Dissertations / Theses on the topic 'Directional statistics'
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Gibson, Richard Stewart. "Wave interactions and wave statistics in directional seas." Thesis, Imperial College London, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.413426.
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Yeh, Shu-Ying. "Reconstruction of foliations from directional information." Thesis, St Andrews, 2007. http://hdl.handle.net/10023/158.
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Zhang, Zhengzheng. "Novel directional statistics for patterns in protein and RNA structure." Thesis, University of Leeds, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.540592.
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黎文傑 and Man-kit Lai. "Some results on the statistical analysis of directional data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1994. http://hub.hku.hk/bib/B31211550.
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Lai, Man-kit. "Some results on the statistical analysis of directional data /." [Hong Kong : University of Hong Kong], 1994. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13787950.
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Doctor, Katarina Zsoldos. "Spectral bands necessary to describe the directional reflective properties of beach sands." Thesis, George Mason University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10192107.
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A common method to identify or model the dominant directional reflective properties of a surface is the bidirectional reflectance distribution function (BRDF). BRDF describes the angular behavior by which light interacts with surfaces. Remote sensing technology has advanced to the stage where hyperspectral sensors, with hundreds of separate wavelength bands, are fairly common. This necessitates examining BRDF in the hyperspectral regime, which implies examining the directional reflective properties of hundreds of narrowly spaced wavelength bands.
In this dissertation I hypothesize that beach sand BRDF is wavelength dependent. Principal component analysis (PCA) and correlation matrix analysis of in situ measurements were used to test whether the spectral variability in the visible, near-infrared and shortwave directional reflectance factor of beach sands with and without freshwater surface films are wavelength dependent. The hyperspectral BRDF of beach sands exhibit weak spectral variability, the majority of which can be described with three to four broad spectral bands. These occur in the absence of a water layer on top of the sand in three wavelength ranges of 350-450 nm, 700-1350 nm, and 1450-2400 nm. When observing sheet flow on sand, a thin layer of water enhances reflectance in the specular direction at all wavelengths, and that spectral variability may be described using four spectral band regions of 350-450 nm, 500-950 nm, 950-1350 nm, and 1450-2400 nm. Spectral variations are more evident in sand surfaces of greater visual roughness than in smooth surfaces, regardless of sheetflow.
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Ball, Adrian Keith. "How Should a Robot Approach a Pair of People?" Thesis, The University of Sydney, 2016. http://hdl.handle.net/2123/17305.
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This thesis experimentally investigates the comfort of pairs of seated people when they are approached by a robot from different directions. While the effect of robot approach direction on the comfort of a lone person has been investigated previously, the extension to a robot approaching pairs of people has not been explored rigorously. Three maximally-different seating configurations of paired people and eight different robot approach directions were considered. The experiment was augmented with a fourth seating configuration of a lone individual, allowing the responses of grouped and lone participants to be compared. Data obtained from the experiment were analysed using both linear and directional statistics. Results from 180 unique participants showed that the comfort of a person when a robot approached is influenced by the presence and location of a second person. Analysis of these data with directional statistics showed that participant comfort preference clusters into angular regions of ‘suitable for robot approach’ and ‘unsuitable for robot approach’. This finding shows the importance of avoiding robot approach directions of low comfort, rather than selecting a singular robot approach direction of high comfort. Rayleigh’s test of uniformity, a directional statistics method, also shows across all participant configurations that robot approach directions that minimize participant discomfort align spatially with regions that allow for good line of sight of the robot by both people, and are centred on the largest open space that a robot could approach the group from. Participants who were grouped also regarded the robot as having more social agency than did lone experimental participants. Grouped participants were less frustrated with the experimental task and also found it less physically and temporally demanding in comparison to lone experimental participants.
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Wu, Jianhua. "Dynamic Path Planning of an Omni-directional Robot in a Dynamic Environment." Ohio University / OhioLINK, 2005. http://www.ohiolink.edu/etd/view.cgi?ohiou1113839523.
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Dayalan, Saravanan, and saravanan dayalan@rmit edu au. "On the Structure Differences of Short Fragments and Amino Acids in Proteins with and without Disulfide Bonds." RMIT University. Computer Science and Information Technology, 2008. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20081128.122615.
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Of the 20 standard amino acids, cysteines are the only amino acids that have a reactive sulphur atom, thus enabling two cysteines to form strong covalent bonds known as disulfide bonds. Even though almost all proteins have cysteines, not all of them have disulfide bonds. Disulfide bonds provide structural stability to proteins and hence are an important constraint in determining the structure of a protein. As a result, disulfide bonds are used to study various protein properties, one of them being protein folding. Protein structure prediction is the problem of predicting the three-dimensional structure of a protein from its one-dimensional amino acid sequence. Ab initio methods are a group of methods that attempt to solve this problem from first principles, using only basic physico-chemical properties of proteins. These methods use structure libraries of short amino acid fragments in the process of predicting the structure of a protein. The protein structures from which these structure libraries are created are not classified in any other way apart from being non-redundant. In this thesis, we investigate the structural dissimilarities of short amino acid fragments when occurring in proteins with disulfide bonds and when occurring in those proteins without disulfide bonds. We are interested in this because, as mentioned earlier, the protein structures from which the structure libraries of ab initio methods are created, are not classified in any form. This means that any significant structural difference in amino acids and short fragments when occurring in proteins with and without disulfide bonds would remain unnoticed as these structure libraries have both fragments from proteins with disulfide bonds and without disulfide bonds together. Our investigation of structural dissimilarities of amino acids and short fragments is done in four phases. In phase one, by statistically analysing the phi and psi backbone dihedral angle distributions we show that these fragments have significantly different structures in terms of dihedral angles when occurring in proteins with and without disulfide bonds. In phase two, using directional statistics we investigate how structurally different are the 20 different amino acids and the short fragments when occurring in proteins with and without disulfide bonds. In phase three of our work, we investigate the differences in secondary structure preference of the 20 amino acids in proteins with and without disulfide bonds. In phase four, we further investigate and show that there are significant differences within the same secondary structure region of amino acids when they occur in proteins with and without disulfide bonds. Finally, we present the design and implementation details of a dihedral angle and secondary structure database of short amino acid fragments (DASSD) that is publicly available. Thus, in this thesis we show previously unknown significant structure differences in terms of backbone dihedral angles and secondary structures in amino acids and short fragments when they occur in proteins with and without disulfide bonds.
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Henry, Legena Albertha. "A study of ocean wave statistical properties using nonlinear, directional, phase-resolved ocean wave-field simulations." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1912/3230.
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Thesis (S.M.)--Joint Program in Oceanography/Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and the Woods Hole Oceanographic Institution), February 2010.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 327-334).
In the present work, we study the statistics of wavefields obtained from non-linear phase-resolved simulations. The numerical model used to generate the waves models wave-wave interactions based on the fully non-linear Zakharov equations. We vary the simulated wavefield's input spectral properties: directional spreading function, Phillips parameter and peak shape parameter. We then investigate the relationships between a wavefield's input spectral properties and its output physical properties via statistical analysis. We investigate surface elevation distribution, wave definition methods in a nonlinear wavefield with a two-dimensional wavenumber, defined waves' distributions, and the occurrence and spacing of large wave events.
by Legena Albertha Henry.
S.M.
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Novikov, Dmitri. "Statistical Methods of Detection of Current Flow Structures in Stretches of Water." Thesis, Montpellier 2, 2011. http://www.theses.fr/2011MON20111/document.
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Ce projet adresse le problème de détection des structures directionnelles précises dans les champs de courant. L'accent est mis surtout sur la détection des structures tourbillonnaires, puisque les chercheurs dans le domaine de la dynamique des fluides considèrent ces structures comme étant particulièrement importantes. Chapitre 1 présente la motivation de ce projet et fournit les détails du contexte environnemental et mathématique du problème en soulignant les parties essentielles de la théorie qu'on utilise après pour la solution. Chapitre 2 propose une méthode statistique, basée sur la proportion des vraisemblances, pour adresser le problème particulier de la détection des tourbillons et montre l'efficacité de cette méthode sur les données simulées et réelles en discutant aussi les limitations. Chapitre 3 développe les idées discutées dans les chapitres 1 et 2 pour trouver un outil statistique plus général qui répare les défauts de la première méthode et permet la détection de toutes les structures directionnelles qui peuvent intéresser les chercheurs. Tous les outils nécessaires pour l'analyse des données en utilisant les deux méthodes développées dans ce projet se trouvent dans les annexes A et B de la thèseThis work addresses the problem of detecting specific directional structures in flows of current. Specific emphasis is placed on vortex detection, as scientists studying fluid dynamics consider this structure to be of particular importance. Chapter 1 presents the motivation behind the project and provides details about the environmental and, subsequently, the mathematical context of the problem, highlighting the essential parts of the theory that is later used to propose the solution. Chapter 2 offers a statistical approach, based on a likelihood ratio, to solving the specific problem of vortex detection and demonstrates the effectiveness of the method on simulated and real data, also discussing the limitations of the approach. Chapter 3 expands on the ideas discussed in Chapters 1 and 2 to derive a generalized statistical test that remedies the flaws of the first approach and extends to the problem of detecting any directional structure of interest. All necessary tools for the analysis of data using the two methods developed in this project are given in Appendix A and B
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Snyder, Mark Alan. "Long-Term Ambient Noise Statistics in the Gulf of Mexico." ScholarWorks@UNO, 2007. http://scholarworks.uno.edu/td/595.
