Onion thrips (Thrips tabaci) and Iris Yellow Spot Virus (IYSV) constitute a pest complex of global concern for allium growers. Yield losses due to onion thrips alone can be heavy, and in tandem with IYSV, these losses can be complete. This study was one phase of a multi-phase research project intent on exploring the potential drivers of thrips and virus infestation in onion. Preliminary observations in participating growers’ fields in northern Utah pointed towards a potential link between weedy field borders and thrips/IYSV infestation in onion. Field work was carried out at the Kaysville experiment station examining the possible relationships between thrips and IYSV incidence in onion and the presence of adjacent weed populations. This research was conducted with the intent of guiding growers’ decisions regarding field border management, and to provide additional insights into onion thrips behavior. While implications for growers remain unclear, thrips preferences regarding host plant utilization were identified, as well as preliminary evidence of additional plant species capable of hosting IYSV.

This dissertation focused on the IRIS design since this will likely be one of the designs of choice for future deployment in the U.S and developing countries. With a net 335 MWe output IRIS novel design falls in the “medium” size category and it is a potential candidate for the so called modular reactors, which may be appropriate for base load electricity generation, especially in regions with smaller electricity grids, but especially well suited for more specialized non-electrical energy applications such as district heating and process steam for desalination. The first objective of this dissertation is to evaluate and quantify the performance of a Nuclear Power Plant (NPP) comprised of two IRIS reactor modules operating simultaneously with a common steam header, which in turn is connected to a single turbine, resulting in a steam-mixing control problem with respect to “load-following” scenarios, such as varying load during the day or reduced consumption during the weekend. To solve this problem a single-module IRIS SIMULINK model previously developed by another researcher is modified to include a second module and was used to quantify the responses from both modules. In order to develop research related to instrumentation and control, and equipment and sensor monitoring, the second objective is to build a two-tank multivariate loop in the Nuclear Engineering Department at the University of Tennessee. This loop provides the framework necessary to investigate and test control strategies and fault detection in sensors, equipment and actuators. The third objective is to experimentally develop and demonstrate a fault-tolerant control strategy using this loop. Using six correlated variables in a single-tank configuration, five inferential models and one Auto-Associative Kernel Regression (AAKR) model were developed to detect faults in process sensors. Once detected the faulty measurements were successfully substituted with prediction values, which would provide the necessary flexibility and time to find the source of discrepancy and resolve it, such as in an operating power plant. Finally, using the same empirical models, an actuator failure was simulated and once detected the control was automatically transferred and reconfigured from one tank to another, providing survivability to the system.

Biometric systems allow identification of human persons based on physiological or behavioral characteristics, such as voice, handprint, iris or facial characteristics. The use of face and iris recognition as a way to authenticate user’s identities has been a topic of research for years. Present iris recognition systems require that subjects stand close (<2m) to the imaging camera and look for a period of about three seconds until the data are captured. This cooperative behavior is required in order to capture quality images for accurate recognition. This will eventually restrict the amount of practical applications where iris recognition can be applied, especially in an uncontrolled environment where subjects are not expected to cooperate such as criminals and terrorists, for example. For this reason, this thesis develops a collection of methods to deal with low quality face and iris images and that can be applied for face and iris recognition in a non-cooperative environment. This thesis makes the following main contributions: I. For eye and face tracking in low quality images, a new robust method is developed. The proposed system consists of three parts: face localization, eye detection and eye tracking. This is accomplished using traditional image-based passive techniques such as shape information of the eye and active based methods which exploit the spectral properties of the pupil under IR illumination. The developed method is also tested on underexposed images where the subject shows large head movements. II. For iris recognition, a new technique is developed for accurate iris segmentation in low quality images where a major portion of the iris is occluded. Most existing methods perform generally quite well but tend to overestimate the occluded regions, and thus lose iris information that could be used for identification. This information loss is potentially important in the covert surveillance applications we consider in this thesis. Once the iris region is properly segmented using the developed method, the biometric feature information is calculated for the iris region using the relative entropy technique. Iris biometric feature information is calculated using two different feature decomposition algorithms based on Principal Component Analysis (PCA) and Independent Component Analysis (ICA). III. For face recognition, a new approach is developed to measure biometric feature information and the changes in biometric sample quality resulting from image degradations. A definition of biometric feature information is introduced and an algorithm to measure it proposed, based on a set of population and individual biometric features, as measured by a biometric algorithm under test. Examples of its application were shown for two different face recognition algorithms based on PCA (Eigenface) and Fisher Linear Discriminant (FLD) feature decompositions.

