Results 201 to 210 of about 4,055 (251)

Enhanced forecasting of shipboard electrical power demand using multivariate input and variational mode decomposition with mode selection. [PDF]

Sci Rep
Fazzini P, La Tona G, Diez M, Di Piazza MC.   +3 more
europepmc   +1 more source

Deep Learning Method Based on Multivariate Variational Mode Decomposition for Classification of Epileptic Signals. [PDF]

Brain Sci
Zhang S, Liu G, Sun S, Cai J.
europepmc   +1 more source

Research on forward multi-step prediction of EU carbon prices considering multiple factors new evidence from a hybrid model combining secondary decomposition technique and transformer. [PDF]

PLoS One
Zheng H, Zhuang S, Zhang T.
europepmc   +1 more source

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Multivariate empirical mode decomposition

Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2009
Despite empirical mode decomposition (EMD) becoming a de facto standard for time-frequency analysis of nonlinear and non-stationary signals, its multivariate extensions are only emerging; yet, they are a prerequisite for direct multichannel data analysis.
Rehman, N., Mandic, D. P.
openaire   +1 more source

Filter Bank Property of Multivariate Empirical Mode Decomposition

IEEE Transactions on Signal Processing, 2011
The multivariate empirical mode decomposition (MEMD) algorithm has been recently proposed in order to make empirical mode decomposition (EMD) suitable for processing of multichannel signals. To shed further light on its performance, we analyze the behavior of MEMD in the presence of white Gaussian noise.
Naveed ur Rehman, Danilo P. Mandic
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FPGA-Based Design for Online Computation of Multivariate Empirical Mode Decomposition

IEEE Transactions on Circuits and Systems I: Regular Papers, 2020
Multivariate or multichannel data have become ubiquitous in many modern scientific and engineering applications, e.g., biomedical engineering, owing to recent advances in sensor and computing technology. Processing these data sets is challenging owing to their large size and multidimensional nature, thus requiring specialized algorithms and efficient ...
Sikender Gull, Muhammad Faisal Siddiqui, Naveed ur Rehman   +2 more
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A joint framework for multivariate signal denoising using multivariate empirical mode decomposition

Signal Processing, 2017
In this paper, a novel multivariate denoising scheme using multivariate empirical mode decomposition (MEMD) is proposed. Unlike previous EMD-based denoising methods, the proposed scheme can align common frequency modes across multiple channels of a multivariate data, thus, facilitating direct multichannel data denoising. The key idea in this work is to
Huan Hao, Huali Wang, Naveed ur Rehman
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Fast and Adaptive Empirical Mode Decomposition for Multidimensional, Multivariate Signals

IEEE Signal Processing Letters, 2018
Over the last decade, empirical mode decomposition (EMD) has developed into a versatile tool for adaptive, scale-based modal decomposition. EMD has proven to be capable of decomposing multivariate signals with cross-channel mode alignment. However, the algorithms for envelope identification in multivariate EMD come with a computational burden rendering
Mruthun R. Thirumalaisamy, Phillip J. Ansell   +1 more
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Epileptic Seizure Classification with Multivariate Empirical Mode Decomposition and Hilbert Vibration Decomposition

2019 27th Signal Processing and Communications Applications Conference (SIU), 2019
EEG signals are frequently used to record seizures of epilepsy. However, observation of these seizures is difficult and time-consuming. Fourier-based approaches are not suitable for the nonlinear and nonstationary nature of EEG. For this reason, empirical methods such as multivariate empirical mode decomposition (MEMD) are used in the analysis of ...
Barkin Büyükçakir, Ali Yener Mutlu
openaire   +1 more source