Results 241 to 250 of about 4,529 (276)

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
openaire   +1 more source

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
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
openaire   +2 more sources

Emotion recognition from EEG signals by using multivariate empirical mode decomposition

Pattern Analysis and Applications, 2016
This is the artifact for the paper titled "POKER: Permutation-based SIMD Execution of Intensive Tree Search by Path Encoding" accepted at CGO 2018. This artifact helps reproduce the results presented in Figures 7 - 9 and Tables 2 - 3 in Section 4. For more information on how to use it, please refer to our paper and the README.txt file in this package ...
Ahmet Mert, Aydin Akan
openaire   +1 more source

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

Pulsar signal de-noising method based on multivariate empirical mode decomposition

2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, 2015
In this paper, the de-noising method based on multivariate empirical mode decomposition (MEMD) is creatively proposed to put use to pulsar signal, filling the void that the previous methods based on the wavelet analysis have the limitations of choosing the basic functions, and the EMD algorithm's bounded that it can't process multiple signals jointly ...
Jing Jin 0003, Xiuxiu Ma, Xiaoyu Li, Yi Shen 0001, Liangwei Huang, Liang He   +5 more
openaire   +1 more source

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

Preterm birth prediction from electrohysterogram using multivariate empirical mode decomposition

Medical & Biological Engineering & Computing
Electrohysterogram (EHG) is an electrophysiological signal describing uterine contractions that can be non-invasively measured on maternal abdominal surface. This signal contains vital physiological and pathological information for assessing delivery abnormalities, such as preterm birth.
Jiawen Cui, Xu Zhang 0002, Xinhui Li, Xuanyu Luo, Xiang Chen 0004, Zongzhi Yin   +5 more
openaire   +2 more sources

Learning multivariate empirical mode decomposition for spectral motion editing

SIGGRAPH Asia 2023 Technical Communications, 2023
Ran Dong, Soichiro Ikuno, Xi Yang 0017
openaire   +1 more source