Relevant bibliographies by topics / EMD (Empirical Mode Decomposition)

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Academic literature on the topic 'EMD (Empirical Mode Decomposition)'

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Published: 4 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "EMD (Empirical Mode Decomposition)"

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TSUI, PO-HSIANG, CHIEN-CHENG CHANG, and NORDEN E. HUANG. "NOISE-MODULATED EMPIRICAL MODE DECOMPOSITION." Advances in Adaptive Data Analysis 02, no. 01 (January 2010): 25–37. http://dx.doi.org/10.1142/s1793536910000410.

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The empirical mode decomposition (EMD) is the core of the Hilbert–Huang transform (HHT). In HHT, the EMD is responsible for decomposing a signal into intrinsic mode functions (IMFs) for calculating the instantaneous frequency and eventually the Hilbert spectrum. The EMD method as originally proposed, however, has an annoying mode mixing problem caused by the signal intermittency, making the physical interpretation of each IMF component unclear. To resolve this problem, the ensemble EMD (EEMD) was subsequently developed. Unlike the conventional EMD, the EEMD defines the true IMF components as the mean of an ensemble of trials, each consisting of the signal with added white noise of finite, not infinitesimal, amplitude. In this study, we further proposed an extension and alternative to EEMD designated as the noise-modulated EMD (NEMD). NEMD does not eliminate mode but intensify and amplify mixing by suppressing the small amplitude signal but the larger signals would be preserved without waveform deformation. Thus, NEMD may serve as a new adaptive threshold amplitude filtering. The principle, algorithm, simulations, and applications are presented in this paper. Some limitations and additional considerations of using the NEMD are also discussed.
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NIAZY, R. K., C. F. BECKMANN, J. M. BRADY, and S. M. SMITH. "PERFORMANCE EVALUATION OF ENSEMBLE EMPIRICAL MODE DECOMPOSITION." Advances in Adaptive Data Analysis 01, no. 02 (April 2009): 231–42. http://dx.doi.org/10.1142/s1793536909000102.

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Empirical mode decomposition (EMD) is an adaptive, data-driven algorithm that decomposes any time series into its intrinsic modes of oscillation, which can then be used in the calculation of the instantaneous phase and frequency. Ensemble EMD (EEMD), where the final EMD is estimated by averaging numerous EMD runs with the addition of noise, was an advancement introduced by Wu and Huang (2008) to try increasing the robustness of EMD and alleviate some of the common problems of EMD such as mode mixing. In this work, we test the performance of EEMD as opposed to normal EMD, with emphasis on the effect of selecting different stopping criteria and noise levels. Our results indicate that EEMD, in addition to slightly increasing the accuracy of the EMD output, substantially increases the robustness of the results and the confidence in the decomposition.
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Chen, Zhongzhe, Baqiao Liu, Xiaogang Yan, and Hongquan Yang. "An Improved Signal Processing Approach Based on Analysis Mode Decomposition and Empirical Mode Decomposition." Energies 12, no. 16 (August 9, 2019): 3077. http://dx.doi.org/10.3390/en12163077.

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Empirical mode decomposition (EMD) is a widely used adaptive signal processing method, which has shown some shortcomings in engineering practice, such as sifting stop criteria of intrinsic mode function (IMF), mode mixing and end effect. In this paper, an improved sifting stop criterion based on the valid data segment is proposed, and is compared with the traditional one. Results show that the new sifting stop criterion avoids the influence of end effects and improves the correctness of the EMD. In addition, a novel AEMD method combining the analysis mode decomposition (AMD) and EMD is developed to solve the mode-mixing problem, in which EMD is firstly applied to dispose the original signal, and then AMD is used to decompose these mixed modes. Then, these decomposed modes are reconstituted according to a certain principle. These reconstituted components showed mode mixing phenomena alleviated. Model comparison was conducted between the proposed method with the ensemble empirical mode decomposition (EEMD), which is the mainstream method improved based on EMD. Results indicated that the AEMD and EEMD can effectively restrain the mode mixing, but the AEMD has a shorter execution time than that of EEMD.
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Zhou, Xiaohang, Deshan Shan, and Qiao Li. "Morphological Filter-Assisted Ensemble Empirical Mode Decomposition." Mathematical Problems in Engineering 2018 (September 17, 2018): 1–12. http://dx.doi.org/10.1155/2018/5976589.

