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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 AgronomiaFire 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 numériqueThis 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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De, Sanctis Silvia [Verfasser], and Hans Robert [Akademischer Betreuer] Kalbitzer. "Application of Singular Spectrum Analysis (SSA), Independent Component Analysis (ICA) and Empirical Mode Decomposition (EMD) for automated solvent suppression and automated baseline and phase correction from multi-dimensional NMR spectra / Silvia De Sanctis. Betreuer: Hans Robert Kalbitzer." Regensburg : Universitätsbibliothek Regensburg, 2011. http://d-nb.info/1030178941/34.
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Janáková, Jaroslava. "Odhad dechové frekvence z elektrokardiogramu a fotopletysmogramu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442594.
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The master thesis deals with the issue of gaining the respiratory rate from ECG and PPG signals, which are not only in clinical practice widely used measurable signals. The theoretical part of the work outlines the issue of obtaining a breath curve from these signals. The practical part of the work is focused on the implementation of five selected methods and their final evaluation and comparison.
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Mettke, Philipp. "Vorhersagbarkeit ökonomischer Zeitreihen auf verschiedenen zeitlichen Skalen." Bachelor's thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-197876.
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This thesis examines three decomposition techniques and their usability for economic and financial time series. The stock index DAX30 and the exchange rate from British pound to US dollar are used as representative economic time series. Additionally, autoregressive and conditional heteroscedastic simulations are analysed as benchmark processes to the real data.
Discrete wavelet transform (DWT) uses wavelike functions to adapt the behaviour of time series on different time scales. The second method is the singular spectral analysis (SSA), which is applied to extract influential reconstructed modes. As a third algorithm, empirical mode decomposition (END) leads to intrinsic mode functions, who reflect the short and long term fluctuations of the time series. Some problems arise in the decomposition process, such as bleeding at the DWT method or mode mixing of multiple EMD mode functions.
Conclusions to evaluate the predictability of the time series are drawn based on entropy - and recurrence - analysis. The cyclic behaviour of the decompositions is examined via the coefficient of variation, based on the instantaneous frequency. The results show rising predictability, especially on higher decomposition levels. The instantaneous frequency measure leads to low values for regular oscillatory cycles, irregular behaviour results in a high variation coefficient. The singular spectral analysis show frequency - stable cycles in the reconstructed modes, but represents the influences of the original time series worse than the other two methods, which show on the contrary very little frequency - stability in the extracted details.
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Vadali, Venkata Akshay Bhargav Krishna. "A Comparative Study of Signal Processing Methods for Fetal Phonocardiography Analysis." Scholar Commons, 2018. https://scholarcommons.usf.edu/etd/7373.
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More than one million fetal deaths occur in the United States every year [1]. Monitoring the long-term heart rate variability provides a great amount of information about the fetal health condition which requires continuous monitoring of the fetal heart rate. All the existing technologies have either complex instrumentation or need a trained professional at all times or both. The existing technologies are proven to be impractical for continuous monitoring [2]. Hence, there is an increased interest towards noninvasive, continuous monitoring, and less expensive technologies like fetal phonocardiography.
Fetal Phonocardiography (FPCG) signal is obtained by placing an acoustic transducer on the abdomen of the mother. FPCG is rich in physiological bio-signals and can continuously monitor the fetal heart rate non-invasively. Despite its high diagnostic potential, it is still not being used as the secondary point of care. There are two challenges as to why it is still being considered as the secondary point of care; in the data acquisition system and the signal processing methodologies. The challenges pertaining to data acquisition systems are but not limited to sensor placement, maternal obesity and multiple heart rates. While, the challenges in the signal processing methodologies are dynamic nature of FPCG signal, multiple known and unknown signal components and SNR of the signal.
Hence, to improve the FPCG based care, challenges in FPCG signal processing methodologies have been addressed in this study. A comparative evaluation was presented on various advanced signal processing techniques to extract the bio-signals with fidelity. Advanced signal processing approaches, namely empirical mode decomposition, spectral subtraction, wavelet decomposition and adaptive filtering were used to extract the vital bio-signals. However, extracting these bio-signals with fidelity is a challenging task in the context of FPCG as all the bio signals and the unwanted artifacts overlap in both time and frequency. Additionally, the signal is corrupted by noise induced from the fetal and maternal movements as well the background and the sensor.
Empirical mode decomposition algorithm was efficient to denoise and extract the maternal and fetal heart sounds in a single step. Whereas, spectral subtraction was used to denoise the signal which was later subjected to wavelet decomposition to extract the signal of interest. On the other hand, adaptive filtering was used to estimate the fetal heart sound from a noisy FPCG where maternal heart sound was the reference input.
The extracted signals were validated by obtaining the frequency ranges computed by the Short Time Fourier Transform (STFT). It was observed that the bandwidths of extracted fetal heart sounds and maternal heart sounds were consistent with the existing gold standards. Furthermore, as a means of additional validation, the heart rates were calculated. Finally, the results obtained from all these methods were compared and contrasted qualitatively and quantitatively.
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Amorim, Felipe Zumba. "Atenua??o de ru?dos coerentes utilizando decomposi??o em modos emp?ricos." Universidade Federal do Rio Grande do Norte, 2010. http://repositorio.ufrn.br:8080/jspui/handle/123456789/12925.
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Previous issue date: 2010-10-23The seismic processing technique has the main objective to provide adequate picture of geological structures from subsurface of sedimentary basins. Among the key steps of this process is the enhancement of seismic reflections by filtering unwanted signals, called seismic noise, the improvement of signals of interest and the application of imaging procedures. The seismic noise may appear random or coherent. This dissertation will present a technique to attenuate coherent noise, such as ground roll and multiple reflections, based on Empirical Mode Decomposition method. This method will be applied to decompose the seismic trace into Intrinsic Mode Functions. These functions have the properties of being symmetric, with local mean equals zero and the same number of zero-crossing and extremes. The developed technique was tested on synthetic and real data, and the results were considered encouraging
O processamento s?smico tem como principal objetivo fornecer imagem adequada das estruturas geol?gicas da sub-superf?cie de bacias sedimentares. Dentre as etapas fundamentais deste processamento est? o enriquecimento das reflex?es s?smicas atrav?s de filtragem de sinais indesej?veis, chamados de ru?dos, a amplifica??o de sinais de interesse e a aplica??o de processos de imageamento. Os ru?dos s?smicos podem aparecer de forma aleat?ria ou coerente. Nesta disserta??o ser? apresentado uma t?cnica para atenuar ru?dos coerentes, como o ground roll e as reflex?es m?ltiplas, baseado na Decomposi??o em Modos Emp?ricos. Este m?todo consiste em decompor o tra?o s?smico em Fun??es de Modo Intr?nseco, que s?o fun??es sim?tricas com m?dia local igual a zero e mesmo n?mero de zeros e extremos. A t?cnica desenvolvida foi testado em dados sint?ticos e reais, e os resultados obtidos foram considerados encorajadores
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Hargis, Brent H. "Analysis of Long-Term Utah Temperature Trends Using Hilbert-Haung Transforms." BYU ScholarsArchive, 2014. https://scholarsarchive.byu.edu/etd/5490.
