Results 241 to 250 of about 5,207,763 (284)

THE MULTI-DIMENSIONAL ENSEMBLE EMPIRICAL MODE DECOMPOSITION METHOD

Advances in Adaptive Data Analysis, 2009
A multi-dimensional ensemble empirical mode decomposition (MEEMD) for multi-dimensional data (such as images or solid with variable density) is proposed here. The decomposition is based on the applications of ensemble empirical mode decomposition (EEMD) to slices of data in each and every dimension involved.
Xianyao Chen, Zhaohua Wu, Norden E. Huang   +2 more
exaly   +3 more sources

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
openaire   +2 more sources

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
openaire   +2 more sources

A complete ensemble empirical mode decomposition with adaptive noise

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011
In this paper an algorithm based on the ensemble empirical mode decomposition (EEMD) is presented. The key idea on the EEMD relies on averaging the modes obtained by EMD applied to several realizations of Gaussian white noise added to the original signal. The resulting decomposition solves the EMD mode mixing problem, however it introduces new ones. In
María Eugenia Torres, Marcelo Alejandro Colominas, Gastón Schlotthauer, Patrick Flandrin   +3 more
openaire   +2 more sources

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
openaire   +1 more source

ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOISE-ASSISTED DATA ANALYSIS METHOD

Advances in Adaptive Data Analysis, 2009
A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. Finite, not infinitesimal, amplitude white noise is necessary to force the ensemble to exhaust all possible solutions in the sifting process, thus making the ...
Zhaohua Wu, Norden E. Huang
exaly   +3 more sources

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
openaire   +1 more source

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
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

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