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In the ensemble empirical mode decomposition (EEMD) algorithm, different realizations of white noise are added to the original signal as dyadic filter banks to overcome the mode mixing problems of empirical mode decomposition (EMD). However, not all the components in white noise are necessary, and the superfluous components will introduce additional mode mixing problems. To address this problem, morphological filter-assisted ensemble empirical mode decomposition (MF-EEMD) was proposed in this paper. First, a new method for determining the structuring element shape and size was proposed to improve the adaptive ability of morphological filter (MF). Then, the adaptive MF was introduced into EMD to remove the superfluous white noise components to improve the decomposition results. Based on the contributions of MF in a single EMD process, the MF-EEMD was proposed by combining EEMD with MF to suppress the mode mixing problems. Finally, an analog signal and a measured signal were used to verify the feasibility of MF-EEMD. The results show that MF-EEMD significantly mitigates the mode mixing problems and achieves a higher decomposition efficiency compared to that of EEMD.
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MHAMDI, FAROUK, JEAN-MICHEL POGGI, and MÉRIEM JAÏDANE. "TREND EXTRACTION FOR SEASONAL TIME SERIES USING ENSEMBLE EMPIRICAL MODE DECOMPOSITION." Advances in Adaptive Data Analysis 03, no. 03 (July 2011): 363–83. http://dx.doi.org/10.1142/s1793536911000696.

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In this paper, we investigate eligibility of trend extraction through the empirical mode decomposition (EMD) and performance improvement of applying the ensemble EMD (EEMD) instead of the EMD for trend extraction from seasonal time series. The proposed method is an approach that can be applied on any time series with any time scales fluctuations. In order to evaluate our algorithm, experimental comparisons with three other trend extraction methods: EMD-energy-ratio approach, EEMD-energy-ratio approach, and the Hodrick–Prescott filter are conducted.
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HUANG, JIANFENG, and LIHUA YANG. "A PIECEWISE MONOTONOUS MODEL FOR EMPIRICAL MODE DECOMPOSITION." Advances in Adaptive Data Analysis 05, no. 04 (October 2013): 1350019. http://dx.doi.org/10.1142/s1793536913500192.

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Empirical mode decomposition (EMD) lacks theoretical support. We propose a piecewise monotonous model for EMD, and prove that the trend-subtracting iteration converges and IMF-separating procedure ends up in finite steps under mild conditions. Experiments are implemented and compared with the classical EMD.
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BEKKA, RAÏS EL'HADI, and YAAKOUB BERROUCHE. "IMPROVEMENT OF ENSEMBLE EMPIRICAL MODE DECOMPOSITION BY OVER-SAMPLING." Advances in Adaptive Data Analysis 05, no. 03 (July 2013): 1350012. http://dx.doi.org/10.1142/s179353691350012x.

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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 producing the residue noise in the signal reconstructed. The residue noise in the IMFs can be reduced with a large number of ensemble trials at the expense of the increase of computational time. Improving the computing time of the EEMD by reducing the number of ensemble trials was thus proposed in this paper by over-sampling the signal to be decomposed. Numerical simulations were conducted to demonstrate proposed approach.
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FALTERMEIER, R., A. ZEILER, A. M. TOMÉ, A. BRAWANSKI, and E. W. LANG. "WEIGHTED SLIDING EMPIRICAL MODE DECOMPOSITION." Advances in Adaptive Data Analysis 03, no. 04 (October 2011): 509–26. http://dx.doi.org/10.1142/s1793536911000891.

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The analysis of nonlinear and nonstationary time series is still a challenge, as most classical time series analysis techniques are restricted to data that is, at least, stationary. Empirical mode decomposition (EMD) in combination with a Hilbert spectral transform, together called Hilbert-Huang transform (HHT), alleviates this problem in a purely data-driven manner. EMD adaptively and locally decomposes such time series into a sum of oscillatory modes, called Intrinsic mode functions (IMF) and a nonstationary component called residuum. In this contribution, we propose an EMD-based method, called Sliding empirical mode decomposition (SEMD), which, with a reasonable computational effort, extends the application area of EMD to a true on-line analysis of time series comprising a huge amount of data if recorded with a high sampling rate. Using nonlinear and nonstationary toy data, we demonstrate the good performance of the proposed algorithm. We also show that the new method extracts component signals that fulfill all criteria of an IMF very well and that it exhibits excellent reconstruction quality. The method itself will be refined further by a weighted version, called weighted sliding empirical mode decomposition (wSEMD), which reduces the computational effort even more while preserving the reconstruction quality.
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WU, ZHAOHUA, and NORDEN E. HUANG. "ON THE FILTERING PROPERTIES OF THE EMPIRICAL MODE DECOMPOSITION." Advances in Adaptive Data Analysis 02, no. 04 (October 2010): 397–414. http://dx.doi.org/10.1142/s1793536910000604.