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We analyzed long-term temperature trends in Utah using a relatively new signal processing method called Empirical Mode Decomposition (EMD). We evaluated the available weather records in Utah and selected 52 stations, which had records longer than 60 years, for analysis. We analyzed daily temperature data, both minimum and maximums, using the EMD method that decomposes non-stationary data (data with a trend) into periodic components and the underlying trend. Most decomposition algorithms require stationary data (no trend) with constant periods and temperature data do not meet these constraints. In addition to identifying the long-term trend, we also identified other periodic processes in the data. While the immediate goal of this research is to characterize long-term temperature trends and identify periodic processes and anomalies, these techniques can be applied to any time series data to characterize trends and identify anomalies. For example, this approach could be used to evaluate flow data in a river to separate the effects of dams or other regulatory structures from natural flow or to look at other water quality data over time to characterize the underlying trends and identify anomalies, and also identify periodic fluctuations in the data. If these periodic fluctuations can be associated with physical processes, the causes or drivers might be discovered helping to better understand the system. We used EMD to separate and analyze long-term temperature trends. This provides awareness and support to better evaluate the extremities of climate change. Using these methods we will be able to define many new aspects of nonlinear and nonstationary data. This research was successful and identified several areas in which it could be extended including data reconstruction for time periods missing data. This analysis tool can be applied to various other time series records.
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Ng, Cheng Man. "Electroencephalogram analysis based on empirical mode decomposition." Thesis, University of Macau, 2011. http://umaclib3.umac.mo/record=b2493507.
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Ayenu-Prah, Albert Yawson Jr. "Empirical mode decomposition and civil infrastructure systems." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file, 210 p, 2008. http://proquest.umi.com/pqdweb?did=1456291101&sid=1&Fmt=2&clientId=8331&RQT=309&VName=PQD.
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Bozzeda, Matteo. "Analisi di emissioni condotte con il metodo Empirical Mode Decomposition." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.
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Il lavoro svolto riguarda lo studio delle emissioni condotte nel range di frequenza compreso fra 2-150 kHz. Il metodo implementato è la trasformata di Hilbert-Huang, la quale è stata implementata in ambiente R.
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Linderhed, Anna. "Adaptive image compression with wavelet packets and empirical mode decomposition /." Linköping : Univ, 2004. http://www.bibl.liu.se/liupubl/disp/disp2004/tek909s.pdf.
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Chen, Jin [Verfasser]. "Using Empirical Mode Decomposition to Process Marine Magnetotelluric Data / Jin Chen." Kiel : Universitätsbibliothek Kiel, 2014. http://d-nb.info/1049189329/34.
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Coughlin, Kathleen T. "Stratospheric and tropospheric signals extracted using the empirical mode decomposition method /." Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/6781.
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Lahmiri, Salim. "Detection of pathologies in retina digital images an empirical mode decomposition approach." Mémoire, École de technologie supérieure, 2011. http://espace.etsmtl.ca/961/1/LAHMIRI_Salim.pdf.
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La détection automatique exacte des pathologies dans les images numériques de la rétine
offre une approche prometteuse dans les applications cliniques. Cette étude emploie la
technique de la transformée discrète en ondelette et la décomposition en modes empiriques
pour extraire six caractéristiques statistiques de la texture de la rétine à partir d’images
numériques. Les caractéristiques statistiques sont la moyenne, l'écart-type, l'aspect lisse, le
troisième moment, l'uniformité et l'entropie. Le but est de classifier les images normales
versus anormales. Cinq différentes pathologies sont considérées. Ils sont le fourreau d'Artère
(la maladie de Manteau), tache d'hémorragie, la dégénérescence rétinienne (circinates), la
dégénérescence maculaire liée à l'âge (drusens) et la rétinopathie diabétique
(microanévrismes et exsudats). Quatre classificateurs sont employés; incluant des machines à
supports de vecteur, l'analyse discriminant quadratique, le k plus proche voisin, et les réseaux
neuronaux probabilistes. Pour chaque expérience, dix plis au hasard sont produits pour
exécuter des épreuves de validation croisée. Pour évaluer la performance de chaque
classificateur, la moyenne et l'écart-type du taux de reconnaissance correct, la sensibilité et la
spécificité sont calculés pour chaque simulation. Les résultats expérimentaux font ressortir
deux conclusions principales. D'abord, ils montrent la performance exceptionnelle des
caractéristiques statistiques obtenues par la décomposition en modes empiriques (DME)
quelque soit le classificateur. Deuxièmement, ils montrent la supériorité des machines à
supports de vecteurs (MSV) par rapport à l'analyse discriminant quadratique, le k plus
proche voisin, et les réseaux neuronaux probabilistes. Finalement, l’analyse en composante
principale a été employée pour réduire le nombre de caractéristiques dans l'espoir d'améliorer
l'exactitude des classificateurs. Nous constatons qu'il n'y a aucune amélioration générale et
significative de la performance. En somme, le système DME-MSV fournit une approche
prometteuse pour la détection de certaines pathologies dans la rétine à partir des images
numériques médicales.
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Poon, Chun Wing. "Identification of nonlinear non-hysteretic and hysteretic structures using empirical mode decomposition /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?CIVL%202007%20POON.
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Yeh, Jia-Rong, and 葉家榮. "APPLICATIONS OF EMPIRICAL MODE DECOMPOSITION (EMD) IN PHYSIOLOGICAL SIGNAL ANALYSIS." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/82879254109505190946.