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Long-term omni-directional ambient noise was collected at several sites in the Gulf of Mexico during 2004 and 2005. The Naval Oceanographic Office deployed bottom moored Environmental Acoustic Recording System (EARS) buoys approximately 159 nautical miles south of Panama City, Florida, in water depths of 3200 meters. The hydrophone of each buoy was 265 meters above the bottom. The data duration ranged from 10-14 months. The buoys were located near a major shipping lane, with an estimated 1.5 to 4.5 ships per day passing nearby. The data were sampled at 2500 Hz and have a bandwidth of 10-1000 Hz. Data are processed in eight 1/3-octave frequency bands, centered from 25 to 950 Hz, and monthly values of the following statistical quantities are computed from the resulting eight time series of noise spectral level: mean, median, standard deviation, skewness, kurtosis and coherence time. Four hurricanes were recorded during the summer of 2004 and they have a major impact on all of the noise statistics. Noise levels at higher frequencies (400-950 Hz) peak during extremely windy months (summer hurricanes and winter storms). Standard deviation is least in the region 100-200 Hz but increases at higher frequencies, especially during periods of high wind variability (summer hurricanes). Skewness is positive from 25-400 Hz and negative from 630-950 Hz. Skewness and kurtosis are greatest near 100 Hz. Coherence time is low in shipping bands and high in weather bands, and it peaks during hurricanes. The noise coherence is also analyzed. The 14-month time series in each 1/3- octave band is highly correlated with other 1/3-octave band time series ranging from 2 octaves below to 2 octaves above the band's center frequency. Spatial coherence between hydrophones is also analyzed for hydrophone separations of 2.29, 2.56 and 4.84 km over a 10-month period. The noise field is highly coherent out to the maximum distance studied, 4.84 km. Additionally, fluctuations of each time series are analyzed to determine time scales of greatest variability. The 14-month data show clearly that variability occurs primarily over three time scales: 7-22 hours (shipping-related), 56-282 hours (2-12 days, weather-related) and over an 8-12 month period.
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Walsh, David Leonard. "Directional statistics, Bayesian methods of earthquake focal mechanism estimation, and their application to New Zealand seismicity data : a thesis submitted to the Victoria University of Wellington in fulfilment of the requirements for the degree of Master of Science in Statistics /." ResearchArchive@Victoria e-Thesis, 2008. http://hdl.handle.net/10063/350.
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Agapito, Cristel da Glória. "A análise da qualidade das previsões macroeconómicas: um estudo comparativo para a economia portuguesa." Master's thesis, Instituto Superior de Economia e Gestão, 2005. http://hdl.handle.net/10400.5/2829.
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Mestrado em Economia Monetária e FinanceiraAs previsões macroeconómicas são de grande importância uma vez que são amplamente utilizadas como um suporte do processo de tomada de decisão. Neste espírito, tem sido desenvolvido um vasto conjunto de literatura respeitante à avaliação de previsões. A presente dissertação tem como objectivo analisar o desempenho relativo do FMI, OCDE, Comissão Europeia e Governo Português, na previsão do crescimento do PIB real e do crescimento do Deflator do PIB para a economia portuguesa. São avaliados dois tipos de previsões: previsões para o ano corrente e para o ano seguinte. Em primeiro lugar, avalio e comparo a qualidade das previsões através de um método puramente descritivo, ou seja, sem qualquer procedimento de inferência estatística. Em particular, analiso algumas estatísticas descritivas como o erro médio, o erro absoluto médio e a raiz da média dos erros ao quadrado, fazendo também a comparação das várias previsões analisadas nesta dissertação com previsões ditas naive. Em segundo lugar, avalio a qualidade usando procedimentos de inferência estatística, analisando nomeadamente a eficiência e o enviesamento das previsões, e avaliando também o sentido da mudança das previsões. Em terceiro lugar, comparo e avalio o desempenho da OCDE, da Comissão Europeia e do Governo Português na detecção do movimento geral do ciclo económico e examino a evolução da qualidade das previsões numa perspectiva temporal. Por todo o trabalho realizado concluiu-se que, de um modo geral, os previsores internacionais estão bastante equilibrados no seu desempenho qualitativo sendo a prestação do governo português igualmente positiva e, nalguns casos, superior à dos previsores internacionais. Concluiu-se igualmente que as várias previsões analisadas são globalmente boas a nível qualitativo.
Macroeconomic forecasts are of great importance since they are widely used as a support of the decision making process. In that spirit, a wide range of literature concerning forecast evaluation has been developed. The present work has as its objective the assessment of the relative (ex-post) performance of the IMF, OECD, European Commission and Portuguese Government, in forecasting the real GDP and GDP Deflator growth for the Portuguese economy. Two types of forecasts are evaluated: current-year and one-year-ahead forecasts. Firstly, I evaluate the accuracy and compare forecasts through a purely descriptive method, that is, without any statistical inference procedures. In particular, I analyse some descriptive statistics like mean error, mean absolute error and root mean squared error, also doing the comparison of the several forecasts analysed in this dissertation with some naive forecasts. Secondly, I evaluate accuracy using statistical inference procedures namely, the efficiency and bias of forecasts, also evaluating the forecasts direction-of-change. Thirdly, I compare and evaluate the performance of OECD, European Commission and Portuguese Government in detecting the broad movement of the business cycle and I examine the forecast accuracy evolution in a temporal perspective. For all the work performed I have concluded that, in a general sense, the international forecasters are rather balanced in their qualitative performance, being the Portuguese Government performance equally positive and, in some cases, superior to that of international forecasters. I have also concluded that the several forecasts analysed are globally good at a qualitative level.
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Jonsson, Fredrik. "Self-Normalized Sums and Directional Conclusions." Doctoral thesis, Uppsala universitet, Matematiska institutionen, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-162168.
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This thesis consists of a summary and five papers, dealing with self-normalized sums of independent, identically distributed random variables, and three-decision procedures for directional conclusions. In Paper I, we investigate a general set-up for Student's t-statistic. Finiteness of absolute moments is related to the corresponding degree of freedom, and relevant properties of the underlying distribution, assuming independent, identically distributed random variables. In Paper II, we investigate a certain kind of self-normalized sums. We show that the corresponding quadratic moments are greater than or equal to one, with equality if and only if the underlying distribution is symmetrically distributed around the origin. In Paper III, we study linear combinations of independent Rademacher random variables. A family of universal bounds on the corresponding tail probabilities is derived through the technique known as exponential tilting. Connections to self-normalized sums of symmetrically distributed random variables are given. In Paper IV, we consider a general formulation of three-decision procedures for directional conclusions. We introduce three kinds of optimality characterizations, and formulate corresponding sufficiency conditions. These conditions are applied to exponential families of distributions. In Paper V, we investigate the Benjamini-Hochberg procedure as a means of confirming a selection of statistical decisions on the basis of a corresponding set of generalized p-values. Assuming independence, we show that control is imposed on the expected average loss among confirmed decisions. Connections to directional conclusions are given.
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Latheef, Mohamed. "Surface wave statistics in directionally spread seas." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/33255.
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A new set of laboratory experiments to examine the short-term statistics of crest elevation and wave heights has been undertaken. Sea states with a range of steepness and directional spreading have been considered. Comparisons between these data and a number of widely adopted short-term statistical models exhibit clearly defined departures. For a given sea state, the extent of these departures is directly proportional to the sea state steepness and inversely proportional to the directional spread. With directional spreading identified as a critical parameter, a detailed study of how best to describe, define and model it has been undertaken. The key finding of this study is that the average directional spread in the steepest sea states reduces. In addition, it has also been shown that on average the largest waves in these steep sea states are more uni-directional when compared to the sea state as a whole. Further consideration of the data show that the two physical mechanisms leading to the alteration of the statistics are nonlinear amplification (leading to increases above second-order) and the dissipative effect of wave breaking. Quantification of the effects arising from these two competing mechanisms has been undertaken based on additional simulations (both numerical and experimental) of focused wave groups. For uni-directional sea states, a classical expansion (truncated at a third order of wave steepness) of the increased surface elevation obtained in a fully nonlinear uni-directional focused wave group has been used to quantify the effect of amplification in the crest height statistics. Similarly, the dissipative effect of wave breaking on crest elevations has been quantified based on the reduction in crest elevations in focused wave groups with linear amplitude sum larger than the limit at which incipient spilling first occurs. These reductions are calculated as the difference in the maximum crest elevation in a breaking wave event and that predicted by the third-order power series used for the quantification of nonlinear amplificiation. Overall the two methods employed in quantifying the effect of nonlinear amplification and wave breaking yield good agreement with the original (random) laboratory data. Finally, directionality is incorporated into these predictions based on the linear reduction in the wave front steepness with increasing directional spread. Both the nonlinear amplification and the dissipative effect of wave breaking are calculated based on this reduced steepness for the directional sea states. The predicted crest heights from this simplified procedure compare well with the laboratory data; the predictions remaining conservative throughout.
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Nasrallah, Alexandre James. "Statistics of gradient directions in natural images." Thesis, University College London (University of London), 2007. http://discovery.ucl.ac.uk/1444886/.