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The empirical mode decomposition (EMD) based time-frequency analysis has been used in many scientific and engineering fields. The mathematical expression of EMD in the time-frequency-energy domain appears to be a generalization of the Fourier transform (FT), which leads to the speculation that the latter may be a special case of the former. On the other hand, the EMD is also known to behave like a dyadic filter bank when used to decompose white noise. These two observations seem to contradict each other. In this paper, we study the filtering properties of EMD, as its sifting number changes. Based on numerical results of the decompositions using EMD of a delta function and white noise, we conjecture that, as the (pre-assigned and fixed) sifting number is changed from a small number to infinity, the EMD corresponds to filter banks with a filtering ratio that changes accordingly from 2 (dyadic) to 1; the filter window does not narrow accordingly, as the sifting number increases. It is also demonstrated that the components of a delta function resulted from EMD with any prescribed sifting number can be rescaled to a single shape, a result similar to that from wavelet decomposition, although the shape changes, as the sifting number changes. These results will lead to further understandings of the relations of EMD to wavelet decomposition and FT.
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Du, Wei, and Quan Liu. "A Novel Empirical Mode Decomposition Denoising Scheme." Advanced Materials Research 143-144 (October 2010): 527–32. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.527.

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This paper presents a novel and fast scheme for signal denoising by using Empirical mode decomposition (EMD). The EMD involves the adaptive decomposition of signal into a series of oscillating components, Intrinsic mode functions(IMFs), by means of a decomposition process called sifting algorithm. The basic principle of the method is to reconstruct the signal with IMFs previously selected and thresholded. The denoising method is applied to four simulated signals with different noise levels and the results compared to Wavelets, EMD-Hard and EMD-Soft methods.
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Dissertations / Theses on the topic "EMD (Empirical Mode Decomposition)"

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Tomás, Ana Raquel Dias. "Application of empirical mode decomposition (EMD) to chronological series of active fires from MODIS satellite." Master's thesis, ISA/UTL, 2011. http://hdl.handle.net/10400.5/4481.

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Mestrado em Engenharia Florestal e dos Recursos Naturais - Instituto Superior de Agronomia
Fire is a global phenomenon, acting as an important disturbance process. Africa is one of the continents that has higher fire density, particularly in savanna regions, making it the subject of innumerous studies about fire regime and behavior. Here, a new method of time series analysis called Empirical Mode Decomposition (EMD) was applied to monthly fire counts time series from MODIS Terra/Aqua sensors. The goals were to analyze the differences between the time series from the two instruments (MODIS Terra and Aqua), the differences in the behavior of the active fire time series from the north and south parts of Africa and they‟re relationships with climatic modes (ENSO and IOD). For most of the time series, the application of the EMD resulted in four IMF‟s and a residue. Although there is always an IMF related with seasonality, the physical meaning of the other isn‟t clear. This may be due to various reasons, some related with intrinsic problems of the method, other with the applicability of the method to this type of series.
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Li, Zhendan. "An Ensemble Empirical Mode Decomposition Approach to Wear Particle Detection in Lubricating Oil Subject to Particle Overlap." Thèse, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/20313.

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With the development of mechanical fault diagnosis technology, complex mechanical systems do not need to be shut down periodically for the maintenance. The working condition of the mechanical systems can be monitored by analyzing the wear metal particles in the systems' lubricating oil. However, the output signals of the monitoring sensor are non-stationary. In some case the particle signals are overlapped with each other. The goal of this thesis is to find a method to decompose those overlapped particle signals, and then count the particle number in the lubricating oil. At the beginning EMD method was introduced in the experiment because of the character of the sensor signals. In this project, because EMD method is sensitive to the noise in the original signals, an improved version of EMD, EEMD method was implemented. Finally, a post processing method was used to get a better result.
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Abderahman, Huthaifa. "An Integrated Compensation System Based on Empirical Mode Decomposition for Robust Noninvasive Blood Pressure Estimation." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/35314.

