Results 211 to 220 of about 4,055 (251)

Characterisation of Physiological Tremor using Multivariate Empirical Mode Decomposition and Hilbert Transform

2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2023
Fatigue-induced physiological tremor (FIPT) is undesirable when performing micromanipulation tasks that require high precision. It is important to characterise this form of tremor to aid in identifying and suppressing it from the intended micromanipulation task. Researchers have used surface electromyography (sEMG) and mechanomyography (MMG) separately
Poongavanam Palani, Vignesh Sompur, Asokan Thondiyath   +2 more
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Speech Enhancement: A Multivariate Empirical Mode Decomposition Approach

2013
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Solé-Casals, Jordi, Gallego Jutglà, Esteve, Martí i Puig, Pere, Travieso, Carlos M., Alonso, Jesús B.   +4 more
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Multivariate empirical mode decomposition approach for adaptive denoising of fringe patterns

Optics Letters, 2012
An adaptive approach is presented for noise reduction of optical fringe patterns using multivariate empirical mode decomposition. Adjacent rows and columns of patterns are treated as multichannel signals and are decomposed into multiscale components.
Xiang, Zhou, Tao, Yang, Haihua, Zou, Hong, Zhao   +3 more
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Application of multivariate empirical mode decomposition for seizure detection in EEG signals

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, 2010
We present a method for the analysis of electroencephalogram (EEG) signals which has the potential to distinguish between ictal and seizure-free intracranial EEG recordings. This is achieved by analyzing common frequency components in multichannel EEG recordings, using the multivariate empirical mode decomposition (MEMD) algorithm.
Naveed, Ur Rehman, Yili, Xia, Danilo P, Mandic   +2 more
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Multivariate empirical mode decomposition based EMG signal analysis for smart prosthesis

2018 26th Signal Processing and Communications Applications Conference (SIU), 2018
Electromyography (EMG) signals are successfully used for human-robot interaction with biomedical applications. One of the basic components of many modern prosthesis is the myoelectric control system which controls prosthetic movements through EMG signals.
Fatih Onay, Ahmet Mert
openaire   +2 more sources

Ring-down oscillation mode identification using multivariate Empirical Mode Decomposition

2016 IEEE Power and Energy Society General Meeting (PESGM), 2016
Inter-area oscillation in a large power systems draws much attention because it might severely influence system security and reduce transmission capability. The recent large-scale deployment of phasor measurement units (PMUs) enables online measurement-based monitoring and analysis on inter-area oscillatory modes.
Shutang You, Jiahui Guo, null Wenxuan Yao, null Siqi Wang, Yong Liu, Yilu Liu   +5 more
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Forecasting using multivariate empirical mode decomposition — Applied to iceberg drift forecast

2017 IEEE Conference on Control Technology and Applications (CCTA), 2017
The prediction of the movement of a floating object in the ocean, such as an iceberg, is a challenging problem. Large uncertainties in the driving forces and possibly in the geometry of the object itself prevent accurate forecasts. However, if observations of the past trajectory of the object are available the forecast can be improved considerably ...
Leif Erik Andersson, Muhammad Faisal Aftab, Francesco Scibilia, Lars Imsland   +3 more
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A New Algorithm for Speech Enhancement Based on Multivariate Empirical Mode Decomposition

2018
Nowadays many systems use speech as a way to interact with them. Therefore, machine learning systems are needed to perform various tasks on these recordings. But speech signals in a real environment are usually mixed with some other signals, such as noise. This may interfere with posterior signal processing applied to the signals.
Pere Martí-Puig, Esteve Gallego-Jutglà, Gerard Masferrer, Jordi Solé-Casals   +3 more
openaire   +1 more source

Sinusoidal Signal Assisted Multivariate Empirical Mode Decomposition for Brain–Computer Interfaces

IEEE Journal of Biomedical and Health Informatics, 2018
A brain-computer interface (BCI) is a communication approach that permits cerebral activity to control computers or external devices. Brain electrical activity recorded with electroencephalography (EEG) is most commonly used for BCI. Noise-assisted multivariate empirical mode decomposition (NA-MEMD) is a data-driven time-frequency analysis method that ...
Sheng Ge, Yanhua Shi, Ruimin Wang, Pan Lin, Jun-Feng Gao, Gao-Peng Sun, Keiji Iramina, Yuankui Yang, Yue Leng, Haixian Wang, Wenming Zheng   +10 more
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EEG epileptic seizures separation with multivariate empirical mode decomposition for diagnostic purposes

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013
We present a successful application of a soft computing approach based on the multivariate empirical mode decomposition (MEMD) method to EEG epileptic seizures separation. The results of the automatic multivatiate intrinsic mode functions (IMF) clustering allowed us to separate the seizure related spikes and sharp waves.
Tomasz M. Rutkowski, Zbigniew R. Struzik, Danilo P. Mandic   +2 more
openaire   +2 more sources