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Interest in finding statistical regularities in natural images has been growing since the advent of information theory and the advancement of the efficient coding hypothesis that the human visual system is optimised to encode natural visual stimuli. In this thesis, a statistical analysis of gradient directions in an ensemble of natural images is reported. Information-theoretic measures have been used to compute the amount of dependency which exists between triples of gradient directions at separate image locations. Control experiments are performed on other image classes: phase randomized natural images, whitened natural images, and Gaussian noise images. The main results show that for an ensemble of natural images the average amount of de pendency between two and three gradient directions is the same as for an ensemble of phase randomized natural images. This result does not extend to i) the amount dependency between gradient magnitudes, ii) gradient directions at high gradient magnitude locations, or iii) individual natural images. Furthermore, no significant synergetic dependencies are found between triples of gradient directions in an ensemble natural images a synergetic dependency is an increase in dependency between a pair of gradient directions given the interaction of a third gradient direction. Additional experiments are performed to establish both the generality and specificity of the main results by studying the gradient direction dependencies of ensembles of noise (random phases) images with varying power law power spectra. The results of the additional experiments indicate that, for ensembles of images with varying power law power spectra, the amount of dependency between two and three gradient directions is determined by the ensemble's mean power spectrum rather than the phase spectrum. A framework is also presented for future work and preliminary results are provided for the dependency between second order derivative measurements (shape index) for up to 9-point configurations.
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DEMNI, Houyem. "Depth-based classification approaches for directional data." Doctoral thesis, Università degli studi di Cassino, 2021. http://hdl.handle.net/11580/83781.
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Supervised learning tasks aim to define a data-based rule by which new objects are assigned to one of the given classes. To this end, a training set containing objects with known memberships is exploited. Directional data are points lying on the surface of circles, spheres or hyper-spheres. Given that they lie on a non-linear manifold, directional observations require specific methods to be analyzed. In this thesis, the main interest is to present novel methodologies and to perform reliable inferences for directional data, within the framework of supervised classification. First, a supervised classification procedure for directional data is introduced. The procedure is based on the cumulative distribution of the cosine depth, that is a directional distance-based
depth function. The proposed method is compared with the max-depth classifier, a well-known depth-based classifier within the literature, through simulations and a real data example. Second, we study the optimality of the depth distribution and the max-depth classifiers from a theoretical perspective. More specifically, we investigate the necessary conditions under which the classifiers are optimal in the sense of the optimal
Bayes rule. Then, we study the robustness of some directional depth-based classifiers in the presence of contaminated data. The performance of the depth distribution classifier, the max-depth classifier and the DD-classifier is evaluated by means of simulations in the presence of both class and attribute noise. Finally, the last part of the thesis is devoted to evaluate the performance of depth-based classifiers on a real directional data set.
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Ganeiber, Asaad Mohammed. "Estimation and simulation in directional and statistical shape models." Thesis, University of Leeds, 2012. http://etheses.whiterose.ac.uk/2585/.
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This thesis is concerned with problems in two related areas of statistical shape analysis in two dimensional landmarks data and directional statistics in various sample spaces. Directional observations can be regarded as points on the circumference of a circle of unit radius in two dimensions or on the surface of a sphere in three dimensions. Special directional methods and models are required which take into account the structure of these sample spaces. Shape analysis involves methods for the study of the shape of objects where location, scale and orientation are removed. Specifically, we consider the situation where the objects are summarized by points on the object called landmarks. The non-Euclidean nature of the shape space causes several problems when defining a distribution on it. Any distribution which could be considered needs to be tractable and a realistic model for landmark data. One aim of this thesis is to investigate the saddlepoint approximations for the normalizing constants of some directional and shape distributions. In particular, we consider the normalizing constant of the CBQ distribution which can be expressed as a one dimensional integral of normalizing constants for Bingham distributions. Two new methods are explored to evaluate this normalizing constant based on saddlepoint approximations namely the Integrated Saddlepoint (ISP) approximation and the Saddlepoint-Integration (SPI) approximation. Another objective of this thesis is to develop new simulation methods for some directional and shape models. We propose an efficient acceptance-rejection simulation algorithm for the Bingham distribution on unit sphere using an angular central Gaussian (ACG) density as an envelope. This envelope is justified using inequalities based on concave functions. An immediate consequence is a method to simulate 3 x 3 matrix Fisher rotation matrices. In addition, a new accept-reject algorithm is developed to generate samples from the complex Bingham quartic (CBQ) distribution. The last objective of this thesis is to develop a new moment method to estimate the parameters of the wrapped normal torus distribution based on the sample sine and cosine moments.
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Hammarhjelm, Gustav. "On the gap statistics of directions in planar quasicrystals." Licentiate thesis, Uppsala universitet, Matematiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-442551.
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Vuollo, V. (Ville). "3D imaging and nonparametric function estimation methods for analysis of infant cranial shape and detection of twin zygosity." Doctoral thesis, Oulun yliopisto, 2018. http://urn.fi/urn:isbn:9789526218557.
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Abstract The use of 3D imaging of craniofacial soft tissue has increased in medical science, and imaging technology has been developed greatly in recent years. 3D models are quite accurate and with imaging devices based on stereophotogrammetry, capturing the data is a quick and easy operation for the subject. However, analyzing 3D models of the face or head can be challenging and there is a growing need for efficient quantitative methods. In this thesis, new mathematical methods and tools for measuring craniofacial structures are developed. The thesis is divided into three parts. In the first part, facial 3D data of Lithuanian twins are used for the determination of zygosity. Statistical pattern recognition methodology is used for classification and the results are compared with DNA testing. In the second part of the thesis, the distribution of surface normal vector directions of a 3D infant head model is used to analyze skull deformation. The level of flatness and asymmetry are quantified by functionals of the kernel density estimate of the normal vector directions. Using 3D models from infants at the age of three months and clinical ratings made by experts, this novel method is compared with some previously suggested approaches. The method is also applied to clinical longitudinal research in which 3D images from three different time points are analyzed to find the course of positional cranial deformation and associated risk factors. The final part of the thesis introduces a novel statistical scale space method, SphereSiZer, for exploring the structures of a probability density function defined on the unit sphere. The tools developed in the second part are used for the implementation of SphereSiZer. In SphereSiZer, the scale-dependent features of the density are visualized by projecting the statistically significant gradients onto a planar contour plot of the density function. The method is tested by analyzing samples of surface unit normal vector data of an infant head as well as data from generated simulated spherical densities. The results and examples of the study show that the proposed novel methods perform well. The methods can be extended and developed in further studies. Cranial and facial 3D models will offer many opportunities for the development of new and sophisticated analytical methods in the futureTiivistelmä Pään ja kasvojen pehmytkudoksen 3D-kuvantaminen on yleistynyt lääketieteessä, ja siihen tarvittava teknologia on kehittynyt huomattavasti viime vuosina. 3D-mallit ovat melko tarkkoja, ja kuvaus stereofotogrammetriaan perustuvalla laitteella on nopea ja helppo tilanne kuvattavalle. Kasvojen ja pään 3D-mallien analysointi voi kuitenkin olla haastavaa, ja tarve tehokkaille kvantitatiivisille menetelmille on kasvanut. Tässä väitöskirjassa kehitetään uusia matemaattisia kraniofakiaalisten rakenteiden mittausmenetelmiä ja -työkaluja. Työ on jaettu kolmeen osaan. Ensimmäisessä osassa pyritään määrittämään liettualaisten kaksosten tsygositeetti kasvojen 3D-datan perusteella. Luokituksessa hyödynnetään tilastollista hahmontunnistusta, ja tuloksia verrataan DNA-testituloksiin. Toisessa osassa analysoidaan pään epämuodostumia imeväisikäisten päiden 3D-kuvista laskettujen pintanormaalivektorien suuntiin perustuvan jakauman avulla. Tasaisuuden ja epäsymmetrian määrää mitataan normaalivektorien suuntakulmien ydinestimaatin funktionaalien avulla. Kehitettyä menetelmää verrataan joihinkin aiemmin ehdotettuihin lähestymistapoihin mittaamalla kolmen kuukauden ikäisten imeväisten 3D-malleja ja tarkastelemalla asiantuntijoiden tekemiä kliinisiä pisteytyksiä. Menetelmää sovelletaan myös kliiniseen pitkittäistutkimukseen, jossa tutkitaan pään epämuodostumien ja niihin liittyvien riskitekijöiden kehitystä kolmena eri ajankohtana otettujen 3D-kuvien perusteella. Viimeisessä osassa esitellään uusi tilastollinen skaala-avaruusmenetelmä SphereSiZer, jolla tutkitaan yksikköpallon tiheysfunktion rakenteita. Toisessa osassa kehitettyjä työkaluja sovelletaan SphereSiZerin toteutukseen. SphereSiZer-menetelmässä tiheysfunktion eri skaalojen piirteet visualisoidaan projisoimalla tilastollisesti merkitsevät gradientit tiheysfunktiota kuvaavalle isoviivakartalle. Menetelmää sovelletaan imeväisikäisen pään pintanormaalivektoridataan ja simuloituihin, pallotiheysfunktioihin perustuviin otoksiin. Tulosten ja esimerkkien perusteella väitöskirjassa esitetyt uudet menetelmät toimivat hyvin. Menetelmiä voidaan myös kehittää edelleen ja laajentaa jatkotutkimuksissa. Pään ja kasvojen 3D-mallit tarjoavat paljon mahdollisuuksia uusien ja laadukkaiden analyysityökalujen kehitykseen myöhemmissä tutkimuksissa
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Hamsici, Onur C. "Bayes Optimality in Classification, Feature Extraction and Shape Analysis." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1218513562.