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When it comes to monitoring human health, accuracy is not a choice. Accuracy in blood pressure (BP) estimation is essential for proper diagnosis and management of hypertension. An error of 5 mmHg is so serious, it can be responsible for doubling or halving number of patients diagnosed with hypertension. Motion artifacts are external sources of inaccuracy and can be due to sudden arm motion, muscle tremor, shivering, and transport vehicle vibration. Medium term drift, due to changing environmental factors, such as ambient temperature, can also contribute to the inaccuracy. Long term drift (ageing), can reach 9 mmHg during the first three months of usage. In this thesis, a new stage is added to current cuff based BP devices. This stage is responsible for adjusting the pressure reading before displaying it to end users. The proposed stage is provided with a 3-axis accelerometer, which makes the detection of motion artifacts during measurement possible. Moreover, it monitors changes in the ambient temperature and sensor ageing, so that it will adaptively compensate for these inaccuracies. These sources of inaccuracy are suppressed using algorithms based on Empirical Mode Decomposition (EMD), which has the feature of removing unwanted noise components little effect on the phase or the frequency distribution of the measured signal. With motion artifacts, measurements show that the proposed algorithms considerably improved the accuracy of the blood pressure estimates in comparison with the commonly-used conventional oscillometric algorithm that does not include a stage for artifact suppression, and allowed the estimates to consistent with the international ANSI/AAMI/ISO standard. Moreover, simulations based on experimental results show that the system is able to compensate for drift due to temperature changes and ageing with excellent performance. Results show promise towards building a robust BP monitor, with very low errors due to motion artifacts, environmental changes, and ageing.
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Šlancar, Matěj. "Potlačení driftu signálu EKG s využitím empirického rozkladu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2017. http://www.nusl.cz/ntk/nusl-316450.

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The aim of this thesis is to introduce with principle of Empirical Mode Decomposition method and possibility use for correction of baseline wander in ECG signals. The thesis describes the main components of the ECG signal, a selection of possible types of signal noise, its property and principles of chosen methods for filtration of ECG signals. In conclusion the evaluation of the effectiveness of the EMD method for filtering a baseline wander and it comparing with effectiveness of the linear filtration. Functionality of used algorithms has been tested on signals of CSE standard library.
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Sadeghi, Mehdi. "Potential of the Empirical Mode Decomposition to analyze instantaneous flow fields in Direct Injection Spark Ignition engine : Effect of transient regimes." Thesis, Orléans, 2017. http://www.theses.fr/2017ORLE2069/document.

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Cette étude introduit une nouvelle approche appelée Bivariate 2D-EMD pour séparer le mouvement organisé à grande échelle, soit la composante basse fréquence de l’écoulement des fluctuations turbulentes, soit la composante haute fréquence dans un champ de vitesse instantané bidimensionnel.Cette séparation nécessite un seul champ de vitesse instantané contrairement aux autres méthodes plus couramment utilisées en mécanique des fluides, comme le POD. La méthode proposée durant cette thèse est tout à fait appropriée à l’analyse des écoulements qui sont intrinsèquement instationnaires et non linéaires comme l'écoulement dans le cylindre lorsque le moteur fonctionne dans des conditions transitoires. Bivariate 2D-EMD est validé à travers différents cas test, sur un écoulement turbulent homogène et isotrope (THI) expérimental, qui a été perturbé par un tourbillon synthétique de type Lamb-Ossen, qui simule le mouvement organisé dans le cylindre. Enfin, Il est appliqué sur un écoulement expérimental obtenu dans un cylindre et les résultats de la séparation d'écoulement sont comparés à ceux basés sur l'analyse POD. L’évolution d’écoulement dans le cylindre pendant le fonctionnement du moteur transitoire, c’est à dire une accélération du régime moteur de 1000 à 2000tr/min en différentes rampes, sont mesurée en utilisant de PIV 2D-2C haute cadence. Les champs de vitesse sont obtenus dans le plan de tumble dans un moteur un moteur GDI mono-cylindre transparent et forment une base de données nécessaire pour valider les résultats de simulation numérique
This study introduces a new approach called Bivariate 2D-EMD to separate large-scale organizedmotion i.e., flow low frequency component from random turbulent fluctuations i.e., high frequency onein a given in-cylinder instantaneous 2D velocity field. This signal processing method needs only oneinstantaneous velocity field contrary to the other methods commonly used in fluid mechanics, as POD.The proposed method is quite appropriate to analyze the flows intrinsically both unsteady and nonlinearflows as in in-cylinder. The Bivariate 2D-EMD is validated through different test cases, by optimize itand apply it on an experimental homogeneous and isotropic turbulent flow (HIT), perturbed by asynthetic Lamb-Ossen vortex, to simulate the feature of in-cylinder flows. Furthermore, it applies onexperimental in-cylinder flows. The results obtained by EMD and POD analysis are compared. Theevolution of in-cylinder flow during transient engine working mode, i.e., engine speed acceleration from1000 to 2000 rpm with different time periods, was obtained by High speed PIV 2D-2C. The velocityfields are obtained within tumble plane in a transparent mono-cylinder DISI engine and provide a database to validate CFD
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Barnhart, Bradley Lee. "The Hilbert-Huang Transform: theory, applications, development." Diss., University of Iowa, 2011. https://ir.uiowa.edu/etd/2670.