The dilemma of diversity is the tension that exists when prescriptive claims are required across reasonable pluralism. Scholar and philosopher Dwight Boyd believes that the dilemma of diversity must be addressed for the continued health of multicultural societies, and suggests that the solution can be found through democratic reciprocity. Though the International Olympic Committee (IOC) markets unity and peace through its Olympic Games, does the Olympics relieve the dilemma of diversity? By critically examining the IOC’s historic and recent treatment of Aboriginals, its encouragement of divisive nationalism, and its educational programs, it is clear that the IOC does not embrace reasonable pluralism. The IOC’s public pedagogy is one that conceals its dominance through diversity. In exposing this dominance, I will argue that the IOC must embrace democratic reciprocity that allows for conversation across difference. Adopting an authentic acceptance of difference will alleviate the IOC’s propagation of Western ideology through neo-imperialism.

The dilemma of diversity is the tension that exists when prescriptive claims are required across reasonable pluralism. Scholar and philosopher Dwight Boyd believes that the dilemma of diversity must be addressed for the continued health of multicultural societies, and suggests that the solution can be found through democratic reciprocity. Though the International Olympic Committee (IOC) markets unity and peace through its Olympic Games, does the Olympics relieve the dilemma of diversity? By critically examining the IOC’s historic and recent treatment of Aboriginals, its encouragement of divisive nationalism, and its educational programs, it is clear that the IOC does not embrace reasonable pluralism. The IOC’s public pedagogy is one that conceals its dominance through diversity. In exposing this dominance, I will argue that the IOC must embrace democratic reciprocity that allows for conversation across difference. Adopting an authentic acceptance of difference will alleviate the IOC’s propagation of Western ideology through neo-imperialism.

Biometric systems allow identification of human persons based on physiological or behavioral characteristics, such as voice, handprint, iris or facial characteristics. The use of face and iris recognition as a way to authenticate user’s identities has been a topic of research for years. Present iris recognition systems require that subjects stand close (<2m) to the imaging camera and look for a period of about three seconds until the data are captured. This cooperative behavior is required in order to capture quality images for accurate recognition. This will eventually restrict the amount of practical applications where iris recognition can be applied, especially in an uncontrolled environment where subjects are not expected to cooperate such as criminals and terrorists, for example. For this reason, this thesis develops a collection of methods to deal with low quality face and iris images and that can be applied for face and iris recognition in a non-cooperative environment. This thesis makes the following main contributions: I. For eye and face tracking in low quality images, a new robust method is developed. The proposed system consists of three parts: face localization, eye detection and eye tracking. This is accomplished using traditional image-based passive techniques such as shape information of the eye and active based methods which exploit the spectral properties of the pupil under IR illumination. The developed method is also tested on underexposed images where the subject shows large head movements. II. For iris recognition, a new technique is developed for accurate iris segmentation in low quality images where a major portion of the iris is occluded. Most existing methods perform generally quite well but tend to overestimate the occluded regions, and thus lose iris information that could be used for identification. This information loss is potentially important in the covert surveillance applications we consider in this thesis. Once the iris region is properly segmented using the developed method, the biometric feature information is calculated for the iris region using the relative entropy technique. Iris biometric feature information is calculated using two different feature decomposition algorithms based on Principal Component Analysis (PCA) and Independent Component Analysis (ICA). III. For face recognition, a new approach is developed to measure biometric feature information and the changes in biometric sample quality resulting from image degradations. A definition of biometric feature information is introduced and an algorithm to measure it proposed, based on a set of population and individual biometric features, as measured by a biometric algorithm under test. Examples of its application were shown for two different face recognition algorithms based on PCA (Eigenface) and Fisher Linear Discriminant (FLD) feature decompositions.

