Statistically more test samples obtained from a single chip would give a better picture of the various noise processes present. Increasing the number of samples while testing one chip would however lead to an increase in the testing time, decreasing the overall throughput. The aim of this report is to investigate the detection of non-Gaussian noise (burst noise) in a random set of data with a small number of samples. In order to determine whether a given set of noise samples has non-Gaussian noise processes present, a Chi-Squared ‘Goodness of Fit’ test on a modeled set of random data is presented. A discussion of test methodologies using a single test measurement pass as well as two passes is presented from the obtained simulation results. / text

Goodness-of-fit and correlation tests are considered for dependent univariate data that arises when multivariate data is projected to the real line with a data-suggested linear transformation. Specifically, tests for multivariate normality are investigated. Let { } i Y be a sequence of independent k-variate normal random vectors, and let 0 d be a fixed linear transform from Rk to R . For a sequence of linear transforms { ( )} 1 , , n d Y Y converging almost surely to 0 d , the weak convergence of the empirical process of the standardized projections from d to a tight Gaussian process is established. This tight Gaussian process is identical to that which arises in the univariate case where the mean and standard deviation are estimated by the sample mean and sample standard deviation (Wood, 1975). The tight Gaussian process determines the limiting null distribution of E.D.F. goodness-of-fit statistics applied to the process of the projections. A class of tests for multivariate normality, which are based on the Shapiro-Wilk statistic and the related correlation statistics applied to the dependent univariate data that arises with a data-suggested linear transformation, is also considered. The asymptotic properties for these statistics are established. In both cases, the statistics based on random linear transformations are shown to be asymptotically equivalent to the statistics using the fixed linear transformation. The statistics based on the fixed linear transformation have same critical points as the corresponding tests of univariate normality; this allows an easy implementation of these tests for multivariate normality. Of particular interest are two classes of transforms that have been previously considered for testing multivariate normality and are special cases of the projections considered here. The first transformation, originally considered by Wood (1981), is based on a symmetric decomposition of the inverse sample covariance matrix. The asymptotic properties of these transformed empirical processes were fully developed using classical results. The second class of transforms is the principal components that arise in principal component analysis. Peterson and Stromberg (1998) suggested using these transforms with the univariate Shapiro-Wilk statistic. Using these suggested projections, the limiting distribution of the E.D.F. goodness-of-fit and correlation statistics are developed.

Job seekers and employers frequently make application and selection decisions based on how well they believe there is a ‘fit’ with the organization and job. The personenvironment fit literature has strongly supported this practice demonstrating that fit is an antecedent to attraction, selection, and attrition. What has been lacking, however, is evidence that once individuals enter the organization their fit relates to performance. Using a social network analytical lens, I develop a framework that integrates PE fit and social networks to explore antecedents to employee performance. Using this framework, I explore how informal workplace relationships may act as catalysts through which fit either enhances or detracts from individual performance, how fit might directly influence performance once the social context is taken into account, and how fit might make an individual an attractive exchange partner benefiting performance. Results suggest that PE fit is related to individual performance (both in- and extra-role) but that this relationship differs depending on how well embedded the employee is in the informal social networks of the organization. It is only when accounting for the ‘who you know’ element of organizational life that we can see how ‘who you are’ relates to performance.

Students’ intention to stay within the higher education system is decreasing by the year, and even though more students are enrolling at universities annually, the percentage of students completing their studies is not satisfactory. The low completion rate is a concern not only in South Africa, but worldwide. Trends are identified as to why students do not complete their studies. This study seeks to focus on perceived social support, the students’ academic fit, the psychological conditions of meaningfulness and availability as well as the students’ engagement levels, and then to investigate if these constructs will influence their intention to stay. The proposed engagement model of May, Gilson, and Harter (2004) originally designed by George Kahn (1990), was used to determine whether social support and academic fit correlates positively with the psychological conditions, which may lead to engagement and increase a student’s intention to stay. A quantitative research design was used to investigate the universal challenge at hand, and it was descriptive in nature in order to gather specific information from the first-year students. A crossectional design was used. The research method consists of a literature review and an empirical study, presented in one research article. A convenience sample was used, and a total of 304 students completed the questionnaires. These questionnaires were based on the Multidimensional Scale of Perceived Social Support, the Psychological Conditions Scale, Academic Fit Scale, the Work Engagement Scale and the Intent to Leave Scale. Structural equation modelling methods were used, and implemented in AMOS to test the measurement and structural models. The fit-indices used to test if the model fit the data included the absolute fit indices such as Chi-square statistic, the Standardized Root Mean Residual (SRMR), and the Root-Means-Square Error of Approximation (RMSEA). The incremental fit indices which were used included the Tucker-Lewis Index (TLI) and the Comparative Fit Index. It was found that social support did not have an impact on the psychological conditions of meaningfulness and availability, but it had a direct and indirect (via academic fit) effect on intention to stay. This implies that the amount of support students receive has an influence on their intention to stay, and also increased their sense of belonging in their field of study. Academic fit was positively associated with the psychological conditions of meaning and availability, which means that if the student’s personality and field of study is aligned the student will feel that the course is meaningful to him, and he will invest more energy in his studies. Academic fit had direct effects on students’ intention to stay, which means that students who feel they belong in their field of study will also be more likely to stay at the educational institution. It was also found that if students experience a sense of psychological meaningfulness and availability they will be more engaged in their studies, which impact their intention to stay / MCom, Industrial Psychology, North-West University, Vaal Triangle Campus, 2012

