scholarly journals Primary Signal Phase Suppression Technique Based On Mutual Information Improved Variational Mode Decomposition

2021 ◽  
Author(s):  
Jiaying Yue ◽  
Sheng Wang ◽  
Pinpin Lyu
Keyword(s):  
Mutual Information ◽  
Recognition Rate ◽  
High Energy ◽  
Individual Identification ◽  
Support Vector ◽  
Signal Phase ◽  
Mode Decomposition ◽  
Primary Signal ◽  
Suppression Technique ◽  
Variable Mode

Abstract Specific emitter identification (SEI) identifies targets mainly by unintentional modulation of the signal. However, due to the high energy of the primary signal, once the primary signal changes, the recognition becomes less effective or even impossible using a feature database that is not updated. In this paper, we propose to use a mutual information improved variable mode decomposition (VMD) algorithm to suppress the primary signal phase of the transmitter. Furthermore, we simulate the feature extraction of the unintentional phase modulation of the transmitter signal and use support vector machine (SVM) for individual identification. The simulation results show that the algorithm improves the recognition rate by about 6% (0 dB) compared to the retained primary signal. The results demonstrate that our proposed phase suppression technique improves the adaptability and accuracy of individual identification of transmitters.

scholarly journals Feature Extraction of Ship-Radiated Noise Based on Regenerated Phase-Shifted Sinusoid-Assisted EMD, Mutual Information, and Differential Symbolic Entropy

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 176 ◽  
Author(s):  
Guohui Li ◽  
Zhichao Yang ◽  
Hong Yang
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Mutual Information ◽  
Empirical Mode Decomposition ◽  
Recognition Rate ◽  
Support Vector ◽  
Svm Classifier ◽  
Radiated Noise ◽  
Mode Decomposition ◽  
Mixing Problem

To improve the recognition accuracy of ship-radiated noise, a feature extraction method based on regenerated phase-shifted sinusoid-assisted empirical mode decomposition (RPSEMD), mutual information (MI), and differential symbolic entropy (DSE) is proposed in this paper. RPSEMD is an improved empirical mode decomposition (EMD) that alleviates the mode mixing problem of EMD. DSE is a new tool to quantify the complexity of nonlinear time series. It not only has high computational efficiency, but also can measure the nonlinear complexity of short time series. Firstly, the ship-radiated noise is decomposed into a series of intrinsic mode functions (IMFs) by RPSEMD, and the DSE of each IMF is calculated. Then, the MI between each IMF and the original signal is calculated; the sum of MIs is taken as the denominator; and each normalized MI (norMI) is obtained. Finally, each norMI is used as the weight coefficient to weight the corresponding DSE, and the weighted DSE (WDSE) is obtained. The WDSEs are sent into the support vector machine (SVM) classifier to classify and recognize three types of ship-radiated noise. The experimental results demonstrate that the recognition rate of the proposed method reaches 98.3333%. Consequently, the proposed WDSE method can effectively achieve the classification of ships.

scholarly journals Multi-Stage Feature Extraction and Classification for Ship-Radiated Noise

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 112
Author(s):  
Hamada Esmaiel ◽  
Dongri Xie ◽  
Zeyad A. H. Qasem ◽  
Haixin Sun ◽  
Jie Qi ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Recognition Rate ◽  
Extraction Methods ◽  
Permutation Entropy ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Radiated Noise ◽  
Passive Sonar ◽  
Multi Stage ◽  
Mode Decomposition

Due to the complexity and unique features of the hydroacoustic channel, ship-radiated noise (SRN) detected using a passive sonar tends mostly to distort. SRN feature extraction has been proposed to improve the detected passive sonar signal. Unfortunately, the current methods used in SRN feature extraction have many shortcomings. Considering this, in this paper we propose a new multi-stage feature extraction approach to enhance the current SRN feature extractions based on enhanced variational mode decomposition (EVMD), weighted permutation entropy (WPE), local tangent space alignment (LTSA), and particle swarm optimization-based support vector machine (PSO-SVM). In the proposed method, first, we enhance the decomposition operation of the conventional VMD by decomposing the SRN signal into a finite group of intrinsic mode functions (IMFs) and then calculate the WPE of each IMF. Then, the high-dimensional features obtained are reduced to two-dimensional ones by using the LTSA method. Finally, the feature vectors are fed into the PSO-SVM multi-class classifier to realize the classification of different types of SRN sample. The simulation and experimental results demonstrate that the recognition rate of the proposed method overcomes the conventional SRN feature extraction methods, and it has a recognition rate of up to 96.6667%.

