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Dissertations / Theses on the topic 'Shape principal component analysis'

Author: Grafiati

Published: 3 June 2025

Last updated: 1 August 2025

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1

Yu, Jie. "Classification of Genotype and Age of Eyes Using RPE Cell Size and Shape." Digital Archive @ GSU, 2012. http://digitalarchive.gsu.edu/math_theses/118.

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Retinal pigment epithelium (RPE) is a principal site of pathogenesis in age-related macular de-generation (AMD). AMD is a main source of vision loss even blindness in the elderly and there is no effective treatment right now. Our aim is to describe the relationship between the morphology of RPE cells and the age and genotype of the eyes. We use principal component analysis (PCA) or functional principal component method (FPCA), support vector machine (SVM), and random forest (RF) methods to analyze the morphological data of RPE cells in mouse eyes to classify their age and genotype. Our analyse

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2

Miller, John William. "Differentiation between causes of optic disc swelling using retinal layer shape features." Thesis, University of Iowa, 2018. https://ir.uiowa.edu/etd/6215.

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The optic disc is the region of the retina where the optic nerve exits the back of the eye. A number of conditions can cause the optic disc to swell. Papilledema, optic disc swelling caused by raised intracranial pressure (ICP), and nonarteritic anterior ischemic optic neuropathy (NAION), swelling caused by reduced blood flow to the back of the eye, are two such conditions. Rapid, accurate diagnosis of the cause of disc swelling is important, as with papilledema the underlying cause of raised ICP could potentially be life-threatening and may require immediate intervention. The current clinical

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3

Bloxson, Julie M. Bloxson. "MINERALOGICAL AND FACIES VARIATIONS WITHIN THE UTICA SHALE, OHIO USING VISIBLE DERIVATIVE SPECTROSCOPY, PRINCIPAL COMPONENT ANALYSIS, AND MULTIVARIATE CLUSTERING." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1498664669872459.

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4

Berguin, Steven Henri. "A method for reducing dimensionality in large design problems with computationally expensive analyses." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53504.

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Strides in modern computational fluid dynamics and leaps in high-power computing have led to unprecedented capabilities for handling large aerodynamic problem. In particular, the emergence of adjoint design methods has been a break-through in the field of aerodynamic shape optimization. It enables expensive, high-dimensional optimization problems to be tackled efficiently using gradient-based methods in CFD; a task that was previously inconceivable. However, adjoint design methods are intended for gradient-based optimization; the curse of dimensionality is still very much alive when it comes t

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5

Dickens, Peter Martin. "Facilitating Emergence: Complex, Adaptive Systems Theory and the Shape of Change." Antioch University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=antioch1339016565.

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6

Šmirg, Ondřej. "Detekce lidské postavy v obrazové scéně." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217296.

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The project consists of two distinct levels i.e. separation level and diagnostic level. At the separation level, statistical models of gaussians and color are separately used to classify each pixel as belonging to backgroung or foreground. Adopted method is mixture of gaussians.A mixture of gaussians model is suitable here because the results of the picture tests will not depend on the lens opening, but rather on the colors in the backgroung. A mixture of gaussians model for return data seems reasonable. The achieved results the used method on the real sequences are presented in the thesis. Di

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7

Appia, Vikram VijayanBabu. "Non-local active contours." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44739.

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This thesis deals with image segmentation problems that arise in various computer vision related fields such as medical imaging, satellite imaging, video surveillance, recognition and robotic vision. More specifically, this thesis deals with a special class of image segmentation technique called Snakes or Active Contour Models. In active contour models, image segmentation is posed as an energy minimization problem, where an objective energy function (based on certain image related features) is defined on the segmenting curve (contour). Typically, a gradient descent energy minimization approach

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8

Strecha, Juraj. "Sledování pohybu srdečního svalstva v ultrazvukovém záznamu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234947.

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he thesis deals with proposal of an algorithm and implementation of a program that tracks a motion of the heart muscle in the captured ultrasound video of the heart. The point position estimation is calculated by optical flow method. The Active Shape Model method is used to confirm the accuracy of point's position tracking. The user annotates desired structure of the heart arch first and the application displays new points which represent a new deformed heart shape.

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9

Kpamegan, Neil Racheed. "Robust Principal Component Analysis." Thesis, American University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10784806.