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Ramler, Ivan Peter. "Improved statistical methods for k-means clustering of noisy and directional data." [Ames, Iowa : Iowa State University], 2008.
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Al-Wali, Azzam Ahmad. "Explicit alternating direction methods for problems in fluid dynamics." Thesis, Loughborough University, 1994. https://dspace.lboro.ac.uk/2134/6840.
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Recently an iterative method was formulated employing a new splitting strategy for the solution of tridiagonal systems of difference equations. The method was successful in solving the systems of equations arising from one dimensional initial boundary value problems, and a theoretical analysis for proving the convergence of the method for systems whose constituent matrices are positive definite was presented by Evans and Sahimi [22]. The method was known as the Alternating Group Explicit (AGE) method and is referred to as AGE-1D. The explicit nature of the method meant that its implementation on parallel machines can be very promising. The method was also extended to solve systems arising from two and three dimensional initial-boundary value problems, but the AGE-2D and AGE-3D algorithms proved to be too demanding in computational cost which largely reduces the advantages of its parallel nature. In this thesis, further theoretical analyses and experimental studies are pursued to establish the convergence and suitability of the AGE-1D method to a wider class of systems arising from univariate and multivariate differential equations with symmetric and non symmetric difference operators. Also the possibility of a Chebyshev acceleration of the AGE-1D algorithm is considered. For two and three dimensional problems it is proposed to couple the use of the AGE-1D algorithm with an ADI scheme or an ADI iterative method in what is called the Explicit Alternating Direction (EAD) method. It is then shown through experimental results that the EAD method retains the parallel features of the AGE method and moreover leads to savings of up to 83 % in the computational cost for solving some of the model problems. The thesis also includes applications of the AGE-1D algorithm and the EAD method to solve some problems of fluid dynamics such as the linearized Shallow Water equations, and the Navier Stokes' equations for the flow in an idealized one dimensional Planetary Boundary Layer. The thesis terminates with conclusions and suggestions for further work together with a comprehensive bibliography and an appendix containing some selected programs.
25
Sharma, Jayant. "Integrated Spatial Reasoning in Geographic Information Systems: Combining Topology and Direction." Fogler Library, University of Maine, 1996. http://www.library.umaine.edu/theses/pdf/Sharma.pdf.
Full text
26
Merrifield, Alistair James. "An Investigation Of Mathematical Models For Animal Group Movement, Using Classical And Statistical Approaches." Thesis, The University of Sydney, 2006. http://hdl.handle.net/2123/1132.
Full textAbstract:
Collective actions of large animal groups result in elaborate behaviour, whose nature can be breathtaking in their complexity. Social organisation is the key to the origin of this behaviour and the mechanisms by which this organisation occurs are of particular interest. In this thesis, these mechanisms of social interactions and their consequences for group-level behaviour are explored. Social interactions amongst individuals are based on simple rules of attraction, alignment and orientation amongst neighbouring individuals. As part of this study, we will be interested in data that takes the form of a set of directions in space. In Chapter 2, we discuss relevant statistical measure and theory which will allow us to analyse directional data. These statistical tools will be employed on the results of the simulations of the mathematical models formulated in the course of the thesis. The first mathematical model for collective group behaviour is a Lagrangian self-organising model, which is formulated in Chapter 3. This model is based on basic social interactions between group members. Resulting collective behaviours and other related issues are examined during this chapter. Once we have an understanding of the model in Chapter 3, we use this model in Chapter 4 to investigate the idea of guidance of large groups by a select number of individuals. These individuals are privy to information regarding the location of a specific goal. This is used to explore a mechanism proposed for honeybee (Apis mellifera) swarm migrations. The spherical theory introduced in Chapter 2 will prove to be particularly useful in analysing the results of the modelling. In Chapter 5, we introduce a second mathematical model for aggregative behaviour. The model uses ideas from electromagnetic forces and particle physics, reinterpreting them in the context of social forces. While attraction and repulsion terms have been included in similar models in past literature, we introduce an orientation force to our model and show the requirement of a dissipative force to prevent individuals from escaping from the confines of the group.
27
Merrifield, Alistair James. "An Investigation Of Mathematical Models For Animal Group Movement, Using Classical And Statistical Approaches." University of Sydney, 2006. http://hdl.handle.net/2123/1132.
Full textAbstract:
Doctor of PhilosophyCollective actions of large animal groups result in elaborate behaviour, whose nature can be breathtaking in their complexity. Social organisation is the key to the origin of this behaviour and the mechanisms by which this organisation occurs are of particular interest. In this thesis, these mechanisms of social interactions and their consequences for group-level behaviour are explored. Social interactions amongst individuals are based on simple rules of attraction, alignment and orientation amongst neighbouring individuals. As part of this study, we will be interested in data that takes the form of a set of directions in space. In Chapter 2, we discuss relevant statistical measure and theory which will allow us to analyse directional data. These statistical tools will be employed on the results of the simulations of the mathematical models formulated in the course of the thesis. The first mathematical model for collective group behaviour is a Lagrangian self-organising model, which is formulated in Chapter 3. This model is based on basic social interactions between group members. Resulting collective behaviours and other related issues are examined during this chapter. Once we have an understanding of the model in Chapter 3, we use this model in Chapter 4 to investigate the idea of guidance of large groups by a select number of individuals. These individuals are privy to information regarding the location of a specific goal. This is used to explore a mechanism proposed for honeybee (Apis mellifera) swarm migrations. The spherical theory introduced in Chapter 2 will prove to be particularly useful in analysing the results of the modelling. In Chapter 5, we introduce a second mathematical model for aggregative behaviour. The model uses ideas from electromagnetic forces and particle physics, reinterpreting them in the context of social forces. While attraction and repulsion terms have been included in similar models in past literature, we introduce an orientation force to our model and show the requirement of a dissipative force to prevent individuals from escaping from the confines of the group.
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Johnson, M. E., and n/a. "The application of statistics to the mesoscale study of wind speed and direction in the Canberra region." University of Canberra. Information Sciences, 1986. http://erl.canberra.edu.au./public/adt-AUC20060802.154807.
Full textAbstract:
The temporal and spatial variability in wind speed and
direction was investigated in a study of the mesoscale wind
fields in the Canberra region. The statistical description of
the winds was based on twelve months of three-hourly data at
seventeen sites obtained in a joint program carried out by the
Division of Land Use Research, CSIRO, the National Capital Development Commission, and the Bureau of Meteorology.
The statistical analysis proceeded in stages. The first
two stages were concerned with the determination and
examination of averages and measures of dipersion. Information
on the temporal variability in regional wind, defined as the
average of the winds at the seventeen collection sites,
provided the first insight into the important determinants of
winds in the region. The data were then categorized on the
basis of the information thus obtained, and the averages over
time for each site were analysed in each category. The
variation between sites revealed the extent of the spatial
variability in the region.
For each category, for each site, there were
perturbations around the average state, and in the last stage
of the study, the analysis examined how the perturbations were
related across sites using correlation coefficients.
Generalized Procrustes Analysis was used, followed by the
extensive use of cluster analysis.
Linear modelling techniques were used at all stages of
the study, not only for wind speed, but also for wind direction
which is an angular variate and thus required different
modelling procedures. The models related the variables of
interest to terrain features such as position, elevation and
surface roughness. These models allowed an informed judgement
to be made on the likelihood of accurately estimating the winds
at other locations in the region using interpolation
techniques.
29
Ban, Hyowon. "Visualization Of Urban Concepts In Two Directions Of Thinking." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1249306949.
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Iacovella, Vittorio. "Directional relationships between BOLD activity and autonomic nervous system fluctuations revealed by fast fMRI acquisition." Doctoral thesis, Università degli studi di Trento, 2012. https://hdl.handle.net/11572/368089.
Full textAbstract:
The problem of the relationship between brain function, characterized by functional magnetic resonance imaging, and physiological fluctuations by means of cardiac / respiratory oscillations is one of the most debated topics in the last decade. In recent literature, a great number of studies are found that focus on both practical and conceptual aspects about this topic.
In this work, we start with reviewing two distinct approaches in considering physiology - related sequences with respect to functional magnetic resonance imaging: one treating physiology - related fluctuations as generators of noise, the other considering them as carriers of cognitively relevant information.
In chapter 2 – “Physiology – related effects in the BOLD signal at rest at 4T†, we consider physiological quantities as generators of noise, and discuss conceptual flaws researchers have to face when dealing with data de-noising procedures. We point out that it can be difficult to show that the procedure has achieved its stated aim, i.e. to remove only physiology - related components from the data. As a practical solution, we present a benchmark for assessing whether correction for physiological noise has achieved its stated aim, based on the principle of permutation testing.
In chapter 3 – “Directional relationships between BOLD activity and autonomic nervous system fluctuations revealed by fast fMRI acquisition†, on the other hand, we will consider autonomic indicants derived from physiological time - series as meaningful components of the BOLD signal. There, we describe a FMRI experiment building on this, where the goal was to localize brain areas whose activity is directionally related to autonomic one, in a top - down modulation fashion.
In chapter 4 we recap the conclusions we found from the two approaches and we summarize the general contributions of our findings. We point out that bringing together the distinct approaches we reviewed lead us to mainly two contributions. On one hand we thought back the validity of almost established procedures in FMRI resting - state pre-processing pipelines. On the other we were able to say something new about general relationship between BOLD and autonomic activity, resting state fluctuations and deactivation theory.