For all types of clothing and body worn technologies it is important to consider how they integrate and interact with the complex shapes that form the unique profile of the human body. This interaction determines the fit of these products and it is often difficult to generate a fit that can simultaneously accommodate these complex shapes. Achieving the correct fit is determined by a number of different factors that must be combined appropriately to create the fit associated with a particular product. This is particularly applicable to Personal Protective Equipment (PPE) to ensure it provides protection while maintaining comfort, mobility and good interaction with the surrounding environment. Integrating suitable anthropometric data into the design and manufacture of this type of clothing plays a critical role in achieving a good fit. By using various processes of Computer Aided Design (CAD) and Computer Aided Manufacture (CAM), the detail contained within these data can be quickly and accurately transferred into physical tools. The aim of this study was to demonstrate and validate a method of enhancing the fit of PPE handwear. This has been achieved through an action research strategy using descriptive and practical research methods. The research tools primarily used are case studies, used to demonstrate how manually collected 2D anthropometric data can be used to generate computer models that represent these data in a 3D form. The products of the case studies are tools that have been introduced into the design and manufacture processes of commercial handwear manufacturing environments. The tools have successfully been used to produce gloves using two different manufacturing methods and been assessed to analyse their fit. An improvement in fit for the gloves has been quantified through user trials to determine the level of increased performance afforded to the wearer. The conclusions drawn from the case studies demonstrate that the integration of anthropometric data and CAD/CAM can greatly influence the fit of handwear and improve the iterative processes of its design. However, the data alone does not achieve this as the added integration of tacit knowledge related to glove design is needed to ensure the correct properties are included to the meet the needs of the target population. The methods developed in the case studies have the potential to be applied to other products where fit and interaction with the human body are important design considerations.

Uncertainties on the parton distribution functions (PDFs), in particular those of the valence quarks, can be constrained at LHC energies using the charge asymmetry in the production of W^± bosons. This thesis presents a measurement of the electron channel, lepton charge asymmetry using 497 pb^-1 of data recorded with the ATLAS detector in 2011. The measurement is included in PDF fits using the machinery of HERAPDF and is found to have some constraining power beyond that of existing W charge asymmetry measurements.

This thesis details the development and evaluation of a new photofitting approach. The motivation for this work is that current photofit systems used by the police - whether manual or computerized - do not appear to work very well. Part of the problem with these approaches is they involve a single facial representation that necessitates a verbal interaction. When a multiple presentation is considered, our innate ability to recognize faces is capitalized (and the potentially disruptive effect of the verbal component is reduced). The approach works by employing Genetic Algorithms to evolve a small group of faces to be more like a desired target. The main evolutionary influence is via user input that specifies the similarity of the presented images with the target under construction. The thesis follows three main phases of development. The first involves a simple system modelling the internal components of a face (eyes, eyebrows, nose and mouth) containing features in a fixed relationship with each other. The second phase applies external facial features (hair and ears) along with an appropriate head shape and changes in the relationship between features. That the underlying model is based on Principal Components Analysis captures the statistics of how faces vary in terms of shading, shape and the relationship between features. Modelling was carried out in this way to create more realistic looking photofits and to guard against implausible featural relationships possible with traditional approaches. The encouraging results of these two sections prompted the development of a full photofit system: EvoFIT. This software is shown to have continued promise both in the lab and in a real case. Future work is directed particularly at resolving issues concerning the anonymity of the database faces and the creation of photofits from the subject's memory of a target.

One of the most useful and best known goodness of fit test is the Kolmogorov one-sample test. The assumptions for the Kolmogorov (one-sample test) test are: 1. A random sample; 2. A continuous random variable; 3. F(x) is a completely specified hypothesized cumulative distribution function. The Kolmogorov one-sample test has a wide range of applications. Knowing the effect fromusing the test when an assumption is not met is of practical importance. The purpose of this research is to analyze the robustness of the Kolmogorov one-sample test to sampling from a finite discrete distribution. The standard tables for the Kolmogorov test are derived based on sampling from a theoretical continuous distribution. As such, the theoretical distribution is infinite. The standard tables do not include a method or adjustment factor to estimate the effect on table values for statistical experiments where the sample stems from a finite discrete distribution without replacement. This research provides an extension of the Kolmogorov test when the hypothesized distribution function is finite and discrete, and the sampling distribution is based on sampling without replacement. An investigative study has been conducted to explore possible tendencies and relationships in the distribution of Dn when sampling with and without replacement for various parameter settings. In all, 96 sampling distributions were derived. Results show the standard Kolmogorov table values are conservative, particularly when the sample sizes are small or the sample represents 10% or more of the population.

The purpose of this research was to determine if perception of fit in jeans will vary by target wearer, sex, educational background, political-social attitudes, and brand name. One hundred and sixteen usable questionnaires were completed by students enrolled in the 1987 spring semester at a university in North Texas. The questionnaire gathered data about perception of fit in jeans, social-political attitudes, clothing interest, and jeans consumption. No differences were found between perception of fit by sex or target wearer. Factors found to affect perception of fit were educational background, political-social attitudes, brand name, and clothing interest. Subjects indicated fit was the most important factor considered when purchasing jeans.

The objective of this thesis is to develop a sequential algorithm to determine accurately and quickly, at which point in time a product is well mixed or reaches a steady state plateau, in terms of the Refractive Index (RI). An algorithm using sequential non-linear model fitting and prediction is proposed. A simulation study representing typical scenarios in a liquid manufacturing process in pharmaceutical industries was performed to evaluate the proposed algorithm. The data simulated included autocorrelated normal errors and used the Gompertz model. A set of 27 different combinations of the parameters of the Gompertz function were considered. The results from the simulation study suggest that the algorithm is insensitive to the functional form and achieves the goal consistently with least number of time points.