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<p> In multivariate analysis, principal component analysis is a widely popular method which is used in many different fields. Though it has been extensively shown to work well when data follows multivariate normality, classical PCA suffers when data is heavy-tailed. Using PCA with the assumption that the data follows a stable distribution, we will show through simulations that a new method is better. We show the modified PCA can be used for heavy-tailed data and that we can more accurately estimate the correct number of components compared to classical PCA and more accurately identify the subs

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10

Akinduko, Ayodeji Akinwumi. "Multiscale principal component analysis." Thesis, University of Leicester, 2016. http://hdl.handle.net/2381/36616.

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The problem of approximating multidimensional data with objects of lower dimension is a classical problem in complexity reduction. It is important that data approximation capture the structure(s) and dynamics of the data, however distortion to data by many methods during approximation implies that some geometric structure(s) of the data may not be preserved during data approximation. For methods that model the manifold of the data, the quality of approximation depends crucially on the initialization of the method. The first part of this thesis investigates the effect of initialization on manif

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11

Der, Ralf, Ulrich Steinmetz, Gerd Balzuweit, and Gerrit Schüürmann. "Nonlinear principal component analysis." Universität Leipzig, 1998. https://ul.qucosa.de/id/qucosa%3A34520.

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We study the extraction of nonlinear data models in high-dimensional spaces with modified self-organizing maps. We present a general algorithm which maps low-dimensional lattices into high-dimensional data manifolds without violation of topology. The approach is based on a new principle exploiting the specific dynamical properties of the first order phase transition induced by the noise of the data. Moreover we present a second algorithm for the extraction of generalized principal curves comprising disconnected and branching manifolds. The performance of the algorithm is demonstrated for both

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12

Solat, Karo. "Generalized Principal Component Analysis." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/83469.

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The primary objective of this dissertation is to extend the classical Principal Components Analysis (PCA), aiming to reduce the dimensionality of a large number of Normal interrelated variables, in two directions. The first is to go beyond the static (contemporaneous or synchronous) covariance matrix among these interrelated variables to include certain forms of temporal (over time) dependence. The second direction takes the form of extending the PCA model beyond the Normal multivariate distribution to the Elliptically Symmetric family of distributions, which includes the Normal, the Student'

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13

Fučík, Vojtěch. "Principal component analysis in Finance." Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-264205.

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The main objective of this thesis is to summarize and possibly interconnect the existing methodology on principal components analysis, hierarchical clustering and topological organization in the financial and economic networks, linear regression and GARCH modeling. In the thesis the clustering ability of PCA is compared with the more conventional approaches on a set of world stock market indices returns in different time periods where the time division is represented by The World Financial Crisis of 2007-2009. It is also observed whether the clustering of DJIA index components is underlied by

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14

Wedlake, Ryan Stuart. "Robust principal component analysis biplots." Thesis, Link to the online version, 2008. http://hdl.handle.net/10019/929.

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15

Brennan, Victor L. "Principal component analysis with multiresolution." [Gainesville, Fla.] : University of Florida, 2001. http://etd.fcla.edu/etd/uf/2001/ank7079/brennan%5Fdissertation.pdf.

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Thesis (Ph. D.)--University of Florida, 2001.<br>Title from first page of PDF file. Document formatted into pages; contains xi, 124 p.; also contains graphics. Vita. Includes bibliographical references (p. 120-123).

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Nair, Binu Muraleedharan. "Learning Latent Temporal Manifolds for Recognition and Prediction of Multiple Actions in Streaming Videos using Deep Networks." University of Dayton / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1429532297.

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17

Cadima, Jorge Filipe Campinos Landerset. "Topics in descriptive Principal Component Analysis." Thesis, University of Kent, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.314686.

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18

Isaac, Benjamin. "Principal component analysis based combustion models." Doctoral thesis, Universite Libre de Bruxelles, 2014. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209278.

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Energy generation through combustion of hydrocarbons continues to dominate, as the most common method for energy generation. In the U.S. nearly 84% of the energy consump- tion comes from the combustion of fossil fuels. Because of this demand there is a continued need for improvement, enhancement and understanding of the combustion process. As computational power increases, and our methods for modelling these complex combustion systems improve, combustion modelling has become an important tool in gaining deeper insight and understanding for these complex systems. The constant state of change in

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19

Alfonso, Miñambres Javier de. "Face recognition using principal component analysis." Master's thesis, Universidade de Aveiro, 2010. http://hdl.handle.net/10773/10221.

