Low-Density EEG for Neural Activity Reconstruction Using Multivariate Empirical Mode Decomposition [PDF]
Frontiers in Neuroscience, 2020 Several approaches can be used to estimate neural activity. The main differences between them concern the a priori information used and its sensitivity to high noise levels.
Andres Soler +5 more
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Multi-Scale Pixel-Based Image Fusion Using Multivariate Empirical Mode Decomposition [PDF]
Sensors, 2015 A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate ...
Naveed ur Rehman +5 more
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Data driven filtering of bowel sounds using multivariate empirical mode decomposition [PDF]
BioMedical Engineering OnLine, 2019 Background The analysis of abdominal sounds can help to diagnose gastro-intestinal diseases. Sounds originating from the stomach and the intestine, the so-called bowel sounds, occur in various forms. They are described as loose successions or clusters of
Konstanze Kölle +4 more
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Matlab Open Source Code: Noise-Assisted Multivariate Empirical Mode Decomposition Based Causal Decomposition for Causality Inference of Bivariate Time Series [PDF]
Frontiers in Neuroinformatics, 2022 Causality inference has arrested much attention in academic studies. Currently, multiple methods such as Granger causality, Convergent Cross Mapping (CCM), and Noise-assisted Multivariate Empirical Mode Decomposition (NA-MEMD) are introduced to solve the
Yi Zhang +11 more
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Early Fault Detection Method for Rotating Machinery Based on Harmonic-Assisted Multivariate Empirical Mode Decomposition and Transfer Entropy [PDF]
Entropy, 2018 It is a difficult task to analyze the coupling characteristics of rotating machinery fault signals under the influence of complex and nonlinear interference signals.
Zhe Wu +4 more
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Multi-Fault Diagnosis of Rolling Bearings via Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition and High Order Singular Value Decomposition [PDF]
Sensors, 2018 Rolling bearings are important components in rotary machinery systems. In the field of multi-fault diagnosis of rolling bearings, the vibration signal collected from single channels tends to miss some fault characteristic information.
Rui Yuan, Yong Lv, Gangbing Song
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Multivariate empirical mode decomposition–based structural damage localization using limited sensors [PDF]
JVC/Journal of Vibration and Control, 2022 Ayan Sadhu, Sandeep Sony
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Applied Sciences, 2022 Multicomponent seismic exploration provides more wavefield information for imaging complex subsurface structures and predicting reservoirs. Ground roll is strongly coherent noise in land multicomponent seismic data and exhibits similar features, which ...
Liying Xiao, Zhifu Zhang, Jianjun Gao
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Ecological Indicators, 2023 Reference evapotranspiration (ETo) is a vital climate parameter affecting plants' water use. ETo can generate large deficits in soil moisture and runoff in different regions and seasons, leading to uncertainties in drought warning systems.
Mumtaz Ali +8 more
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Multivariate Nonlinear Sparse Mode Decomposition and Its Application in Gear Fault Diagnosis
IEEE Access, 2021 Multi-channel signal has more abundant and accurate state characteristic information than single channel signal. How to separate fault characteristic information from the multi-channel signal is the key of fault diagnosis.
Haiyang Pan +3 more
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