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Approved for public release; distribution in unlimited. / The relational database model has become the most popular and widespread database model. Most current database systems are based upon or related to -he relational model. However, the relational model is beset with significant limitations, pitfalls and deficiencies. The relational model can be substantially improved with graphical interfaces. To this end, the Graphics Language for Accessing Database (GLAD) can provide easy to use and learn graphics interfaces for the relational model. Data structures and algorithms for GLAD will be presented to extend the relational model. / http://archive.org/details/datastructuresal00gren / Captain, United States Marine Corps

M.Com. (Informatics) / In this study the issue on User Interface Management Systems (UIMSs) is addressed. Specific attention is given to the design principles regarding development of user interface management systems that allow the creation of graphical user interfaces for existing text-oriented command-line applications. As an introduction the concept of user interface management systems is discussed and a working and formal definition is derived. Thereafter a survey is discussed concerning user interface tools. A user interface management system architecture is proposed; \he planning, design and development aspects of user interface management systems are addressed, and the effect of human factors in the design of a user interface management system is discussed. Attention is also given to various interaction and dialogue models ...

Notre recherche concerne les nouvelles approches qui améliorent l’exploration 3D dans des environnements virtuels pour les utilisateurs. De ce fait, nous nous focalisons sur la conception de nouvelles techniques d’interaction 3D transversales, qui sont simples, efficaces et bénéficient de la nouvelle périphérique d’entrée, comme les interfaces tactiles. Notre approche permet aux utilisateurs d’effectuer une interaction 3D complexes à l’aide d’entrée touche simple et multiples. Les utilisateurs effectuer des gestes en utilisent des touches simple et des esquisses pour interagir avec le contenu. Pour faire des conclusions sur la facilité d’utilisation de nos interfaces nous évaluons les techniques d’interaction en menant des études utilisateurs. Ensuite, nous adoptons des techniques traditionnelles et d’illustration de visualisation pour mettre en avant des caractéristiques différentes de l’objet en cible tout en préservant les informations de contexte. En outre, nous proposons de nouvelles méthodes de collaboration entre les utilisateurs qui interagissent avec les données 3D en utilisant la technologie divers, comme des périphériques mobiles de petite taille et des environnements virtuels immersifs. / Our research concerns the investigation of new approaches that enhance 3D exploration in virtual environments for users. Thereby, we concentrate on design of novel transversal 3D interaction techniques that are simple, efficient and benefit from new input technology, like tactile interfaces. Our approach allows users to perform complex 3D interaction using single and multiple 2D touch input. Users carry out simple touch, stroke or sketch-based gestures to interact with content. To draw conclusions about the usability of our interfaces we evaluate the interaction techniques by conducting users studies. In addition, we adopt different traditional and illustrative visualization techniques to highlight object characteristics in focus while preserving context information. Furthermore, we propose new methods for collaboration between users, who interact together with 3D data using varying technology, from small mobile devices to immersive virtual environments.

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, in ful llment of the requirements for the degree of Master of Science. October 31, 2016. / Brain Computer Interface (BCI) technology is a promising new channel for communication between humans and computers, and consequently other humans. This technology has the potential to form the basis for a paradigm shift in communication for people with disabilities or neuro-degenerative ailments. The objective of this work is to create an asynchronous BCI that is based on a commercial-grade electroencephalography (EEG) sensor. The BCI is intended to allow a user of possibly low income means to issue control signals to a computer by using modulated cortical activation patterns as a control signal. The user achieves this modulation by performing a mental task such as imagining waving the left arm until the computer performs the action intended by the user. In our work, we make use of the Emotiv EPOC headset to perform the EEG measurements. We validate our models by assessing their performance when the experimental data is collected using clinical-grade EEG technology. We make use of a publicly available data-set in the validation phase. We apply signal processing concepts to extract the power spectrum of each electrode from the EEG time-series data. In particular, we make use of the fast Fourier transform (FFT). Specific bands in the power spectra are used to construct a vector that represents an abstract state the brain is in at that particular moment. The selected bands are motivated by insights from neuroscience. The state vector is used in conjunction with a model that performs classification. The exact purpose of the model is to associate the input data with an abstract classification result which can then used to select the appropriate set of instructions to be executed by the computer. In our work, we make use of probabilistic graphical models to perform this association. The performance of two probabilistic graphical models is evaluated in this work. As a preliminary step, we perform classification on pre-segmented data and we assess the performance of the hidden conditional random fields (HCRF) model. The pre-segmented data has a trial structure such that each data le contains the power spectra measurements associated with only one mental task. The objective of the assessment is to determine how well the HCRF models the spatio-spectral and temporal relationships in the EEG data when mental tasks are performed in the aforementioned manner. In other words, the HCRF is to model the internal dynamics of the data corresponding to the mental task. The performance of the HCRF is assessed over three and four classes. We find that the HCRF can model the internal structure of the data corresponding to different mental tasks. As the final step, we perform classification on continuous data that is not segmented and assess the performance of the latent dynamic conditional random fields (LDCRF). The LDCRF is used to perform sequence segmentation and labeling at each time-step so as to allow the program to determine which action should be taken at that moment. The sequence segmentation and labeling is the primary capability that we require in order to facilitate an asynchronous BCI protocol. The continuous data has a trial structure such that each data le contains the power spectra measurements associated with three different mental tasks. The mental tasks are randomly selected at 15 second intervals. The objective of the assessment is to determine how well the LDCRF models the spatio-spectral and temporal relationships in the EEG data, both within each mental task and in the transitions between mental tasks. The performance of the LDCRF is assessed over three classes for both the publicly available data and the data we obtained using the Emotiv EPOC headset. We find that the LDCRF produces a true positive classification rate of 82.31% averaged over three subjects, on the validation data which is in the publicly available data. On the data collected using the Emotiv EPOC, we find that the LDCRF produces a true positive classification rate of 42.55% averaged over two subjects. In the two assessments involving the LDCRF, the random classification strategy would produce a true positive classification rate of 33.34%. It is thus clear that our classification strategy provides above random performance on the two groups of data-sets. We conclude that our results indicate that creating low-cost EEG based BCI technology holds potential for future development. However, as discussed in the final chapter, further work on both the software and low-cost hardware aspects is required in order to improve the performance of the technology as it relates to the low-cost context. / LG2017

