Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, September 2003. / Thesis advisor(s): Xiaoping Yun, Fotis Papoulias. Includes bibliographical references (p. 61-62). Also available online.

Thesis (Ph. D.)--Ohio State University, 2004. / Title from first page of PDF file. Document formatted into pages; contains xvi, 149 p.; also includes graphics (some col.). Includes abstract and vita. Advisor: Sheikh Akbar, Dept. of Materials Science and Engineering. Includes bibliographical references (p. 138-149).

Thesis (M. Sc.)--University of Canterbury, 2009. / Typescript (photocopy). Includes bibliographical references (p. 51-55). Also available via the World Wide Web.

Thesis (M. Phil.)--University of Hong Kong, 1981.

Thesis (MTech (Electrical Engineering))--Peninsula Technikon, 1998. / Word processed copy. Summary in English. Includes bibliographical references (leaves 97-104). Also available online.

Thesis (DTech (Electrical Engineering))--Peninsula Technikon, 2002. / Word processed copy. Summary in English. Includes bibliographical references (leaves 189-199). Also available online.

Thesis (Ph. D.)--University of Texas at Austin, 2001. / Vita. Includes bibliographical references (leaves 214-232). Available also in a digital version from Dissertation Abstracts.

Thesis (Ph.D.)--University of Central Florida, 2008. / Adviser: Hyoung Jin Cho. Includes bibliographical references (p. 105-117).

Autonomous mobile robots have the potential to be extremely beneficial to society due to their ability to perform tasks that are difficult or dangerous for humans. These robots will necessarily interact with their environment through the two fundamental processes of acting and sensing. Robots learn about the state of the world around them through their sensations, and they influence that state through their actions. However, in order to interact with their environment effectively, these robots must have accurate models of their sensors and actions: knowledge of what their sensations say about the state of the world and how their actions affect that state. A mobile robot’s action and sensor models are typically tuned manually, a brittle and laborious process. The robot’s actions and sensors may change either over time from wear or because of a novel environment’s terrain or lighting. It is therefore valuable for the robot to be able to autonomously learn these models. This dissertation presents a methodology that enables mobile robots to learn their action and sensor models starting without an accurate estimate of either model. This methodology is instantiated in three robotic scenarios. First, an algorithm is presented that enables an autonomous agent to learn its action and sensor models in a class of one-dimensional settings. Experimental tests are performed on a four-legged robot, the Sony Aibo ERS-7, walking forward and backward at different speeds while facing a fixed landmark. Second, a probabilistically motivated model learning algorithm is presented that operates on the same robot walking in two dimensions with arbitrary combinations of forward, sideways, and turning velocities. Finally, an algorithm is presented to learn the action and sensor models of a very different mobile robot, an autonomous car. / text

Motion coding in the brain undoubtedly reflects the statistics of retinal image motion occurring in the natural environment. Measuring the statistics of motion in natural scenes is an important tool for building our understanding of how the brain works. Unfortunately, there are statistics that are either impossible or prohibitively difficult to measure. For this reason, it is useful to measure scene statistics in artificial movies derived from simulated environments. This is a novel and important methodological approach that allows us to ask questions about optimal coding that are impossible otherwise. This dissertation describes a course of research that develops this research methodology, the simulated scene statistical approach. This dissertation applied the artificial scene statistical approach to understanding the visual statistics of motion during navigation through forest environments. An environmental model of forest scenes was developed based on previously measured range and surface texture statistics. Spatiotemporal power spectra were measured in both simulated and natural scenes for the task of first person motion through a forest environment. These image statistics measurements helped validate the environmental model. Next, the environmental model was used to simulate across-domain statistics to study the ideal aperture size of motion sensors. It was found that across a variety of different scene conditions, the optimal aperture size of motion sensors increases with the speed to which the sensor is tuned. This is an important constraint for understanding both how the brain encodes motion as well as for designing computer motion detectors. This theoretical research inspired a psychophysical experiment estimating the receptive-field size of human foveal motion discrimination. It was found that for narrow-band stimuli the ideal aperture size increases with spatial frequency, but is unchanging with respect to velocity or temporal frequency. This dissertation shows an approach to the study of vision that has applications in psychophysics, neuroscience and computer vision. The emphasis on accurate and validated environmental models for simulating scene statistics can help improve our understanding of the structure and function of the human visual system and also help us build more accurate and robust computer vision systems. / text