The aims of this thesis are: 1. To examine patterns of morphological variation in the crania of extant species {Pan, Gorilla, Pongo, and H. sapiens) to determine if any common pattern of primate sexual dimorphism exists which could be used in the assessment of fossil hominid sexual dimorphism; 2. To examine patterns of between species variability among the crania of the above extant species to determine if characters exist which could be useful as taxonomic indicators, especially of specific distinctiveness in fossil Hominidae; and 3. To assess the validity of using traits which are dimorphic and/ or variable within species as taxonomic indicators in systematic analyses. This thesis entails an analysis of inter- and intra-specific diversity among the early hominids based on models derived from samples of modern H. sapiens and pongids. Metrical cranial characters were surveyed in order to assess the implications of their variability within the available early hominid sample {A. afarensis, A. africanus, A. robustus, A. boisei, H. habilis, and H. erectus) using univariate, multi variate, and cladistic analytical techniques. The univariate analysis found no common pattern of primate sexual dimorphism but it did identify characters of low sexual dimorphism and low variability common to all the extant hominoids. These were used to test the homogeneity of the fossil groups and indicated the possible heterogeneity of H. erectus, H. habilis, A. afarensis, and A. boisei. The remaining characters revealed an apparent trend among the hominids (fossil and modern) of dimorphic regions of the skull including the areas of nuchal and temporal muscle attachment, kyphosis of the basicrania, width of the palate, mandible, and base, and facial prognathism. The multivariate analyses used the patterns of variability and dimorphism known from the modern comparators to assess sex, degrees of sexual dimorphism, and homo geneity of the fossil samples. These analyses revealed the possible heterogeneity of H. erectus and A. afarensis, the sex of some individual specimens, and some interesting contrasts in the patterns of sexual dimorphism between the fossil and modern species. They also isolated KNM-ER 1805 as having unique basicranial proportions. Two different types of characters were used in cladistic analyses to determine which type produced the most parsimonious trees and the implications of their use for future cladistic analyses. The results show that non-variable, non-dimorphic traits generally produce more parsimonious trees than variable, dimorphic ones, thus demonstrating the importance of assessing within- and between-group variability of characters prior to cladistic analyses. The method of coding the data prior to the cladistic analysis was tested for its objectivity. The analyses showed that the constant used to code the data into discrete character states had a substantial effect upon the resultant trees. This study has demonstrated that characters have different properties due to the amount they vary or are dimorphic within groups and that utilising these characters for different purposes has the potential to enhance future systematic/ phyletic studies.

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This paper presents a study on classifying receipts and invoices using Machine Learning. Furthermore, Naïve Bayes Algorithm and the advantages of using it will be discussed.  With information gathered from theory and previous research, I will show how to classify images into a receipt or an invoice. Also, it includes pre-processing images using a variety of pre-processing methods and text extraction using Optical Character Recognition (OCR). Moreover, the necessity of pre-processing images to reach a higher accuracy will be discussed. A result shows a comparison between Tesseract OCR engine and FineReader OCR engine. After embracing much knowledge from theory and discussion, the results showed that combining FineReader OCR engine and Machine Learning is increasing the accuracy of the image classification.

Thesis (Ph. D.)--University of Chicago, Dept. of Mathematics, August 2000. / Includes bibliographical references. Also available on the Internet.