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博士元智大學
機械工程學系
97
The functions of the human body are frequently associated with physiological signals, which convey hidden information, determined by the various complicated underlying mechanisms. Moreover, physiological signal processing and analysis are interdisciplinary topics. A physiological system is non-linear and non-stationary, therefore, most of traditional algorithms based on linear assumption cannot satisfy the requirements for physiological signal analysis. Recently, empirical mode decomposition (EMD) was proposed as a signal processing and analysis algorithm for nonlinear and non-stationary systems. EMD also performs as an adaptive analysis algorithm, which doesn’t need a priori. In 2006, we used EMD and found that helps in research on physiological signal analysis. Therefore, we decide to focus our research on the processing algorithms of EMD and its correlated applications. In the processing algorithm, we proposed a noise enhanced algorithm of complementary ensemble empirical mode decomposition (CEEMD) to solve the mode-mixing problem of the original EMD and to improve the efficiency of EEMD. According to signals with or without dominant components, physiological signals are assorted into two different categories. A broad-band signal is defined as a signal without dominant components and a narrow-band signal is a signal with dominant components. Moreover, EMD acts as a natural filter bank for narrow-band signals and as a dyadic filter bank for broad-band signals. Therefore, we developed different applications of EMD according to the essential characteristics of the signals. These applications include the complexity quantification, verification of high-frequency fluctuation in signals, and the intrinsic component extraction. In this thesis, we present three different applications of EMD on physiological signal analysis to demonstrate the functions of EMD. In the first application of complexity quantification, EMD acts as a dyadic filter bank to decompose a human heartbeat interval into several IMFs adaptive to the intrinsic timescales and power-law distributions of data. The power-law distribution presents a long-term correlation, just as Hurst exponent and DFA scaling exponents do. Moreover, the distribution of intrinsic timescales of signals presents an extra property in a signal. Thus, the two-parameter scheme of complexity quantification was developed using the intrinsic timescale and power-law distributions. In addition, we developed two different approaches, the EMD-based DFA and the intrinsic mode analysis (IMA), to investigate human heartbeat interval. We found that the distribution of intrinsic timescales performs as a good indicator for patients with of without heart disease (i.e., CHF or AF) and the power-law distribution performs as an indicator for aging. In addition, EMD associates with Monte Carlo verification to act as a filter, which can be used to filter high-frequency noise from a signal. In the second application of EMD associating with linguistic analysis, we demonstrate the use of EMD as a filter. In such an application, the blocking index is designed using the distant measurement of similarity. Moreover, the blocking index is succeeded in verifying the fluctuation pattern of blood pressure (BP) during artery clamping or relaxing. In the third and last application, EEMD acts to decompose the intrinsic components from narrow-band signals, such as ECG and BP. We demonstrate two approaches of intrinsic component extraction. Here, an intrinsic component is defined as an IMF, which presents the response of a particular physiological mechanism. These two approaches of intrinsic component extraction are the EEMD-based reflected wave quantification and multi-modal analysis. EEMD works to extract the reflected waves from BP in the EEMD-based reflected wave quantification and the cardiac oscillations from ECG and BP in the multi-modal analysis. In this application, these EEMD-based analysis methods are succeeded in figuring out the correlations among systolic arterial pressure (SAP), arterial stiffness, and dynamic property of the circulation system. Without doubt, EMD is a powerful signal processing and analysis algorithm for signals measured from nonlinear and non-stationary systems. Although, the development of processing algorithm and the application of EMD is still at an early stage, we derived useful information from the physiological signals by these analysis algorithms based on EMD or EEMD. We believe that we can create more applications of EMD for physiological signal analysis in the future.
26
Tseng, Ji-Yu, and 曾紀宇. "Memory-Efficient & Scalable Empirical Mode Decomposition (EMD) and its Hardware Implementation." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/4z6mzx.
Full textAbstract:
碩士國立中正大學
資訊工程研究所
101
Empirical mode decomposition (EMD) has outstanding performance in non-linear and non-stationary signal analysis. But it is not widely adopted in embedded and real-time signal processing applications due to its high computing complexity and high memory requirement. This thesis proposes a memory-efficient EMD design with parallel architecture, which has been integrated into an embedded system successfully. First, a memory-efficient segmented cubic spline computation is proposed to reduce the memory requirements in EMD. Then, a systematic exploration is proposed for the segment size & overlapped points of the segmented cubic spline, which affect computing time, quality and memory sizes. Finally, a scalable hardware architecture is proposed for our memory-efficient EMD. The above design techniques have been implemented and verified using FPGA and a real-time processing system has been demonstrated. Compared with the original approach, our proposed algorithm can reduce 95.3% memory in spline computations for 2,048-points EMD.
27
Khaldi, Kais. "Processing and analysis of sounds signals by Huang transform (Empirical Mode Decomposition: EMD)." Phd thesis, 2012. http://tel.archives-ouvertes.fr/tel-00719637.
Full textAbstract:
This dissertation explores the potential of EMD as analyzing tool for audio and speech processing. This signal expansion into IMFs is adaptive and without any prior assumptions (stationarity and linearity) on the signal to be analyzed. Salient properties of EMD such as dyadic filter bank structure, quasi-symmetry of IMF and fully description of IMF by its extrema, are exploited for denoising, coding and watermarking purposes. In speech signals denoising, we initially proposed a technique based on IMFs thresholding. A comparative analysis of performance of this technique compared to the denoising technique based on the wavelet. Then, to remedy the problem of the MMSE filters which requires an estimation of the spectral properties of noise, we introduced the ACWA filter in the denoising procedure. The proposed approach is consisted to filter all IMFs of the noisy signal by ACWA filter. This filtering approach is implemented in the time domain, and also applicable in the context of colored noise. Finally, to handle the case of hybrid speech frames, that is composed of voiced and unvoiced speech, we introduced a stationarity index in the denoising approach to detect the transition between the mixture of voiced and unvoiced sounds. In audio signals coding, we proposed four compression approaches. The first two approaches are based on the EMD, and the other two approaches exploit the EMD in association with Hilbert transform. In particular, we proposed to use a predictive coding of the instantaneous amplitude and frequency of the IMFs Finally, we studied the problem of audio signals watermarking in context of copyright protection. The number of IMFs can be variable depending on the attack type. The proposed approach involves inserting the mark in the extrema of last IMFs. In addition, we introduced a synchronization code in the procedure in order to facility the extraction of the mark. These contributions are illustrated on synthetic and real data and results compared to well established methods such as MMSE filter, wavelets approach, MP3 and AAC coders showing the good performances of EMD based signal processes. These findings demonstrate the real potential of EMD as analyzing tool (in adaptive way) in speech and audio processing.
28
Zhendan, Li. "An Ensemble Empirical Mode Decomposition Approach to Wear Particle Detection in Lubricating Oil Subject to Particle Overlap." Thèse, 2011. http://hdl.handle.net/10393/20313.
Full textAbstract:
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.
29
Mouton, Jacques. "Combining empirical mode decomposition with neural networks for the prediction of exchange rates / Jacques Mouton." Thesis, 2014. http://hdl.handle.net/10394/15448.
Full textAbstract:
The foreign exchange market is one of the largest and most active financial markets with enormous daily trading volumes. Exchange rates are influenced by the interactions of a large number of agents, each operating with different intentions and on different time scales. This gives rise to nonlinear and non-stationary behaviour which complicates modelling. This research proposes a neural network based model trained on data filtered with a novel Empirical Mode Decomposition (EMD) filtering method for the forecasting of exchange rates.
One minor and two major exchange rates are evaluated in this study. Firstly the ideal prediction horizons for trading are calculated for each of the exchange rates. The data is filtered according to this ideal prediction horizon using the EMD-filter. This EMD-filter dynamically filters the data based on the apparent number of intrinsic modes in the signal that can contribute towards prediction over the selected horizon. The filter is employed to filter out high frequency noise and components that would not contribute to the prediction of the exchange rate at the chosen timescale. This results in a clearer signal that still includes nonlinear behaviour. An artificial neural network predictor is trained on the filtered data using different sampling rates that are compatible with the cut-off frequency. The neural network is able to capture the nonlinear relationships between historic and future filtered data with greater certainty compared to a neural network trained on unfiltered data.
