Results 41 to 50 of about 8,950,043 (334)
On the behavior of EMD and MEMD in presence of symmetric alpha-stable noise [PDF]
, 2014 EmpiricalMode Decomposition (EMD) and its extended versions such as Multivariate EMD (MEMD) are data-driven techniques that represent nonlinear and non-stationary data as a sum of a finite zero-mean AM-FM components referred to as Intrinsic Mode ...
NOLAN, John +4 more
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Correction of blink artifacts using independent component analysis and empirical mode decomposition. [PDF]
Blink-related ocular activity is a major source of artifacts in electroencephalogram (EEG) data. Independent component analysis (ICA) is a well-known technique for the correction of such ocular artifacts, but one of the limitations of ICA is that the ICs
Bhattacharya, Joydeep, Lindsen, Job P.
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IEEE Open Access Journal of Power and Energy, 2022 Day-ahead energy forecasting systems struggle to provide accurate demand predictions due to pandemic mitigation measures. Decomposition-Residuals Deep Neural Networks (DR-DNN) are hybrid point-forecasting models that can provide more accurate electricity
Konstantinos Theodorakos +3 more
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Tensor Decomposition for EEG Signals Retrieval [PDF]
2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2019 Prior studies have proposed methods to recover multi-channel electroencephalography (EEG) signal ensembles from their partially sampled entries. These methods depend on spatial scenarios, yet few approaches aiming to a temporal reconstruction with lower loss.
Zehong Cao +4 more
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Angle of arrival estimation for broadband signals : a comparison [PDF]
, 2013 This paper reviews and compares three different linear algebraic signal subspace techniques for angle of arrival estimation. These include a polynomial matrix approach to multiple signal classification (MUSIC), a parameterised spatial covariance matrix ...
Weiss, Stephan +4 more
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Multi-band network fusion for Alzheimer’s disease identification with functional MRI
Frontiers in Psychiatry, 2022 IntroductionThe analysis of functional brain networks (FBNs) has become a promising and powerful tool for auxiliary diagnosis of brain diseases, such as Alzheimer’s disease (AD) and its prodromal stage.
Lingyun Guo +4 more
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Dynamic decomposition of spatiotemporal neural signals [PDF]
PLOS Computational Biology, 2017 Neural signals are characterized by rich temporal and spatiotemporal dynamics that reflect the organization of cortical networks. Theoretical research has shown how neural networks can operate at different dynamic ranges that correspond to specific types of information processing.
Ambrogioni, L. +4 more
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Improved EMD Using Doubly-Iterative Sifting and High Order Spline Interpolation [PDF]
, 2008 Empirical mode decomposition (EMD) is a signal analysis method which has received much attention lately due to its application in a number of fields. The main disadvantage of EMD is that it lacks a theoretical analysis and, therefore, our understanding ...
Yannis Kopsinis +4 more
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Transient Signal Spaces and Decompositions
CoRR, 2015 In this paper, we study the problem of transient signal analysis. A signal-dependent algorithm is proposed which sequentially identifies the countable sets of decay rates and expansion coefficients present in a given signal. We qualitatively compare our method to existing techniques such as orthogonal exponential transforms generated from orthogonal ...
Tarek A. Lahlou, Anuran Makur
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Fault Diagnosis of Rotating Machinery Based on Adaptive Stochastic Resonance and AMD-EEMD
Shock and Vibration, 2016 An adaptive stochastic resonance and analytical mode decomposition-ensemble empirical mode decomposition (AMD-EEMD) method is proposed for fault diagnosis of rotating machinery in this paper.
Peiming Shi, Cuijiao Su, Dongying Han
doaj +1 more source