This dissertation investigates how to plan paths for Unmanned Aerial Vehicles (UAV) for the task of covering an environment. Three increasingly complex coverage problems based on the environment that needs to be covered are studied. The dissertation starts with a 2D point coverage problem where the UAV needs to visit a set of sites on the ground plane by flying on a fixed altitude plane parallel to the ground. The UAV has limited battery capacity which may make it infeasible to visit all the points. A novel symbiotic UAV and Unmanned Ground Vehicle (UGV) system where the UGV acts as a mobile recharging station is proposed. A practical, efficient algorithm for solving this problem using Generalized Traveling Salesperson Problem (GTSP) solver is presented. Then the algorithm is extended to a coverage problem that covers 2D regions on the ground with a UAV that can operate in fixed-wing or multirotor mode. The algorithm is demonstrated through proof-of-concept experiments. Then this algorithm is applied to covering 2D regions, not all of which lie on the same plane. This is motivated by bridge inspection application, where the UAV is tasked with visually inspecting planar regions on the bridge. Finally, a general version of the problem where the UAV is allowed to fly in complete 3D space and the environment to be covered is in 3D as well is presented. An algorithm that clusters viewpoints on the surface of a 3D structure and has an UAV autonomously plan online paths to visit all viewpoints is presented. These online paths are re-planned in real time as the UAV obtains new information on the structure and strives to obtain an optimal 3D coverage path. / Doctor of Philosophy / This dissertation investigates how to plan paths for Unmanned Aerial Vehicles (UAV). Three increasingly complex coverage problems based on the environment that needs to be covered are studied. The dissertation starts with a 2D point coverage problem where the UAV needs to visit a set of sites on the ground by flying at a fixed altitude. The UAV has limited battery capacity which may make it impossible to visit all the points. A novel symbiotic UAV and Unmanned Ground Vehicle (UGV) system where the UGV acts as a mobile recharging station is proposed. A practical, efficient algorithm for solving this problem using Generalized Traveling Salesperson Problem (GTSP) solver is presented. Then the algorithm is extended to coverage of 2D regions on the ground with a hybrid UAV. The algorithm is demonstrated through proof-of-concept experiments. Then this algorithm is applied to covering 2D regions on 3D structures. This is motivated by bridge inspection application, where the UAV is tasked with visually inspecting regions on the bridge. Finally, a general version of the problem where the UAV is allowed to fly in 3D space and the environment to be covered is in 3D as well is presented. An algorithm that clusters points on the surface of a 3D structure and has an UAV autonomously plan online paths to visit all viewpoints is presented. These online paths are re-planned in real time as the UAV obtains new information on the structure and strives to obtain an optimal 3D coverage path.

In this thesis, we present a new register-transfer level (RTL) test generation method that makes use of two coverage metrics, Branch Coverage, and Mutation Coverage across two stages, to cover hard-to-reach points previously unreached. We start with a preprocessing stage by converting the RTL source to a C++ equivalent using a modified Verilator, which also automatically creates mutants and the corresponding mutated C++ design, based on arithmetic, logical and relational operators during conversion. With the help of extracted Data Dependency and Control Flow Graphs, in every <golden, mutation> pair, branches containing variables dependent on the mutated statement are instrumented to track them. The first stage uses Evolutionary algorithms with Ant Colony Optimization to generate test vectors with mutation coverage as the metric. Two new filtering techniques are also proposed which optimize the first stage by eliminating the need for generating tests for redundant mutants. The next stage is the original BEACON which now takes the generated mutation test vectors as the initial population unlike random vectors, and output final test vectors. These test vectors succeed in improving the coverage up to 70%, compared to the previous approaches for most of the ITC99 benchmarks. With the application of filtering techniques, we also observed a speedup by 85% in the test generation runtime and also up to 78% reduction in test vector size when compared with those generated by the previous techniques. / MS / In the recent years, Verification has become one of the major bottlenecks in integrated circuit design process, which is exacerbated by the increasing design complexities today. Today designers start the design process by abstracting the initial design in a manner similar to software programming language using a higher abstraction language called Hardware Descriptive Language(HDL). Hence, an HDL based design also contains a number of case statements and if-else statements, also called branches, similar to a software design. Branches indicate decision points in the design and high branch coverage based tests can give us an assurance that the design is properly exercised as compared to those given by randomly generated tests. In this thesis, we introduce a new test generation methodology which generates tests using the help of user introduced mutants to ensure higher branch coverage. Mutation testing is similar to a fault testing method, in which an error or a fault is deliberately introduced into the design and we check if the tests generated are able to detect the fault. An important property of a mutant is that: when a mutant is applied and if the mutated part of the design is exercised by the given test suite, then the following data and control flow path taken can be different from that taken on the original design. This important property along with proper guidance is used in our work to reach some branches which are difficult to cover by random test vectors, and this is the main basis of this thesis. Applying this method, we observed that the branch coverage increased with a decrease in test generation runtime and test vector length when compared to previously proposed techniques.

Wireless sensor networks provide a wide range of applications, such as environment surveillance, hazard monitoring, traffic control, and other commercial or military applications. The quality of service provided by a sensor network relies on its coverage, i.e., how well an event can be tracked by sensors. This research studies issues about sensor coverage: (1) how to optimally deploy new sensors in order to improve the coverage of an existing network, (2) how to properly measure the coverage when the path is a line. The best- and worst-case coverage problems that are related to the observability of a path are addressed and formulated into computational geometry problems. We prove that there exists a duality between the two coverage problems, and then solve the two problems together. The presented new-node placement algorithm is shown to deploy new nodes optimally in polynomial time. However, in some applications, such as highway monitoring and anti-missile interception systems, the trajectory of a target is linear but we can not find suitable coverage measurement for the straight-line path in previous research. Therefore, this research presents novel algorithms for coverage measurement of straight-line paths. Based on computational geometry and graph theory, we propose plane sweep algorithms to find the optimal straight-line paths for both the best-case and worst-case coverage problems in polynomial time. Both mathematical analysis and simulations are used to prove the optimality of our algorithms.

