Thesis--Wisconsin. / Vita. Includes bibliographical references (leaves 119-146).

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.

No description available.

Thesis (M.Sc.)--Memorial University of Newfoundland, 2000. / Bibliography: p. 44-46.

Thesis (M.S.)--California Polytechnic State University, 2009. / Title from PDF title page; viewed on March 12, 2009. "February 2009." "In partial fulfillment of the requirements for the degree [of] Master of Science in Kinesiology." "Presented to the faculty of California Polytechnic State University, San Luis Obispo." Major professor: Kellie Green Hall, Ph.D. Includes bibliographical references (p. 43-45). Also available on microfiche.

Thesis (Ph.D)--Psychology, Georgia Institute of Technology, 2009. / Committee Chair: Ackerman, Phillip; Committee Member: Catrambone, Richard; Committee Member: James, Lawrence; Committee Member: Kanfer, Ruth; Committee Member: Parsons, Chuck. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Thesis (Ph.D.)--Duquesne University, 2006. / Title from document title page. Abstract included in electronic submission form. Includes bibliographical references (p. 231-241) and index.

This study examines how serious games may be designed and developed to contain engaging working memory (WM) training exercises. Video games have a high potential as WM exercise environment for children with ADHD, but it is still difficult to create a long-term training solution due to their problem to retain attention. This thesis aims for better understanding of ADHD children’s motivation to be trained by describing the creation of a game prototype that was designed with the help from WM experts, workshops with designers working on previous prototypes of the same game, guidance from medical doctors specialized on ADHD, and by using a methodical formal game design approach along with frequent user tests. The results of the presented prototype showed an increase in play-time and in retention of player’s attention over time in comparison to the previous prototype. The study concludes how game design resonated with the target audience.

The advancements in robotic perception in the recent years have empowered robots to better execute tasks in various environments. The perception of objects in the robot work space significantly relies on how sensory data is represented. In this context, 3D models of object’s surfaces have been studied as a means to provide useful insights on shape of objects and ultimately enhance robotic perception. This involves several challenges, because sensory data generally presents artifacts, such as noise and incompleteness. To tackle this problem, we employ Gaussian Process Implicit Surface (GPIS), a non-parametric probabilistic reconstruction of object’s surfaces from 3D data points. This thesis investigates different configurations for GPIS, as a means to tackle the extraction of shape information. In our approach we interpret an object’s surface as the level-set of an underlying sparse Gaussian Process (GP) with variational formulation. Results show that the variational formulation for sparse GP enables a reliable approximation to the full GP solution. Experiments are performed on a synthetic and a real sensory data set. We evaluate results by assessing how close the reconstructed surfaces are to the ground-truth correspondences, and how well objects from different categories are clustered based on the obtained representation. Finally we conclude that the proposed solution derives adequate surface representations to reason about object shape and to discriminate objects based on shape information. / Framsteg inom robotperception de senaste åren har resulterat i robotar som är bättre på attutföra uppgifter i olika miljöer. Perception av objekt i robotens arbetsmiljö är beroende avhur sensorisk data representeras. I det här sammanhanget har 3D-modeller av objektytorstuderats för att ge användbar insikt om objektens form och i slutändan bättre robotperception. Detta innebär flera utmaningar, eftersom sensoriska data ofta innehåller artefakter, såsom brus och brist på data. För att hantera detta problem använder vi oss av Gaussian Process Implicit Surface (GPIS), som är en icke-parametrisk probabilistisk rekonstruktion av ett objekts yta utifrån 3D-punkter. Detta examensarbete undersöker olika konfigurationer av GPIS för att på detta sätt kunna extrahera forminformation. I vår metod tolkar vi ett objekts yta som nivåkurvor hos en underliggande gles variational Gaussian Process (GP) modell. Resultat visar att en gles variational GP möjliggör en tillförlitlig approximation av en komplett GP-lösningen. Experiment utförs på ett syntetisk och ett reellt sensorisk dataset. Vi utvärderar resultat genom att bedöma hur nära de rekonstruerade ytorna är till grundtruth- korrespondenser, och hur väl objektkategorier klustras utifrån den erhållna representationen. Slutligen konstaterar vi att den föreslagna lösningen leder till tillräckligt goda representationer av ytor för tolkning av objektens form och för att diskriminera objekt utifrån forminformation.