This interdisciplinary thesis involves the design and analysis of coordination algorithms on networks, identification of dynamic networks and estimation on networks with random geometries with implications for networks that support the operation of dynamic systems, e.g., formations of robotic vehicles, distributed estimation via sensor networks. The results have ramifications for fault detection and isolation of large-scale networked systems and optimization models and algorithms for next generation aircraft power systems. The author finds novel applications of the methodology in energy systems, such as residential and industrial smart energy management systems.

This book explores human polity with respect to its nature, context, and evolution. Specifically, it examines how individual wills translate into political ideologies, investigates what social forces converge to shape governmental operations, and probes whether human polity progresses in focus from individual wills to group interests to social integrations. The book entertains five hypotheses. The first is commonsensical: where there are people there is politics. The second is analogous: humans govern themselves socially in a way that is comparable to how a body regulates itself physically. The third is rational: humans set rules, organize activities, and establish institutions upon facts, following reasons, for the purpose of effectiveness and efficiency. The fourth is random: human affairs take place haphazardly under specific circumstances while they overall exhibit general patterns and trends. The final hypothesis is inevitable: human governance evolves from autocracy to democracy to technocracy. The book presents systematic information about human polity, its form, content, operation, impact, and evolution. It sheds light on multivariate interactions among human wills, rights, and obligations, political thoughts, actions, and mechanisms, and social structures, processes, and order maintenances. Pragmatically, it offers invaluable insights into individuals as agents, groupings as agencies, and polity as structuration across the human sphere.

The techniques used for the extraction of information from received or ob served signals are applicable in many diverse areas such as radar, sonar, communications, geophysics, remote sensing, acoustics, meteorology, med ical imaging systems, and electronics warfare. The received signal is usually disturbed by thermal, electrical, atmospheric, channel, or intentional inter ferences. The received signal cannot be predicted deterministically, so that statistical methods are needed to describe the signal. In general, therefore, any received signal is analyzed as a random signal or process. The purpose of this book is to provide an elementary introduction to random signal analysis, estimation, filtering, and identification. The emphasis of the book is on the computational aspects as well as presentation of com mon analytical tools for systems involving random signals. The book covers random processes, stationary signals, spectral analysis, estimation, optimiz ation, detection, spectrum estimation, prediction, filtering, and identification. The book is addressed to practicing engineers and scientists. It can be used as a text for courses in the areas of random processes, estimation theory, and system identification by undergraduates and graduate students in engineer ing and science with some background in probability and linear algebra. Part of the book has been used by the author while teaching at State University of New York at Buffalo and California State University at Long Beach. Some of the algorithms presented in this book have been successfully applied to industrial projects.

For dynamic distributed systems modeled by partial differential equations, existing methods of sensor location in parameter estimation experiments are either limited to one-dimensional spatial domains or require large investments in software systems. With the expense of scanning and moving sensors, optimal placement presents a critical problem.

The book is about the possibilities of involvement of the well-known Green’s function method in exact or approximate controllability analysis for dynamic systems. Due to existing extensions of the Green’s function notion to nonlinear systems, the approach developed here is valid for systems with both linear and nonlinear dynamics. The book offers a number of particular examples, covering specific issues that make the controllability analysis sophisticated, such as coordinate dependent characteristics, point sources, unbounded domains, higher dimensions, and specific nonlinearities. It also offers extensive numerical analysis, which reveals both advantages and drawbacks of the approach. As such, the book will be of interest to researchers interested in the theory and practice of control, as well as PhD and Master’s students.