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Access to abstract permanently restricted to Ball State community only / Access to thesis permanently restricted to Ball State community only / Department of Communication Studies

Condemned prisoners in Texas and most other states are given an opportunity to make a final statement in the last moments before death. An anecdotal review by the author of this study over the last 15 years indicates that condemned prisoners use the opportunity for a variety of purposes. They ask forgiveness, explain themselves, lash out at accusers, rail at the system, read poems, say goodbyes to friends and family, praise God, curse fate - and assert their innocence with their last breaths. The final words also are typically heard by a select group of witnesses, which may include a prisoner's family and friends, victim's relatives, and one or more journalists. What the public knows about a particular condemned person's statement largely depends on what the journalists who witness the executions chose to include in their accounts of executions, the accuracy of their notes, and the completeness of the statements that are recorded on departments of correction websites or records. This paper will examine, through rhetorical and content analyses, the final words of the 355 prisoners who were executed in Texas between 1976 and 2005, identify those who made unequivocal claims of innocence in their final statements, and analyze news coverage of their executions by the Associated Press.

International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada / The coverage characteristics of the GLONASS constellation is analyzed. The almanac data of GLONASS navigation message are used in the computation according to the operation of the satellites. The ground traces of the GLONASS satellites are plotted. And the probability of visible satellite number is calculated under different latitude conditions. The results are analyzed to give descriptions of the GLONASS constellation. And they are compared with those of GPS's. The conclusion is verified that GLONASS constellation provides better coverage at high latitude.

Testing a PCB assembly can be very time consuming due to its complexity andcompactness. Tests are desired to be consistent and test coverage should be as highas possible, which is perfect for automated testing software.This thesis intends to develop computer controlled tests of faulty PCB assembliesusing boundary scan, which is meant to quickly locate the error so that an analysisengineer can evaluate it and prevent it from happening in future versions of theproduct. Boundary scan is even able to test the inner circuitry.Testing with boundary scan has been around for quite some time, but in recentyears it has shown to be truly valuable and time saving, due to the increasingcomplexity of PCB assemblies. The conclusions reached in this study are promisingfor future tests and development of PCBs using boundary scan, which has shownto be quite the powerful tool.

There is a growing recognition that we cannot afford the provision of all new health care technologies, even those that are proven to be beneficial. This is increasingly true in the US, where health care spending is on an unsustainable upward trajectory. US health care spending is greatly in excess of that of other countries; however, with respect to key health metrics, the US health care system performs relatively poorly. Despite this, unlike many other developed countries economic evaluation, and more specifically cost effectiveness evidence, is used sparingly in the US health care system. Notably, the Centers for Medicare and Medicaid Services (CMS), administrators of the Medicare programme, state that cost-effectiveness evidence is not relevant to coverage decisions for medical technology and interventions evaluated as part of National Coverage Determinations (NCDs). The empirical aspect of this thesis evaluates the current use and potential value of using cost-effectiveness evidence in CMS NCDs. A database was built using data obtained from NCD decision memoranda, the medical literature, a Medicare claims database, and Medicare reimbursement information. The findings of the empirical work show that, CMS’s stated position notwithstanding, cost-effectiveness evidence has been cited or discussed in a number of coverage decisions, and there is a statistically significant difference between positive and non-coverage decisions with respect to cost effectiveness. When controlling for factors likely to have an effect on coverage decisions, the availability of cost-effectiveness evidence is a statistically significant predictor of coverage. In addition, the quality of the supporting clinical evidence, the availability of alternative interventions, and the recency of the decision are statistically significant variables. Further, when hypothetically reallocating resources in accordance with cost-effectiveness substantial gains in aggregate health are estimated. It is shown that using cost-effectiveness to guide resource allocation has an effect on resource allocation across patient populations and types of technology.

Spatial facility location problems (SFLPs) involve the placement of facilities in continuous demand regions. One approach to solving SFLPs is to aggregate demand into discrete points, and then solve the point-based model as a conventional facility location problem (FLP) according to a surrogate model. Solution performance is measured in terms of the percentage of continuous space actually covered in the original SFLP. In this dissertation I explore this approach and examine factors contributing to solution quality. Three error sources are discussed: point representation spacing, multiple possible solutions to the surrogate point-based model, and round-off errors induced by the computer representation of numbers. Some factors—including boundary region surrogate points and surrogate point location—were also found to make significant contributions to coverage errors. A surrogate error measure using a point-based surrogate model was derived to characterize relationships among spacing, facility coverage area, and spatial coverage error. Locating continuous space facilities with full coverage is important but challenging. Demand surrogate points were initially used as a continuous space for constructing the MIP model, and a point-based surrogate FLP was enhanced for extracting multiple solutions with additional constraints that were found to reduce coverage error. Next, a best initial solution was applied to a proposed heuristic algorithm to serve as an improvement procedure. Algorithm performance was evaluated and applied to a problem involving the location of emergency warning sirens in the city of Dublin, Ohio. The effectiveness of the proposed method for solving this and other facility location/network design problems was demonstrated by comparing the results with those reported in recently published papers.