Thesis (M.A.) -- Central Connecticut State University, 2009. / Thesis advisor: Antonio García-Lozada. "... in partial fulfillment of the requirements for the degree of Master of Arts in Spanish Language and Hispanic Cultures." Includes bibliographical references (leaves 191-201). Abstract available via the World Wide Web.

La répartition spatiale des fonctions et des populations urbaines n'est pas due au hasard, elle peut mettre en évidence des inégalités qui interrogent nos sociétés démocratiques fondées avant tout sur le principe d'égalité. Celui-ci s'appuie sur les relations entre le " social " et le " spatial " qui consistent principalement à explorer les inégalités sociales dans les espaces urbains et ce qui est ressenti comme une injustice par les individus. Pour saisir ces dynamiques socio-spatiales, nous focalisons notre recherche sur l'histoire urbaine de Besançon, Mulhouse et Strasbourg ainsi que sur les rapports entre les habitants et leur environnement résidentiel. À l'aide des grandes bases de données géo-localisées de l'INSEE, nous étudions la distribution spatiale des ménages ainsi que son évolution sur une période de près de vingt années (de 1990 à 2007). Par ailleurs, nous cherchons à observer les différents modes d'habiter des Bisontins, c'est-à-dire leurs stratégies résidentielles ainsi que leurs pratiques et représentations spatiales à l'aide d'enquêtes de terrain. Cette thèse s'appuie donc sur une double approche : l'une macroscopique, historique et quantitative et l'autre microscopique et qualitative. Elle présente finalement les structures socio-spatiales des zones d'étude et les trajectoires des quartiers (plus précisément des IRIS) afin de montrer les diverses tendances d'évolution (gentrification, paupérisation...) dans une optique comparative. Des facteurs d'évolution spécifiques apparaissent également en lien avec la localisation des villes d'étude ainsi qu'avec leurs fonctions urbaines et sociales, leur organisation interne et leur taille. Les enquêtes révèlent, pour leur part, plusieurs règles comportementales qu'il s'agit de comparer compte tenu de la diversité sociale des quartiers. Ces informations collectées à partir d'enquêtes sont plus subjectives, mais elles complètent les données des recensements et leur donnent du sens. Au final, elles montrent que l'appartenance à un groupe sociospatial ne détermine pas systématiquement les comportements résidentiels.

Biometric systems allow identification of human persons based on physiological or behavioral characteristics, such as voice, handprint, iris or facial characteristics. The use of face and iris recognition as a way to authenticate user’s identities has been a topic of research for years. Present iris recognition systems require that subjects stand close (<2m) to the imaging camera and look for a period of about three seconds until the data are captured. This cooperative behavior is required in order to capture quality images for accurate recognition. This will eventually restrict the amount of practical applications where iris recognition can be applied, especially in an uncontrolled environment where subjects are not expected to cooperate such as criminals and terrorists, for example. For this reason, this thesis develops a collection of methods to deal with low quality face and iris images and that can be applied for face and iris recognition in a non-cooperative environment. This thesis makes the following main contributions: I. For eye and face tracking in low quality images, a new robust method is developed. The proposed system consists of three parts: face localization, eye detection and eye tracking. This is accomplished using traditional image-based passive techniques such as shape information of the eye and active based methods which exploit the spectral properties of the pupil under IR illumination. The developed method is also tested on underexposed images where the subject shows large head movements. II. For iris recognition, a new technique is developed for accurate iris segmentation in low quality images where a major portion of the iris is occluded. Most existing methods perform generally quite well but tend to overestimate the occluded regions, and thus lose iris information that could be used for identification. This information loss is potentially important in the covert surveillance applications we consider in this thesis. Once the iris region is properly segmented using the developed method, the biometric feature information is calculated for the iris region using the relative entropy technique. Iris biometric feature information is calculated using two different feature decomposition algorithms based on Principal Component Analysis (PCA) and Independent Component Analysis (ICA). III. For face recognition, a new approach is developed to measure biometric feature information and the changes in biometric sample quality resulting from image degradations. A definition of biometric feature information is introduced and an algorithm to measure it proposed, based on a set of population and individual biometric features, as measured by a biometric algorithm under test. Examples of its application were shown for two different face recognition algorithms based on PCA (Eigenface) and Fisher Linear Discriminant (FLD) feature decompositions.

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