singular value decomposition, grouping and reconstruction, the denoised time series are obtained. The actual gear vibration signals from the QPZZ-II rotating machinery fault simulation rig are used to verify the effectiveness of the proposed method. The results of comparing with the different denoising methods show that the presented method can effectively depress the strong noise, and extract weak fault feature.

In recent years, due to the non-stationary behavior of data samples, modeling and forecasting the stock price has been challenging for the business community and researchers. In order to address these mentioned issues, enhanced machine learning algorithms can be employed to establish stock forecasting algorithms. Accordingly, introducing the idea of “decomposition and ensemble” and the theory of “granular computing”, a hybrid model in this paper is established by incorporating the complementary ensemble empirical mode decomposition (CEEMD), sample entropy (SE), independent component analysis (ICA), particle swarm optimization (PSO), and long short-term memory (LSTM). First, aiming at reducing the complexity of the original data of stock price, the CEEMD approach decomposes the data into different intrinsic mode functions (IMFs). To alleviate the cumulative error of IMFs, SE is performed to restructure the IMFs. Second, the ICA technique separates IMFs, describing the internal foundation structure. Finally, the LSTM model is adopted for forecasting the stock price results, in which the LSTM hyperparameters are optimized by synchronously utilizing the PSO algorithm. The experimental results on four stock prices from China stock market reveal the accuracy and robustness of the established model from the aspect of statistical efficiency measures. In theory, a useful attempt is made by integrating the idea of “granular computing” with “decomposition and ensemble” to construct the forecasting model of non-stationary data. In practice, the research results will provide scientific reference for the business community and researchers.

A frequency-domain cepstrum correlation and temporal transient filtered radio frequency fingerprints (RFF) blind extraction method is proposed for long term evolution uplink device authentication in this work. With the merits of the constant-modulus nature of the Zadoff-Chu (ZC) sequence and the cyclic prefix (CP) structure of the physical random access channel (PRACH) frame, the device RFF integrated in both the frequency-domain ZC sequence and the transient ramp-on/ramp-off sequences are extracted. For the frequency-domain RFF extraction, the cepstrum of the ZC sequence is calculated to enlarge the gap between the constant RFF and the random fading effects, and the correlation between two cepstrum fragments is then calculated to mitigate the random fading components. For the temporal RFF extraction, the ramp-on and ramp-off features induced by the device-specific power amplifier are extracted by exploiting the CP structure of the PRACH frame, where the CP part and the duplicate part are adaptively filtered and the final adapted weight of the least mean square filter is used as RFF. No prior PRACH parameter knowledge is required in RFF extraction and both the frequency-domain and the temporal RFF are combined as the input of the convolutional neural network to enhance the classification accuracy. With four-months sampling of PRACH signals emitted from universal software radio peripheral devices, the experimental results show that the authentication accuracy of the proposed combined RFF can reach as high as 99.9% in the line-of-sight (LOS) scenario and 99.6% in the non-LOS scenario.

To solve the problem that the neural network-based specific emitter identification (SEI) method is limited by the training scene and has poor generalization ability in the complex scene, an SEI method based on deep adversarial domain adaptation (DADA) is proposed. DADA integrates deep neural networks and adversarial methods into the domain adaptation problem of transfer learning. It trains the feature extractor by maximizing the domain discrimination loss and transfers the high-quality deep radio frequency fingerprint (RFF) features extracted under weak interference conditions to the unlabeled emitter of the same class affected by the channel and noise. At the same time, the loss of the sample classifier is minimized, and the deep RFF features of unlabeled emitters can be accurately classified and identified. By repeating the above-mentioned adversarial process, the network realizes unsupervised transfer learning based on deep RFF features. The analysis of the identification results of 20 CC2530 devices under the Rayleigh channel and different signal-to-noise ratio (SNR) conditions proves that DADA can effectively improve the network's identification performance for unlabeled emitters under different conditions and has good performance of adaptability and generalization.