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Mestrado em Engenharia Electrónica e Telecomunicações<br>The purpose of this dissertation was to analyze the image processing method known as Principal Component Analysis (PCA) and its performance when applied to face recognition. This algorithm spans a subspace (called facespace) where the faces in a database are represented with a reduced number of features (called feature vectors). The study focused on performing various exhaustive tests to analyze in what conditions it is best to apply PCA. First, a facespace was spanned using the images of all the people in the database. We obtaine

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20

Roveroni, Alessandro <1997&gt. "Principal Component Analysis on ESG data." Master's Degree Thesis, Università Ca' Foscari Venezia, 2021. http://hdl.handle.net/10579/19941.

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The objective of the dissertation is to perform a principal component analysis on ESG data. The ESG database is provided by the ESG-Credit.eu project and contains information of 11.104 firms of all Europe. The research focuses on companies residing in French, Italy and Germany. The data present in the database are extracted from three different sources of information: Bloomberg, Thomson Reuters Eikon and CDP. The number of overall measures collected is 609 distributed among the three main pillars of the ESG score: Environment, Social and Governance. In the 20th century, ESG has played a crucia

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21

Burka, Zak. "Perceptual audio classification using principal component analysis /." Online version of thesis, 2010. http://hdl.handle.net/1850/12247.

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22

Patak, Zdenek. "Robust principal component analysis via projection pursuit." Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/29737.

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In principal component analysis (PCA), the principal components (PC) are linear combinations of the variables that minimize some objective function. In the classical setup the objective function is the variance of the PC's. The variance of the PC's can be easily upset by outlying observations; hence, Chen and Li (1985) proposed a robust alternative for the PC's obtained by replacing the variance with an M-estimate of scale. This approach cannot achieve a high breakdown point (BP) and efficiency at the same time. To obtain both high BP and efficiency, we propose to use MM- and τ-estimates in pl

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23

Monahan, Adam Hugh. "Nonlinear principal component analysis of climate data." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ48678.pdf.

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24

Nilsson, Jakob, and Tim Lestander. "Detecting network failures using principal component analysis." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-132258.

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The dataset is ﬁrst analyzed on a basic level by looking at the correlations between number of measurements and average download speed for every day. Second, our PCA-based methodology applied on the dataset, taking into account many factors, including the number of correlated measurements. The results from each analysis is compared and evaluated. Based on the results, we give insights to just how efﬁcient the tested methods are and what improvements that can be made on the methods.This thesis investigates the efﬁciency of a methodology that ﬁrst performs a Principal Component Analysis (PCA), f

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25

Dauwe, Alexander. "Principal component analysis of the yield curve." Master's thesis, NSBE - UNL, 2009. http://hdl.handle.net/10362/9439.

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A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics<br>This report deals with one of the remaining key problems in financial decision taking: the forecast of the term structure at different time horizons. Specifically: I will forecast the Euro Interest Rate Swap with a macro factor augmented autoregressive principal component model. I achieve forecasts that significantly outperform the Random Walk for medium to long term horizons when using a short rolling time window. Including macro factors lead

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26

Graner, Johannes. "On Asymptotic Properties of Principal Component Analysis." Thesis, Uppsala universitet, Tillämpad matematik och statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-420649.

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27

Li, Liubo Li. "Trend-Filtered Projection for Principal Component Analysis." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1503277234178696.

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28

Peloušek, Jan. "Sledování obličejových rysů v reálném čase." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-218823.

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This thesis considers the problematic of the object recognition in a digital picture, particularly about the human face recognition and its components. There are described the basics of the computer vision, the object detector Viola-Jones, its computer realization with help of the OpenCV libraries and the test results. This thesis also describes the accurate system of the facial features detection per the algorithm of the Active Shape Models and also related mechanism of the classifier training, including the software implementation.

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Chen, Shaokang. "Robust discriminative principal component analysis for face recognition /." [St. Lucia, Qld.], 2005. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe18934.pdf.

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30

Dimitrov, Darko [Verfasser]. "Geometric applications of principal component analysis / Darko Dimitrov." Berlin : Freie Universität Berlin, 2009. http://d-nb.info/102346392X/34.

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31

Binongo, Jose Nilo G. "Stylometry and its implementation by principal component analysis." Thesis, University of Ulster, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.311585.

