Turbulence Time Series Data Hole Filling using Karhunen-Loeve and ARIMA methods [PDF]
, 2007 Measurements of optical turbulence time series data using unattended instruments over long time intervals inevitably lead to data drop-outs or degraded signals.
Beran J +15 more
core +2 more sources
A comparative study of single-channel signal processing methods in fetal phonocardiography.
PLoS ONE, 2022 Fetal phonocardiography is a non-invasive, completely passive and low-cost method based on sensing acoustic signals from the maternal abdomen. However, different types of interference are sensed along with the desired fetal phonocardiography.
Katerina Barnova +4 more
doaj +1 more source
Analysis of Vehicle Platform Vibration Based on Empirical Mode Decomposition
Shock and Vibration, 2021 Vehicle platform vibration (VPV) directly affects the measurement accuracy of precise measuring instrument (PMI) fixed on it. In order to reduce the influences of VPV on measurement accuracy, it is necessary to perform vibration isolation between vehicle
Chengwu Shen +5 more
doaj +1 more source
Surface electromyography signal denoising via EEMD and improved wavelet thresholds
Mathematical Biosciences and Engineering, 2020 The acquisition of good surface electromyography (sEMG) is an important prerequisite for correct and timely control of prosthetic limb movements. sEMG is nonlinear, nonstationary, and vulnerable against noise and a new sEMG denoising method using ...
Ziyang Sun +4 more
doaj +1 more source
Seizure detection from EEG signals using Multivariate Empirical Mode Decomposition [PDF]
, 2017 We present a data driven approach to classify ictal (epileptic seizure) and non-ictal EEG signals using the multivariate empirical mode decomposition (MEMD) algorithm.
Ehsan, Shoaib +4 more
core +1 more source
IEEE Access, 2017 Effective signal processing methods are essential for machinery fault diagnosis. Most conventional signal processing methods lack adaptability, thus being unable to well extract the embedded meaningful information.
Zhipeng Feng, Dong Zhang, Ming J. Zuo
doaj +1 more source
Empirical mode decomposition with shape-preserving spline interpolation
Results in Applied Mathematics, 2020 Empirical mode decomposition (EMD) is a popular, novel, user-friendly algorithm to decompose a given signal into its constituting components, utilizing spline interpolation.
Maria D. van der Walt
doaj +1 more source
Time-frequency representation of earthquake accelerograms and inelastic structural response records using the adaptive chirplet decomposition and empirical mode decomposition [PDF]
, 2007 In this paper, the adaptive chirplet decomposition combined with the Wigner-Ville transform and the empirical mode decomposition combined with the Hilbert transform are employed to process various non-stationary signals (strong ground motions and ...
A. Giaralis +31 more
core +1 more source
An improved genetic algorithm for optimizing ensemble empirical mode decomposition method
Systems Science & Control Engineering, 2019 This paper proposes an improved ensemble empirical mode decomposition method based on genetic algorithm to solve the mode mixing problem in empirical mode decomposition (EMD) algorithm as well as the parameters selection issue in ensemble empirical mode ...
Dabin Zhang +3 more
doaj +1 more source
Empirical Mode Decomposition Based Multi-Modal Activity Recognition
Sensors, 2020 This paper aims to develop an activity recognition algorithm to allow parents to monitor their children at home after school. A common method used to analyze electroencephalograms is to use infinite impulse response filters to decompose the ...
Lingyue Hu +4 more
doaj +1 more source