scholarly journals Chaotic signals denoising using empirical mode decomposition inspired by multivariate denoising

2020 ◽  
Vol 10 (2) ◽  
pp. 1352
Author(s):  
Fadhil Sahib Hasan
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Chaotic Signal ◽  
Intrinsic Mode Functions ◽  
Signal To Noise ◽  
Mode Decomposition ◽  
Chaotic Signals ◽  
Different Types ◽  
Multivariate Signals

Empirical mode decomposition (EMD) is an effective noise reduction method to enhance the noisy chaotic signal over additive noise. In this paper, the intrinsic mode functions (IMFs) generated by EMD are thresholded using multivariate denoising. Multivariate denoising is multivariable denosing algorithm that is combined wavelet transform and principal component analysis to denoise multivariate signals in adaptive way. The proposed method is compared at a various signal to noise ratios (SNRs) with different techniques and different types of noise. Also, scale dependent Lyapunov exponent (SDLE) is used to test the behavior of the denoised chaotic signal comparing with clean signal. The results show that EMD-MD method has the best root mean square error (RMSE) and signal to noise ratio gain (SNRG) comparing with the conventional methods.

scholarly journals Empirical Mode Decomposition in Discrete Time Signals Denoising

2019 ◽  
Vol 16 (1) ◽  
pp. 10-13 ◽  
Author(s):  
Zoltán Germán-Salló
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal To Noise Ratio ◽  
Main Idea ◽  
Discrete Wavelet ◽  
Intrinsic Mode Functions ◽  
Nonlinear Signal Processing ◽  
Mode Decomposition ◽  
Time Signals ◽  
Nonlinear Signal ◽  
Filtering Procedure

Abstract This study explores the data-driven properties of the empirical mode decomposition (EMD) for signal denoising. EMD is an acknowledged procedure which has been widely used for non-stationary and nonlinear signal processing. The main idea of the EMD method is to decompose the analyzed signal into components without using expansion functions. This is a signal dependent representation and provides intrinsic mode functions (IMFs) as components. These are analyzed, through their Hurst exponent and if they are found being noisy components they will be partially or integrally eliminated. This study presents an EMD decomposition-based filtering procedure applied to test signals, the results are evaluated through signal to noise ratio (SNR) and mean square error (MSE). The obtained results are compared with discrete wavelet transform based filtering results.

Distributed Optical Fiber Raman Signal Noise Cancellation Based on Empirical Mode Decomposition

2012 ◽  
Vol 198-199 ◽  
pp. 1621-1626 ◽  
Author(s):  
You Ping Zhong ◽  
Qi Zhang ◽  
Di Zhou
Keyword(s):  
Optical Fiber ◽  
Empirical Mode Decomposition ◽  
Signal To Noise Ratio ◽  
Noise Cancellation ◽  
Raman Signal ◽  
Signal To Noise ◽  
Mode Decomposition ◽  
Improve Measurement ◽  
Distributed Optical Fiber ◽  
Anti Stokes

The distributed optical fiber Raman sensor system was widely used for real-time measurement temperature, but the Anti-Stokes and Stokes scattering Raman signal is very weak. In order to improve measurement accuracy the signal must be denoised before obtaining the temperature. In this paper, a new noise cancellation of empirical mode decomposition is proposed for enhancing signal-to-noise ration of the Anti-Stokes and Stokes scattering Raman signal. The signal-to-noise ratio was enhanced by using this method and it is easy to be realized in computer.

scholarly journals Extraction method of weak underwater acoustic signal based on the combination of wavelet transform and empirical mode decomposition

2021 ◽  
Vol 12 ◽  
pp. 7
Author(s):  
Junbing Shi ◽  
Yingmin Wang ◽  
Xiaoyong Zhang ◽  
Libo Yang
Keyword(s):  
Wavelet Transform ◽  
Empirical Mode Decomposition ◽  
Signal To Noise Ratio ◽  
Intrinsic Mode Functions ◽  
Threshold Method ◽  
Underwater Acoustic ◽  
Baseline Drift ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Combined Algorithm

When studying underwater acoustic exploration, tracking and positioning, the target signals collected by hydrophones are often submerged in strong intermittent noise and environmental noise. In this paper, an algorithm that combines empirical mode decomposition and wavelet transform is proposed to achieve the efficient extraction of target signals in the environment with strong noise. First the calibration of baseline drift is performed on the algorithm, and then it is decomposed into different intrinsic mode functions via empirical mode. The wavelet threshold processing is conducted according to the correlation coefficient of each mode component and the original signal, and finally the signals are reconstructed. The simulation and experiment results show that compared with the conventional empirical mode decomposition method and wavelet threshold method, when the signal-to-noise ratio is low and there exist high-frequency intermittent jamming and baseline drift, the combined algorithm can better extract the target signal, laying the foundation for direction-of-arrival estimation and target positioning in the next step.

