Typescript (photocopy). / Digitized by Kansas Correctional Industries

In recent years, multi-touch has been heralded as a revolution in humancomputer interaction. Multi-touch provides features such as gestural interaction, tangible interfaces, pen-based computing, and interface customization – features embraced by an increasingly tech-savvy public. However, multi-touch platforms have not been adopted as "everyday" computer interaction devices; that is, multi-touch has not been applied to general-purpose computing. The questions this thesis seeks to address are: Will the general public adopt these systems as their chief interaction paradigm? Can multi-touch provide such a compelling platform that it displaces the desktop mouse and keyboard? Is multi-touch truly the next revolution in human-computer interaction? As a first step toward answering these questions, we observe that generalpurpose computing relies on text input, and ask: "Can multi-touch, without a text entry peripheral, provide a platform for efficient text entry? And, by extension, is such a platform viable for general-purpose computing?" We investigate these questions through four user studies that collected objective and subjective data for text entry and word processing tasks. The first of these studies establishes a benchmark for text entry performance on a multi-touch platform, across a variety of input modes. The second study attempts to improve this performance by iv examining an alternate input technique. The third and fourth studies include mousestyle interaction for formatting rich-text on a multi-touch platform, in the context of a word processing task. These studies establish a foundation for future efforts in general-purpose computing on a multi-touch platform. Furthermore, this work details deficiencies in tactile feedback with modern multi-touch platforms, and describes an exploration of audible feedback. Finally, the thesis conveys a vision for a general-purpose multi-touch platform, its design and rationale.

No description available.

Work-related musculoskeletal disorders (WMSDs) negatively impact worker's health, ability to work, and their quality of life. Non-invasive methods for assessing the physiological responses to workload may provide information on physiological markers leading to increased risk of WMSDs. The following study aimed to evaluate the feasibility of using thermography to quantify differences in thermal readings of participants during and following a data entry task and assess the repeatability of thermal readings. Skin surface temperature measurements of the dorsal forearm were obtained from 12 participants (6 females, 6 males) during a data entry task (35 minutes) and a 30-minute post-task period. Participants also reported their perceived forearm discomfort during data entry and recovery. Three forearm analysis regions were analyzed based on statistical findings; Upper Left, Lower Left and Right regions. Temperature trends were found to increase during data entry and decrease during recovery. The Upper Left region was warmer during both data entry and recovery phases in comparison to the other regions. Repeatability of surface temperatures, based on intraclass correlations (ICCs), was found to be fair for magnitudes and trends during data entry, and poor for magnitudes and trends during recovery, despite higher significant correlations in the latter. Positive correlations were evident between subjective feelings of forearm discomfort trends and temperature trends in response to workload. No gender differences were found with regard to temperature measurements. This work contributes to the understanding of surface responses of the forearm during and following an applied stress, and to the literature supporting thermography as a non-invasive evaluative tool for assessing physiological responses during job tasks. / Master of Science

Thesis (MScIng)--University of Stellenbosch, 2004. / ENGLISH ABSTRACT: Data capturing equipment are an expensive part of testing the coverage of a deployed or planned wireless service. This thesis presents the development of such a data capturing system that make use of 1800MHz GSM base stations as transmitters and a mobile phone connected to a laptop as receiver. The measurements taken, are then verified against know propagation models. Datavaslegging toerusting wat gebruik word om die dekking van draadlose stelsels te toets is baie duur en moeilik bekombaar. Hierdie tesis beskryf die ontwikkeling van so ’n datavaslegger wat baie goedkoper is en maklik gebruik kan word. Dit maak gebruik van ’n sellulêr foon en GPS gekoppel aan ’n skootrekenaar, wat die ontvanger is. Cell C basis staties word gebruik as die senders. Die data wat gemeet is word dan geverifieer deur gebruik te maak van bestaande radio frekwensie voortplanting modelle.