El trabajo desarrollado en la presente tesis consiste en el diseño e implementación de un emulador de redes de alta capacidad, como entorno de pruebas de nuevas tecnologías previo su despliegue. El emulador está conformado por módulos implementados usando el lenguaje de programación Python. A largo plazo, se desea integrar el presente emulador en un rack de alta capacidad (con enlaces de 10 Gbps), el cual se encuentra en el laboratorio del Grupo de Investigación en Redes Avanzadas (GIRA) de la Pontificia Universidad Católica del Perú. En dicho rack, se ha instalado el software de Cloud Computing OpenStack, el cual emplea diferentes servicios para la creación de las máquinas virtuales a usar en el emulador. El objetivo principal del emulador es que las pruebas realizadas en él presenten una alta fidelidad: no solo se desea capturar el comportamiento de la red al limitar la tasa de bits o introducir latencia en un enlace, sino también evitar generar fenómenos espurios –como pérdida de paquetes– debido a las limitaciones de procesamiento del hardware sobre el cual funciona el emulador. Por ello, el emulador incluye un proceso de calibración del hardware subyacente, así como un módulo de validación de recursos (p.ej. RAM, cores, etc.) para asegurar que el emulador puede soportar la topología de interés. En el primer capítulo, se describe la situación actual de las redes —específicamente la de los proveedores de servicios— y se identifican los problemas que surgen con la evolución y desarrollo de nuevos servicios. Asimismo, se presentan algunas tecnologías emergentes como posibles soluciones a los problemas mencionados. En el segundo capítulo, se presentan los tipos de entornos de prueba que se utilizan para evaluar distintos prototipos de redes; en especial, los que proponen las tecnologías emergentes. Adicionalmente, se realiza un análisis de las ventajas y desventajas de cada entorno y se determina cuál es más conveniente para los objetivos del presente trabajo. En la última parte del capítulo, se presentan los objetivos del trabajo de tesis, en base a la problemática y al entorno de prueba determinado. El tercer capítulo está orientado a las tecnologías de virtualización. Primero, se introduce el concepto de virtualización y se presenta el modelo de Cloud Computing. Luego, se desarrollan las áreas de virtualización, mostrando las diversas técnicas existentes en cada una y la necesidad de monitorear el estado de la infraestructura. Por último, se realiza una descripción de OpenStack, dado que es el software empleado en el presente trabajo de tesis. En el cuarto capítulo, se detalla el diseño completo del emulador. Se inicia con las consideraciones y requerimientos del emulador; a continuación, se presenta la arquitectura del emulador, indicando sus módulos y el funcionamiento de cada uno; luego, se detallan los cambios realizados a OpenStack, seguido del módulo de validación del emulador –que lo diferencia de los demás existentes; finalmente, se muestra la interfaz de usuario para la creación de los experimentos. En el capítulo final, se presentan los resultados obtenidos al realizar pruebas en el emulador. Estas pruebas tienen la finalidad de demostrar que el emulador funciona en base a los objetivos y requerimientos planteados. También se muestra el análisis realizado en la etapa de calibración, en donde se obtiene la cantidad de recursos requeridos por cada nodo. Por último, se presentan las conclusiones obtenidas de la implementación y de las pruebas desarrolladas, basadas en los objetivos de la tesis y los requerimientos del emulador, así como las recomendaciones sugeridas para un mejor desempeño del mismo. / Tesis

Typescript (photocopy). / Digitized by Kansas Correctional Industries