Results 151 to 160 of about 50,188 (187)

ASSESSING DISCONTINUOUS DATA USING ENSEMBLE EMPIRICAL MODE DECOMPOSITION

Advances in Adaptive Data Analysis, 2011
This investigation presents an improved ensemble empirical mode decomposition (EEMD) algorithm that can be applied to discontinuous data. The quality of the algorithm is assessed by creating artificial data gaps in continuous data, then comparing the extracted intrinsic mode functions (IMFs) from both data sets.
Bradley Lee Barnhart, Honda Kahindo Wa Nandage, William Eichinger   +2 more
openaire   +1 more source

Ensemble Empirical Mode Decomposition of Photoplethysmogram Signals in Biometric Recognition

2019 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS), 2019
This research focuses on using photoplethysmogram (PPG) signals for biometric recognition. Specifically, the biometric traits studied are the ensemble empirical mode decomposition (EEMD) and power spectral density (PSD) of the PPG signals. The classifiers used for testing the performance of the algorithm were K-nearest neighbors algorithm (KNN ...
Lea Monica B. Alonzo, Homer S. Co
openaire   +1 more source

BIDIMENSIONAL ENSEMBLE EMPIRICAL MODE DECOMPOSITION OF FUNCTIONAL BIOMEDICAL IMAGES

Advances in Adaptive Data Analysis, 2014
Positron emission tomography (PET) provides a functional imaging modality to detect signs of dementias in human brains. Two-dimensional empirical mode decomposition (2D EMD) provides means to analyze such images. It extracts characteristic textures from these images which may be fed into powerful classifiers trained to group these textures into ...
A. Neubauer, Ana Maria Tomé, Andreas Kodewitz, J. M. Górriz, Carlos García Puntonet, Elmar Wolfgang Lang   +5 more
openaire   +1 more source

Ensemble Empirical Mode Decomposition for atherosclerosis in high-risk subjects

2011 8th International Conference on Information, Communications & Signal Processing, 2011
Photoplethysmography (PPG) is a popular non-invasive method of waveform contour analysis in assessing arterial stiffness. Although stiffness index (SI) is a good parameter in assessing atherosclerosis, it dose not perform in aged and arterial stiffness patients, because of their ill-defined diastolic peaks in digital volume pulse (DVP) waveforms.
Ching-Shuen Chen, Cyuan-Cin Liu, Hsien-Tsai Wu, An-Bang Liu   +3 more
openaire   +1 more source

Automatic contrast enhancement using ensemble empirical mode decomposition

IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 2011
Ultrasound nonlinear contrast imaging using microbubble-based contrast agents has been widely investigated. However, the degree of contrast enhancement is often limited by overlap between the spectra of the tissue and microbubble nonlinear responses, which makes it difficult to separate them.
Shang-Ching, Lin, Pai-Chi, Li
openaire   +2 more sources

Ensemble Empirical Mode Decomposition for Machine Health Diagnosis

Key Engineering Materials, 2009
Ensemble Empirical Mode Decomposition (EEMD) is a new signal processing technique aimed at solving the problem of mode mixing present in the original Empirical Mode Decomposition (EMD) algorithm. This paper investigates its utility for machine health monitoring and defect diagnosis. The mechanism of EEMD is first introduced.
Jian Zhang, Ru Qiang Yan, Robert X. Gao
openaire   +1 more source

Purification of Axis Trace by Ensemble Empirical Mode Decomposition

Advanced Materials Research, 2013
Aiming at the purification of axis trace, a novel method was proposed by using ensemble empirical mode decomposition (EEMD). Ensemble empirical mode decomposition decomposed a complicated signal into a collection of intrinsic mode functions (IMFs). Then according to prior knowledge of rotating machinery, chose intrinsic mode function components and ...
Jia Xing Zhu, Wen Bin Zhang, Ya Song Pu, Yan Jie Zhou   +3 more
openaire   +1 more source

An Optimal Ensemble Empirical Mode Decomposition Method for Vibration Signal Decomposition

Journal of Vibration and Acoustics, 2017
The vibration signal decomposition is a critical step in the assessment of machine health condition. Though ensemble empirical mode decomposition (EEMD) method outperforms fast Fourier transform (FFT), wavelet transform, and empirical mode decomposition (EMD) on nonstationary signal decomposition, there exists a mode mixing problem if the two critical ...
Shi-Chang Du, Tao Liu, De-Lin Huang, Gui-Long Li   +3 more
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Rainfall Forecasting Based on Ensemble Empirical Mode Decomposition and Neural Networks

2013
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Juan Beltrán-Castro, Juliana Valencia-Aguirre, Mauricio Orozco-Alzate, Germán Castellanos-Domínguez, Carlos Manuel Travieso-González   +4 more
openaire   +2 more sources

Ensemble empirical mode decomposition based feature enhancement of cardio signals

Medical Engineering & Physics, 2012
This paper presents an application of ensemble empirical mode decomposition method for enhancement of specific biological signal features. The application for two types of cardiological signals is presented in this article. Detection of fiducial points is a routine task for analyzing these signals.
Artūras, Janušauskas, Vaidotas, Marozas, Arūnas, Lukoševičius   +2 more
openaire   +2 more sources