The objectives of the research were to determine whether there is a preferred device in the home environment that the consumer would perceive to be easy to use, offer a sense of security and allow the seamless purchase of commodity items. The research involved interviews with leading managers in large corporations that influence the development of products used in the home environment as well as a survey to a controlled group of consumer electronic experts. The research findings show that although there is much hype about the future of certain technology devices for use in the home environment, the consumer is interested in an easy to use technology that offers seamless connectivity in a secure environment with trusted service providers. Lastly, the findings established that further in depth research is required on the impact of the new wireless networks and mobile connectivity. / Mrs. Nikki Kettles

Thesis (M.S.)--Missouri University of Science and Technology, 2010. / Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed Dec. 23, 2009). Includes bibliographical references (p. 82-83).

published_or_final_version / Computer Science / Master / Master of Philosophy

No description available.

The basic goal of the Adaptive Optical Music Recognition system presented herein is to create an adaptive software for the recognition of musical notation. The focus of this research has been to create a robust framework upon which a practical optical music recognizer can be built. / The strength of this system is its ability to learn new music symbols and handwritten notations. It also continually improves its accuracy in recognizing these objects by adjusting internal parameters. Given the wide range of music notation styles, these are essential characteristics of a music recognizer. / The implementation of the adaptive system is based on exemplar-based incremental learning, analogous to the idea of "learning by example," that identifies unknown objects by their similarity to one or more of the known stored examples. The entire process is based on two simple, yet powerful algorithms: k-nearest neighbour classifier and genetic algorithm. Using these algorithms, the system is designed to increase its accuracy over time as more data are processed.

Thesis (Ph. D.)--Ohio University, March, 1995. / Title from PDF t.p.

Thesis (M.S.)--Ohio University, August, 1992. / Title from PDF t.p.

Thesis (M.S.)--Ohio University, November, 1992. / Title from PDF t.p.

Thesis (M. Phil.)--University of Hong Kong, 1992.

Thesis (M. Phil.)--University of Hong Kong, 1998. / Includes bibliographical references.