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Hilbert-Huang Transform (HHT) is a data analysis tool, first developed in 1998, which can be used to extract the periodic components embedded within oscillatory data. This thesis is dedicated to the understanding, application, and development of this tool. First, the background theory of HHT will be described and compared with other spectral analysis tools. Then, a number of applications will be presented, which demonstrate the capability for HHT to dissect and analyze the periodic components of different oscillatory data. Finally, a new algorithm is presented which expands HHT ability to analyze discontinuous data. The sum result is the creation of a number of useful tools developed from the application of HHT, as well as an improvement of the HHT tool itself.
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Waindim, Mbu. "On Unsteadiness in 2-D and 3-D Shock Wave/Turbulent Boundary Layer Interactions." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1511734224701396.

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Ramirez, Saul Gallegos. "Toward Using Empirical Mode Decomposition to Identify Anomalies in Stream FlowData and Correlations with other Environmental Data." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7574.

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I applied empirical mode decomposition (EMD) and the Hilbert-Herbert transforms, as tools to analyze streamflow data. I used the EMD method to extract and analyze periodic processes and trends in several environmental datasets including daily stream flow, daily precipitation, and daily temperature on data from the watersheds of two rivers in the Upper Colorado River Basin, the Yampa and the Upper-Green rivers. I used these data to identify forcing functions governing streamflow. Forcing functions include environmental factors such as temperature and precipitation and anthropogenic factors such as dams or diversions. The Green and Yampa Rivers have similar headwaters, but the Yampa has minimal diversions or controls while Flaming George Dam on the Green river significantly affects flow. This provides two different flow regimes with similar large watersheds. In addition to flow data, I analyzed several time series data sets, including temperature and precipitation from Northeast Utah, North Western Colorado, and Southern Wyoming. These data are from the area that defines the Yampa River and Green River watersheds, which stretch from Flaming Gorge Dam to Ouray Colorado. The EMD method is a relatively new technique that allows any time series data set, including non-linear and non-stationary datasets that are common in earth observation data, to be decomposed into a small quantity of composite finite data series, called intrinsic mode functions (IMFs). The EMD method can decompose any complicated data into several IMFs that represent independent signals in the original data. These IMFs may represent periodic forcing functions, such as environmental conditions or dam operations, or they may be artifacts of the decomposition method and not have an associated physical meaning. This study attempts to assign physical meaning to some IMFs resulting from the decomposition of the Green and Yampa flows where possible. To assign physical meaning to the IMFs, I analyzed frequencies of each IMF using the Hilbert-Hung transform, part of the Empirical Mode Decomposition method, and then compared frequencies of the IMFs with the known frequencies of physical processes. I performed these calculations on both flow, temperature, and precipitation. I found significant correlation between IMF components of flow, precipitation, and temperature data with El Niño Southern Oscillation (ENSO) events. The EMD process also extracts the long-term trend in non-linear data sets that can provide insights into the effects of climate change on the flow system. Though in preliminary stages of research, these analysis methods may lead to further understanding the availability of water within the upper Yampa and Green River Watersheds.
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Procházka, Petr. "Odstraňovaní kolísání izolinie v EKG pomocí empirické modální dekompozice." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221366.