The application of threshold empirical mode decomposition de-noising algorithm for battlefield ambient noise

2018 ◽  
Vol 09 (04) ◽  
pp. 1850027
Author(s):  
Shaocheng Zhu ◽  
Limin Liu ◽  
Zhigang Yao
Keyword(s):  
Empirical Mode Decomposition ◽  
Acoustic Signal ◽  
Ambient Noise ◽  
Signal To Noise Ratio ◽  
Intrinsic Mode Functions ◽  
Noisy Signal ◽  
Mode Decomposition ◽  
Smoothness Index ◽  
Acoustic Target ◽  
Low Altitude

The detection of the low-altitude acoustic target is an important way to compensate for the weakness of radar. Removing the noise mixed in acoustic signal as much as possible to retain the useful information is a challenging task. Inspired by the wavelet threshold, the de-noising method for low-altitude battlefield acoustic signal based on threshold empirical mode decomposition (EMD-T) is proposed in this paper. Firstly, the noisy signal is decomposed by empirical mode decomposition (EMD) to get the intrinsic mode functions (IMFs). Then the IMFs, whose actual energy exceeds its estimated energy, are processed by the EMD threshold. Finally, the processed IMFs are summed to reconstruct the de-noised signal. To evaluate the performance of the proposed method, extensive simulations are performed using helicopter sound corrupted with four types of typical low-altitude ambient noise under different signal-to-noise ratio (SNR) input values. The performance is evaluated in terms of SNR, root mean square error (RMSE) and smoothness index (SI). The simulations results reveal that the proposed method de-noising method has the perspective of the highest SNR, smallest RMSE and SI in de-noising low-altitude ambient noise compared to other methods, including the wavelet transform (WT) and conventional EMD.

scholarly journals Empirical Mode Decomposition De-Noising Method Based on Improved Soft-Threshold

2011 ◽  
Vol 1 ◽  
pp. 421-425 ◽  
Author(s):  
Jian Hui Xi ◽  
Jia Chen
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal To Noise Ratio ◽  
Adaptive Threshold ◽  
Threshold Function ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Soft Threshold ◽  
Simulation Results ◽  
Improved Function ◽  
Constant Deviation

In this paper, an improved soft-threshold function is constructed, combined the improved function and empirical mode decomposition (EMD) methods, a new de-noising method has been proposed. Set the adaptive threshold for the intrinsic mode functions (IMFs) of the EMD, and then de-noise the each IMF respectively. Finally, the de-noised signal is reconstructed by the de-noised IMF components. Through the simulation results of quantitative analysis by signal-to-noise ratio (SNR) and mean square error (MSE), the algorithm in this paper has better de-noising effect. Also, this method can effectively improve the constant deviation between the original signal and the de-noised signal by traditional soft-threshold.

scholarly journals Ultrasonic Flaw Echo Enhancement Based on Empirical Mode Decomposition

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 236 ◽  
Author(s):  
Wei Feng ◽  
Xiaojun Zhou ◽  
Xiang Zeng ◽  
Chenlong Yang
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal To Noise Ratio ◽  
Electronic Noise ◽  
Intrinsic Mode Functions ◽  
Simulated Signal ◽  
Mode Decomposition ◽  
Ultrasonic Flaw ◽  
Otsu’S Method ◽  
Different Depths ◽  
Backscattered Signals

The detection of flaw echoes in backscattered signals in ultrasonic nondestructive testing can be challenging due to the existence of backscattering noise and electronic noise. In this article, an empirical mode decomposition (EMD) methodology is proposed for flaw echo enhancement. The backscattered signal was first decomposed into several intrinsic mode functions (IMFs) using EMD or ensemble EMD (EEMD). The sample entropies (SampEn) of all IMFs were used to select the relevant modes. Otsu’s method was used for interval thresholding of the first relevant mode, and a window was used to separate the flaw echoes in the relevant modes. The flaw echo was reconstructed by adding the residue and the separated flaw echoes. The established methodology was successfully employed for simulated signal and experimental signal processing. For the simulated signals, an improvement of 9.42 dB in the signal-to-noise ratio (SNR) and an improvement of 0.0099 in the modified correlation coefficient (MCC) were achieved. For experimental signals obtained from two cracks at different depths, the flaw echoes were also significantly enhanced.

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