This thesis explores a border security system based on agent-based modeling and simulation (ABMS). The ABMS software platform, map aware non-uniform automata, is used to model various scenarios and evaluate the border security system given a set of infiltrators who have evolutionary behavior governed by a genetic algorithm (GA). The GA is used to represent adaptive behavior of the enemy when the friendly force has deployed our border security at a maximum level. By using a near optimal Latin hypercube design, our simulation runs are implemented efficiently and the border security system is analyzed using four different kinds of measures of effectiveness. / text

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Many areas of science now generate huge volumes of data that present visualization, modeling, and interpretation challenges. Methods for effectively representing the original data in a reduced coordinate space are therefore receiving much attention. The purpose of this research is to test the hypothesis that molecular computing of vectors for transformation matrices enables spectra to be represented in any arbitrary coordinate system. New coordinate systems are selected to reduce the dimensionality of the spectral hyperspace and simplify the mechanical/electrical/computational construction of a spectrometer. A novel integrated sensing and processing system, termed Molecular Factor Computing (MFC) based near infrared (NIR) spectrometer, is proposed in this dissertation. In an MFC -based NIR spectrometer, spectral features are encoded by the transmission spectrum of MFC filters which effectively compute the calibration function or the discriminant functions by weighing the signals received from a broad wavelength band. Compared with the conventional spectrometers, the novel NIR analyzer proposed in this work is orders of magnitude faster and more rugged than traditional spectroscopy instruments without sacrificing the accuracy that makes it an ideal analytical tool for process analysis. Two different MFC filter-generating algorithms are developed and tested for searching a near-infrared spectral library to select molecular filters for MFC-based spectroscopy. One using genetic algorithms coupled with predictive modeling methods to select MFC filters from a spectral library for quantitative prediction is firstly described. The second filter-generating algorithm designed to select MFC filters for qualitative classification purpose is then presented. The concept of molecular factor computing (MFC)-based predictive spectroscopy is demonstrated with quantitative analysis of ethanol-in-water mixtures in a MFC-based prototype instrument.

The problem of relative spacecraft motion estimation is considered with application to various reference and relative orbits. Mean circular and elliptic orbits are analyzed, with relative orbits ranging in size from 1 km to 10 km. Estimators are built for three propagation models: (i) Gim-Alfriend State Transition Matrix, (ii) the J2-Linearized Equations of Motion for Circular Orbits, and (iii) the Clohessy-Wiltshire Equations of Motion. Two alternative models were developed in an attempt to ac- count for unmodeled nonlinearities: (i) Biased Clohessy-Whiltshire Equations, and (ii) J2 -Linearized State Transition Matrix. Two estimation techniques are presented in an attempt to explore and determine which propagation model minimizes the error residual: the linear Kalman filter is presented under the assumption of vector based, GPS-type measurements; the extended Kalman filter is analyzed assuming angle-range, optical-type measurements. Sampling time is varied to look at the effect of measurement frequency. It is assumed that the orbit of one of the satellites, the chief, is known reasonably well. This work showed that the error residuals from the state estimates were minimized when the propagation technique utilized was the Gim-Alfriend State Transition Matrix. This supports conclusions that are obtained outside of the estimation problem. Additionally, the error residuals obtained when the propagation technique was the Clohessy-Wiltshire Equations is comparable to the more complicated models. Unmodeled nonlinearities affect the magnitude of the error residuals. As expected, the Gim-Alfriend STM comes closest to the truth; for smaller eccentricities (0.005), the Clohessy-Wiltshire EOM show minor deviations from the truth. As the eccentricity increases, the linear models begin to diverge greatly from the true response. The additional two models (the biased CW equations, and the linear STM) show decent performance under specific conditions. The former accounts for some of the unaccounted for nonlinearities. The latter exhibits comparable performance to the Gim-Alfrien STM for circular reference orbits. However, in each case, as the nonlinearity of the problem increases, the accuracy of the model decreases.