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In this semestral thesis, realizations of chosen linear filters for baseline wander are described. These filters are then used on artificial ECG signals from CSE database with added baseline wander. These methods are compared and results are evaluated. After that, literature search of Empirical mode decomposition method is utilized. Realization of designed filters in MATLAB programming language are described, then results are evaluated with respect to filtration success.
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Fayad, Farah. "Apprentissage et annulation des bruits impulsifs sur un canal CPL indoor en vue d'améliorer la QoS des flux audiovisuels." Phd thesis, Université Blaise Pascal - Clermont-Ferrand II, 2012. http://tel.archives-ouvertes.fr/tel-00769953.

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Le travail présenté dans cette thèse a pour objectif de proposer et d'évaluer les performances de différentes techniques de suppression de bruit impulsif de type asynchrone adaptées aux transmissions sur courants porteurs en lignes (CPL) indoor. En effet, outre les caractéristiques physiques spécifiques à ce type de canal de transmission, le bruit impulsif asynchrone reste la contrainte sévère qui pénalise les systèmes CPL en terme de QoS. Pour remédier aux dégradations dues aux bruits impulsifs asynchrones, les techniques dites de retransmission sont souvent très utilisées. Bien qu'elles soient efficaces, ces techniques de retransmission conduisent à une réduction de débit et à l'introduction de délais de traitement supplémentaires pouvant être critiques pour des applications temps réel. Par ailleurs, plusieurs solutions alternatives sont proposées dans la littérature pour minimiser l'impact du bruit impulsif sur les transmissions CPL. Cependant, le nombre de techniques, qui permettent d'obtenir un bon compromis entre capacité de correction et complexité d'implantation reste faible pour les systèmes CPL. Dans ce contexte, nous proposons dans un premier temps d'utiliser un filtre linéaire adaptatif : le filtre de Widrow, nommé aussi ADALINE (ADAptive LInear NEuron), que nous utilisons comme méthode de débruitage pour les systèmes CPL. Pour améliorer les performances du débruitage effectué à l'aide d'ADALINE, nous proposons d'utiliser un réseau de neurones (RN) non linéaire comme méthode de débruitage. Le réseau de neurones est un bon outil qui est une généralisation de la structure du filtre ADALINE. Dans un deuxième temps, pour améliorer les performances du débruitage par un réseau de neurones, nous proposons un procédé d'annulation du bruit impulsif constitué de deux algorithmes : EMD (Empirical Mode Decomposition) associé à un réseau de neurones de type perceptron multicouches. L'EMD effectue le prétraitement en décomposant le signal bruité en signaux moins complexes et donc plus facilement analysables. Après quoi le réseau de neurones effectue le débruitage. Enfin, nous proposons une méthode d'estimation du bruit impulsif utilisant la méthode GPOF (Generalized Pencil Of Function). L'efficacité des deux méthodes, EMD-RN et la technique utilisant l'algorithme GPOF, est évaluée en utilisant une chaîne de simulation de transmission numérique compatible avec le standard HPAV.
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Book chapters on the topic "EMD (Empirical Mode Decomposition)"

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Labate, Domenico, Fabio La Foresta, Giuseppe Morabito, Isabella Palamara, and Francesco Carlo Morabito. "On the Use of Empirical Mode Decomposition (EMD) for Alzheimer’s Disease Diagnosis." In Advances in Neural Networks: Computational and Theoretical Issues, 121–28. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18164-6_12.

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Wang, Heming, Richard Mann, and Edward R. Vrscay. "A Diffusion-Based Two-Dimensional Empirical Mode Decomposition (EMD) Algorithm for Image Analysis." In Lecture Notes in Computer Science, 295–305. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93000-8_34.

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Chang, H. C., P. L. Lee, and C. H. Wu. "Empirical Mode Decomposition (EMD) – Based Spatiotemporal Approach for Single-Trial Extraction of Post-Movement MEG Beta Synchronization." In IFMBE Proceedings, 1091–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03882-2_290.

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Tabrizi, A., L. Garibaldi, A. Fasana, and S. Marchesiello. "Influence of Stopping Criterion for Sifting Process of Empirical Mode Decomposition (EMD) on Roller Bearing Fault Diagnosis." In Lecture Notes in Mechanical Engineering, 389–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39348-8_33.

