The development methodology for today’s computer systems is lacking in the area of preliminary design. Decisions that greatly influence the product’s life-cycle cost are made during this stage of the design with minimal knowledge. One of the key decisions made during preliminary design is the allocation of the system’s functions to hardware, software, or firmware implementations. This research investigates methods to automate the decision making process during preliminary design. These decisions include the hardware, software, and firmware allocation process and the reduction of the preliminary design space by ordering the design space and determining a cut-off point. A general system model is presented based on the top-down design methodology. A decision process is applied to the lowest level functions of the system model to order the allocation design space. The methods explored to perform decisions are the general linear model and fuzzy logic membership functions. A computer based tool is developed that applies the decision methods to a system model. The output of the tool is a set of optimum hardware/software/firmware function allocations. The application of this tool greatly reduces the overall allocation design space which allows the human designer to make improved decisions during preliminary design. / Master of Science

This thesis presents Taskmaster.2, a graphical environment for interactive task specification, execution and monitoring. Problem solving in the Taskmaster environment can be accomplished with top-down programming, bottom-up programming, or a mixture of the two. The use of top-down programming permits the user to start with a high level task and refine this task into successively lower level subtasks until, at the lowest level, each subtasks represents a software tool. Bottom-up programming is accomplished by beginning with the lowest level subtasks, software tools, and then combining these tools into successively higher level subtasks until, at the highest level, the high level subtask represents the original problem task. These programming methods provide the user with abstraction capabilities. Another abstraction capability within the Taskmaster.2 environment is the network tools. The user creates network tools by selecting several software tools that, combined, provide a certain functionality. These network tools can then be reused in solving other problem tasks. In fact, these tools appear no different to the user than the low level software tools: they are both single indivisible units. Providing complete abstraction capabilities, i.e., mixing programming styles (top-down and bottom-up) and network tools, maintains the consistency of the Taskmaster.2 environment. This makes the environment an easy one to learn, as well as remember. / Master of Science

This dissertation deals with the use of Shewhart control charts, modified to have variable sampling intervals, to simultaneously monitor a set of parameters. Fixed sampling interval control charts are modified to utilize sampling intervals that vary depending on what is being observed from the data. Two problems are emphasized, namely, the simultaneous monitoring of the mean and the variance and the simultaneous monitoring of several means. For each problem, two basic strategies are investigated. One strategy uses separate control charts for each parameter. A second strategy uses a single statistic which combines the information in the entire sample and is sensitive to shifts in any of the parameters. Several variations on these two basic strategies are studied. Numerical studies investigate the optimal number of sampling intervals and the length of the sampling intervals to be used. Each procedure is compared to corresponding fixed interval procedures in terms of time and the number of samples taken to signal. The effect of correlation on multiple means charts is studied through simulation. For both problems, it is seen that the variable sampling interval approach is substantially more efficient than fixed interval procedures, no matter which strategy is used. / Ph. D.

Indiana University-Purdue University Indianapolis (IUPUI) / Multiresolution image fusion is an emerging area of research for use in military and commercial applications. While many methods for image fusion have been developed, improvements can still be made. In many cases, image fusion methods are tailored to specific applications and are limited as a result. In order to make improvements to general image fusion, novel methods have been developed based on the wavelet transform and empirical variance. One particular novelty is the use of directional filtering in conjunction with wavelet transforms. Instead of treating the vertical, horizontal, and diagonal sub-bands of a wavelet transform the same, each sub-band is handled independently by applying custom filter windows. Results of the new methods exhibit better performance across a wide range of images highlighting different situations.

Indiana University-Purdue University Indianapolis (IUPUI) / Reliability of the wide-area power system is becoming a greater concern as the power grid is growing. Delivering electric power from the most economical source through fewest and shortest transmission lines to customers frequently increases the stress on the system and prevents it from maintaining its stability. Events like loss of transmission equipment and phase to ground faults can force the system to cross its stability limits by causing the generators to lose their synchronism. Therefore, a helpful solution is detection of these dynamic events and prediction of instability. Decision Trees (DTs) were used as a pattern recognition tool in this thesis. Based on training data, DT generated rules for detecting event, predicting loss of synchronism, and selecting stabilizing control. To evaluate the accuracy of these rules, they were applied to testing data sets. To train DTs of this thesis, direct system measurements like generator rotor angles and bus voltage angles as well as calculated indices such as the rate of change of bus angles, the Integral Square Bus Angle (ISBA) and the gradient of ISBA were used. The initial method of this thesis included a response based DT only for instability prediction. In this method, time and location of the events were unknown and the one shot control was applied when the instability was predicted. The control applied was in the form of fast power changes on four different buses. Further, an event detection DT was combined with the instability prediction such that the data samples of each case was checked with event detection DT rules. In cases that an event was detected, control was applied upon prediction of instability. Later in the research, it was investigated that different control cases could behave differently in terms of the number of cases they stabilize. Therefore, a third DT was trained to select between two different control cases to improve the effectiveness of the methodology. It was learned through internship at Midwest Independent Transmission Operators (MISO) that post-event steady-state analysis is necessary for better understanding the effect of the faults on the power system. Hence, this study was included in this research.