Results show that the neural network trained on EMD-filtered data is significantly more accurate at prediction of exchange rates compared to the benchmark models of a neural network trained on unfiltered data and a random walk model for all the exchange rates. The EMD-filtered neural network’s predicted returns for the higher sample rates show higher correlations with the actual returns, and significant profits can be made when applying a trading strategy based on the predictions. Lower sample rates that just marginally satisfy the Nyquist criterion perform comparably with the neural network trained on unfiltered data; this may indicate that some aliasing occurs for these sampling rates as the EMD low-pass filter has a gradual cut-off, leaving some high frequency noise within the signal.
The proposed model of the neural network trained on EMD-filtered data was able to uncover systematic relationships between the filtered inputs and actual outputs. The model is able to deliver profitable average monthly returns for most of the tested sampling rates and forecast horizons of the different exchange rates. This provides evidence that systematic predictable behaviour is present within exchange rates, and that this systematic behaviour can be modelled if it is properly separated from high frequency noise.MIng (Computer and Electronic Engineering), North-West University, Potchefstroom Campus, 2015
30
Lin, En-Hung, and 林恩弘. "Extraction of MEG steady-state auditory evoked field in tinnitus patient using empirical mode decomposition (EMD)." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/60535972634273410917.
Full textAbstract:
碩士國立中央大學
電機工程研究所
100
This dissertation adopted multi-channel MEG to study the steady-state auditory evoked field (SSAEF) responses in tinnitus patients. In this study, 10 right-handed subjects (5 single-side tinnitus patients), aged from 22 to 50 years (mean age at 33 years) were recruited. MEG experiments were performed in a sound-proof magnetic shielding room. MEG data were recorded at 1000 Hz sampling rate. Auditory stimuli were given to subject’s left ear and right ear separately. Preceding the SSAEF study, pure tone stimulations were given to each subject to ensure the sound loudness was within subject’s acceptable range. The stimulation material of SSAEF was 1000Hz sound modulated by 37 Hz modulation frequency. MEG data were segmented into epochs and decomposed by empirical mode decomposition (EMD) into several intrinsic mode functions (IMF). Task-related IMFs with 37Hz information were identified to reconstruct noise-suppressed SSAEFs. In this study, we found the SSAEFs have the following characteristics in normal subjects: 1. right brain energy is always greater than the left hemisphere, and 2. Greater responses induced by contralateral auditory stimulation. Neverthelss, no similar finding was concluded in tinnitus patients. We guess it is caused by cerebral cortex plasticity, it makes the brain not normal discharge. And We also found disinhibition of SSAEF response in affected side (tinntus ear), it might caused by the some reason.
31
Nyaga, Muriithi Job. "The use of empirical mode decomposition (EMD) and variable length boostrap (VLB) for stochastic rainfall generation." Thesis, 2015. http://hdl.handle.net/10539/17668.
Full textAbstract:
This Research Report sets out to find out how the use of Empirical Mode Decomposition (EMD) for block selection impacts on the performance of the Variable Length Bootstrap (VLB) stochastic rainfall generator. Empirical Mode Decomposition (EMD), a relatively new data-adaptive approach, decomposes a time series into a group of component time series’ termed Intrinsic Mode Functions (IMFs) that are considered to quantify the impact of the multiple physical processes that affect the variability in the original time series. Therefore using IMFs may be better than the subjective method currently used in the VLB for block determination. The performance of the resulting model is tested by comparing historic with generated rainfall statistics using a 10-site rainfall generator problem. The hybrid EMD-VLB model is further compared with the standard VLB model using 8 statistics. The EMD-VLB generator is found to replicate the statistics at par with the VLB generator on a monthly time scale while the standard VLB model performs better on a yearly time scale.
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Μπάρκουλα, Κωνσταντίνα. "Ευφυής ψηφιακή επεξεργασία σημάτων με μεθόδους EMD, TKO και συνδυασμοί." Thesis, 2010. http://nemertes.lis.upatras.gr/jspui/handle/10889/3545.
Full textAbstract:
Στα πλαίσια της διπλωματικής αυτής εργασίας στόχος μας είναι η εφαρμογή τεχνικών
ψηφιακής επεξεργασίας σήματος σε γεωφυσικά σήματα και συγκεκριμένα κατά κύριο λόγο
σε σήματα γεωηλεκτρικού δυναμικού μακράς διάρκειας και σε σήματα σεισμικών κυμάτων.
Έχουμε εστιάσει την επεξεργασία μας στην διερεύνηση της εφαρμογής δυο τεχνικών
επεξεργασίας ξεχωριστά και σε συνδυασμό. Η πρώτη αφορά την εκτίμηση της στιγμιαίας
συχνότητας ενός σήματος μέσω του τελεστή Teager-Kaiser [Maragos_1991] στοχεύοντας
σε μια εναλλακτική στα προβλήματα συμβιβασμού της διακριτικής ικανότητας, στην
υπολογιστική πολυπλοκότητα, στην ανίχνευση αρνητικών συχνοτήτων και στην εμφάνιση
cross-terms άλλων κλασικών μεθόδων. Η δεύτερη που είναι η Empirical Mode
Decomposition (EMD) [Huang_1998] αφορά την ανάλυση των δεδομένων σε συνιστώσες
AM-FM μορφής καθοδηγούμενη από τα δεδομένα, για την ανάδειξη χαρακτηριστικών
δεδομένων μη-στάσιμων και μη-γραμμικών διαδικασιών. Ο συνδυασμός των μεθόδων είναι
η εύρεση του συχνοτικού περιεχομένου ενός σήματος με την χρήση του τελεστή Teager-
Kaiser από τις επιμέρους συνιστώσες που έχουν προκύψει με την μέθοδο (EMD) και
ονομάζεται μετασχηματισμός Teager-Huang και αποτελεί μια εναλλακτική δυνατότητα στην
εύρεση του φάσματος ενός σήματος.