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Srivastava, Ashita, Vikrant Bhateja, Deepak Kumar Tiwari, and Deeksha Anand. "AWGN Suppression Algorithm in EMG Signals Using Ensemble Empirical Mode Decomposition." In Intelligent Computing and Information and Communication, 515–24. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7245-1_50.

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Fatmawati, T. Y., A. Yuliani, M. A. Afandi, and D. Zulherman. "Comparative Analysis of the Phonocardiogram Denoising System Based-on Empirical Mode Decomposition (EMD) and Double-Density Discrete Wavelet Transform (DDDWT)." In Proceedings of the 1st International Conference on Electronics, Biomedical Engineering, and Health Informatics, 593–604. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6926-9_52.

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Panda, Saroj Kumar, Papia Ray, and Debani Prasad Mishra. "Short Term Load Forecasting Using Empirical Mode Decomposition (EMD), Particle Swarm Optimization (PSO) and Adaptive Network-Based Fuzzy Interference Systems (ANFIS)." In Advances in Intelligent Systems and Computing, 161–68. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49339-4_17.

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Liu, Shing-Hong, Li-Te Hsu, Cheng-Hsiung Hsieh, and Yung-Fa Huang. "Denoising of ECG Signal with Power Line and EMG Interference Based on Ensemble Empirical Mode Decomposition." In Recent Advances in Intelligent Information Hiding and Multimedia Signal Processing, 175–82. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03748-2_21.

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Mahgoun, H., Fakher Chaari, A. Felkaoui, and Mohamed Haddar. "Early Detection of Gear Faults in Variable Load and Local Defect Size Using Ensemble Empirical Mode Decomposition (EEMD)." In Applied Condition Monitoring, 13–22. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41459-1_2.

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Cong, Fengyu, X. Xu, T. Ristaniemi, and H. Lyytinen. "Empirical Mode Decomposition on Mismatch Negativity." In IFMBE Proceedings, 206–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-69367-3_56.

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Conference papers on the topic "EMD (Empirical Mode Decomposition)"

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Fan, Xianfeng, and Ming J. Zuo. "Gearbox Fault Detection Using Empirical Mode Decomposition." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-59349.

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Local faults in a gearbox cause impacts and the collected vibration signal is often non-stationary. Identification of impulses within the non-stationary vibration signal is key to fault detection. Recently, the technique of Empirical Mode Decomposition (EMD) was proposed as a new tool for analysis of non-stationary signal. EMD is a time series analysis method that extracts a custom set of bases that reflects the characteristic response of a system. The Intrinsic Mode Functions (IMFs) within the original data can be obtained through EMD. We expect that the change in the amplitude of the special IMF’s envelope spectrum will become larger when fault impulses are present. Based on this idea, we propose a new fault detection method that combines EMD with Hilbert transform. The proposed method is compared with both the Hilbert-Huang transform and the wavelet transform using simulated signal and real signal collected from a gearbox. The results obtained show that the proposed method is effective in capturing the hidden fault impulses.
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Shahbakhti, Mohammad, Vahidreza Khalili, and Golnoosh Kamaee. "Removal of blink from EEG by Empirical Mode Decomposition (EMD)." In 2012 5th Biomedical Engineering International Conference (BMEiCON). IEEE, 2012. http://dx.doi.org/10.1109/bmeicon.2012.6465451.

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Alam, MD Erfanul, and Biswanath Samanta. "Performance Evaluation of Empirical Mode Decomposition for EEG Artifact Removal." In ASME 2017 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/imece2017-71647.

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Electroencephalography measures the sum of the post-synaptic potentials generated by many neurons having the same radial orientation with respect to the scalp. The electroen-cephalographic signals (EEG) are weak and often contaminated with different artifacts that have biological and external sources. Reliable pre-processing of the noisy, non-linear, and non-stationary brain activity signals is needed for successful extraction of characteristic features in motor imagery based brain-computer interface (MI-BCI). In this work, a signal processing technique, namely, empirical mode decomposition (EMD), has been proposed for processing EEG signals acquired from volunteer subjects for characterization and identification of motor imagery (MI) activities. EMD has been used for removal of artifacts like electrooculography (EOG) that strongly appears in frontal electrodes of EEG and the power line noise that is mainly produced by the fluorescent light. The performance of EMD has been compared with two extensions, ensemble empirical mode decomposition (EEMD) and multivariate empirical mode decomposition (MEMD)using signal to noise ratio (SNR). The maximum SNR values found for EMD, EEMD and MEMD are 4.30, 7.64 and 10.62 respectively for the EEG signals considered.
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Gan, Xilin, Weigen Huang, Jingsong Yang, and Bin Fu. "Internal Wave Packet Characterization from SAR Images Using Empirical Mode Decomposition (EMD)." In 2008 Congress on Image and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/cisp.2008.136.

