<p> Coastal hazards including storm surge, sea-level rise, and cyclone winds continue to have devastating effects on infrastructure systems and communities despite the costly investments already being made in risk management to mitigate predicted consequences. Risk management has generally not been sufficiently focused on coastal resilience with community stakeholders involved in the process of making their coastlines more resilient to damaging storms. Thus, without earlier stakeholder involvement in coastal resilient planning for their community, they are frustrated after disasters occur. The US National Academies has defined resilience as &ldquo;the ability to prepare and plan for, absorb, recover from and more successfully adapt to adverse events&rdquo; (National Research Council (NRC), 2012). This dissertation introduces a methodology for enabling stakeholder-involved resilience evaluation across the physical, information, cognitive and social domains (DiMase, Collier, Heffner, &amp; Linkov, 2015; Linkov et al., 2013). The methodology addresses the stages of resilience: prepare, absorb, recover and adapt and integrates performance assessment of risk management project initiatives with scenario analysis to characterize disruptions of risk-management priorities (Linkov, Fox-Lent, Keisler, Della Sala, &amp; Sieweke, 2014b). The goal of the methodology is not to find the &ldquo;right&rdquo; solution set of priorities by quantitative means., but to develop a methodology for dialogue among the stakeholders. Rather, the purpose is to develop a methodology that would allow stakeholder involvement in the process of making their coastal communities more resilient by determining important resilience stages and domains, critical functions of the system, project initiatives for consideration, and potential future scenarios of concern. Stakeholder qualitative comments are transformed into quantitative inputs to produce qualitative outputs. The results of the methodology allow the stakeholders to easily &ldquo;see&rdquo; the priorities and the resilience stages and domains. The methodology is illustrated through a case study at Mobile Bay, Alabama, USA and then illustrated again through a second case study of Southeast Region of Florida and produces more focused results for the stakeholders. The research findings as broadly implemented will benefit federal and local policymakers and emergency responders, business and community leaders, and individual homeowners and residents in the United States and the International Community.</p>

Evaluation enable the Government to effectively and efficiently fulfill its mission. The current transition to Performance Based Service Acquisition (PBSA) and Multiple Award Contracts (MACs) have profound impact on program cubes, and many organizations are venturing concurrently into these strategies for the first time. The intent of PBSA is to maximize performance, innovation, and competition often at a savings." MACs are intended to benefit Government and industry by helping to ensure healthy competition and fair and reasonable contract prices. This thesis will examine the impact of PBSA and MAC contracting strategies on the Government program offices, along with the inherent cultural effects on both Government and industry.

The United States Navy's Cryptologic Carry-On Program Office manages a portfolio of Information Warfare (IW) systems. This research and case study demonstrate how the Knowledge Value Added (KVA) Methodology can be used to formulate a framework for extracting and analyzing performance parameters and measures of effectiveness for each system. KVA measures the effectiveness and efficiency of CCOP systems and the impact they have on the Intelligence Collection Process (ICP) on board U.S. Navy Ships. By analyzing the outputs of the subprocesses involved in the ICP in common units of change, a price per unit of output can be generated to allocate both cost and revenue at the subprocess level. With this level of financial detail, a return on investment (ROI) analysis can be conducted for each process, or asset.

This study examined the effectiveness of Introductory Flight Screening (IFS) for U.S. Navy and Marine Corps student pilots. It compared a non-IFS group to an IFS-complete group to determine if IFS had any effect on Primary drop-on-request (DOR) and flight-failure (FF) attrition. It then examined the return on investment (ROI) of the IFS program utilizing T-34 flying-hour costs, active-duty costs, and opportunity cost-savings of IFS-screened students who did not enter undergraduate pilot training. Results suggest that IFS did not have an effect on the DOR rate and may have produced the undesired effect of delaying the DOR-student's decision until later in the syllabus. IFS had a desirable effect on the FF attrition rate with no significant change in T-34 flight hours per FF. The combined Primary DOR and FF rate, although significantly lower, did not achieve expectations. The ROI analysis was completed with both composite-pay costs and Individual Account costs. In both cases, the IFS-investment costs significantly outweighed the IFS savings resulting in a net loss and an undesirable ROI. Several alternatives were discussed as possible improvements to the current IFS program.