The major purpose of this investigation was to study the structure, attributes , and supplemental aspects of equivalence classifying of words and pictures made by sixth graders, freshmen college students and junior and senior college students . A particular point of interest was whether or not increasing or decreasing the size of the groups results in different types of equivalence classifying. Other minor goals were to determine the effects of direction of grouping, type of stimuli, and class level on unique reasons produced and recall of stimuli. The results of the study support the thesis that there is a cognitive developmental progression which supports the works of Piaget, Bruner, Vinacke and others. Children, compared to college levels, use a less efficient grouping structure, lower level simple association and concrete and perceptual grouping attributes, lower quality responses, and more specific reasons for grouping. College levels, compared to six grade, use more representational grouping attributes, have higher quality responses, and have a more general level of specificity. In addition, decreasing grouping structure results in a high level of cognitive performance in all aspects of grouping. There were no main effect differences between the words and pictures. However, there were significant interaction effects involving directions, stimuli, and class levels. There were more stimuli recalled and unique reasons produced by the college students. More pictures than words were recalled by all groups. One implication of the results is that decreasing the size of groupings apparently results in a higher level of cognitive functioning. One reason ventured for this result is that decreasing group size results in more divergent thinking and allows the individual to contemplate more possibilities for his groupings. Increasing group size is related to convergent thinking in which the subject is hindered by a previous set. Another implication is that there is a cognitive development a l progression which results in an increasingly more sophisticated ability to deal with the complex stimuli of the environment. Also, there is some evidence that different grade levels handle pictures differently than they do words. More study is needed to clarify the role of different forms of stimuli in classifying. From this study it is clear that there are no main effect differences between words and pictures. However, there certainly appear to be some interaction effects, and these need further clarification.

Title: Investigating factors that can have an impact on customer loyalty -an empirical study of IKEA     Level: thesis for Bachelor Degree in Business Administration   Author: Nana Wei 8502202-T141   Supervisor: Dr. Aihie Osarenkhoe   Date: 2010 – May   Aim: The aim of this thesis is to investigate which factors will have impact on customer’ loyalty strategy. And in this study, both markets of China and Sweden will be discussed. Therefore, the focus of this thesis is on the international companies.   Method: As the purposes of this thesis are descriptive and explanatory, the method used in this thesis is the qualitative method. Interview method is used in the thesis to collect data and information. Besides that, case study method is used in the thesis to analyze the data and information.   Findings: when considering how to retain customer loyalty, service quality, communication, advertisements is important for companies. Moreover, trust and commitment are also important for companies. Besides that, there are some differences between Chinese and Swedish markets, such as different attitude towards customer classifying method, and the different reasons of implementing customer loyalty strategy. Finally, some problems of customer loyalty strategy are studied.   Contribution: This research is about customer loyalty strategy and the focus of it is the comparison of Chinese and Swedish markets. Therefore, this thesis helps to better understand customer loyalty strategy in different markets.    Key words: customer loyalty, customer classifying, trust, commitment, communication

O presente trabalho visa a utilização da transformada Wavelet no monitoramento do sistema elétrico no que diz respeito a problemas de qualidade da energia com o intuito de detectar, localizar e classificar os mesmos. A transformada Wavelet tem surgido na literatura como uma nova ferramenta para análise de sinais, utilizando funções chamadas Wavelet mãe para mapear sinais em seu domínio, fornecendo informações simultâneas nos domínios tempo e freqüência. A transformada Wavelet é realizada através de filtros decompondo-se um dado sinal em análise multiresolução. Por esta, obtém-se a detecção e a localização de distúrbios relacionados com a qualidade da energia decompondo-se o sinal em dois outros que representam uma versão de detalhes (correspondente as altas freqüências do sinal) e uma versão de aproximação (correspondente as baixas freqüências do sinal). A versão de aproximação é novamente decomposta obtendo-se novos sinais de detalhes e aproximações e assim sucessivamente. Sendo assim, os distúrbios podem ser detectados e localizados no tempo em função do seu conteúdo de freqüência. Estas informações fornecem também características únicas pertinentes a cada distúrbio, permitindo classificá-los. Desta forma, propõe-se neste trabalho o desenvolvimento de um algoritmo classificador automático de distúrbios relacionados com a qualidade da energia baseado unicamente nas decomposições obtidas da análise multiresolução. / The aim of the present dissertation is to apply the Wavelet transform to analyze power quality problems, detecting, localizing and classifying them. The topic Wavelet transform, has appeared in the literature as a new tool for the analysis of signals, using functions called mother Wavelet to map signals in its domain, supplying information in the time and frequency domain, simultaneously. Wavelet transform is accomplished through filters decomposing a provided signal in multiresolution analysis. The detection and localization of disturbances are obtained by decomposing a signal into two other signals that represent, a detailed version (high frequency signals) and a smoothed version (low frequency signals). The smoothed version is decomposed again, and new detailed and smoothed signals are obtained. This process is repeated as many times as necessary and the disturbances can be detected and localized in the time as a function of its level frequency. This information also supplies characteristics to each disturbance, allowing classifying them. Thus, this research presents a way to develop an automatic classifying algorithm of power quality disturbances, based only on multiresolution analysis.

