Results 31 to 40 of about 23,305 (264)
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
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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
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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
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IEEE Photonics Journal, 2016 Low-coherence interferometry (LCI) has proved to be a useful tool in optical measurement and detection. However, the noise that is present in practical applications makes interference term retrieval (ITR) difficult.
Hongxia Zhang +4 more
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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
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An Alternative Formulation for the Empirical Mode Decomposition [PDF]
IEEE Transactions on Signal Processing, 2012 The Empirical Mode Decomposition (EMD) is a relatively new adaptive method for multicomponent signal representation which allows for analyzing nonlinear and nonstationary signals. In spite of its lack of mathematical foundations, very few papers are dedicated to defining new decompositions that would preserve the interesting properties of the EMD while
Thomas Oberlin +2 more
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WAVELET TRANSFORMS FOR EEG SIGNAL DENOISING AND DECOMPOSITION
International Journal of Advances in Signal and Image Sciences, 2023 EEG signal analysis is difficult because there are so many unwanted impulses from non-cerebral sources. Presently, methods for eliminating noise through selective frequency filtering are afflicted with a notable deprivation of EEG information. Therefore,
Ibtihal Hassan Elshekhidris +2 more
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Empirical mode decomposition with missing values [PDF]
SpringerPlus, 2016 This paper considers an improvement of empirical mode decomposition (EMD) in the presence of missing data. EMD has been widely used to decompose nonlinear and nonstationary signals into some components according to intrinsic frequency called intrinsic mode functions.
Kim, Donghoh, Oh, Hee-Seok
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Structure‐forward targeting of claudins with synthetic binders
FEBS Letters, EarlyView.Claudins form the paracellular barriers between epithelial and endothelial tissues at tight junctions and are targets for molecular binders with the goal of modulating barrier permeability. Claudin‐binding molecules are relevant in drug delivery or in altering claudin interactions with disease‐causing proteins.
Alex J. Vecchio
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FEBS Open Bio, EarlyView.Evolutionary analysis across 32 placental mammals identified positive selection at residues H148 and W149 in the immune receptor FcγR1. Ancestral reconstruction combined with molecular dynamics simulations reveals how these mutations may influence receptor structure and dynamics, providing insight into the evolution of antibody recognition and immune ...
David A. Young +7 more
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