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Call number: LD2668 .T4 1984 H92 / Master of Science

The project investigated the problem of performance optimisation of computer systems at the systems level. It was ascertained that no generally accepted technique for approaching this problem exist. A theoretical approach was thus developed which describes the system, the workload and the performance in terms of matrices which are deduced from measured data. An attempt is then made to verify this theory by applying it to a real system in a controlled environment. A dummy workload is used and measurements are made on the computer system for various configurations. The results thus obtained are compared with the expected trends in system performance and conclusions are drawn which appear to verify the validity of the theory proposed. / Thesis (M.Sc.Eng.)-University of Natal, 1987.

Indiana University-Purdue University Indianapolis (IUPUI) / To access interactive systems, blind and visually impaired users can leverage their auditory senses by using non-speech sounds. The current structure of non-speech sounds, however, is geared toward conveying user interface operations (e.g., opening a file) rather than large theme-based information (e.g., a history passage) and, thus, is ill-suited to signify the complex meanings of primary learning material (e.g., books and websites). In order to address this problem, this dissertation introduces audemes, a new category of non-speech sounds, whose semiotic structure and flexibility open new horizons for facilitating the education of blind and visually impaired students. An experiment with 21 students from the Indiana School for the Blind and Visually Impaired (ISBVI) supports the hypothesis that audemes increase the retention of theme-based information. By acting as memory catalysts, audemes can play an important role in enhancing the aural interaction and navigation in future sound-based user interfaces. For this dissertation, I designed an Acoustic EDutainment INterface (AEDIN) that integrates audemes as a way by which to vividly anticipate text-to-speech theme-based information and, thus, act as innovative aural covers. The results of two iterative usability evaluations with total of 20 blind and visually impaired participants showed that AEDIN is a highly usable and enjoyable acoustic interface. Yet, designing well-formed audemes remains an ad hoc process because audeme creators can only rely on their intuition to generate meaningful and memorable sounds. In order to address this problem, this dissertation presents three experiments, each with 10 blind and visually impaired participants. The goal was to examine the optimal combination of audeme attributes, which can be used to facilitate accurate recognitions of audeme meanings. This work led to the creation of seven basic guidelines that can be used to design well-formed audemes. An interactive application tool (ASCOLTA: Advanced Support and Creation-Oriented Library Tool for Audemes) operationalized these guidelines to support individuals without an audio background in designing well-formed audemes. An informal evaluation conducted with three teachers from the ISBVI, supports the hypothesis that ASCOLTA is a useful tool by which to facilitate the integration of audemes into the teaching environment.

Indiana University-Purdue University Indianapolis (IUPUI) / Recent technological applications have made the life of people too much dependent on Science, Technology, Engineering, and Mathematics (STEM) and its applications. Understanding basic level science is a must in order to use and contribute to this technological revolution. Science education in middle and high school levels however depends heavily on visual representations such as models, diagrams, figures, animations and presentations etc. This leaves visually impaired students with very few options to learn science and secure a career in STEM related areas. Recent experiments have shown that small aural clues called Audemes are helpful in understanding and memorization of science concepts among visually impaired students. Audemes are non-verbal sound translations of a science concept. In order to facilitate science concepts as Audemes, for visually impaired students, this thesis presents an automatic system for audeme generation from STEM textbooks. This thesis describes the systematic application of multiple Natural Language Processing tools and techniques, such as dependency parser, POS tagger, Information Retrieval algorithm, Semantic mapping of aural words, machine learning etc., to transform the science concept into a combination of atomic-sounds, thus forming an audeme. We present a rule based classification method for all STEM related concepts. This work also presents a novel way of mapping and extracting most related sounds for the words being used in textbook. Additionally, machine learning methods are used in the system to guarantee the customization of output according to a user's perception. The system being presented is robust, scalable, fully automatic and dynamically adaptable for audeme generation.