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<p>Grain structure and secondary phases play a critical role in determining the mechanical properties of industrial alloys. The spatial variation of such phases is very closely correlated to the liquid pooling established during late stage solidification and grain boundary coalescence. Obtaining a theory that correlates the evolution of length scales during grain boundary coalescence is a critical step toward the optimization of commercial alloys. This thesis highlights various phenomena that enter such a theory. They include coarsening and coalescence of dendrites, nucleation mechanisms and changes in composition of inter-dendritic liquid where second phases tend to initially form. Quantitative phase field models of solidification to simulate casting conditions and microstructure evolution are used in combination with characterization techniques to illustrate the connection between number, size, and distribution of liquid pools. Characterization techniques include spectral analysis, and clustering analysis by way of the Hoshen-Kopleman algorithm. By characterizing late-stage liquid pools, this thesis aims to be a first step towards developing a statistical scaling theory of length scale of liquid pooling.</p> / Master of Applied Science (MASc)

<p>Infectious diseases spreading in a human population can occasionally exhibit sudden transitions in their qualitative dynamics. Previous work has been very successful in predicting such transitions in New York City's measles incidence rates using the standard SIR model (susceptible, infected, recovered). This work relied on a dataset spanning 45 years, which we have extended to 93 years (1891-1984). We continue previous research in transition analysis on this larger dataset, and compare resonant and transient periods predicted to exist in NYC's measles incidence rates with those observed through a continuous wavelet transform of the data. We find good agreement between SIR predictions and observation, and in particular note the likely existence of previously unobserved hysteresis early in our new time-series.</p> / Master of Science (MSc)

Point processes are common in many physical applications found in engineering and biology. These processes can be observed in one-dimension as a time series or two-dimensions as a spatial point pattern with extensive amounts of literature devoted to their analyses. However, if the observed process is a hybrid of spatial and temporal point process, very few practical methods exist. In such cases, practitioners often remove the temporal component and analyze the spatial dependencies. This marginal spatial analysis may lead to misleading results if time is an important factor in the process. In this dissertation we extend the current analysis of spatial point patterns to include a temporal dimension. First- and second-order intensity measures for analyzing spatiotemporal point patterns are explicitly defined. Estimation of first-order intensities are examined using 3-dimensional smoothing techniques. Conditions for weak stationarity are provided so that subsequent second-order analysis can be conducted. We consider second-order analysis of spatiotemporal point patterns first in the space-time domain through an extension of Ripley's Κ-function. An alternative analysis is given in the frequency domain though construction of a spatiotemporal periodogram. The methodology provided is tested through simulation of spatiotemporal point patterns and by analysis of a real data set. The biological application concerns the estimation of the homerange of groups of the endangered red-cockaded woodpecker in the Fort Bragg area of North Carolina. Monthly or bimonthly point patterns of the bird distribution are analyzed and integrated over a 23 month period. / Ph. D.

License plate recognition/identification (LPR/I) applies image processing and character recognition technology to identify vehicles by automatically reading their license plates. In the United States, however, each state has its own standard-issue plates, plus several optional styles, which are referred to as special license plates or varieties. There is a clear absence of standardization and multi-colored, complex backgrounds are becoming more frequent in license plates. Commercially available optical character recognition (OCR) systems generally fail when confronted with textured or poorly contrasted backgrounds, therefore creating the need for proper image segmentation prior to classification. The image segmentation problem in LPR is examined in two stages: license plate region detection and license plate character extraction from background. Three different approaches for license plate detection in a scene are presented: region distance from eigenspace, border location by edge detection and the Hough transform, and text detection by spectral analysis. The experiments for character segmentation involve the RGB, HSV/HSI and 1976 CIE L*a*b* color spaces as well as their Karhunen-Loéve transforms. The segmentation techniques applied include multivariate hierarchical agglomerative clustering and minimum-variance color quantization. The trade-off between accuracy and computational expense is used to select a final reliable algorithm for license plate detection and character segmentation. The spectral analysis approach together with the K-L L*a*b* transformed color quantization are found experimentally as the best alternatives for the two identified image segmentation stages for US license plate recognition. / Master of Science

No / Abstract: We develop a novel approach to estimate the n unknown constituent frequencies of a sinusoidal signal that comprises of unknown number, n, of sinusoids of unknown phases and unknown amplitudes. The approach has been applied to multiple sinusoidal signals in the presence of white Gaussian noise with varying signal to noise ratio (SNR). The approach is based on eigenspace analysis of Hankel matrix formed with the samples from averaged frequency spectrum of the signal obtained through multiple measurements. The eigenspace analysis is based on the newly developed 3M relationship which reflects and exploits the relationship between the consecutive sets of Maximum, Middle and Minimum eigenvalues of square symmetric matrix of the Hankel matrix. The 3M relationship exhibits a pattern in line with the order of the Hankel matrix and leads to parametric estimation of the constituent sinusoids. This paper also presents the relationship equation between the size of 3M relationship pattern and the dimensions of the Hankel matrix. The performance of the developed approach has been tested to correctly estimate multiple constituent frequencies within a noisy signal.

Arrays of fully hydrogel-supported, artificial hair cell (AHC) sensors based on bilayer membrane mechanotransduction are designed and characterized to determine sensitivity to multiple stimuli. The work draws upon key engineering design principles inspired by the characteristics of biological hair cells, primarily the use of slender hair-like structures as flow measurement elements. Many hair cell microelectromechanical (MEMS) devices to sense fluid flow have already been built based on this principle. However, recent developments in lipid bilayer applications, namely physically encapsulated bilayers and hydrogel interface bilayers, have facilitated the development of AHCs made primarily from biomolecular materials. The most current research in this field of "membrane based AHCs," shows promise, yet still lacks the modularity to create large sensor arrays similar to those in nature. This paper presents a novel bilayer based AHC platform, developed for array implementation by applying some of the core design principles of biological hair cells. These principles are translated into key design, fabrication and material considerations toward improved sensor sensitivity and modularity. Single hair cell responses to base excitation and short air pulses are to investigate the dynamic coupling between hair and bilayer membrane transducer. In addition, a spectral analysis of the AHC system under varying voltages and air flow velocities helps to build simple, predictive models for the sensitivity properties of the AHC. And finally, based on these results, we implement a spatial sensing strategy that involves mapping frequency content to stimulus location by "tuning" linear, three-unit arrays of AHCs. Individual AHC sensors characterization results demonstrate peak current outputs in the nanoamp range and measure flow velocities as high as 72 m/s. Characterization of the AHC response to base excitation and air pulses show that membrane current oscillates with the first three bending modes of the hair. Output magnitudes reflect of vibrations near the base of the hair. A 2 degree-of-freedom Rayleigh-Ritz approximation of the system dynamics yields estimates of 19 N/m and 0.0011 Nm/rad for the equivalent linear and torsional stiffness of the hair's hydrogel base, although double modes suggest non-symmetry in the gel's linear stiffness. The sensor output scales linearly with applied voltage (1.79 pA/V), avoiding a higher-order dependence on electrowetting effects. The free vibration amplitude of the sensor also increases in a linear fashion with applied airflow pressure (3.39 pA/m s??). Array sensing tests show that the bilayers' consistent spectral responses allow for an accurate localization of the airflow source. However, temporal variations in bilayer size affect sensitivity properties and make airflow magnitude estimation difficult. The overall successful implementation of the array sensing method validates the sensory capability of the bilayer based AHC. / Master of Science