Philip E. Pare

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Necessary and Sufficient Conditions for State-Space Network Realization Philip E. Paré Department of Computer Science, BYU Master of Science This thesis presents the formulation and solution of a new problem in systems and control theory, called the Network Realization Problem. Its relationship to other problems, such as State Realization [1] and Structural(More)
Virus models are used commonly for modeling and analysis of biological networks, computer networks, and human contact networks. The dynamic modeling of such systems in prior work has mainly been focused on networks with static graph structures, which we posit are unrealistic and/or oversimplified for the purpose of understanding and analyzing disease(More)
Network reconstruction is an important research topic in many different applications, including biochemical reactions, critical infrastructures, social media, and wireless mesh networks. This paper shows that, for a certain important class of systems, all the states in a system must be measured in order to ensure correct reconstruction of the network.(More)
Accurate measurements of photosynthesis are vital for understanding the response of our planet to climate change and developing novel strategies for improving food production. Since photosynthesis is sensitive to a myriad of inputs, including temperature, these measurements require precise control to produce meaningful and accurate data. This paper develops(More)
Epidemic processes are used commonly for modeling and analysis of biological networks, computer networks, and human contact networks. The idea of competing viruses has been explored recently, motivated by the spread of different ideas along different social networks. Previous studies of competitive viruses have focused only on two viruses and on static(More)
This paper demonstrates that both Balanced Truncation and Balanced Singular Perturbation Approximations can be viewed as limiting approximations of the same parameterization of Linear Time Invariant (LTI) systems. First, we introduce a specific parameterization of LTI systems that distinguishes dynamic and structural parameters. Next, we apply the Model(More)
This paper considers how much one must know, a priori, about a particular state space system to recover it from its transfer function. Knowing that one has access to full state measurements is clearly sufficient to uniquely specify a specific state space model from a given transfer function, but identifying what information is necessary for such state(More)
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