Improved Wavelet Threshold Method Research Articles

Research on Noise Reduction Method of Underwater Acoustic Signal Based on CEEMDAN Decomposition-Improved Wavelet Threshold

Due to the complex noise in the ocean environment, the signal-to-noise ratio of the hydrophone receiving signal is often low, making subsequent signal processing difficult. To solve this problem, this paper proposes using CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) decomposition algorithm combined with an improved wavelet threshold algorithm to process the signal, and obtain the reconstructed signal after denoising. In this method, the noise-containing signal is transformed by the function and decomposed into multiple natural mode components with frequencies ranging from high to low using the CEEMDAN algorithm. The correlation component and the non-correlation component are then determined using the cross-correlation function. The non-correlated compinents are denoised using the improved wavelet threshold method and the denoised signal is obtained by reconstructing the signal. Experimental results show that this method can improve the performance of underwater acoustic signal denoising.

Combined denoising method of acceleration signal during mobile robot collision

Collision safety of mobile robot is one of the important indexes of robot safety evaluation. The acceleration signal of the robot can completely feed-back the state of the collision process, but it is also vulnerable to noise interference such as vibration and impact, which is difficult to be used for direct analysis. In order to solve the problem of mixing acceleration signal with impact noise and periodic vibration noise during robot collision, a combined filtering noise reduction method based on empirical mode decomposition (EMD), improved wavelet threshold method and dynamic sliding window is proposed. And a wavelet adaptive threshold function is constructed. The wavelet coefficients are automatically adjusted using the information of decomposition degree and noise level, and a dynamic adjustment strategy of sliding window length is designed. The acceleration information of robot can be effectively extracted in the process of strong noise collision. The experimental results show that this method has better noise reduction effect than other methods.

Fault transient feature extraction method of 66kV transmission line based on improved wavelet threshold and CEEMDAN-FME

In the 66kV transmission line, the compensation effect of the arc suppression coil, so the amplitude of fault zero sequence current is weak after the occurrence of single-phase grounding responsibility. The fault characteristics are not obvious, and there is environmental noise interference during fault signal acquisition, which further increases the difficulty of ground fault feature extraction. To solve the above problems, a fault transient feature extraction method of 66kV transmission line is proposed, based on improved wavelet threshold and Complete Ensemble Empirical Mode Decomposition with adaptive noise (CEEMDAN) - Fuzzy measure entropy (FME). The characteristics of single-phase grounding transient fault in small current grounding system are analyzed. The transient fault signal was preprocessed by an improved wavelet threshold method, and the transient fault characteristics were extracted accurately by CEEMDAN-FME processing. The proposed method has good anti-noise performance through the simulation comparison and verification of denoising processing and transient feature extraction by MATLAB. It can effectively extract fault features when the interference degree is significant.

Research on weak signal detection method for power system fault based on improved wavelet threshold

Corresponding weak signals will be generated in case of power system failure in order to ensure the safe and stable operation of power system and detect the weak signal in real time, a denoising method based on improved wavelet threshold algorithm is proposed by analyzing the change of weak signal under noise interference. Firstly, the monitoring signal is decomposed by EMD. Secondly, each IMF component is transformed by wavelet transform to eliminate the noise in the wavelet coefficients of each scale. Finally, the denoised signal is obtained through the signal reconstruction mechanism. The experimental results show that the denoising method based on the improved wavelet threshold method not only removes the noise, but also retains more fault information features. Therefore, this method overcomes the interference of noise on signal stability and detects weak signals accurately and effectively. When there is strong noise in the power system, this method can still accurately observe the weak signal, effectively shield the influence of noise on the signal, improve the detection efficiency and accuracy, and is of great significance to ensure the stable operation of the power system.

A new adaptive variable step size natural gradient BSS algorithm

The traditional blind source separation (BSS) algorithm is mainly used to deal with signal separation under the noiseless model, but it does not apply to data with the low signal to noise ratio (SNR). To solve the problem, an adaptive variable step size natural gradient BSS algorithm based on an improved wavelet threshold is proposed in this paper. Firstly, an improved wavelet threshold method is used to reduce the noise of the signal. Secondly, the wavelet coefficient layer with obvious periodicity is denoised using a morphological component analysis (MCA) algorithm, and the processed wavelet coefficients are recombined to obtain the ideal model. Thirdly, the recombined signal is pre-whitened, and a new separation matrix update formula of natural gradient algorithm is constructed by defining a new separation degree estimation function. Finally, the adaptive variable step size natural gradient blind source algorithm is used to separate the noise reduction signal. The results show that the algorithm can not only adaptively adjust the step size according to different signals, but also improve the convergence speed, stability and separation accuracy.