Στα επόμενα κεφάλαια περιγράφουμε τα υπό εξέταση σήματα, τους μηχανισμούς
καταγραφής τους, μια ανασκόπηση των βασικών επεξεργασιών που έχουν υποστεί και την
ανάλυση στην οποία υποβλήθηκαν στα πλαίσια της συγκεκριμένης εργασίας. Έτσι στο
κεφάλαιο 2 παρουσιάζουμε τα υπό ανάλυση σήματα, αναφέροντας τις διαδικασίες με τις
οποίες προκύπτουν και τις διατάξεις μέτρησής τους. Στο κεφάλαιο 3 κάνουμε μια
ανασκόπηση των τεχνικών επεξεργασίας των σημάτων γεωηλεκτρικού δυναμικού μέχρι
σήμερα. Στα κεφάλαια 4, 5 και 6 αναφέρουμε το θεωρητικό υπόβαθρο, την πειραματική
διαδικασία επεξεργασίας που ακολουθήθηκε και τα συμπεράσματα με την εφαρμογή του
τελεστή Teager, της μεθόδου EMD και του μετασχηματισμού Teager-Huang αντίστοιχα που
αποτελούν πρωτότυπη εφαρμογή στον χώρο των γεωφυσικών σημάτων. Στο κεφάλαιο 7
αναφερόμαστε σε επεξεργασία που έχει γίνει υποβοηθητικά ή συμπληρωματικά της
επεξεργασίας των προηγούμενων κεφαλαίων. Για τα πειράματα των κεφαλαίων 4-7,
αναφέρουμε το όνομα του script αρχείου που τα υλοποιεί εντός παρενθέσεων δίπλα στο
όνομα του πειράματος. Ο σχετικός κώδικας μπορεί να βρεθεί στο συνοδευτικό CD της
διπλωματικής εργασίας. Στο κεφάλαιο 8 καταλήγουμε με τα κύρια συμπεράσματα και
ορισμένες κατευθύνσεις περαιτέρω επεξεργασίας ως επέκταση της τρέχουσας
διπλωματικής.This work concerns the study and implementation of the Teager-Kaiser Operator, the Empirical Mode Decomposition method, and the combination of them known as Teager-Huang transformation.
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Chang, Chung-Yu, and 張仲宇. "An Approach to Eliminating EMG noise from ECG using Ensemble Empirical Mode Decomposition." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/16381634943074798556.
Full textAbstract:
碩士國立臺灣大學
電子工程學研究所
101
Cardiovascular disease has been listed as the second rank of the top ten leading causes of death. Electrocardiogram(ECG) has played an important role and has been widely used clinically because it is a non-invasive, real-time, quick and easy-to-implement technique. Cardiovascular disease was diagnosed traditionally by inspection from doctors. For doctors, ECG noise can be easily ignored by visual inspection. Nevertheless, with the advance of science and technology, remote monitoring and diagnosis have become important processes to automatically detecting cardiovascular disease. However, in holter devices, ECG recordings are often corrupted by artifacts in some real practice, such as 50/60Hz power line interference, muscle contraction induced electromyogram(EMG), movement(or breath) induced baseline wandering or motion artifact. These aforementioned noises might result in misleading ECG detection. Thus, pre-processing of ECG noise is a very important task in such ECG analysis systems. In this thesis, an effective approach to eliminate baseline wander and EMG noise from ECG based on modified moving average filter and ensemble empirical mode decomposition (EEMD) was proposed. Modified moving average filter is used to eliminate ECG base line drift. It can be viewed as a pre-processing of the EEMD-based EMG reduction method. If data is interfered by EMG noise, EEMD is first used to decompose ECG data into different frequency components. By combination of proper QRS detection algorithms, only noise part will be extracted without affecting QRS complex or other ECG component. Finally, EMG noise can be estimated and removed from original ECG data. Then, by moving variance detection method, EMG positions can be detected and marked as reference to users. Cross correlation coefficient (Corr-Coef), percentage root-mean-square difference (PRD) and ECG morphology were used to examine the artificial data performance of proposed algorithm. Results showed that proposed de-noising framework successfully eliminate baseline wander and EMG interferences without significantly distorting the ECG waveform.
34
Lanka, Karthikeyan. "Predictability of Nonstationary Time Series using Wavelet and Empirical Mode Decomposition Based ARMA Models." Thesis, 2013. http://etd.iisc.ernet.in/2005/3363.
Full textAbstract:
The idea of time series forecasting techniques is that the past has certain information about future. So, the question of how the information is encoded in the past can be interpreted and later used to extrapolate events of future constitute the crux of time series analysis and forecasting. Several methods such as qualitative techniques (e.g., Delphi method), causal techniques (e.g., least squares regression), quantitative techniques (e.g., smoothing method, time series models) have been developed in the past in which the concept lies in establishing a model either theoretically or mathematically from past observations and estimate future from it. Of all the models, time series methods such as autoregressive moving average (ARMA) process have gained popularity because of their simplicity in implementation and accuracy in obtaining forecasts. But, these models were formulated based on certain properties that a time series is assumed to possess. Classical decomposition techniques were developed to supplement the requirements of time series models. These methods try to define a time series in terms of simple patterns called trend, cyclical and seasonal patterns along with noise. So, the idea of decomposing a time series into component patterns, later modeling each component using forecasting processes and finally combining the component forecasts to obtain actual time series predictions yielded superior performance over standard forecasting techniques. All these methods involve basic principle of moving average computation. But, the developed classical decomposition methods are disadvantageous in terms of containing fixed number of components for any time series, data independent decompositions. During moving average computation, edges of time series might not get modeled properly which affects long range forecasting. So, these issues are to be addressed by more efficient and advanced decomposition techniques such
as Wavelets and Empirical Mode Decomposition (EMD). Wavelets and EMD are some of the most innovative concepts considered in time series analysis and are focused on processing nonlinear and nonstationary time series. Hence, this research has been undertaken to ascertain the predictability of nonstationary time series using wavelet and Empirical Mode Decomposition (EMD) based ARMA models.
The development of wavelets has been made based on concepts of Fourier analysis and Window Fourier Transform. In accordance with this, initially, the necessity of involving the advent of wavelets has been presented. This is followed by the discussion regarding the advantages that are provided by wavelets. Primarily, the wavelets were defined in the sense of continuous time series. Later, in order to match the real world requirements, wavelets analysis has been defined in discrete scenario which is called as Discrete Wavelet Transform (DWT). The current thesis utilized DWT for performing time series decomposition. The detailed discussion regarding the theory behind time series decomposition is presented in the thesis. This is followed by description regarding mathematical viewpoint of time series decomposition using DWT, which involves decomposition algorithm.
EMD also comes under same class as wavelets in the consequence of time series decomposition. EMD is developed out of the fact that most of the time series in nature contain multiple frequencies leading to existence of different scales simultaneously. This method, when compared to standard Fourier analysis and wavelet algorithms, has greater scope of adaptation in processing various nonstationary time series. The method involves decomposing any complicated time series into a very small number of finite empirical modes (IMFs-Intrinsic Mode Functions), where each mode contains information of the original time series. The algorithm of time series decomposition using EMD is presented post conceptual elucidation in the current thesis. Later, the proposed time series forecasting algorithm that couples EMD and ARMA model is presented that even considers the number of time steps ahead of which forecasting needs to be performed.