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32

Tan, Murat Hasan. "Principal component analysis for signal-based system identification." Thesis, University of Southampton, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.430735.

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33

Kharva, Mohamed. "Monitoring of froth systems using principal component analysis." Thesis, Stellenbosch : Stellenbosch University, 2002. http://hdl.handle.net/10019.1/52945.

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Thesis (MScEng)--Stellenbosch University, 2002.<br>ENGLISH ABSTRACT: Flotation is notorious for its susceptibility to process upsets and consequently its poor performance, making successful flotation control systems an elusive goal. The control of industrial flotation plants is often based en the visual appearance of the froth phase, and depends to a large extent on the experience and ability of a human operator. Machine vision systems provide a novel solution to several of the problems encountered in conventional flotation systems for monitoring and control. The rapid development in co

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34

Nunes, Madalena Baioa Paraíso. "Portfolio selection : a study using principal component analysis." Master's thesis, Instituto Superior de Economia e Gestão, 2017. http://hdl.handle.net/10400.5/14598.

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Mestrado em Finanças<br>Nesta tese aplicámos a análise de componentes principais ao mercado bolsista português usando os constituintes do índice PSI-20, de Julho de 2008 a Dezembro de 2016. Os sete primeiros componentes principais foram retidos, por se ter verificado que estes representavam as maiores fontes de risco deste mercado em específico. Assim, foram construídos sete portfólios principais e comparámo-los com outras estratégias de alocação. Foram construídos o portfólio 1/N (portfólio com investimento igual para cada um dos 26 ativos), o PPEqual (portfólio com igual investimento em cada

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35

See, Kyoungah. "Three-mode principal component analysis in designed experiments." Diss., Virginia Tech, 1993. http://hdl.handle.net/10919/40079.

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36

Al-Kandari, Noriah Mohammed. "Variable selection and interpretation in principal component analysis." Thesis, University of Aberdeen, 1998. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU067766.

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In many research fields such as medicine, psychology, management and zoology, large numbers of variables are sometimes measured on each individual. As a result, the researcher will end up with a huge data set consisting of large number of variables, say p. Using this collected data set in any statistical analyses may cause several troubles. Thus, many cases demand a prior selection of the best subset of variables of size q, with q « p, to represent the entire data set in any data analysis. Evidently, the best subset of size q for some specified objective can always be determined by investigati

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37

Li, Xiaomeng. "Human Promoter Recognition Based on Principal Component Analysis." Thesis, The University of Sydney, 2008. http://hdl.handle.net/2123/3656.

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This thesis presents an innovative human promoter recognition model HPR-PCA. Principal component analysis (PCA) is applied on context feature selection DNA sequences and the prediction network is built with the artificial neural network (ANN). A thorough literature review of all the relevant topics in the promoter prediction field is also provided. As the main technique of HPR-PCA, the application of PCA on feature selection is firstly developed. In order to find informative and discriminative features for effective classification, PCA is applied on the different n-mer promoter and exon combin

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Li, Xiaomeng. "Human Promoter Recognition Based on Principal Component Analysis." University of Sydney, 2008. http://hdl.handle.net/2123/3656.

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Master of Engineering<br>This thesis presents an innovative human promoter recognition model HPR-PCA. Principal component analysis (PCA) is applied on context feature selection DNA sequences and the prediction network is built with the artificial neural network (ANN). A thorough literature review of all the relevant topics in the promoter prediction field is also provided. As the main technique of HPR-PCA, the application of PCA on feature selection is firstly developed. In order to find informative and discriminative features for effective classification, PCA is applied on the different n-mer

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39

Broadbent, Lane David. "Recognition of Infrastructure Events Using Principal Component Analysis." BYU ScholarsArchive, 2016. https://scholarsarchive.byu.edu/etd/6197.

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Information Technology systems generate system log messages to allow for the monitoring of the system. In increasingly large and complex systems the volume of log data can overwhelm the analysts tasked with monitoring these systems. A system was developed that utilizes Principal Component Analysis to assist the analyst in the characterization of system health and events. Once trained, the system was able to accurately identify a state of heavy load on a device with a low false positive rate. The system was also able to accurately identify an error condition when trained on a single event. The

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Khawaja, Antoun. "Automatic ECG analysis using principal component analysis and wavelet transformation." Karlsruhe Univ.-Verl. Karlsruhe, 2007. http://www.uvka.de/univerlag/volltexte/2007/227/.

