scholarly journals Electrocardiogram Signal Denoising Using Extreme-Point Symmetric Mode Decomposition and Nonlocal Means

Sensors ◽  
2016 ◽  
Vol 16 (10) ◽  
pp. 1584 ◽  
Author(s):  
Xiaoying Tian ◽  
Yongshuai Li ◽  
Huan Zhou ◽  
Xiang Li ◽  
Lisha Chen ◽  
...  
Keyword(s):  
Extreme Point ◽  
Signal Denoising ◽  
Symmetric Mode ◽  
Nonlocal Means ◽  
Mode Decomposition ◽  
Electrocardiogram Signal

scholarly journals A Morphology Filter-Assisted Extreme-Point Symmetric Mode Decomposition (MF-ESMD) Denoising Method for Bridge Dynamic Deflection Based on Ground-Based Microwave Interferometry

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xianglei Liu ◽  
Mengzhuo Jiang ◽  
Ziqi Liu ◽  
Hui Wang
Keyword(s):  
Extreme Point ◽  
Filter Method ◽  
Signal Denoising ◽  
Symmetric Mode ◽  
Intrinsic Mode Functions ◽  
Important Indicator ◽  
Mode Decomposition ◽  
Reconstructed Signal ◽  
Microwave Interferometry ◽  
Morphology Filter

Bridge dynamic deflection is an important indicator of structure safety detection. Ground-based microwave interferometry is widely used in bridge dynamic deflection monitoring because it has the advantages of noncontact measurement and high precision. However, due to the influences of various factors, there are many noises in the obtained dynamic deflection of bridges obtained by ground-based microwave interferometry. To reduce the impacts of noise for bridge dynamic deflection obtained with ground-based microwave interferometry, this paper proposes a morphology filter-assisted extreme-point symmetric mode decomposition (MF-ESMD) for the signal denoising of bridge dynamic deflection obtained by ground-based microwave interferometry. First, the original bridge dynamic deflection obtained with ground-based microwave interferometry was decomposed to obtain a series of intrinsic mode functions (IMFs) with the ESMD method. Second, the noise-dominant IMFs were removed according to Spearman’s rho algorithm, and the other decomposed IMFs were reconstructed as a new signal. Finally, the residual noises in the reconstructed signal were further eliminated using the morphological filter method. The results of both the simulated and on-site experiments showed that the proposed MF-ESMD method had a powerful signal denoising ability.

Adaptive Nonlocal Means Method for Electrocardiogram Signal Denoising

2015 ◽  
Vol 5 (7) ◽  
pp. 1455-1461 ◽  
Author(s):  
Xiang Li ◽  
Yongshuai Li ◽  
Huan Zhou ◽  
Mingyue Ding ◽  
Xuming Zhang
Keyword(s):  
Signal Denoising ◽  
Nonlocal Means ◽  
Electrocardiogram Signal

Extraction of Bridge Frequencies Inclusive of the Higher Modes by the ESMD Using the Contact-Point Response

2020 ◽  
Vol 20 (04) ◽  
pp. 2050045 ◽  
Author(s):  
Y. B. Yang ◽  
F. Xiong ◽  
Z. L. Wang ◽  
H. Xu
Keyword(s):  
Extreme Point ◽  
Systematic Study ◽  
Contact Point ◽  
Effective Procedure ◽  
Symmetric Mode ◽  
University Campus ◽  
Intrinsic Mode Functions ◽  
Higher Modes ◽  
Mode Decomposition ◽  
Better Than

An effective procedure is proposed for extracting bridge frequencies including the higher modes using the vehicle collected data. This is enabled by the use of the contact-point response, rather than the vehicle response, for processing by the extreme-point symmetric mode decomposition (ESMD). The intrinsic mode functions (IMFs) so decomposed are then processed by the FFT to yield the bridge frequencies. A systematic study is conducted to compare the proposed procedure with existing ones, while assessing the effects of various parameters involved. The proposed procedure was verified in the field for a two-span bridge located at the Chongqing University campus. It was confirmed to perform better than the existing ones in extracting bridge frequencies inclusive of the higher modes. The following are the reasons: (1) the ESMD is more efficient than the EMD in that remarkably less IMFs are generated; (2) the modal aliasing problem is largely alleviated, which helps enhancing the visibility of bridge frequencies in general; and (3) the contact-point response adopted is free of the vehicle frequency, which makes the higher frequencies more outstanding and detectable.

scholarly journals Complexity Analysis of Precipitation and Runoff Series Based on Approximate Entropy and Extreme-Point Symmetric Mode Decomposition

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1388 ◽  
Author(s):  
Dongyong Sun ◽  
Hongbo Zhang ◽  
Zhihui Guo
Keyword(s):  
Climate Change ◽  
Correlation Coefficient ◽  
Extreme Point ◽  
Time Scales ◽  
Approximate Entropy ◽  
Monthly Precipitation ◽  
Symmetric Mode ◽  
Mode Decomposition ◽  
Different Time Scales ◽  
The Imf

Many regional hydrological regime changes are complex under the influences of climate change and human activities, which make it difficult to understand the regional or basin al hydrological status. To investigate the complexity of precipitation and the runoff time series from 1960 to 2012 in the Jing River Basin on different time scales, approximate entropy, a Bayesian approach and extreme-point symmetric mode decomposition were employed. The results show that the complexity of annual precipitation and runoff has decreased since the 1990sand that the change occurred in 1995. The Intrinsic Mode Function (IMF)-6 component decomposed by extreme-point symmetric mode decomposition of monthly precipitation and runoff was consistent with precipitation and runoff. The IMF-6 component of monthly precipitation closely followed the 10-year cycle of change, and it has an obvious correlation with sunspots. The correlation coefficient is 0.6, representing a positive correlation before 1995 and a negative correlation after 1995. However, the IMF-6 component of monthly runoff does not have a significant correlation with sunspots, and the correlation coefficient is only 0.41, which indicates that climate change is not the dominant factor of runoff change. Approximate entropy is an effective analytical method for complexity, and furthermore, it can be decomposed by extreme-point symmetric mode decomposition to obtain the physical process of the sequences at different time scales, which helps us to understand the background of climate change and human activity in the process of precipitation and runoff.

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