The diversity and dynamic nature of disaster management environments necessitate the use of convenient, yet reliable, tools for technology. While there have been many improvements in mitigating the effects of disasters, it is clearly evident by recent events, such as Hurricane Katrina that issues related to emergency response and management require considerable research and improvement to effectively respond to these situations. One of the links in a disaster management chain is the Emergency Operations Center (EOC). The EOC is a physical command center responsible for the overall strategic control of the disaster response and functions as an information and communication hub. The effectiveness and accuracy of the disaster response greatly depends on the quality and timeliness of inter-personnel communication within an EOC. The advent of handheld mobile communication devices have introduced new avenues of communication that been widely adopted by disaster management officials. The portability afforded by these devices allows users to exchange, manage and access vital information during critical situations. While their use and importance is gaining momentum, little is still known about the ergonomic and human reliability implications of human-handheld interaction, particularly in an Emergency Operations Center setting. The purpose of this effort is to establish basic human error probabilities (bHEP's) for handheld QWERTY data entry and to study the effects of various performance shaping factors, specifically, environmental conditions, communication load, and cognitive load. The factors selected are designed to simulate the conditions prevalent in an Emergency Operations Center. The objectives are accomplished through a three-factor between-subjects randomized full factorial experiment in which a bHEP value of 0.0296 is found. It is also determined that a combination of cognitive loading and environmental conditions has a statistically significant detrimental impact on the HEP.

No description available.

Textual data entry is an increasingly-important part of Human-Computer Interaction (HCI), but there is room for improvement in this domain. First, the keyboard -- a foundational text-entry device -- presents ergonomic challenges in terms of comfort and accuracy for even well-trained typists. Second, touch-screen smartphones -- some of the most ubiquitous mobile devices -- lack the physical space required to implement a full-size physical keyboard, and settle for a reduced input that can be slow and inaccurate. This thesis proposes and examines "DeepType" to begin addressing both of these problems in the form of a fully-virtual keyboard, realized through a deep recurrent neural network (DRNN) trained to recognize skeletal movement during typing. This network enables typing data to be extracted without a physical keyboard: a user can type on a flat surface as though on a keyboard, and the movement of their fingers (as recorded via monocular camera and estimated using a pre-trained model) is input into the DeepType network to provide output compatible with that output by a physical keyboard with 91.2% accuracy without any autocorrection. We show that this architecture is computationally feasible and sufficiently accurate for use when tailored to a specific subject, and suggest optimizations that may enable generalization. We also present a novel data capture system used to generate the training dataset for DeepType, including effective hand pose data normalization techniques.

This dissertation examined the effects of display spatial luminance nonuniformities on operator performance and perception. The objectives of this research were to develop definitions of nonuniformity, develop accurate measurement techniques, determine acceptable levels of nonuniformities, and to develop a predictive model based on user performance data. Nonuniformities were described in terms of spatial frequency, amplitude, display luminance, gradient shape, and number of dimensions. Performance measures included a visual random search task and a subjective measure to determine users' perceptions of the nonuniformities. Results showed that users were able to perform the search task in the presence of appreciable nonuniformities. lt was concluded that current published recommendations for acceptable levels of nonuniformities are adequately specified. Results from the subjective task showed that users were sensitive to the presence of nonuniformities in terms of their perceptions of uniformity. Specifically, results showed that as spatial frequency increased, perceived uniformity ratings increased. That is, users rated nonuniformities to be less noticeable. As amplitude and display luminance increased, the users' ratings of perceived uniformity decreased; that is, they rated the display as being farther from a uniform field. There were no differences in impressions between a sine and triangle gradient shape, while a square gradient shape resulted in lower ratings of perceived uniformity. Few differences were attributed to the dimension (1-D versus 2- D) of the nonuniformity and results were inconclusive because dimension was confounded with the display luminance. Nonuniformities were analyzed using Fourier techniques to determine the amplitudes of the coefficients for each nonuniformity pattern. These physical descriptors were used to develop models to predict users' perceptions of the nonuniformities. A few models yielded good fits of the subjective data. lt was concluded that the method for describing and measuring nonuniformities was successful. Also, the results of this research were in strong concurrence with previous research in the area of spatial vision. / Ph. D.

This thesis presents the development and use of MECHIN, an interactive graphical preprocessor for data input to spatial mechanism analysis and synthesis codes. A goal in the development of this preprocessor is to produce a graphical data input program that is both graphics device-independent and not structured for the input of data to any particular mechanism processing program. To achieve device-independence, the proposed graphics standard PHIGS (Programmer's Hierarchical Interactive Graphics System) is used for the graphics support software. Program development strategies including screen layout and user interfaces for three-dimensional data input are discussed. The program structure is also described and presented along with a complete listing of the program code to aid in future modifications and additions. Finally, a description of the use of the program is presented along with several examples of mechanism data input for synthesis and analysis. / Master of Science