Forecasting of the true satellite carbon monoxide data with ensemble empirical mode decomposition, singular value decomposition and moving average. [PDF]
J Appl StatPoongadan S, Lineesh MC.
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Beyond Frequency Bands: Complementary-Ensemble-Empirical-Mode-Decomposition-Enhanced Microstate Sequence Non-Randomness Analysis for Aiding Diagnosis and Cognitive Prediction of Dementia. [PDF]
Brain SciWan W, Gu Z, Peng CK, Cui X.
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Global Navigation Satellite System Receiver Positioning in Harsh Environments via Clock Bias Prediction by Empirical Mode Decomposition and Back Propagation Neural Network Method. [PDF]
Sensors (Basel)Du L, Chen H, Yuan Y, Song L, Meng X.
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Quadrivariate Empirical Mode Decomposition
The 2010 International Joint Conference on Neural Networks (IJCNN), 2010We introduce a quadrivariate extension of Empirical Mode Decomposition (EMD) algorithm, termed QEMD, as a tool for the time-frequency analysis of nonlinear and non-stationary signals consisting of up to four channels. The local mean estimation of the quadrivariate signal is based on taking real-valued projections of the input in different directions in
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Shunt sound decomposition by empirical mode decomposition
2020 IEEE REGION 10 CONFERENCE (TENCON), 2020Unpredictable disequilibrium syndrome and blood pressure fluctuations can occur during hemodialysis therapy. We have proposed a method for analyzing shunt sounds using EMD to predict these symptoms. The shunt sound is the sound of turbulent blood flow generated in the shunt, which can be measured from the puncture needle of the dialyzer.
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Online Empirical Mode Decomposition
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017The success of Empirical Mode Decomposition (EMD) resides in its practical approach to dissect non-stationary data. EMD repetitively goes through the entire data span to iteratively extract Intrinsic Mode Functions (IMFs). This approach, however, is not suitable for data stream as the entire data set has to be reconsidered every time a new point is ...
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Sliding Empirical Mode Decomposition
The 2010 International Joint Conference on Neural Networks (IJCNN), 2010Biomedical signals are in general non-linear and non-stationary which renders them difficult to analyze with classical time series analysis techniques. Empirical Mode Decomposition (EMD) in conjunction with a Hilbert spectral transform, together called Hilbert-Huang Transform, is ideally suited to extract informative components which are characteristic
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Flow Empirical Mode Decomposition
2021Decomposing non-stationary signals using Empirical Mode Decomposition (EMD) highly facilitates signal analyses and processing. According to the original algorithm, EMD decomposes the input signal into useful Intrinsic Mode Functions (IMFs). However, EMD has some drawbacks.
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Empirical Mode Decomposition - an introduction
The 2010 International Joint Conference on Neural Networks (IJCNN), 2010Due to external stimuli, biomedical signals are in general non-linear and non-stationary. Empirical Mode Decomposition in conjunction with a Hilbert spectral transform, together called Hilbert-Huang Transform, is ideally suited to extract essential components which are characteristic of the underlying biological or physiological processes.
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