Results 171 to 180 of about 1,862 (208)

ENSEMBLE EMPIRICAL MODE DECOMPOSITION WITH SUPERVISED CLUSTER ANALYSIS

Advances in Adaptive Data Analysis, 2013
Ensemble empirical mode decomposition (EEMD) is a noise-assisted data analysis method which decomposes a signal into a collection of intrinsic mode functions (IMFs). There nevertheless appears a multi-mode problem where signals with a similar timescale are decomposed into different IMF components.
Chih-Yu Kuo, Shao-Kuan Wei, Pi-Wen Tsai
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IMPROVEMENT OF ENSEMBLE EMPIRICAL MODE DECOMPOSITION BY OVER-SAMPLING

Advances in Adaptive Data Analysis, 2013
The empirical mode decomposition (EMD) is a useful method for the analysis of nonlinear and nonstationary signals and found immediate applications in diverse areas of signal processing. However, the major inconvenience of EMD is the mode mixing. The ensemble EMD (EEMD) was proposed to solve the problem of mode-mixing with the assistance of added noises
Raïs El'hadi Bekka, Yaakoub Berrouche
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Recognizing thoracic breathing by ensemble empirical mode decomposition

2013 9th International Conference on Information, Communications & Signal Processing, 2013
Recognizing breathing pattern is important in many fields of medicine. Ensemble empirical mode decomposition (an adaptive algorithm) was used to investigate breathing pattern, including thoracic breathing (TB) and abdominal breathing (AB). This study recognizes TB and AB by correlation coefficient and power proportion.
Jin-Long Chen, Ya-Chen Chen, Tzu-Chien Hsiao   +2 more
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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
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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
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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
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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
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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
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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
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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
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