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Schmid, Martin. "Anwendung der Principal Component Analysis auf die Commodity-Preise." St. Gallen, 2007. http://www.biblio.unisg.ch/org/biblio/edoc.nsf/wwwDisplayIdentifier/02282663001/$FILE/02282663001.pdf.

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Skittides, Christina. "Statistical modelling of wind energy using Principal Component Analysis." Thesis, Heriot-Watt University, 2015. http://hdl.handle.net/10399/2930.

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The statistical method of Principal Component Analysis (PCA) is developed here from a time-series analysis method used in nonlinear dynamical systems to a forecasting tool and a Measure-Correlate-Predict (MCP) and then applied to wind speed data from a set of Met. Office stations from Scotland. PCA for time-series analysis is a method to separate coherent information from noise of measurements arising from some underlying dynamics and can then be used to describe the underlying dynamics. In the first step, this thesis shows that wind speed measurements from one or more weather stations can be

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Shannak, Kamal Majed. "On Non-Linear Principal Component Analysis for Process Monitoring." Fogler Library, University of Maine, 2004. http://www.library.umaine.edu/theses/pdf/ShannakKM2004.pdf.

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ABDELWAHAB, MOATAZ MAHMOUD. "NOVEL FACIAL IMAGE RECOGNITION TECHNIQUES EMPLOYING PRINCIPAL COMPONENT ANALYSIS." Doctoral diss., University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2181.

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Recently, pattern recognition/classification has received considerable attention in diverse engineering fields such as biomedical imaging, speaker identification, fingerprint recognition, and face recognition, etc. This study contributes novel techniques for facial image recognition based on the Two dimensional principal component analysis in the transform domain. These algorithms reduce the storage requirements by an order of magnitude and the computational complexity by a factor of 2 while maintaining the excellent recognition accuracy of the recently reported methods. The proposed recogniti

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Ragozzine, Brett A. "Modeling the Point Spread Function Using Principal Component Analysis." Ohio University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1224684806.

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Brock, James L. "Acoustic classification using independent component analysis /." Link to online version, 2006. https://ritdml.rit.edu/dspace/handle/1850/2067.

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Söderström, Ulrik. "Very Low Bitrate Video Communication : A Principal Component Analysis Approach." Doctoral thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-1808.

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A large amount of the information in conversations come from non-verbal cues such as facial expressions and body gesture. These cues are lost when we don't communicate face-to-face. But face-to-face communication doesn't have to happen in person. With video communication we can at least deliver information about the facial mimic and some gestures. This thesis is about video communication over distances; communication that can be available over networks with low capacity since the bitrate needed for video communication is low. A visual image needs to have high quality and resolution to be seman

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Verdebout, Thomas. "Optimal inference for one-sample and multisample principal component analysis." Doctoral thesis, Universite Libre de Bruxelles, 2008. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210448.

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Parmi les outils les plus classiques de l'Analyse Multivariée, les Composantes Principales sont aussi un des plus anciens puisqu'elles furent introduites il y a plus d'un siècle par Pearson (1901) et redécouvertes ensuite par Hotelling (1933). Aujourd'hui, cette méthode est abondamment utilisée en Sciences Sociales, en Economie, en Biologie et en Géographie pour ne citer que quelques disciplines. Elle a pour but de réduire de façon optimale (dans un certain sens) le nombre de variables contenues dans un jeu de données.<p>A ce jour, les méthodes d'inférence utilisées en Analyse en Composantes P

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CHEN, YAO-WEN, and 陳耀文. "A two-dimensional shape recognition scheme based on principal component analysis." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/67256918296198324652.

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Fan, Ruei-Hao, and 范睿豪. "Three- Dimensional Breast Tumor Shape Principle Component Analysis for Horizontal Exploration of Breast Tumor." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/34863405901861694780.

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碩士<br>國立臺灣大學<br>生物產業機電工程學研究所<br>100<br>Generally breast tumor tissue is harder than the adjacent normal breast tissue, malignant tumors are also harder than benign tumors, this phenomenon is often used in clinical palpation. However each physician''s diagnosis has no quantitative criteria and the judgment is all subject to their expertise and judgment. In addition, it is very difficult to use palpation to diagnose the inner and smaller breast tumors. Thus there are increasing researches aimed for providing physicians secondary opinions, such as tumor hardness and mobility, in order t

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