Results 141 to 150 of about 49,831 (187)

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 HYPERSPECTRAL IMAGE CLASSIFICATION

Advances in Adaptive Data Analysis, 2012
Ensemble empirical mode decomposition (EEMD) is a novel adaptive time-frequency analysis method, which is particularly suitable for extracting useful information from noisy nonlinear or nonstationary data. This paper presents the utilization of EEMD for hyperspectral images to extract signals from them, generated in noisy nonlinear and nonstationary ...
MIN ZHANG, YI SHEN
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Pavement profile analysis using ensemble empirical mode decomposition

International Journal of Vehicle Systems Modelling and Testing, 2009
Pavement profile analysis is a major component in pavement infrastructure management decision making for maintenance and rehabilitation. Road profile data are non-stationary and inherently non-Gaussian. This paper presents the application ensemble empirical mode decomposition (EEMD) to profile data analysis.
Y.O. Adu Gyamfi, Nii O. Attoh Okine, A.Y. Ayenu Prah   +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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MODEL VALIDATION BASED ON ENSEMBLE EMPIRICAL MODE DECOMPOSITION

Advances in Adaptive Data Analysis, 2010
We proposed a new model validation method through ensemble empirical mode decomposition (EEMD) and scale separate correlation. EEMD is used to analyze the nonlinear and nonstationary ozone concentration data and the data simulated from the Taiwan Air Quality Model (TAQM).
YU-MEI CHANG, ZHAOHUA WU, JULIUS CHANG, NORDEN E. HUANG   +3 more
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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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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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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.
null Jin-Long Chen, null Ya-Chen Chen, Tzu-Chien Hsiao   +2 more
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Kinematic Data Smoothing Using Ensemble Empirical Mode Decomposition

Journal of Medical Imaging and Health Informatics, 2014
Kinematic data measured via a motion capture system tends to be contaminated by noise. In order to obtain velocities and accelerations, kinematic data is usually numerically differentiated using finite difference method. The differentiation process could amplify those high frequency components in the data, which are probably from noise, and ...
Jun Chen, Meng-Shi Zhang, Yong-Fang Zhao, Hong-Sheng Zhan   +3 more
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Cardiogenic oscillations extraction in inductive plethysmography: Ensemble empirical mode decomposition

2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009
The purpose of this study is to investigate the potential of the ensemble empirical mode decomposition (EEMD) to extract cardiogenic oscillations from inductive plethysmography signals in order to measure cardiac stroke volume. First, a simple cardio-respiratory model is used to simulate cardiac, respiratory, and cardio-respiratory signals.
Abdulhay, Enas, Guméry, Pierre-Yves, Fontecave Jallon, Julie, Baconnier, Pierre   +3 more
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