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Tait, Daniel Beale. "Electromagnetic Vector-Sensor Direction-of-Arrival Estimation in the Presence of Interference." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/99961.
Full textAbstract:
This research investigates signal processing involving a single electromagnetic vector-sensor, with an emphasis on the problem regarding signal-selective narrowband direction-of-arrival (DOA) estimation in the presence of interference. The approach in this thesis relies on a high-resolution ESPRIT-based algorithm. Unlike spatially displaced arrays, the sensor cannot estimate the DOA of sources using phase differences between the array elements, as the elements are spatially co-located. However, the sensor measures the full electromagnetic field vectors, so the DOA can be estimated through the Poynting vector. Limited information is available in the open literature regarding signal-selective DOA estimation for a single electromagnetic vector-sensor. In this thesis, it is shown how the Uni-Vector-Sensor-ESPRIT (UVS-ESPRIT) algorithm that relies on a time-series invariance and was originally devised for deterministic harmonic sources can be applied to non-deterministic sources. Additionally, two algorithms, one based on cyclostationarity and the other based on fourth-order cumulants, are formulated based on the UVS-ESPRIT algorithm and are capable of selectively estimating the source DOA in the presence of interference based on the statistical properties of the sources. The cyclostationarity-based UVS-ESPRIT algorithm is capable of selectively estimating the signal-of-interest DOA when the sources have the same carrier frequency, and thus overlap in frequency. The cumulant-based UVS-ESPRIT algorithm devised for this sensor relies on the independent component analysis algorithm JADE and is capable of selectively estimating the signal-of-interest DOA through the fourth-order cumulants only, is robust to spatially colored noise, and is capable of estimating the DOA of more sources than sensor elements.Master of Science
Electromagnetic vector-sensors are specialized sensors capable of capturing the full electromagnetic field vectors at a single point in space. Direction-of-arrival (DOA) estimation is the problem of estimating the spatial-angular parameters of one or more wavefronts impinging on an array. For a single electromagnetic vector-sensor, the array elements are not spatially displaced, but it is still possible to estimate the direction-of-arrival through the Poynting vector, which relates the electric and magnetic field vectors to the direction of propagation of an electromagnetic wave. Although direction-of-arrival estimation is a well-established area of research, there is limited discussion in the open literature regarding signal-selective DOA estimation in the presence of interference for a single electromagnetic vector-sensor. This research investigates this problem and discusses how the high-resolution Uni-Vector-Sensor-ESPRIT (UVS-ESPRIT) algorithm may be applied to non-deterministic sources. ESPRIT based algorithms capable of selectively estimating the source DOA are formulated based on the cyclostationarity and higher-order statistics of the sources, which are approaches known to be robust to interference. The approach based on higher-order statistics is also robust to spatially colored noise and is capable of estimating the DOA of more sources than sensor elements. The formulation of the UVS-ESPRIT for higher-order statistics relies on the application of the independent component analysis algorithm JADE, an unsupervised learning technique. Overall, this research investigates signal-selective direction-of-arrival estimation using an ESPRIT-based algorithm for a single electromagnetic vector-sensor.
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Aktas, Metin. "Online Calibration Of Sensor Arrays Using Higher Order Statistics." Phd thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614054/index.pdf.
Full textAbstract:
Higher Order Statistics (HOS) and Second Order Statistics (SOS) approaches have certain advantages and disadvantages in signal processing applications. HOS approach provides more statistical information for non-Gaussian signals. On the other hand, SOS approach is more robust to the estimation errors than the HOS approach, especially when the number of observations is small. In this thesis, HOS and SOS approaches are jointly used in order to take advantage of both methods. In this respect, the joint use of HOS and SOS approaches are introduced for online calibration of sensor arrays with arbitrary geometries. Three different problems in online array calibration are considered and new algorithms for each of these problems are proposed. In the first problem, the positions of the randomly deployed sensors are completely unknown except the two reference sensors and HOS and SOS approaches are used iteratively for the joint Direction of Arrival (DOA) and sensor position estimation. Iterative HOS-SOS algorithm (IHOSS) solves the ambiguity problem in sensor position estimation by observing the source signals at least in two different frequencies and hence it is applicable for wideband signals. The conditions on these frequencies are presented. IHOSS is the first algorithm in the literature which finds the DOA and sensor position estimations in case of randomly deployed sensors with unknown coordinates. In the second problem, narrowband signals are considered and it is assumed that the nominal sensor positions are known. Modified IHOSS (MIHOSS) algorithm uses the nominal sensor positions to solve the ambiguity problem in sensor position estimation. This algorithm can handle both small and large errors in sensor positions. The upper bound of perturbations for unambiguous sensor position estimation is presented. In the last problem, an online array calibration method is proposed for sensor arrays where the sensors have unknown gain/phase mismatches and mutual coupling coefficients. In this case, sensor positions are assumed to be known. The mutual coupling matrix is unstructured. The two reference sensors are assumed to be perfectly calibrated. IHOSS algorithm is adapted for online calibration and parameter estimation, and hence CIHOSS algorithm is obtained. While CIHOSS originates from IHOSS, it is fundamentally different in many aspects. CIHOSS uses multiple virtual ESPRIT structures and employs an alignment technique to order the elements of rows of the actual array steering matrix. In this thesis, a new cumulant matrix estimation technique is proposed for the HOS approach by converting the multi-source problem into a single source one. The proposed algorithms perform well even in the case of correlated source signals due to the effectiveness of the proposed cumulant matrix estimate. The iterative procedure in all the proposed algorithms is guaranteed to converge. Closed form expressions are derived for the deterministic Cram´er-Rao bound (CRB) for DOA and unknown calibration parameters for non-circular complex Gaussian noise with unknown covariance matrix. Simulation results show that the performances of the proposed methods approach to the CRB for both DOA and unknown calibration parameter estimations for high SNR.
33
Iacovella, Vittorio. "Directional relationships between BOLD activity and autonomic nervous system fluctuations revealed by fast fMRI acquisition." Doctoral thesis, University of Trento, 2012. http://eprints-phd.biblio.unitn.it/838/1/2012.10.31.vittorio.iacovella.phd.thesis.pdf.
Full textAbstract:
The problem of the relationship between brain function, characterized by functional magnetic resonance imaging, and physiological fluctuations by means of cardiac / respiratory oscillations is one of the most debated topics in the last decade. In recent literature, a great number of studies are found that focus on both practical and conceptual aspects about this topic.
In this work, we start with reviewing two distinct approaches in considering physiology - related sequences with respect to functional magnetic resonance imaging: one treating physiology - related fluctuations as generators of noise, the other considering them as carriers of cognitively relevant information.
In chapter 2 – “Physiology – related effects in the BOLD signal at rest at 4T”, we consider physiological quantities as generators of noise, and discuss conceptual flaws researchers have to face when dealing with data de-noising procedures. We point out that it can be difficult to show that the procedure has achieved its stated aim, i.e. to remove only physiology - related components from the data. As a practical solution, we present a benchmark for assessing whether correction for physiological noise has achieved its stated aim, based on the principle of permutation testing.
In chapter 3 – “Directional relationships between BOLD activity and autonomic nervous system fluctuations revealed by fast fMRI acquisition”, on the other hand, we will consider autonomic indicants derived from physiological time - series as meaningful components of the BOLD signal. There, we describe a FMRI experiment building on this, where the goal was to localize brain areas whose activity is directionally related to autonomic one, in a top - down modulation fashion.
In chapter 4 we recap the conclusions we found from the two approaches and we summarize the general contributions of our findings. We point out that bringing together the distinct approaches we reviewed lead us to mainly two contributions. On one hand we thought back the validity of almost established procedures in FMRI resting - state pre-processing pipelines. On the other we were able to say something new about general relationship between BOLD and autonomic activity, resting state fluctuations and deactivation theory.
34
Salah, Aghiles. "Von Mises-Fisher based (co-)clustering for high-dimensional sparse data : application to text and collaborative filtering data." Thesis, Sorbonne Paris Cité, 2016. http://www.theses.fr/2016USPCB093/document.
