Considering blood scattering effect in noninvasive optical detection of blood components using dynamic spectrum along with time varying filter based empirical mode decomposition

2022 ◽  
Vol 71 ◽  
pp. 103266
Author(s):  
Yussif Moro Awelisah ◽  
Gang Li ◽  
Yuyu Wang ◽  
Wei Tang ◽  
Ling Lin
Keyword(s):  
Empirical Mode Decomposition ◽  
Optical Detection ◽  
Dynamic Spectrum ◽  
Time Varying ◽  
Blood Components ◽  
Scattering Effect ◽  
Mode Decomposition

scholarly journals Short Term Traffic Flow Prediction of Urban Road Using Time Varying Filtering Based Empirical Mode Decomposition

2020 ◽  
Vol 10 (6) ◽  
pp. 2038 ◽  
Author(s):  
Yanpeng Wang ◽  
Leina Zhao ◽  
Shuqing Li ◽  
Xinyu Wen ◽  
Yang Xiong
Keyword(s):  
Traffic Flow ◽  
Root Mean Square ◽  
Empirical Mode Decomposition ◽  
Original Data ◽  
Time Varying ◽  
Mean Square ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Mode Decomposition

Short-term traffic flow prediction is important to realize real-time traffic instruction. However, due to the existing strong nonlinearity and non-stationarity in short-term traffic volume data, it is hard to obtain a satisfactory result through the traditional method. To this end, this paper develops an innovative hybrid method based on the time varying filtering based empirical mode decomposition (TVF-EMD) and least square support vector machine (LSSVM). Specifically, TVF-EMD is firstly used to deal with the implied non-stationarity in the original data by decomposing them into several different subseries. Then, the LSSVM models are established for each subseries to capture the linear and nonlinear characteristics embedded in the original data, and the corresponding prediction results are superimposed to obtain the final one. Finally, case studies based on two groups of data measured from an arterial road intersection are employed to evaluate the performance of the proposed method. The experimental results indicate it outperforms the other involved models. For example, compared with the LSSVM model, the average improvements by the proposed method in terms of the indexes of mean absolute error, mean relative percentage error, root mean square error and root mean square relative error are 7.397, 15.832%, 10.707 and 24.471%, respectively.

Measurement While Drilling Mud Pulse Signal Denoising and Extraction Approach Based on Particle-Swarm-Optimized Time-Varying Filtering Empirical Mode Decomposition

2020 ◽  
pp. 1-11
Author(s):  
Xianyou Zhong ◽  
Li Cen ◽  
Yankun Zhao ◽  
Tianwei Huang ◽  
Jinjin Shi
Keyword(s):  
Particle Swarm Optimization ◽  
Empirical Mode Decomposition ◽  
Drilling Fluid ◽  
Particle Swarm ◽  
Pulse Signal ◽  
Permutation Entropy ◽  
Time Varying ◽  
Swarm Optimization ◽  
Mode Decomposition ◽  
Reconstructed Signal

Summary At present, mud pulse transmission is widely used in underground wireless transmission. To extract more accurately the original drilling fluid pulse signals while drilling, in this paper, we developed an algorithm for optimal denoising shaping based on particle-swarm-optimized time-varying filtering empirical mode decomposition (TVFEMD). The performance of TVFEMD heavily depends on its parameters (i.e., B-spline order and bandwidth threshold). In the traditional TVFEMD method, the parameters are given in advance and may not be optimized, so it is difficult to achieve satisfactory decomposition results. To tackle this issue, the correlation coefficient was used as the objective function, and the particle-swarm-optimization algorithm was used to optimize the parameters of TVFEMD in this paper. First, the particle swarm optimization was used to search for the best combination of parameters. Then, the TVFEMD was applied to obtain a series of intrinsic mode functions (IMFs). Subsequently, the optimal denoising and shaping algorithm was used to determine the best reconstructed signal by low-pass filtering. Permutation entropy was taken as the evaluation index to obtain a reconstruction signal. Finally, the reconstructed signal was processed by square wave shaping to obtain accurate drilling fluid pulse signals. The approximation of the algorithm is 0.7581, and relevance is as high as 0.8535. The simulation signal and drilling fluid pulse signal analysis results showed that the proposed approach can extract the original pulse signal accurately.

Seismic denoising based on time-varying filtering and empirical mode decomposition in the fx domain

2021 ◽  
pp. 1-1
Author(s):  
Siyuan Chen ◽  
Siyuan Cao ◽  
Yaoguang Sun ◽  
Fang Huang ◽  
Guoming Cao
Keyword(s):  
Empirical Mode Decomposition ◽  
Time Varying ◽  
Mode Decomposition

A time varying filter approach for empirical mode decomposition

2017 ◽  
Vol 138 ◽  
pp. 146-158 ◽  
Author(s):  
Heng Li ◽  
Zhi Li ◽  
Wei Mo
Keyword(s):  
Empirical Mode Decomposition ◽  
Time Varying ◽  
Mode Decomposition ◽  
Filter Approach
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