Recognition of Muscle Fatigue Status Based on Improved Wavelet Threshold and CNN-SVM

This study proposed a muscle fatigue classification method based on surface electromyography (sEMG) signals to achieve accurate muscle fatigue detection and classification. A total of 20 healthy young participants (14 men and 6 women) were recruited for fatigue testing on a cycle ergometer, and sEMG signals and oxygen uptake were recorded during the test. First, the measured sEMG signals were denoised with an improved wavelet threshold method. Second, the V-slope method was used to identify the ventilation threshold (VT) to reflect the muscle fatigue state. The time- and frequency-domain features of the sEMG signals were extracted, including root mean square, integrated electromyography, median frequency, mean power frequency, and band spectral entropy. Third, the time- and frequency-domain features of the sEMG signals were labeled either “normal” or “fatigued” based on the VT. Finally, the statistical features of 16 participants were selected as the training data set of the Convolutional Neural Network-Support Vector Machine (CNN-SVM), Support Vector Machine, Convolutional Neural Network, and Particle Swarm Optimization-Support Vector Machine algorithms. In addition, the statistical features of the four remaining participants were used as the test data set to analyze the classification accuracy of the four aforementioned algorithms. Experimental results indicated that the denoising effect of the improved wavelet threshold algorithm proposed in this study was satisfactory. The CNN-SVM algorithm achieved accurate muscle fatigue classification and 80.33%-86.69% classification accuracy.

Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System.

Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias.

Image denoising based on edge detection and prethresholding Wiener filtering of multi-wavelets fusion

In this paper, we describe a method for removing Gaussian noise from digital images, based on edge detection and prethresholding Wiener filtering of multi-wavelets fusion. First, we decompose the noisy image by using multiple wavelets, then the edge of image is detected via wavelet multi-scale edge detection. On this basis, the wavelet coefficients belonging to the edge position are dealt with the improved wavelet threshold method and the others are dealt with the prethresholding Wiener filtering. Finally, we use the fusion algorithm based on wavelet analysis to obtain the denoised image. The experimental results show that this method not only can remove the noise without blurring the edges and the important characteristics of the images effectively, but also can highlight the characteristics of image edge compared with the existing methods. The denoised images have higher peak signal to noise ratio (PSNR) and mean structural similarity (MSSIM), hence the method is of great application value.

Study on MEMS Gyro Signal De-Noising Based on Improved Wavelet Threshold Method

An effective filtering method for MEMS gyro random drift is discussed. On the basis of the fundamentals and defect analysis of wavelet threshold de-noising, a new continuous threshold function at λ is presented, signal concussion and distortion in wavelet hard and soft threshold method is effectively overcome. The improved wavelet de-noising algorithm is applied to MEMS gyro signal filtering experiment and experimental results show that the proposed method can effectively reduce the high frequency noise, restrain the MEMS gyro random drift, increases the SNR and decrease the MSE.

Application of Improved Wavelet Threshold Method in SEMG De-Noising Processing

The surface electromyography signal is often submerged by the noise background while being gathered and recorded. To some extent, the useful signal and noise signal can be separated by applying the wavelet de-noising method effectively to eliminate the noisy signal. However, there are discontinuous points and constant deviations in the traditional wavelet threshold methods. Taking these problems into consideration, the improved threshold method is proposed and based on soft threshold function to use a transition of the nonlinear function to deal with the threshold function and de-noise the SEMG. Experimental results show that the improved threshold method has better de-noising effect and higher signal-noise ratio than the traditional threshold method.

An Approach Based on Wavelet Transform to Remove the Noises of ECG

In the acquisition process of ECG, noise, which mainly consists of power line interference baseline drift and the EMG interference, often exists due to the instrument, the human body and other aspects. This noise mixed with the ECG, will causes ECG distortion, which makes the whole ECG waveform blurred, and impacts the subsequent signal processing and analysis. In this paper, Coif4 wavelet is used to make the ECG decomposed by 8 scale; at the same time, the wavelet decomposition and reconstruction method is used to remove baseline drift, and then the improved wavelet threshold method is used to remove power line interference and the EMG interference waveform to obtain accurate geocentric, providing a more accurate basis for the medical diagnosis.

Image De-Noising in Mixed Noises Based on Wavelet Transform

A method of image de-noising in mixed noises based on wavelet transform is presented. Firstly, 2D multi-scale wavelets transforming the images to get the low-frequency sub-band images and the high-frequency sub-band images. Secondly, de-noising both the low-frequency sub-band images with the improved neighborhood average filters and the high-frequency sub-bands images with the improved wavelet threshold method. Lastly, wavelet refactoring the treated sub-band wavelet coefficients to get the de-noised images. The result shows that, this algorithm not only de-noises the image mixed noise, but also preserves the image edges and details well.