In order to test the methodologies of wavelet and EMD based algorithms for prediction of time series with non stationarity, series of streamflow data from USA and rainfall data from India are used in the study. Four non-stationary streamflow sites (USGS data resources) of monthly total volumes and two non-stationary gridded rainfall sites (IMD) of monthly total rainfall are considered for the study. The predictability by the proposed algorithm is checked in two scenarios, first being six months ahead forecast and the second being twelve months ahead forecast. Normalized Root Mean Square Error (NRMSE) and Nash Sutcliffe Efficiency Index (Ef) are considered to evaluate the performance of the proposed techniques.
Based on the performance measures, the results indicate that wavelet based analyses generate good variations in the case of six months ahead forecast maintaining harmony with the observed values at most of the sites. Although the methods are observed to capture the minima of the time series effectively both in the case of six and twelve months ahead predictions, better forecasts are obtained with wavelet based method over EMD based method in the case of twelve months ahead predictions. It is therefore inferred that wavelet based method has better prediction capabilities over EMD based method despite some of the limitations of time series methods and the manner in which decomposition takes place.
Finally, the study concludes that the wavelet based time series algorithm could be used to model events such as droughts with reasonable accuracy. Also, some modifications that could be made in the model have been suggested which can extend the scope of applicability to other areas in the field of hydrology.
35
Hsiao, You-Ren, and 蕭祐仁. "Characterization of Nonstationary and Nonlinear Hydrologic, Environmental and Epidemic Time Series Based on Empirical Mode Decomposition (EMD)-based Algorithms and Time-dependent Intrinsic Correlation (TDIC)." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/q73t4p.
Full textAbstract:
碩士國立臺灣大學
土木工程學研究所
107
Time frequency analysis is a powerful tool to investigate the characteristic time scale and energy distribution of a signal. However, assumptions of the linearity and non-stationary character of the signal limit the estimation of any correlation between two variables using traditional time-frequency techniques (such as short-time Fourier transform, wavelet transform and others.). Thus, a method of noise-assisted multivariate empirical mode decomposition (NAMEMD)-based spectral analysis is introduced. A time-dependent intrinsic correlation (TDIC) algorithm is also introduced to gain some insight into variation of any correlation over time. A time-dependent intrinsic cross-correlation (TDICC) algorithm is introduced to elucidate the time-varying lag effect. The above algorithms are applied to data on air pollution and dengue fever. In the application to the air pollution problem, the association among 〖PM〗_2.5 and hydro-meteorological variables are characterized at three monitoring stations in Kaohsiung. The annual, diurnal and semi-diurnal scale are identified to be significant. The correlation obtained from filtered signal is found to be physically more representative than the Pearson correlation. The seasonal switchover of correlation is observed by time dependent intrinsic correlation analysis in the association among 〖PM〗_2.5 and temperature and relative humidity at diurnal and semi-diurnal scales. It is identified that the concentration of 〖PM〗_2.5 is related to the land breeze at diurnal scale, which corresponds to the monsoon during the winter at annual scale. A novel measurement of nonlinearity is introduced to quantify the difference between empirical mode decomposition (EMD)-based methods and Fourier-based methods. In the application of dengue fever issue, the long-term association among dengue fever incidences and hydro-meteorological variables are characterized. The inter-annual (4-year) and annual scale are identified to be significant in dengue fever incidences. The fluctuation of lag effect is observed by TDICC among dengue fever incidences, precipitation, relative humidity and temperature at annual scale, indicating the diverse mechanism during the epidemic periods and normal time. It is confirmed that the outbreak of dengue fever is associated with the El Niño-Southern Oscillation (ENSO) events by TDIC. It is revealed in this thesis that the NAMEMD algorithm to be the best filtering technique while dealing with complicated multivariate data compared to EMD and continuous wavelet transform (CWT) when the multiple data resolution is identical to each other.
36
Hong, Huei-Cheng, and 洪暉程. "Applications of Ensemble Empirical Mode Decomposition (EEMD) and Auto-Regressive (AR) Model for Diagnosing Looseness Faults of Rotating Machinery." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/umbye9.
Full textAbstract:
碩士國立中央大學
光機電工程研究所
97
Post processing of Ensemble Empirical Mode Decomposition (EEMD) can be utilized to decompose the vibration signals of rotating machinery into finite number of Intrinsic Mode Functions (IMFs) without mode mixing problem. The basis of the post processing of EEMD will satisfy the well-defined conditions of IMF. The Autoregressive (AR) model of information-contained IMFs can be used to predict the unmeasured vibration signal, and the coefficients of AR model represent the feature of systematic dynamic behavior. In this paper, the post-processing of EEMD combining the AR model is proposed for diagnosing the looseness faults at different conponents of rotating machinery. The information-contained IMFs are selected to build the AR model. The looseness types are identified by analyzing the coefficients of AR model. The effectiveness of the proposed method is validated through the analysis of the experimental data.
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Chen, Chun-Erh, and 陳均爾. "Predicting Arterial Stiffness With The Aid of Ensemble Empirical Mode Decomposition(EEMD) Algorithm of the Wrist Pulse Sigmals." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/49706152693930296506.
Full textAbstract:
碩士國立東華大學
電機工程學系
98
In this study, we propose an easy-to-use noninvasive arterial stiffness assessment instrument that can be used to record the radial arterial pressure signals from the wrist. The system combines the ensemble empirical mode decomposition (EEMD) algorithm with the signals to derive a modified reflection index (MRI) and modified stiffness index (MSI). The performance of MRI and MSI was verified based on 46 subjects (35 men and 11 women, 20 to 27 years of age). Early self-monitoring of cardiovascular dysfunction and arterial stiffness can be easily and effectively achieved by MRI and MSI. Only few minutes are needed for conducting at home.
38
Wang, KeSheng. "Approaches to the improvement of order tracking techniques for vibration based diagnostics in rotating machines." Thesis, 2011. http://hdl.handle.net/2263/28747.