This thesis consists of three articles treating topics in the theory of algebraic stacks. The first two papers deal with motivic invariants. In the first, we show that the class of the classifying stack BPGLn is the inverse of the class of PGLn in the Grothendieck ring of stacks for n ≤ 3. This shows that the multiplicativity relation holds for the universal torsors, although it is known not to hold for torsors ingeneral for the groups PGL2 and PGL3. In the second paper, we introduce an exponential function which can be viewed as a generalisation of Kapranov's motivic zeta function. We use this to derive a binomial theorem for a power operation defined on the Grothendieck ring of varieties. As an application, we give an explicit expression for the motivic class of a universal quasi-split torus, which generalises a result by Rökaeus. The last paper treats destackification. We give an algorithm for removing stackiness from smooth, tame stacks with abelian stabilisers by repeatedly applying stacky blow-ups. As applications, we indicate how the result can be used for destackifying general Deligne–Mumford stacks in characteristic zero, and to obtain a weak factorisation theorem for such stacks. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 1: Manuscript. Paper 2: Manuscript. Paper 3: Manuscript.</p>

O presente trabalho visa a utilização da transformada Wavelet no monitoramento do sistema elétrico no que diz respeito a problemas de qualidade da energia com o intuito de detectar, localizar e classificar os mesmos. A transformada Wavelet tem surgido na literatura como uma nova ferramenta para análise de sinais, utilizando funções chamadas Wavelet mãe para mapear sinais em seu domínio, fornecendo informações simultâneas nos domínios tempo e freqüência. A transformada Wavelet é realizada através de filtros decompondo-se um dado sinal em análise multiresolução. Por esta, obtém-se a detecção e a localização de distúrbios relacionados com a qualidade da energia decompondo-se o sinal em dois outros que representam uma versão de detalhes (correspondente as altas freqüências do sinal) e uma versão de aproximação (correspondente as baixas freqüências do sinal). A versão de aproximação é novamente decomposta obtendo-se novos sinais de detalhes e aproximações e assim sucessivamente. Sendo assim, os distúrbios podem ser detectados e localizados no tempo em função do seu conteúdo de freqüência. Estas informações fornecem também características únicas pertinentes a cada distúrbio, permitindo classificá-los. Desta forma, propõe-se neste trabalho o desenvolvimento de um algoritmo classificador automático de distúrbios relacionados com a qualidade da energia baseado unicamente nas decomposições obtidas da análise multiresolução. / The aim of the present dissertation is to apply the Wavelet transform to analyze power quality problems, detecting, localizing and classifying them. The topic Wavelet transform, has appeared in the literature as a new tool for the analysis of signals, using functions called mother Wavelet to map signals in its domain, supplying information in the time and frequency domain, simultaneously. Wavelet transform is accomplished through filters decomposing a provided signal in multiresolution analysis. The detection and localization of disturbances are obtained by decomposing a signal into two other signals that represent, a detailed version (high frequency signals) and a smoothed version (low frequency signals). The smoothed version is decomposed again, and new detailed and smoothed signals are obtained. This process is repeated as many times as necessary and the disturbances can be detected and localized in the time as a function of its level frequency. This information also supplies characteristics to each disturbance, allowing classifying them. Thus, this research presents a way to develop an automatic classifying algorithm of power quality disturbances, based only on multiresolution analysis.