scholarly journals Feature Extraction of Ship-Radiated Noise Based on Enhanced Variational Mode Decomposition, Normalized Correlation Coefficient and Permutation Entropy

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 468 ◽  
Author(s):  
Dongri Xie ◽  
Hamada Esmaiel ◽  
Haixin Sun ◽  
Jie Qi ◽  
Zeyad A. H. Qasem
Keyword(s):  
Feature Extraction ◽  
Correlation Coefficient ◽  
Extraction Method ◽  
Recognition Rate ◽  
Permutation Entropy ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Feature Extraction Method ◽  
Radiated Noise ◽  
Mode Decomposition

Due to the complexity and variability of underwater acoustic channels, ship-radiated noise (SRN) detected using the passive sonar is prone to be distorted. The entropy-based feature extraction method can improve this situation, to some extent. However, it is impractical to directly extract the entropy feature for the detected SRN signals. In addition, the existing conventional methods have a lack of suitable de-noising processing under the presence of marine environmental noise. To this end, this paper proposes a novel feature extraction method based on enhanced variational mode decomposition (EVMD), normalized correlation coefficient (norCC), permutation entropy (PE), and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, EVMD is utilized to obtain a group of intrinsic mode functions (IMFs) from the SRN signals. The noise-dominant IMFs are then eliminated by a de-noising processing prior to PE calculation. Next, the correlation coefficient between each signal-dominant IMF and the raw signal and PE of each signal-dominant IMF are calculated, respectively. After this, the norCC is used to weigh the corresponding PE and the sum of these weighted PE is considered as the final feature parameter. Finally, the feature vectors are fed into the PSO-SVM multi-class classifier to classify the SRN samples. The experimental results demonstrate that the recognition rate of the proposed methodology is up to 100%, which is much higher than the currently existing methods. Hence, the method proposed in this paper is more suitable for the feature extraction of SRN signals.

Individual Identification Method for Group-Housed Pigs Based on Optimal Feature Extraction

2014 ◽  
Vol 614 ◽  
pp. 436-439 ◽  
Author(s):  
Wei Xing Zhu ◽  
Jia Li Chen ◽  
Yi Zheng Guo
Keyword(s):  
Recognition Rate ◽  
Gaussian Mixture ◽  
Morphological Processing ◽  
Individual Identification ◽  
Practical Significance ◽  
Locally Linear Embedding ◽  
Support Vector ◽  
Hole Filling ◽  
Set Up ◽  
Prediction Mechanism

In order to reduce the workload of farmers on individual identification of pigs, reduce interference to pigs, and raise automation and intelligent level of farms, a Matlab-based automatic classification system is set up. Firstly, top view video sequences are captured for group-housed pigs from pig barn, and the adaptive Gaussian mixture model based on prediction mechanism is used to detect the foreground objects. Then with morphological processing and hole filling, the targets are extracted as completely as possible from the background. Secondly, after building the sample image databases for each pig, color, texture and shape features are extracted and combined as the feature vector. Next, LLE (Locally Linear Embedding) algorithm is adopted to reduce the data dimension. Finally, hybrid kernel SVM (Support Vector Machine) classifier is designed to identify the pigs in test frames. Experimental results show that the recognition rate of pigs is 95.2%. For improving the recognition rate, decreasing cost and interference to pigs, the above method is proposed. Moreover, it can lay a foundation for future researches on group-housed pigs in behaviors, emotions, breathings and so on, which has good practical significance.

scholarly journals Optimized Variational Mode Decomposition and Permutation Entropy with Their Application in Feature Extraction of Ship-Radiated Noise

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 503
Author(s):  
Dongri Xie ◽  
Shaohua Hong ◽  
Chaojun Yao
Keyword(s):  
Feature Extraction ◽  
Recognition Rate ◽  
Permutation Entropy ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Marine Animals ◽  
Practical Applications ◽  
Radiated Noise ◽  
Mode Decomposition

The complex and changeable marine environment surrounded by a variety of noise, including sounds of marine animals, industrial noise, traffic noise and the noise formed by molecular movement, not only interferes with the normal life of residents near the port, but also exerts a significant influence on feature extraction of ship-radiated noise (S-RN). In this paper, a novel feature extraction technique for S-RN signals based on optimized variational mode decomposition (OVMD), permutation entropy (PE), and normalized Spearman correlation coefficient (NSCC) is proposed. Firstly, with the mode number determined by reverse weighted permutation entropy (RWPE), OVMD decomposes the target signal into a set of intrinsic mode functions (IMFs). The PE of all the IMFs and SCC between each IMF with the raw signal are then calculated, respectively. Subsequently, feature parameters are extracted through the sum of PE weighted by NSCC for the IMFs. Lastly, the obtained feature vectors are input into the support vector machine multi-class classifier (SVM) to discriminate various types of ships. Experimental results indicate that five kinds of S-RN samples can be accurately identified with a recognition rate of 94% by the proposed scheme, which is higher than other previously published methods. Hence, the proposed method is more advantageous in practical applications.