Full textAbstract:
Conventional rotating machine vibration monitoring techniques are based on the assumption that changes in the measured structural response are caused by deterioration in the condition of the rotating machine. However, due to variations of the rotational speed, the measured signal may be non-stationary and difficult to interpret. For this reason, the order tracking technique is introduced. One of main advantages of order tracking over traditional vibration monitoring lies in its ability to clearly identify non-stationary vibration data and to a large extent exclude the influences of varying rotational speed. In recent years, different order tracking techniques have been developed. Each of these has their own pros and cons in analyzing rotating machinery vibration signals. In this research, three existing order tracking techniques are extensively investigated and combined to further explore their abilities in the context of condition monitoring. Firstly, computed order tracking is examined. This allows non-stationary effects due to the variation of rotational speed to be largely excluded. However, this technique was developed to deal with the entire raw signal and therefore looses the ability to focus on each individual order of interest. Secondly, Vold-Kalman filter order tracking is considered. It is widely reported that this technique overcomes many of the limitations of other order tracking methods and extracts order signals into the time domain. However because of the adaptive nature of the Vold-Kalman filter, the non-stationary effects due to the rotational speed will remain in the extracted order waveform, which is not ideal for conventional signal processing methods such as Fourier analysis. Yet, the strict mathematical filter (the Vold-Kalman filter is based upon two rigorous mathematical equations, namely the data equation and the structural equation, to realize the filter) gives this technique an excellent ability to focus on the orders of interest. Thirdly, the empirical mode decomposition method is studied. In the literature, this technique is claimed to be an effective diagnostic tool for various kinds of applications including diagnosis of rotating machinery faults. Its unique empirical way of extracting non-stationary and non-linear signals allows it to capture machine fault information which is intractable by other order tracking methods. But since there is no precise mathematical definition for an intrinsic mode function in empirical mode decomposition and – as far as could be ascertained – no published assessment of the relationship between an order and an intrinsic mode function, this technique has not been properly considered by analysts in terms of order tracking. As a result, its abilities have not really been explored in the context of order related vibrations in rotating machinery. In this research, the relationship between an order and an intrinsic mode function is discussed and it is treated as a special kind of order tracking method. In stead of focusing individually on each order tracking technique, the current work synthesizes different order tracking techniques. Through combination, exchange and reconciliation of ideas between these order tracking techniques, three improved order tracking techniques are developed for the purpose of enhancing order tracking analysis in condition monitoring. The techniques are Vold-Kalman filter and computed order tracking (VKC-OT), intrinsic mode function and Vold-Kalman filter order tracking (IVK-OT) and intrinsic cycle re-sampling (ICR). Indeed, these improved approaches contribute to current order tracking practice, by providing new order tracking methods with new capabilities for condition monitoring of systems which are intractable by traditional order tracking methods, or which enhances results obtained by these traditional methods. The work commences with a discussion of the inter-relationship between the order tracking methods which are considered in the thesis, and exposition of the scope of the work and an explanation of the way these independent order tracking techniques are integrated in the thesis. To demonstrate the abilities of the improved order tracking techniques, two simulation models are established. One is a simple single-degree-of-freedom (SDOF) rotor model with which VKC-OT and IVK-OT techniques are demonstrated. The other is a simplified gear mesh model through which the effectiveness of the ICR technique is proved. Finally two experimental set-ups in the Sasol Laboratory for Structural Mechanics at the University of Pretoria are used for demonstrating the improved approaches for real rotating machine signals. One test rig was established to monitor an automotive alternator driven by a variable speed motor. A stator winding inter-turn short was artificially introduced. Advantages of the VKC-OT technique are presented and features clear and clean order components under non-stationary conditions. The diagnostic ability of the IVK-OT technique of further decomposing an intrinsic mode function is also demonstrated via signals from this test rig, so that order signals and vibrations that modulate orders in IMFs can be separated and used for condition monitoring purposes. The second experimental test rig is a transmission gearbox. Artificially damaged gear teeth were introduced. The ICR technique provides a practical alternative tool for fault diagnosis. It proves to be effective in diagnosing damaged gear teeth.Thesis (PhD)--University of Pretoria, 2011.
Mechanical and Aeronautical Engineering
unrestricted
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Shastry, Mahesh C. Narayanan Ram Mohan. "An empirical mode decomposition based approach for through-the-wall radar sensing of human activity." 2009. http://etda.libraries.psu.edu/theses/approved/WorldWideIndex/ETD-4446/index.html.
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Mettke, Philipp. "Vorhersagbarkeit ökonomischer Zeitreihen auf verschiedenen zeitlichen Skalen." Bachelor's thesis, 2015. https://tud.qucosa.de/id/qucosa%3A29258.
Full textAbstract:
This thesis examines three decomposition techniques and their usability for economic and financial time series. The stock index DAX30 and the exchange rate from British pound to US dollar are used as representative economic time series. Additionally, autoregressive and conditional heteroscedastic simulations are analysed as benchmark processes to the real data.
Discrete wavelet transform (DWT) uses wavelike functions to adapt the behaviour of time series on different time scales. The second method is the singular spectral analysis (SSA), which is applied to extract influential reconstructed modes. As a third algorithm, empirical mode decomposition (END) leads to intrinsic mode functions, who reflect the short and long term fluctuations of the time series. Some problems arise in the decomposition process, such as bleeding at the DWT method or mode mixing of multiple EMD mode functions.
Conclusions to evaluate the predictability of the time series are drawn based on entropy - and recurrence - analysis. The cyclic behaviour of the decompositions is examined via the coefficient of variation, based on the instantaneous frequency. The results show rising predictability, especially on higher decomposition levels. The instantaneous frequency measure leads to low values for regular oscillatory cycles, irregular behaviour results in a high variation coefficient. The singular spectral analysis show frequency - stable cycles in the reconstructed modes, but represents the influences of the original time series worse than the other two methods, which show on the contrary very little frequency - stability in the extracted details.:1. Einleitung
2. Datengrundlage
2.1. Auswahl und Besonderheiten ökonomischer Zeitreihen
2.2. Simulationsstudie mittels AR-Prozessen
2.3. Simulationsstudie mittels GARCH-Prozessen
3. Zerlegung mittels modernen Techniken der Zeitreihenanalyse
3.1. Diskrete Wavelet Transformation
3.2. Singulärsystemanalyse
3.3. Empirische Modenzerlegung
4. Bewertung der Vorhersagbarkeit
4.1. Entropien als Maß der Kurzzeit-Vorhersagbarkeit
4.2. Rekurrenzanalyse
4.3. Frequenzstabilität der Zerlegung
5. Durchführung und Interpretation der Ergebnisse
5.1. Visuelle Interpretation der Zerlegungen
5.2. Beurteilung mittels Charakteristika
6. Fazit
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Yi-Huan, Lai. "Speaker Identification by Empirical Mode Decomposition." 2006. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-3007200621440200.
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Lai, Yi-Huan, and 賴亦桓. "Speaker Identification by Empirical Mode Decomposition." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/20500645615568765596.
Full textAbstract:
碩士國立臺灣大學
電機工程學研究所
94
Timbre is a main feature that one verifies who is speaking. It is the information that is hidden inside the acoustic properties. Using the differences of timbre features in speaker identification has been an open issue over the years. In the literature, most speaker identification systems use LPC-derived Cesptral Coefficients (LPCC) or Mel Frequency Cesptral Coefficients (MFCC) as timbre models. The linear and stationary assumptions of above techniques limit identification performance. In this thesis, we apply an adaptive time-frequency distribution, Hilbert-Huang transform. By decomposing original signal into simple oscillation modes empirically, we can obtain meaningful instantaneous frequencies. These instantaneous frequencies are taken as the input pattern to train the Neural Network classifier. Using these timbre features in the proposed system, we achieve a nice accuracy.
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Huang, Yen-Hui, and 黃彥勳. "Empirical mode decomposition representation of VLF data." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/28117764906208507394.
