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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Multivariate empirical mode decomposition-based structural damage localization using limited sensors. [PDF]
J Vib Control, 2022 In this article, multivariate empirical mode decomposition is proposed for damage localization in structures using limited measurements. Multivariate empirical mode decomposition is first used to decompose the acceleration responses into their mono-component modal responses.
Sony S, Sadhu A.
europepmc +6 more sources
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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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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Fast Multivariate Empirical Mode Decomposition
IEEE Access, 2018 The multivariate empirical mode decomposition (MEMD) has been pioneered recently for adaptively processing of multichannel data. Despite its high efficiency on time-frequency analysis of nonlinear and nonstationary signals, high computational load and ...
Xun Lang +6 more
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Baseline wander removal of electrocardiogram signals using multivariate empirical mode decomposition. [PDF]
Healthc Technol Lett, 2015 A new method for removing the baseline wander (BW) noise based on multivariate empirical mode decomposition is presented. The proposed method is compared with recently introduced technique for BW removal using Hilbert vibration decomposition in terms of correlation coefficient criterion and signal‐to‐noise ratio.
Gupta P, Sharma KK, Joshi SD.
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Multivariate Empirical Mode Decomposition for Quantifying Multivariate Phase Synchronization [PDF]
EURASIP Journal on Advances in Signal Processing, 2011 Quantifying the phase synchrony between signals is important in many different applications, including the study of the chaotic oscillators in physics and the modeling of the joint dynamics between channels of brain activity recorded by electroencephalogram (EEG).
Ali Yener Mutlu, Selin Aviyente
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Noise-assisted multivariate empirical mode decomposition for multichannel EMG signals. [PDF]
Biomed Eng Online, 2017 Ensemble Empirical Mode Decomposition (EEMD) has been popularised for single-channel Electromyography (EMG) signal processing as it can effectively extract the temporal information of the EMG time series. However, few papers examine the temporal and spatial characteristics across multiple muscle groups in relation to multichannel EMG signals.The ...
Zhang Y +7 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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Noise-Assisted Multivariate Empirical Mode Decomposition Based Emotion Recognition
Electrica, 2018 Emotion state detection or emotion recognition cuts across different disciplines because of the many parameters that embrace the brain's complex neural structure, signal processing methods, and pattern recognition algorithms.
Pınar Özel +2 more
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