The state-of-art technologies keep generating new ways of improving on the performance of the old systems. Array antennas, one of the continuously improving technologies, brought many benefits to our life. The superiorities of array antennas remove the disadvantages of the old technology radars such as great sidelobes, vulnerability to the jammers, and degradation effect of the clutter. Array antennas find many applications on different areas. Today, unmanned aerial vehicles (UAVs) have begun to be seen in our life more often than before. UAVs prevent pilot loss of life. They carry out a variety of military and civilian missions such as surveillance and reconnaissance, target recognition, battle damage assessment, EW, search and rescue, and traffic monitoring. An important use of the UAVs is troop support, carrying out reconnaissance and surveillance missions, which requires maintaining a data-link with troops in order to send any data collected, such as video images, or audio. During operations it is necessary to continuously maintain a data and control link with the operator. This requires the ground station antenna to track the UAV so the antenna beam is pointed properly. The purpose of this research is to design and build an array to angle-track a UAV and, eventually, to accomplish the data transfer from the UAV to the ground station.

Personal computing is currently undergoing radical enhancements with the current technological advancements that have been made in the areas of high resolution displays, GUIs (graphic user interfaces), high quality sound and full motion video. Multimedia stands at the convergence of these technological advances. The pervasiveness of networks will result in a new generation of distributed services that include multimedia as the fundamental characteristic. The current hyper-activity in the commercial arena is testimony to the future of distributed mu1timedia services. The anticipation of the data superhighways has led to an industrial scramble filled with takeovers and acquisitions as companies battle to acquire the infrastructure that will set the scene for the services of the future. [Abbreviated Abstract. Open document to view full version] / GR 2016

This dissertation focuses on challenging static and dynamic problems encountered in cooperative multi-agent systems. First, a unified optimization framework is proposed for a wide range of tasks including consensus, optimal coverage, and resource allocation problems. It allows gradient-based algorithms to be applied to solve these problems, all of which have been studied in a separate way in the past. Gradient-based algorithms are shown to be distributed for a subclass of problems where objective functions can be decoupled. Second, the issue of global optimality is studied for optimal coverage problems where agents are deployed to maximize the joint detection probability. Objective functions in these problems are non-convex and no global optimum can be guaranteed by gradient-based algorithms developed to date. In order to obtain a solution close to the global optimum, the selection of initial conditions is crucial. The initial state is determined by an additional optimization problem where the objective function is monotone submodular, a class of functions for which the greedy solution performance is guaranteed to be within a provable bound relative to the optimal performance. The bound is known to be within 1 − 1/e of the optimal solution and is improved by exploiting the curvature information of the objective function. The greedy solution is subsequently used as an initial point of a gradient-based algorithm for the original optimal coverage problem. In addition, a novel method is proposed to escape a local optimum in a systematic way instead of randomly perturbing controllable variables away from a local optimum. Finally, optimal dynamic formation control problems are addressed for mobile leader-follower networks. Optimal formations are determined by maximizing a given objective function while continuously preserving communication connectivity in a time-varying environment. It is shown that in a convex mission space, the connectivity constraints can be satisfied by any feasible solution to a Mixed Integer Nonlinear Programming (MINLP) problem. For the class of optimal formation problems where the objective is to maximize coverage, the optimal formation is proven to be a tree which can be efficiently constructed without solving a MINLP problem. In a mission space constrained by obstacles, a minimum-effort reconfiguration approach is designed for obtaining the formation which still optimizes the objective function while avoiding the obstacles and ensuring connectivity.

<p> System design at the component level seeks to construct a design trade space of alternate solutions comprising mapping(s) of system function(s) to physical hardware or software product components. The design space is analyzed to determine a near-optimal next-level allocated architecture solution that system function and quality requirements. Software product components are targeted to increasingly complex computer systems that provide heterogeneous combinations of processing resources. These processing technologies facilitate performance (speed) optimization via algorithm parallelization. However, speed optimization can conflict with electrical energy and thermal constraints. A multi-disciplinary architecture analysis method is presented that considers all attribute constraints required to synthesize a robust, optimum, extensible next-level solution. This paper presents an extensible, executable model-based architecture attribute framework that efficiently constructs a component-level design trade space. A proof-of-concept performance attribute model is introduced that targets single-CPU systems. The model produces static performance estimates that support optimization analysis and dynamic performance estimation values that support simulation analysis. This model-based approach replaces current architecture analysis of alternatives spreadsheet approaches. The capability to easily model computer resource alternatives that produces attribute estimates improves design space exploration productivity. Performance estimation improvements save time and money through reduced prototype requirements. Credible architecture attribute estimates facilitate more informed design tradeoff discussions with specialty engineers. This paper presents initial validation of a model-based architecture attribute analysis method and model framework using a single computation thread application on two laptop computers with different CPU configurations. Execution time estimates are calibrated for several data input sizes using the first laptop. Actual execution times on the second laptop are shown to be within 10 percent of execution time estimates for all data input sizes.</p><p>

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