Full textAbstract:
La classification automatique, qui consiste à regrouper des objets similaires au sein de groupes, également appelés classes ou clusters, est sans aucun doute l’une des méthodes d’apprentissage non-supervisé les plus utiles dans le contexte du Big Data. En effet, avec l’expansion des volumes de données disponibles, notamment sur le web, la classification ne cesse de gagner en importance dans le domaine de la science des données pour la réalisation de différentes tâches, telles que le résumé automatique, la réduction de dimension, la visualisation, la détection d’anomalies, l’accélération des moteurs de recherche, l’organisation d’énormes ensembles de données, etc. De nombreuses méthodes de classification ont été développées à ce jour, ces dernières sont cependant fortement mises en difficulté par les caractéristiques complexes des ensembles de données que l’on rencontre dans certains domaines d’actualité tel que le Filtrage Collaboratif (FC) et de la fouille de textes. Ces données, souvent représentées sous forme de matrices, sont de très grande dimension (des milliers de variables) et extrêmement creuses (ou sparses, avec plus de 95% de zéros). En plus d’être de grande dimension et sparse, les données rencontrées dans les domaines mentionnés ci-dessus sont également de nature directionnelles. En effet, plusieurs études antérieures ont démontré empiriquement que les mesures directionnelles, telle que la similarité cosinus, sont supérieurs à d’autres mesures, telle que la distance Euclidiennes, pour la classification des documents textuels ou pour mesurer les similitudes entre les utilisateurs/items dans le FC. Cela suggère que, dans un tel contexte, c’est la direction d’un vecteur de données (e.g., représentant un document texte) qui est pertinente, et non pas sa longueur. Il est intéressant de noter que la similarité cosinus est exactement le produit scalaire entre des vecteurs unitaires (de norme 1). Ainsi, d’un point de vue probabiliste l’utilisation de la similarité cosinus revient à supposer que les données sont directionnelles et réparties sur la surface d’une hypersphère unité. En dépit des nombreuses preuves empiriques suggérant que certains ensembles de données sparses et de grande dimension sont mieux modélisés sur une hypersphère unité, la plupart des modèles existants dans le contexte de la fouille de textes et du FC s’appuient sur des hypothèses populaires : distributions Gaussiennes ou Multinomiales, qui sont malheureusement inadéquates pour des données directionnelles. Dans cette thèse, nous nous focalisons sur deux challenges d’actualité, à savoir la classification des documents textuels et la recommandation d’items, qui ne cesse d’attirer l’attention dans les domaines de la fouille de textes et celui du filtrage collaborative, respectivement. Afin de répondre aux limitations ci-dessus, nous proposons une série de nouveaux modèles et algorithmes qui s’appuient sur la distribution de von Mises-Fisher (vMF) qui est plus appropriée aux données directionnelles distribuées sur une hypersphère unitéCluster analysis or clustering, which aims to group together similar objects, is undoubtedly a very powerful unsupervised learning technique. With the growing amount of available data, clustering is increasingly gaining in importance in various areas of data science for several reasons such as automatic summarization, dimensionality reduction, visualization, outlier detection, speed up research engines, organization of huge data sets, etc. Existing clustering approaches are, however, severely challenged by the high dimensionality and extreme sparsity of the data sets arising in some current areas of interest, such as Collaborative Filtering (CF) and text mining. Such data often consists of thousands of features and more than 95% of zero entries. In addition to being high dimensional and sparse, the data sets encountered in the aforementioned domains are also directional in nature. In fact, several previous studies have empirically demonstrated that directional measures—that measure the distance between objects relative to the angle between them—, such as the cosine similarity, are substantially superior to other measures such as Euclidean distortions, for clustering text documents or assessing the similarities between users/items in CF. This suggests that in such context only the direction of a data vector (e.g., text document) is relevant, not its magnitude. It is worth noting that the cosine similarity is exactly the scalar product between unit length data vectors, i.e., L 2 normalized vectors. Thus, from a probabilistic perspective using the cosine similarity is equivalent to assuming that the data are directional data distributed on the surface of a unit-hypersphere. Despite the substantial empirical evidence that certain high dimensional sparse data sets, such as those encountered in the above domains, are better modeled as directional data, most existing models in text mining and CF are based on popular assumptions such as Gaussian, Multinomial or Bernoulli which are inadequate for L 2 normalized data. In this thesis, we focus on the two challenging tasks of text document clustering and item recommendation, which are still attracting a lot of attention in the domains of text mining and CF, respectively. In order to address the above limitations, we propose a suite of new models and algorithms which rely on the von Mises-Fisher (vMF) assumption that arises naturally for directional data lying on a unit-hypersphere
35
Leishman, Natalie. "Model Sensitivity, Performance and Evaluation Techniques for The Air Pollution Model in Southeast Queensland." Thesis, Queensland University of Technology, 2005. https://eprints.qut.edu.au/16148/1/Natalie_Leishman.pdf.
Full textAbstract:
One important component for successful air quality modelling is the utilisation of a reliable meteorological simulator. Evaluating the model with respect to its overall performance in predicting natural processes is no easy task. The problem is twofold, firstly there is the availability and suitability of field data with which to compare a model with and secondly there is the method of evaluation. The Air Pollution Model (TAPM), developed by the CSIRO was used to simulate the winds in Southeast Queensland (SEQ). The complex nature of the airshed makes it difficult to compare modelled data with observational data as the observational data may be influenced by local phenomena. Evaluation of the model through the use of standard statistics and monthly and seasonal statistics illustrated that overall the model predicted the annual average wind speeds and temperatures well. Through the use of synoptic clustering, more detail on model performance was gained and it was found that TAPM predicted sea breezes that occurred on high pollution days. The sensitivity of the model to the selection of input parameters such as soil type, land use, vegetation, and rain processes was also investigated.
36
Leishman, Natalie. "Model Sensitivity, Performance and Evaluation Techniques for The Air Pollution Model in Southeast Queensland." Queensland University of Technology, 2005. http://eprints.qut.edu.au/16148/.
Full textAbstract:
One important component for successful air quality modelling is the utilisation of a reliable meteorological simulator. Evaluating the model with respect to its overall performance in predicting natural processes is no easy task. The problem is twofold, firstly there is the availability and suitability of field data with which to compare a model with and secondly there is the method of evaluation. The Air Pollution Model (TAPM), developed by the CSIRO was used to simulate the winds in Southeast Queensland (SEQ). The complex nature of the airshed makes it difficult to compare modelled data with observational data as the observational data may be influenced by local phenomena. Evaluation of the model through the use of standard statistics and monthly and seasonal statistics illustrated that overall the model predicted the annual average wind speeds and temperatures well. Through the use of synoptic clustering, more detail on model performance was gained and it was found that TAPM predicted sea breezes that occurred on high pollution days. The sensitivity of the model to the selection of input parameters such as soil type, land use, vegetation, and rain processes was also investigated.
37
Lundbäck, Jonas. "On signal processing and electromagnetic modelling : applications in antennas and transmission lines." Doctoral thesis, Ronneby : Blekinge Institute of Technology, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-00363.
Full textAbstract:
This doctoral thesis is comprised of five parts. The first three parts concern signal processing and electromagnetic modelling of multiport antennas. The last two parts concern signal processing and transmission line theory applied to wave splitting on transmission lines. In Part I, the spherical vector wave expansion of the electromagnetic field is used to completely characterize a multiport antenna. A general framework for modelling an antenna configuration based on measurement data and numerical computation is obtained. The generic electromagnetic model for arbitrary multiport antennas or vector sensors is applied in direction of arrival (DOA) estimation. Next, in Part II using the generic electromagnetic model (from Part I), we obtain the Cramér–Rao bound (CRB) for DOA and polarization estimation using arbitrary multiport antennas. In the Gaussian case, the CRB is given in terms of the transmission matrix, the spherical vector harmonics and its spatial derivatives. Numerical examples using an ideal Tripole antenna array and a non-ideal Tetrahedron antenna array are included. In Part III, the theory of optimal experiments is applied to a cylindrical antenna near-field measurement setup. The D-optimal (determinant) formulation using the Fisher information matrix of the multipole coefficients in the spherical wave expansion of the electrical field result in the optimal measurement positions. The estimation of the multipole coefficients and corresponding electric field using the optimal measurement points is studied using numerical examples and singular value analysis. Further, Part IV describes a Digital Directional Coupler (DDC), a device for wave splitting on a transmission line. The DDC is a frequency domain digital wave splitter based on two independent wide-band measurements of the voltage and the current. A calibration of the digital processor is included to account for the particular transmission line and the sensors that are employed. Properties of the DDC are analyzed using the CRB and an experiment where wave splitting was conducted on a coaxial–cable is accounted for. Finally, in Part V the DDC has been designed and implemented for wave splitting on a medium voltage power cable in a power distribution station using low cost wide–band sensors. Partial discharge measurements are conducted on cross–linked polyethylene insulated power cables. The directional separation capabilities of the DDC are visualized and utilized to separate multiple reflections from partial discharges based on the direction of travel.
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Nafkha, Kamel. "Étude des directions d'arrivée du rayonnement dans une chambre réverbérante." Phd thesis, Université Paris-Est, 2009. http://tel.archives-ouvertes.fr/tel-00628411.
Full textAbstract:
Ce travail porte sur l'étude de l'environnement électromagnétique dans une chambre réverbérante à brassage mécanique de modes. Les chambres réverbérantes constituent un outil fondamental pour réaliser des mesures de compatibilité électromagnétique. Leur étude se focalise généralement sur les propriétés statistiques de la puissance du champ stationnaire. Cependant, d'autres études de ce champ stationnaire ont été faites sous un autre angle qui consiste à utiliser la décomposition en spectre d'ondes planes pour décrire ce champ. Les méthodes d'estimation spectrale à haute résolution constituent un outil puissant pour identifier les directions d'arrivée d'ondes planes. Ces méthodes réputées puissantes sont paramétriques, elles requièrent la connaissance a priori du nombre d'ondes planes à séparer. Une méthode complète basée sur le critère MDL (Minimum Description Length) pour l'estimation d'ordre et de l'algorithme MUSIC (MUltiple SIgnal Classification) a été codée pour l'estimation du nombre, des directions d'arrivée, des amplitudes et des phases des ondes planes. L'adaptation de l'estimateur du spectre d'ondes planes à l'environnement de la chambre réverbérante, qui est un milieu à forte corrélation, nécessite l'utilisation de la technique de lissage spatial pour décorréler les fronts d'onde. La simulation du fonctionnement de la chambre réverbérante par la méthode numérique FDTD (Finite Difference Time Domain) a permis d'avoir les cartographies du champ électrique pour réaliser l'étude de la variation du spectre angulaire par rotation du brasseur mettant en évidence l'effet du mouvement du brasseur sur les directions d'arrivée des ondes et sur leurs puissances respectives. Ce travail se termine par une étude statistique des directions d'arrivée et des amplitudes des ondes planes de la décomposition
39
Salah, Aghiles. "Von Mises-Fisher based (co-)clustering for high-dimensional sparse data : application to text and collaborative filtering data." Electronic Thesis or Diss., Sorbonne Paris Cité, 2016. https://wo.app.u-paris.fr/cgi-bin/WebObjects/TheseWeb.woa/wa/show?t=1858&f=11557.
