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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.

Ferromagnetic shape memory alloys FSMAs have diverse application, especially in the aerospace and bio-medical industries. They are a class of active and smart materials exhibiting strains under the influence of an applied magnetic field. These magnetic properties are mainly attributed to the martensitic structural phase trans- formation these material experience in response to temperature variation. Co-Ni-Al based alloys are one of the most promising ferromagnetic shape memory alloy FSMA that has been put recently under extensive study by researchers. They have shown extensive and promising features specifically those related to self-actuation. The effect of valence electron concentration and magnetic properties of Co-Ni-Al based ferromagnetic shape memory alloys on the martensitic transformations were analyzed utilizing Ab-initio first principle calculations. The variations of martensite start temperatures (Ms) and magnetic properties of a number of stoichiometric and mnon-stoichiometric Co-Ni-Al ferromagnetic shape memory alloys (FSMA’s) with B2 austenite structure were studied and analyzed as a function of composition and lattice site ordering and site preference. A major conclusion of this thesis suggests that the magnetic valence number (Zm) should be considered in conjunction to the e/a ratio if the composition profile of the Ms is to be determined. Both Monte-Carlo and Ab-initio simulations were implemented to obtain the magnetic Heisenberg’s exchange coupling parameters (J m) and model the magnetic transformations in stoichiometric Co2NiAl FSMAs. Two different cubic structures, ordered and disordered were compared to their tetragonal distortions martensitic phases and their Curie temperature (TC ) were obtained from the Monte-Carlo magnetic susceptibility temperature profile.