Specific Emitter Identification (SEI) is the technique that identifies the individual radio emitter using the Radio Frequency Fingerprint (RFF), which are originated from the imperfections and differences of transmitters. Previous SEI techniques are sensitive to noise and need enough sampled points. In this paper, a novel SEI approach to extracting fingerprint features of energy envelope of transient signals is proposed. A linear system model is utilized to fit the energy envelope, and the fingerprinting features are constructed by the polynomial coefficients estimated with least-squares algorithm. The results of experiments on actual burst signals demonstrate that the method is effective.

Accurately identifying faults in rolling bearing systems by analyzing vibration signals, which are often nonstationary, is challenging. To address this issue, a new approach based on complementary ensemble empirical mode decomposition (CEEMD) and time series modeling is proposed in this paper. This approach seeks to identify faults appearing in a rolling bearing system using proper autoregressive (AR) model established from the nonstationary vibration signal. First, vibration signals measured from a rolling bearing test system with different defect conditions are decomposed into a set of intrinsic mode functions (IMFs) by means of the CEEMD method. Second, vibration signals are filtered with calculated filtering parameters. Third, the IMF which is closely correlated to the filtered signal is selected according to the correlation coefficient between the filtered signal and each IMF, and then the AR model of the selected IMF is established. Subsequently, the AR model parameters are considered as the input feature vectors, and the hidden Markov model (HMM) is used to identify the fault pattern of a rolling bearing. Experimental study performed on a bearing test system has shown that the presented approach can accurately identify faults in rolling bearings.

Traction machine is the power source of traction elevator, through fault diagnosis of traction machine can effectively reduce the occurrence of elevator safety accidents. In view of the problems of weak fault characteristics of traction machine and the influence of the vibration signal by the transmitting path, which lead to the poor diagnosis effect, a vibration signal acquisition scheme and fault diagnosis method of elevator traction machine based on improved Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Support Vector Machines (SVM) Algorithm are proposed. Firstly, the vibration signal acquisition scheme of traction machine is designed by finite element simulation of vibration response of traction machine by Abaqus. A fault diagnosis method based on improved CEEMD and SVM Algorithm is proposed to solve the problem of weak fault feature and poor fault diagnosis effect of traction machine. The improved CEEMD method introduces the integrated fault factors to calculate the fault feature correlation degree of each IMF component, and sorts them according to the integrated fault factors from large to small, the Intrinsic Mode Functions (IMF) with larger integrated fault factors is selected and its time-frequency characteristic is calculated to construct the feature vector, and then the tractor fault is identified and classified by SVM. Finally, the effect and recognition rate of this method and the unimproved CEEMD method are compared by fault injection test. Experimental results show that the CEEMD method based on integrated fault factors can effectively extract the fault features of the tractor and improve the recognition rate of the tractor fault diagnosis.

Resistance signals of a faulty building electrical system contain a large amount of information about the electrical systems operating status. However, it is difficult to extract the fault features completely because of their characteristics of nonlinearity and non-stationarity which brings a problem of a relatively low fault identification rate of the current fault diagnosis methods based on pattern recognition. Aiming at improving the accuracy of fault diagnosis further, this paper proposes a fault diagnosis method of a building electrical system based on the complementary ensemble empirical mode decomposition and mutual dimensionless index extraction (CEEMD-MDI) combined with the multi-kernel relevance vector machine (MK-RVM). First, the resistance signals of a faulty building electrical system are decomposed into a series of intrinsic mode functions (IMFs) by using an adaptive decomposition ability of the CEEMD. Second, the IMFs are used to extract the MDI and to form a feature vector with the resistance signal. Finally, the processed feature vector is input into the MK-RVM for modeling, and the fault diagnosis result of the building electrical system is provided in the form of a probability output. The experimental results show that the fault diagnosis accuracy rate of the proposed method based on CEEMD-MDI and MK-RVM can reach 97.22%, which has better fault diagnosis performance compared with other methods.