Full textAbstract:
La classification automatique, qui consiste à regrouper des objets similaires au sein de groupes, également appelés classes ou clusters, est sans aucun doute l’une des méthodes d’apprentissage non-supervisé les plus utiles dans le contexte du Big Data. En effet, avec l’expansion des volumes de données disponibles, notamment sur le web, la classification ne cesse de gagner en importance dans le domaine de la science des données pour la réalisation de différentes tâches, telles que le résumé automatique, la réduction de dimension, la visualisation, la détection d’anomalies, l’accélération des moteurs de recherche, l’organisation d’énormes ensembles de données, etc. De nombreuses méthodes de classification ont été développées à ce jour, ces dernières sont cependant fortement mises en difficulté par les caractéristiques complexes des ensembles de données que l’on rencontre dans certains domaines d’actualité tel que le Filtrage Collaboratif (FC) et de la fouille de textes. Ces données, souvent représentées sous forme de matrices, sont de très grande dimension (des milliers de variables) et extrêmement creuses (ou sparses, avec plus de 95% de zéros). En plus d’être de grande dimension et sparse, les données rencontrées dans les domaines mentionnés ci-dessus sont également de nature directionnelles. En effet, plusieurs études antérieures ont démontré empiriquement que les mesures directionnelles, telle que la similarité cosinus, sont supérieurs à d’autres mesures, telle que la distance Euclidiennes, pour la classification des documents textuels ou pour mesurer les similitudes entre les utilisateurs/items dans le FC. Cela suggère que, dans un tel contexte, c’est la direction d’un vecteur de données (e.g., représentant un document texte) qui est pertinente, et non pas sa longueur. Il est intéressant de noter que la similarité cosinus est exactement le produit scalaire entre des vecteurs unitaires (de norme 1). Ainsi, d’un point de vue probabiliste l’utilisation de la similarité cosinus revient à supposer que les données sont directionnelles et réparties sur la surface d’une hypersphère unité. En dépit des nombreuses preuves empiriques suggérant que certains ensembles de données sparses et de grande dimension sont mieux modélisés sur une hypersphère unité, la plupart des modèles existants dans le contexte de la fouille de textes et du FC s’appuient sur des hypothèses populaires : distributions Gaussiennes ou Multinomiales, qui sont malheureusement inadéquates pour des données directionnelles. Dans cette thèse, nous nous focalisons sur deux challenges d’actualité, à savoir la classification des documents textuels et la recommandation d’items, qui ne cesse d’attirer l’attention dans les domaines de la fouille de textes et celui du filtrage collaborative, respectivement. Afin de répondre aux limitations ci-dessus, nous proposons une série de nouveaux modèles et algorithmes qui s’appuient sur la distribution de von Mises-Fisher (vMF) qui est plus appropriée aux données directionnelles distribuées sur une hypersphère unitéCluster analysis or clustering, which aims to group together similar objects, is undoubtedly a very powerful unsupervised learning technique. With the growing amount of available data, clustering is increasingly gaining in importance in various areas of data science for several reasons such as automatic summarization, dimensionality reduction, visualization, outlier detection, speed up research engines, organization of huge data sets, etc. Existing clustering approaches are, however, severely challenged by the high dimensionality and extreme sparsity of the data sets arising in some current areas of interest, such as Collaborative Filtering (CF) and text mining. Such data often consists of thousands of features and more than 95% of zero entries. In addition to being high dimensional and sparse, the data sets encountered in the aforementioned domains are also directional in nature. In fact, several previous studies have empirically demonstrated that directional measures—that measure the distance between objects relative to the angle between them—, such as the cosine similarity, are substantially superior to other measures such as Euclidean distortions, for clustering text documents or assessing the similarities between users/items in CF. This suggests that in such context only the direction of a data vector (e.g., text document) is relevant, not its magnitude. It is worth noting that the cosine similarity is exactly the scalar product between unit length data vectors, i.e., L 2 normalized vectors. Thus, from a probabilistic perspective using the cosine similarity is equivalent to assuming that the data are directional data distributed on the surface of a unit-hypersphere. Despite the substantial empirical evidence that certain high dimensional sparse data sets, such as those encountered in the above domains, are better modeled as directional data, most existing models in text mining and CF are based on popular assumptions such as Gaussian, Multinomial or Bernoulli which are inadequate for L 2 normalized data. In this thesis, we focus on the two challenging tasks of text document clustering and item recommendation, which are still attracting a lot of attention in the domains of text mining and CF, respectively. In order to address the above limitations, we propose a suite of new models and algorithms which rely on the von Mises-Fisher (vMF) assumption that arises naturally for directional data lying on a unit-hypersphere
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XIA, QI. "Sufficient Dimension Reduction with Missing Data." Diss., Temple University Libraries, 2017. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/469880.
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StatisticsPh.D.
Existing sufficient dimension reduction (SDR) methods typically consider cases with no missing data. The dissertation aims to propose methods to facilitate the SDR methods when the response can be missing. The first part of the dissertation focuses on the seminal sliced inverse regression (SIR) approach proposed by Li (1991). We show that missing responses generally affect the validity of the inverse regressions under the mechanism of missing at random. We then propose a simple and effective adjustment with inverse probability weighting that guarantees the validity of SIR. Furthermore, a marginal coordinate test is introduced for this adjusted estimator. The proposed method share the simplicity of SIR and requires the linear conditional mean assumption. The second part of the dissertation proposes two new estimating equation procedures: the complete case estimating equation approach and the inverse probability weighted estimating equation approach. The two approaches are applied to a family of dimension reduction methods, which includes ordinary least squares, principal Hessian directions, and SIR. By solving the estimating equations, the two approaches are able to avoid the common assumptions in the SDR literature, the linear conditional mean assumption, and the constant conditional variance assumption. For all the aforementioned methods, the asymptotic properties are established, and their superb finite sample performances are demonstrated through extensive numerical studies as well as a real data analysis. In addition, existing estimators of the central mean space have uneven performances across different types of link functions. To address this limitation, a new hybrid SDR estimator is proposed that successfully recovers the central mean space for a wide range of link functions. Based on the new hybrid estimator, we further study the order determination procedure and the marginal coordinate test. The superior performance of the hybrid estimator over existing methods is demonstrated in simulation studies. Note that the proposed procedures dealing with the missing response at random can be simply adapted to this hybrid method.
Temple University--Theses
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Thungtong, Anurak. "Synchronization, Variability, and Nonlinearity Analysis: Applications to Physiological Time Series." Case Western Reserve University School of Graduate Studies / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1364316597.
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Tang, Shuhan. "Spectral Analysis Using Multitaper Whittle Methods with a Lasso Penalty." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1586863604571678.
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Horiki, Yasutaka. "A self calibration technique for a DOA array in the presence of mutual coupling and resonant scatterers." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1158610758.
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Karlsson, Roger. "Theory and Applications of Tri-Axial Electromagnetic Field Measurements." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-5916.
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Debbah, Samir. "Le management du travail collaboratif en réseau numérique comme levier de bien-être, de performance collaborative et d'innovation : vers une modélisation." Electronic Thesis or Diss., Toulon, 2019. http://www.theses.fr/2019TOUL2002.
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La collaboration humaine a toujours été une façon de s’entraider, de travailler ensemble, afin de surmonter des tâches difficiles. Cependant, avec les outils numériques, ces actions se sont amplifiées permettant ainsi la mutualisation des expériences et des savoirs pour un objectif commun. Le travail collaboratif en réseau numérique (TCRN) a pour fondement la notion de collaboration, dotée d’une particularité technologique. Pour Spinuzzi (2012), le travail collaboratif est un système d’activités collectives où plusieurs collaborateurs participent à des tâches de manière cyclique pour résoudre des problèmes. Kitade (2000), nomme cette façon de travailler comme étant une communication médiatisée par ordinateur (CMO), il trouve ainsi, que l’interaction et la collaboration forme le socle de cette nouvelle forme de travail. Cette recherche a ainsi pour objectif de comprendre le fonctionnement du travail collaboratif en réseau numérique, et la manière dont les décideurs se saisissent d’outils pour assurer le bon fonctionnement. Pour mieux explorer ce type de travail sur le terrain, deux études ont été menées séparément. La première étude est de nature qualitative avec une approche abductive, les allers-retours fructueux sur le terrain, ont permis d’observer un ensemble de variables utiles à cette forme de travail. Quant à la deuxième étude, elle est de nature quantitative, d’où l’intérêt d’utiliser des statistiques inférentielles de première génération pour valider ou invalider nos hypothèses. Les résultats de nos recherches indiquent un ensemble de variables qui cadrent ce type de travail comme l’intelligence collective, la culture communautaire ou encore le partage de connaissances. De ce travail, il ressort également que la mobilité, la collaboration et le partage de documents, forment le socle des environnements numériques de travailHuman collaboration has always been a way to help one another, to work together, to overcome difficult tasks. However, with digital tools, these actions have been amplified allowing the sharing of experiences and knowledge for a common goal. Collaborative digital network work (TCRN) is based on the notion of collaboration, with a particular technological feature. For Spinuzzi (2012), collaborative work is a system of collective activity where several employees participate in tasks in a cyclical way to solve problems. Kitade (2000), calls this way of working as a computer-mediated communication (CMO), and finds that interaction and collaboration form the basis of this new form of work.The goal of this research is to understand the workings of collaborative work in a digital network, and how decision-makers are seizing tools to ensure smooth operation. To better explore this type of fieldwork, two studies were conducted separately. The first study is of a qualitative nature with an abductive approach, the successful round trips in the field, allowed to observe a set of variables useful for this form of work. As for the second study, it is of a quantitative nature, hence the interest of using first-generation inferential statistics to validate or invalidate our hypotheses. The results of our research indicate a set of variables that fit this type of work such as collective intelligence, community culture or knowledge sharing. From this work, it is also apparent that mobility, collaboration and document sharing form the foundation of digital work environments
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Link, Adam John. "Identifying Potential Patterns of Wildfires in California in Relation to Soil Moisture using Remote Sensing." Bowling Green State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu158646328387007.