The Extraction of Differential MFCC Based on EMD

2013 ◽  
Vol 313-314 ◽  
pp. 1167-1170 ◽  
Author(s):  
Yun Yun Chu ◽  
Wei Hua Xiong ◽  
Wei Wei Shi ◽  
Yu Liu
Keyword(s):  
Feature Extraction ◽  
Recognition Rate ◽  
Measured Data ◽  
Support Vector ◽  
Emotional Speech ◽  
Characteristic Parameters ◽  
Order Difference ◽  
First Order ◽  
Mode Decomposition ◽  
Original Speech

Feature extraction is the key to the object recognition. How to obtain effective, reliable characteristic parameters from the limited measured data is a question of great importance in feature extraction. This paper presents a method based on Empirical Mode Decomposition (EMD) for the extraction of Mel Frequency Cepstrum Coefficients (MFCCs) and its first order difference from original speech signals that contain four kinds of emotions such as anger, happiness, surprise and natural for emotion recognition. And the experiments compare the recognition rate of MFCC, differential MFCC (Both of them are extracted based on EMD) or their combination through using Support Vector Machine (SVM) to recognize speakers' emotional speech identity. It proves that the combination of MFCC and its first order difference has a highest recognition rate.

MULTISCALE ENTROPY ALGORITHMS AND THEIR APPLICATIONS IN CARDIAC DISEASES DISCRIMINATION

2020 ◽  
Vol 20 (08) ◽  
pp. 2050052
Author(s):  
XIANGKUI WAN ◽  
BINRU ZHU ◽  
ZHIYAO JIN ◽  
MINGRUI ZHANG ◽  
YAN LI
Keyword(s):  
Cardiovascular Diseases ◽  
Recognition Rate ◽  
Entropy Method ◽  
Multiscale Entropy ◽  
Support Vector ◽  
Ecg Signals ◽  
The People ◽  
Mode Decomposition ◽  
The Difference ◽  
Ecg Data

In recent years, the number of cardiac disease patients has been increasing. Modern medical research has shown that the complexity of electrocardiogram (ECG) signals is related to cardiovascular diseases. This paper investigates the difference in complexity of ECG data from the people with different cardiovascular diseases, such as atrial fibrillation (AF), ventricular arrhythmia (VA) and congestive heart failure (CHF). The empirical mode decomposition (EMD) and multiscale entropy method are used to analyze the ECG data, and a mathematical model established by a support vector machine is used to identify different diseases. The accuracy recognition rate of the AF recognition is 96.25%, and that of the CHF and VA reach 90.26% and 92.20%, respectively. The experimental results show that the recognition method proposed in this paper is successful.

scholarly journals Individual Identification of Radar Emitters Based on a One-Dimensional LeNet Neural Network

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1215
Author(s):  
Yue Chen ◽  
Zi-Long Wu ◽  
Ying-Ke Lei
Keyword(s):  
Neural Network ◽  
Dimensionality Reduction ◽  
Recognition Rate ◽  
Pulse Signal ◽  
Individual Identification ◽  
One Dimensional ◽  
Mode Decomposition ◽  
Radio Signals ◽  
The Individual ◽  
The Military

Specific emitter identification involves extracting the fingerprint features that represent the individual differences of the emitter through processing the received signals. By identifying the extracted fingerprint features, one can also identify the emitter to which the received signals belong. Due to differences in transmitter hardware, this fingerprint cannot be duplicated. Therefore, SEI plays an important role in the field of information security and can reduce the information leakages caused by key theft. This method can also be used in the military field to support communication countermeasures via emitter individual identification. In this paper, empirical mode decomposition is carried out for each radar pulse signal, and then the bispectral features are extracted. Dimensionality reduction is carried out according to the symmetry of the bispectral features. The features after dimensionality reduction are input into a one-dimensional LeNet neural network as the fingerprint features of the emitter, and the identification of 10 radar emitter sources is completed. Based on the verification of real signals, the SEI identification strategy in this paper achieved a recognition rate of 96.4% for 10 radar signals, 98.9% for 10 data emitter signals, and 88.93% for 5 communication radio signals.

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