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Shafqat, K., S. K. Pal, S. Kumari, and P. A. Kyriacou. "Empirical mode decomposition (EMD) analysis of HRV data from locally anesthetized patients." In 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2009. http://dx.doi.org/10.1109/iembs.2009.5335000.

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Li, Ying, and Dongzi Pan. "Analysis of Dynamic Test Signal for Bolt Using Empirical Mode Decomposition (EMD)." In 2010 International Conference on E-Product E-Service and E-Entertainment (ICEEE 2010). IEEE, 2010. http://dx.doi.org/10.1109/iceee.2010.5660614.

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Ye, Min, and Ting Jiang. "Dynamic body postures recognition with WiFi based on empirical mode decomposition (EMD)." In 2017 17th International Symposium on Communications and Information Technologies (ISCIT). IEEE, 2017. http://dx.doi.org/10.1109/iscit.2017.8261185.

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Hsiao, Fu-Jung, and Chen-Chuan Hung. "Noninvasive blood pressure analysis using empirical mode decomposition (EMD) - Based oscillometric method." In 2010 3rd International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2010. http://dx.doi.org/10.1109/bmei.2010.5639803.

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Setiawan, Nugroho Syarif, Amien Widodo, Wien Lestari, Firman Syaifuddin, Ahmad Zarkasyi, Dwa Desa Warnana, and Juan Pandu Gya Nur Rochman. "Application of empirical mode decomposition (EMD) filtering at magnetotelluric time-series data." In INTERNATIONAL CONFERENCE ON ELECTROMAGNETISM, ROCK MAGNETISM AND MAGNETIC MATERIAL (ICE-R3M) 2019. AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0015767.

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Morishita, Helio Mitio, Leonardo Kubota, Michaelli Sforsin Vestri, Solenn Greuell, and La´zaro Moratelli. "The Empirical Mode Decomposition Applied to Dynamic Positioning Systems." In ASME 2011 30th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2011. http://dx.doi.org/10.1115/omae2011-50025.

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Dynamic Positioning Systems installed in marine vessels require a filtering block to attenuate the wave frequency components captured by the position and heading sensors. In this paper a filtering algorithm based on the Empirical Mode Decomposition (EMD) is suggested. The EMD is an algorithm that claims to properly identify the time scales featuring the physical characteristics of any given system by analyzing a time series representative of the system dynamics. It can handle both non stationary and non linear signals and it requires no previous knowledge on the system dynamics. Details of the algorithm are provided and the results obtained from simulation and experimental tests are presented.
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Reports on the topic "EMD (Empirical Mode Decomposition)"

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Khatri, Hiralal, Kenneth Ranney, Kwok Tom, and Romeo del Rosario. Empirical Mode Decomposition Based Features for Diagnosis and Prognostics of Systems. Fort Belvoir, VA: Defense Technical Information Center, April 2008. http://dx.doi.org/10.21236/ada486738.

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Zhou, Feng, Lijun Yang, Hao-min Zhou, and Lihua Yang. Optimal Averages for Nonlinear Signal Decompositions - Another Alternative for Empirical Mode Decomposition. Fort Belvoir, VA: Defense Technical Information Center, October 2014. http://dx.doi.org/10.21236/ada610276.

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Huang, Norden E. The Application of the Empirical Mode Decomposition and Hilbert Spectral Analysis to Field Data and Future Experimental Designs. Fort Belvoir, VA: Defense Technical Information Center, August 2001. http://dx.doi.org/10.21236/ada627728.

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Huang, Norden E. The Application of the Empirical Mode Decomposition and Hilbert Spectral Analysis to Field Data and Future Experimental Designs. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada636671.

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Huang, Norden E. Development a Statistical Measure for Empirical Mode Decomposition and Hilbert Spectral Analysis and its Applications to Oceanographic Data. Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada634054.

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