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Boudjellal, Abdelouahab. "Contributions à la localisation et à la séparation de sources." Thesis, Orléans, 2015. http://www.theses.fr/2015ORLE2063.
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Les premières recherches en détection, localisation et séparation de signaux remontent au début du 20ème siècle. Ces recherches sont d’actualité encore aujourd’hui, notamment du fait de la croissance rapide des systèmes de communications constatée ces deux dernières décennies. Par ailleurs, la littérature du domaine consacre très peu d’études relatives à certains contextes jugés difficiles dont certains sont traités dans cette thèse. Ce travail porte sur la localisation de signaux par détection des temps d’arrivée ou estimation des directions d’arrivée et sur la séparation de sources dépendantes ou à module constant. L’idée principale est de tirer profit de certaines informations a priori disponibles sur les signaux sources telles que la parcimonie, la cyclostationarité, la non-circularité, le module constant, la structure autoregressive et les séquences pilote dans un contexte coopératif. Une première partie détaille trois contributions : (i) un nouveau détecteur pour l’estimation des temps d’arrivée basé sur la minimisation de la probabilité d’erreur ; (ii) une estimation améliorée de la puissance du bruit, basée sur les statistiques d’ordre ; (iii) une quantification de la précision et de la résolution de l’estimation des directions d’arrivée au regard de certains a priori considérés sur les sources. Une deuxième partie est consacrée à la séparation de sources exploitant différentes informations sur celles-ci : (i) la séparation de signaux de communication à module constant ; (ii) la séparation de sources dépendantes connaissant la nature de la dépendance et (iii) la séparation de sources autorégressives dépendantes connaissant la structure autorégressiveSignal detection, localization, and separation problems date back to the beginning of the twentieth century. Nowadays, this subject is still a hot topic receiving more and more attention, notably with the rapid growth of wireless communication systems that arose in the last two decades and it turns out that many challenging aspects remain poorly addressed by the available literature relative to this subject. This thesis deals with signal detection, localization using temporal or directional measurements, and separation of dependent source signals. The main objective is to make use of some available priors about the source signals such as sparsity, cyclo-stationarity, non-circularity, constant modulus, autoregressive structure or training sequences in a cooperative framework. The first part is devoted to the analysis of (i) signal’s time-of-arrival estimation using a new minimum error rate based detector, (ii) noise power estimation using an improved order-statistics estimator and (iii) side information impact on direction-of-arrival estimation accuracy and resolution. In the second part, the source separation problem is investigated at the light of different priors about the original sources. Three kinds of prior have been considered : (i) separation of constant modulus communication signals, (ii) separation of dependent source signals knowing their dependency structure and (iii) separation of dependent autoregressive sources knowing their autoregressive structure
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Li, Weiyu. "Quelques contributions à l'estimation des modèles définis par des équations estimantes conditionnelles." Thesis, Rennes 1, 2015. http://www.theses.fr/2015REN1S065/document.
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Dans cette thèse, nous étudions des modèles définis par des équations de moments conditionnels. Une grande partie de modèles statistiques (régressions, régressions quantiles, modèles de transformations, modèles à variables instrumentales, etc.) peuvent se définir sous cette forme. Nous nous intéressons au cas des modèles avec un paramètre à estimer de dimension finie, ainsi qu’au cas des modèles semi paramétriques nécessitant l’estimation d’un paramètre de dimension finie et d’un paramètre de dimension infinie. Dans la classe des modèles semi paramétriques étudiés, nous nous concentrons sur les modèles à direction révélatrice unique qui réalisent un compromis entre une modélisation paramétrique simple et précise, mais trop rigide et donc exposée à une erreur de modèle, et l’estimation non paramétrique, très flexible mais souffrant du fléau de la dimension. En particulier, nous étudions ces modèles semi paramétriques en présence de censure aléatoire. Le fil conducteur de notre étude est un contraste sous la forme d’une U-statistique, qui permet d’estimer les paramètres inconnus dans des modèles générauxIn this dissertation we study statistical models defined by condition estimating equations. Many statistical models could be stated under this form (mean regression, quantile regression, transformation models, instrumental variable models, etc.). We consider models with finite dimensional unknown parameter, as well as semiparametric models involving an additional infinite dimensional parameter. In the latter case, we focus on single-index models that realize an appealing compromise between parametric specifications, simple and leading to accurate estimates, but too restrictive and likely misspecified, and the nonparametric approaches, flexible but suffering from the curse of dimensionality. In particular, we study the single-index models in the presence of random censoring. The guiding line of our study is a U-statistics which allows to estimate the unknown parameters in a wide spectrum of models
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El, Korso Mohammed Nabil. "Analyse de performances en traitement d'antenne : bornes inférieures de l'erreur quadratique moyenne et seuil de résolution limite." Thesis, Paris 11, 2011. http://www.theses.fr/2011PA112074/document.
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Ce manuscrit est dédié à l’analyse de performances en traitement d’antenne pour l’estimation des paramètres d’intérêt à l’aide d’un réseau de capteurs. Il est divisé en deux parties :– Tout d’abord, nous présentons l’étude de certaines bornes inférieures de l’erreur quadratique moyenne liées à la localisation de sources dans le contexte champ proche. Nous utilisons la borne de Cramér-Rao pour l’étude de la zone asymptotique (notamment en terme de rapport signal à bruit avec un nombre fini d’observations). Puis, nous étudions d’autres bornes inférieures de l’erreur quadratique moyenne qui permettent de prévoir le phénomène de décrochement de l’erreur quadratique moyenne des estimateurs (on cite, par exemple, la borne de McAulay-Seidman, la borne de Hammersley-Chapman-Robbins et la borne de Fourier Cramér-Rao).– Deuxièmement, nous nous concentrons sur le concept du seuil statistique de résolution limite, c’est-à-dire, la distance minimale entre deux signaux noyés dans un bruit additif qui permet une ”correcte” estimation des paramètres. Nous présentons quelques applications bien connues en traitement d’antenne avant d’étendre les concepts existants au cas de signaux multidimensionnels. Par la suite, nous étudions la validité de notre extension en utilisant un test d’hypothèses binaire. Enfin, nous appliquons notre extension à certains modèles d’observation multidimensionnelsThis manuscript concerns the performance analysis in array signal processing. It can bedivided into two parts :- First, we present the study of some lower bounds on the mean square error related to the source localization in the near eld context. Using the Cramér-Rao bound, we investigate the mean square error of the maximum likelihood estimator w.r.t. the direction of arrivals in the so-called asymptotic area (i.e., for a high signal to noise ratio with a nite number of observations.) Then, using other bounds than the Cramér-Rao bound, we predict the threshold phenomena.- Secondly, we focus on the concept of the statistical resolution limit (i.e., the minimum distance between two closely spaced signals embedded in an additive noise that allows a correct resolvability/parameter estimation.) We de ne and derive the statistical resolution limit using the Cramér-Rao bound and the hypothesis test approaches for the mono-dimensional case. Then, we extend this concept to the multidimensional case. Finally, a generalized likelihood ratio test based framework for the multidimensional statistical resolution limit is given to assess the validity of the proposed extension
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Hunter, Brandon. "Channel Probing for an Indoor Wireless Communications Channel." BYU ScholarsArchive, 2003. https://scholarsarchive.byu.edu/etd/64.
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The statistics of the amplitude, time and angle of arrival of multipaths in an indoor environment are all necessary components of multipath models used to simulate the performance of spatial diversity in receive antenna configurations. The model presented by Saleh and Valenzuela, was added to by Spencer et. al., and included all three of these parameters for a 7 GHz channel. A system was built to measure these multipath parameters at 2.4 GHz for multiple locations in an indoor environment. Another system was built to measure the angle of transmission for a 6 GHz channel. The addition of this parameter allows spatial diversity at the transmitter along with the receiver to be simulated. The process of going from raw measurement data to discrete arrivals and then to clustered arrivals is analyzed. Many possible errors associated with discrete arrival processing are discussed along with possible solutions. Four clustering methods are compared and their relative strengths and weaknesses are pointed out. The effects that errors in the clustering process have on parameter estimation and model performance are also simulated.