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Yu-ZenLi and 李育任. "Effective Breast Density Classification: Empirical Mode Decomposition." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/96573863357590945748.
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碩士國立成功大學
醫學資訊研究所
101
Literatures indicate that dense breast in mammography generally contains more glandular tissue and less fatty tissue. The breast density, which is hereditary characteristics, is also related to the chance of getting breast cancer in many studies. Detecting the breast cancer from mammogram is more important than other genetic factors. An effective and automatic segmentation method to determine the glandular tissue from mammogram becomes a fundamental and important issue for further breast density related research. Therefore, this paper proposes an empirical mode decomposition-based mammogram gland enhancement method to perform efficiently automatic segmentation of gland. First, the proposed method uses fast and adaptive bidimensional empirical mode decomposition (FABEMD) to enhance the mammogram for dense glandular tissue, skin lines, and fatty tissue. Second, the skin lines are removed from the image by using morphology technologies. The segmented results are used to be the coordinate locations of the glandular tissue in the mammogram. Third, we adopt k-means algorithm to classify glandular and fatty tissue in the image to determine a threshold. Fourth, we improve a region growing method to adaptively tune the threshold from the original mammogram. The segmented results which get point 4 or 5 occupy 75% in our mammogram database. According to the characteristic of the dense tissue of each BIRADS density category, we extract the fractal dimension, morphology features, and texture features. The experimental results show that the accuracy rate of the PCA+BPN classifier is about 97% which is significantly higher than the 86% accuracy rate of the PCA+kNN classifier.
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Wang, Yung-Ling, and 王詠令. "Empirical Mode Decomposition for Hyperspectral Data Analysis." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/69519747245803435720.
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博士國立中央大學
資訊工程學系
102
Optical remote sensing can distinguish different materials because each material has its own unique absorption characteristics to form a unique spectrum. This information can be adopted to discriminate different materials in optical remote sensing images. Traditional approach for spectra similarity measurement is calculating the Euclidean distance or spectral angle between two spectra directly. However, in reality the spectra usually contain noise or interference which cannot be tolerated by traditional measurements. In this study, we propose a new approach to measure the similarity between the spectra to discriminate materials. It adopts Empirical Mode Decomposition (EMD) to decompose the spectrum into several components, called Intrinsic Mode Functions (IMFs). The absorption features are highlighted and the noise is reduced in the first few IMFs, so the ability of material discrimination is improved. For evaluation purpose, we compare the proposed method with several commonly used measurements, including Euclidean distance, Spectral Angle and Mahalanobis distance. The sample spectra used for experiment are provided by the spectral library of U. S. Geological Survey (USGS). The experiments results have demonstrated that EMD can extract the spectral features more effectively than common spectral similarity measurements and improve the classification performance.
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Lin, Shang-Ching, and 林上景. "Automatic Contrast Enhancement usingEnsemble Empirical Mode Decomposition." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/92736636397812088964.
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碩士國立臺灣大學
生醫電子與資訊學研究所
99
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. The use of ensemble empirical mode decomposition (EEMD) in the Hilbert-Huang transform (HHT) was previously explored with the aim of alleviating this problem. The HHT is designed for analyzing nonlinear and nonstationary data, whereas EEMD is a method associated with the HHT that allows decomposition of data into a finite number of intrinsic mode functions (IMFs). It was found that the contrast can be effectively improved in certain IMFs, but manual selection of appropriate IMFs is still required. This prompted the present study to test the hypothesis that the contrast can be enhanced without requiring manual selection by summing appropriately weighted IMFs and demodulating the signal at appropriate frequencies. That is, a data-driven mechanism for automatically determining weights and demodulation frequencies was derived and tested. Users only have to specify the microbubble distribution in the training data set, and the contrasts in testing data sets can be improved. Phantom results show that an overall contrast enhancement of up to 12.5 dB can be achieved. A fused-image representation that simultaneously displays the conventional B-mode image and the new contrast mode image is also presented. The proposed method outperforms second-harmonic imaging significantly, but is only slightly better than subharmonic imaging on experimental data. However, there is a limitation that the imaging setups should be identical for obtaining training and testing data. Though there are other means to determine the weights, as long as they are determined through a training process, the contrast improvement and the reliability of the results will mainly depend on the size of the training data set. Finally, in general the proposed method demands more computations than conventional methods. Hence, future studies will not only tempt to apply the method to other imaging configurations and clinical data, but also seek for a set of computational parameters or utilize other algorithms derived from ensemble empirical decomposition (EMD) to better balance computational complexity and contrast improvement.
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Du, Tzung-Tze, and 杜宗澤. "Signal Recognition by Using Empirical Mode Decomposition." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/39258799449139483328.
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Sheng-MaoWang and 王晟懋. "Automated program of Ensemble Empirical Mode Decomposition." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/2yg4c2.
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碩士國立成功大學
航空太空工程學系
107
The ensemble empirical mode decomposition (EEMD) method is applied for wind data analysis in the current research. However, calculations could take a very long time. Therefore, an attempt is made to accelerate the calculations. MATLAB and Python are used to explore the characteristics of different programming language operations, and a user-friendly graphical interface is also developed, and the execution process will be operated automatically and continuously. The wind data analyzed were collected by the wind turbines located on campus of Case Western Reserve University in the United States. The wind data have been collecting since 2012 and the amount of data keeps growing. Thus, reducing the analyzing time is important. This study not only wants to use the graphics processor to try to shorten the time required for the operation process, but also finds the approximation trend in EEMD and refines the algorithm to shorten the operation time by about 65%.
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Tzu-ChengYang and 楊子徵. "The Study of Improved Empirical Mode Decomposition." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/37940715787449593045.
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Wei, Shao-Kuan, and 魏韶寬. "Ensemble Empirical Mode Decomposition with Clustering Analysis." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/82011189687957252619.
Full textAbstract:
碩士國立臺灣師範大學
數學系
100
Ensemble Empirical Mode Decomposition (EEMD) is an adaptive time-frequency data analysis method. Time series or signals can be decomposed into a collection of intrinsic mode functions (IMFs). Nevertheless, there appears a multi-mode problem where signals with a similar time scale are decomposed into different IMFs. A possible solution to this problem is to combine the multi-modes into a proper single mode, but there is no general rule on how to combine IMFs in the literature. In this paper, we propose to modify EEMD algorithm using the statistical clustering analysis and to provide a framework to combine the IMFs into a condensed set of clustered intrinsic mode functions (CIMFs). The method is applied to two artificially synthesized signals, wind turbine signal at Chunan Miaoli, and a seismic signal during the earthquake at Chi-Chi in 1999. Especially, this seismic signal contains not only the main seismic information but also the seismic motion from a landslide in Tsaoling area. The present method can separate the two signal from different sources correctly, and these applications of other examples demonstrate that, the present method offers great improvement over EEMD for extracting useful information.