This research assessed the effects of inconsistencies in graphic direct manipulation interfaces. Objective and subjective measurement techniques were employed to determine how inconsistencies affected performance in an Apple Macintosh-based computer application called “The Personal Organizer.” Three groups of 11 participants, all familiar with the Macintosh computer, were given a set of similar tasks on different versions of the application in a pretest (control version), treatment (control or one of two inconsistent versions), post-test (control version) experimental design. Performance was measured using two objective measures: task completion time and the number of input control actions. Analysis of variance and correlational procedures were used to interpret these measures. A set of 29 bipolar semantic differentials were used to form a subjective measure of consistency. The linear sum of the scores on a subset of these items was used to create a composite measure of consistency. An analysis of variance procedure was performed on the composite measure, called the Preference Index. Results show that time and subjective measures are not identical in their ability to discriminate between inconsistent versions of the interface. It is concluded that the inconsistency of the interface has different effects on subject’s ability to complete tasks as compared to subject’s ability to rate interface consistency. Interface designers should be aware that subjective ratings of interface consistency need to be collected in concert with objective performance measures to assess the effects of graphical user interface inconsistencies upon human performance. / Master of Science

As the use of computers in the workplace becomes more commonplace, the levels of interconnection and interoperability increases. The desire to pass large amounts of data almost instantaneously is the basis of the high speed local area network (LAN). To meet the needs of these LANs, the American National Standards Institute has developed the Fiber Distributed Data Interface (FDDI) standard. This new LAN can provides high speed fiber optic based communication between computers. In meeting the computer/LAN interface requirements, new methods for data transfer will be required. Trade-offs between the reliability, architecture and buffer sizes must be developed. These concepts must include variables of data transfer widths, protocol processing, transfer architecture and packet length distributions. This dissertation addresses these hardware requirements in using the high speed computer interface known as the Fiber Data Distributed Interface. / Ph. D.

Indiana University-Purdue University Indianapolis (IUPUI) / A Brain Computer Interface (BCI) is a hardware and software system that establishes direct communication between human brain and the environment. In a BCI system, brain messages pass through wires and external computers instead of the normal pathway of nerves and muscles. General work ow in all BCIs is to measure brain activities, process and then convert them into an output readable for a computer. The measurement of electrical activities in different parts of the brain is called electroencephalography (EEG). There are lots of sensor technologies with different number of electrodes to record brain activities along the scalp. Each of these electrodes captures a weighted sum of activities of all neurons in the area around that electrode. In order to establish a BCI system, it is needed to set a bunch of electrodes on scalp, and a tool to send the signals to a computer for training a system that can find the important information, extract them from the raw signal, and use them to recognize the user's intention. After all, a control signal should be generated based on the application. This thesis describes the step by step training and testing a BCI system that can be used for a person who has lost speaking skills through an accident or surgery, but still has healthy brain tissues. The goal is to establish an algorithm, which recognizes different vowels from EEG signals. It considers a bandpass filter to remove signals' noise and artifacts, periodogram for feature extraction, and Support Vector Machine (SVM) for classification.