<p>For agricultural soils to be perpetually productive, farmers must maintain and improve the physical, chemical, and biological properties of the soil. The loss of soil to erosion is a major challenge to soil health, contributing to farmland loss and declines in productivity. This is a long-term problem for agriculture because there is a limited amount of topsoil available. Another costly loss happens when<em> residual nitrogen is lost to leaching or carried away in runoff. This is a particular problem in the fall and winter months when fields lie fallow, and there are no plants to take up excess nitrogen. Losing nitrogen is a problem for both the nutrient content of the soil as well as a serious concern in terms of water contamination.</em><em> </em>Cover crops provide a way to at least partially address each of these and many other agronomic and soil health issues. Although there has been a steady increase in cover crop use, adoption has been relatively slow. This is likely due to a lack of economic information and understanding of the associated risk. To address this problem, field level data was gathered from farmers across central and northeastern Indiana. The data included information on cash crop yield, cover crops grown, fertilizer use, among many other variables. The sample was trimmed based on the estimated propensity to cover crop, in order to reduce selection bias. Using this data, the effect of cover crops on the mean and variation of the subsequent cash crop yield was estimated using regression analysis. This information was combined in a stochastic analysis of a farm enterprise budget. The effects of cover crops on farm finance and risk were evaluated. These final analyses provide agricultural producers with more information to make informed decisions regarding the adoption of cover crops. The information may also provide insight to policy makers, who may wish to understand more completely the private economics of cover crops. The results indicated that cover crops have the ability to provide economic benefits when grown prior to corn in our study region. These include increased yield, reduced need for nitrogen fertilizer, and increased temporal yield stability. These benefits translate into higher revenue from the sale of the grain, lower input costs, and lower risk and uncertainty. However, the results for soybeans showed cover crops had a negative, albeit statistically insignificant, effect on desirable measures. This led to lower projected revenue, higher projected costs, and increased expected risk. Even so, the average corn-soybean contribution margin with cover crops was nearly equal to the baseline scenario. Furthermore, the analysis of risk showed that the corn-soybean two-year average would be preferred by farmers with moderate to high risk aversion. The difference between the effect of cover crops in corn and soybeans may be due to differences in the crop’s inherent nitrogen needs and the difficulty of cover crop establishment after corn in the region.<br></p>

The recent emergencies on college campuses including the Virginia Tech massacre of April, 2007, the Northern Illinois University shootings and the Union University tornado highlight the importance of disaster preparedness within the university community. This study is a survey exploring the daily rhythms of student life, the communication channels open to students and students composition and characteristics. This survey finds that students have frequent access to communication technologies which can be utilized as warning channels. The study also concludes that many students do not actively seek out information relative to emergency preparedness and the gaps in the populations knowledge require increased disaster education by the university.

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The purpose of this research project was to examine whether frontline nurse managers who had attended a leadership program, perceived their leadership style as containing behaviors representative of transformational leadership. A secondary purpose was to determine the participant's opinions about the value of a leadership program for their practice. Current literature was utilized to support this research project examining a nursing systems issue.The primary instrument used to collect data about leader practice was the Leadership Practices Inventory (LPI) (University of Georgia, 2002). An evaluation tool was also designed and utilized to gather information about the participant's perception of their leadership behaviors after completion of a training program. Survey participants were selected from nurses who completed the Arizona Healthcare Leadership Academy (AzHCLA) (2007) course in the last four years.A course survey and results from the LPI revealed that study participants perceived an increase in their behaviors related to leading others as well as having learned new skills by having completed the AzHCLA course. Nurse's educational levels were compared to the five leadership practice subgroups from the LPI to examine whether a nurse's educational level could better account for an increase in leadership competencies. Research data revealed that no relationship between educational levels existed but that certain leadership skills were gained by having completed a leadership educational program. By using descriptive statistics, mean scores were used to identify differences in how nurses perceived their individual competencies and behaviors after having completed leadership education. Reported perceptions of competencies and behaviors indicated that educational programs can be beneficial to frontline nurse leaders.While results from an ANOVA showed there was no statistical significance related to education and LPI subgroups, there was a trend in the mean differences for those individuals with a master's degree. Qualitative data revealed that course participants perceived having gained new leadership skills and behaviors. The data from this study created a baseline of information that warrants further investigation to identify if indeed education makes a difference in perceived leadership practices.

Researchers have always found difficulty in attaining funding from the National Research Foundation (NRF) for new research interests. The field of Artificial Intelligence (AI) holds the promise of improving the matching of research proposals to funding sources in the area of Intelligent Information Retrieval (IIR). IIR is a fairly new AI technique that has evolved from the traditional IR systems to solve real-world problems. Typically, an IIR system contains three main components, namely, a knowledge base, an inference engine and a user-interface. Due to its inferential capabilities. IIR has been found to be applicable to domains for which traditional techniques, such as the use of databases, have not been well suited. This applicability has led it to become a viable AI technique from both, a research and an application perspective. This dissertation concentrates on researching and implementing an IIR system in LPA Prolog, that we call FUND, to assist in the matching of research proposals of prospective researchers to funding sources within the National Research Foundation (NRF). FUND'S reasoning strategy for its inference engine is backward chaining that carries out a depth-first search over its knowledge representation structure, namely, a semantic network. The distance constraint of the Constrained Spreading Activation (CSA) technique is incorporated within the search strategy to help prune non-relevant returns by FUND. The evolution of IIR from IR was covered in detail. Various reasoning strategies and knowledge representation schemes were reviewed to find the combination that best suited the problem domain and programming language chosen. FUND accommodated a depth 4, depth 5 and an exhaustive search algorithm. FUND'S effectiveness was tested, in relation to the different searches with respect to their precision and recall ability and in comparison to other similar systems. FUND'S performance in providing researchers with better funding advice in the South African situation proved to be favourably comparable to other similar systems elsewhere. / Thesis (M.Sc.)- University of Natal, Pietermaritzburg, 2001.