The purpose of this study was to evaluate variations of the Natural Resources Conservation Service (NRCS) curve number (CN) method for estimating near real-time runoff for naturalized flow, using high resolution radar rainfall data for watersheds in various agro-climatic regions of Texas. The CN method is an empirical method for calculating surface runoff which has been tested on various systems over a period of several years. Many of the findings of previous studies indicate the need to develop variations of this method to account for regional and seasonal changes in weather patterns and land cover that might affect runoff. This study seeks to address these issues, as well as the inherent spatial variability of rainfall, in order to develop a means of predicting runoff in near real-time for water resource management. In the past, raingauge networks have provided data for hydrologic models. However, these networks are generally unable to provide data in real-time or capture the spatial variability associated with rainfall. Radar networks, such as the Next Generation Weather Radar (NEXRAD) of the National Weather Service (NWS), which are widely available and continue to improve in quality and resolution, can accomplish these tasks. In general, a statistical comparison of the raingauge and NEXRAD data, where both were available, shows that the radar data is as representative of observed rainfall as raingauge data. In this study, watersheds of mostly homogenous land cover and naturalized flow were used as study areas. Findings indicate that the use of a dry antecedent moisture condition CN value and an initial abstraction (Ia) coefficient of 0.1 produced statistically significant results for eight out of the ten watersheds tested. The urban watershed used in this study produced more significant results with the use of the traditional 0.2 Ia coefficient. The predicted results before and during the growing season, in general, more closely agreed with the observed runoff than those after the growing season. The overall results can be further improved by altering the CN values to account for seasonal vegetation changes, conducting field verification of land cover condition, and using bias-corrected NEXRAD rainfall data.

La mise en forme de l'alliage d'aluminium de corroyage 7075 de pièces fonctionnelles à géométrie complexe se fait la plupart du temps par usinage. Pour un nombre de pièces important, le coût peut être réduit en réalisant une ébauche par forgeage. Il est cependant plus intéressant de réaliser une ébauche proche des dimensions finales pour limiter l'usinage (aspect near-net-shape). Pour mettre en forme une pièce à géométrie complexe en une seule étape, la résistance à la déformation de la matière doit être suffisamment faible. Cette faible déformation nécessite la génération d'une phase liquide, ce qui introduit des défauts dans l'alliage de corroyage tels que la fissuration à chaud, porosité, retassure, retrait important, macroségrégation solide-liquide. L'apparition de ces défauts peut être réduite en limitant la fraction volumique de la phase liquide à 0.1. AA 7075 étant particulièrement sensible à la fissuration à chaud, un faible retrait est un atout. Nous choisirons le procédé RAP pour obtenir une matière globulaire semi-solide. Ce procédé utilise la recristallisation d'une matière extrudée pour réaliser une matière qui a un comportement rhéofluidifiant-thixotrope. Le procédé de mise en forme de cette matière semi-solide globulaire à faible phase liquide est le thixoforgeage. Des essais de chauffage de la matière extrudée et de filage permettront d'étudier l'influence des divers paramètres du procédé sur l'effort de mise en forme et sur la qualité des pièces produites. Nous adapterons les paramètres de chauffage et de mise en forme sur un outillage spécialement conçu pour le thixoforgeage pour des pièces à géométrie simple et pour des pièces à géométrie complexe. Une étude de l'influence des paramètres sur les caractéristiques mécaniques et sur la qualité des pièces a montré le très bon potentiel de mise en forme de AA 7075 par thixoforgeage.

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The near- through mid-infrared offers a unique and, as this thesis aims to show, essential view of extragalactic stellar populations both nearby, at intermediate redshifts and at very high redshift. In chapter 2, I demonstrate that rest-frame near-IR photometry obtained by the Spitzer Space Telescope provides more robust stellar mass estimates for a spectroscopic sample of ~100 galaxies in the redshift desert (0.5<z<2), and is crucial for modeling galaxies with young star-forming populations. From this analysis, a surprising result emerges in the data. Although the rest-frame light short of 2 micron improves stellar mass estimates, the models and observations disagree beyond 2 micron and emission from non-stellar sources becomes significant. At wavelengths from 1-30 micron, stellar and non-stellar emission contribute equally to a galaxy's global spectral energy distribution. This is unlike visible wavelengths where stellar emission dominates or the far-IR where dust emission provides the bulk of a galaxy's luminosity. Using the sample of high-z galaxies, in chapter 3, I quantify the statistical significance of the excess emission at 2-5 micron and find the emission to correlate with the OII luminosity, suggesting a link between the excess emission and star formation. The origin of the excess emission is not clear, although I explore a number of non-stellar candidates in this chapter. Nearby resolved observations provide a clearer picture of the excess by spatially resolving 68 nearby galaxies. By analyzing the pixel-by-pixel near-IR colours within each galaxy at ~1-5 micron, increasingly red near-IR colors are mapped to spatial regions in chapter 4. For regions with red NIR colors and high star formation rates, I find the broad near- through mid-IR spectrum is constant, varying only in amplitude as a function of the intensity of star formation, suggesting the infrared emission of a young, dusty stellar populations can be added to stellar population synthesis models as an additional component tied to the star formation rate. In closing the thesis, the focus is moved to the detection of stellar populations in the earliest star-forming galaxies. By z>6, all visible wavelength emission is redshifted into near-IR wavelengths. In chapter 5, I show how a tunable near-IR filter I have helped develop holds promise for finding bright Lyman alpha emitting galaxies at redshifts of 8<z<11.

Brain-computer interface (BCI) technologies allow users to control external devices through brain activity alone, circumventing the somatic nervous system and the need for overt physical movement. BCIs may potentially benefit individuals with severe neuromuscular disorders who experience significant, and often total, loss of voluntary muscle control (e.g. amyotrophic lateral sclerosis, multiple sclerosis, brainstem stroke). Though a majority of BCI research to date has focused on electroencephalography (EEG) for brain signal acquisition, recently researchers have noted the potential of an optical imaging technology called near-infrared spectroscopy (NIRS) for BCI applications. This thesis investigates the feasibility of a practical, online optical BCI based on conscious modulation of prefrontal cortex activity through the performance of different cognitive tasks, specifically mental arithmetic (MA) and mental singing (MS). The thesis comprises five studies, each representing a step toward the realization of a practical optical BCI. The first study demonstrates the feasibility of a two-choice synchronized optical BCI based on intentional control states corresponding to MA and MS. The second study explores a more user-friendly alternative - a two-choice system-paced BCI supporting a single intentional control state (either MA or MS) and a natural baseline, or "no-control (NC)", state. The third study investigates the feasibility of a three-choice system-paced BCI supporting both MA and MS, as well as the NC state. The fourth study examines the consistency with which the relevant mental states can be differentiated over multiple sessions. The first four studies involve healthy adult participants; in the final study, the feasibility of optical BCI use by a user with Duchenne muscular dystrophy is explored. In the first study, MA and MS were classified with an average accuracy of 77.2% (n=10), while in the second, MA and MS were differentiated individually from the NC state with average accuracies of 71.2% and 62.7%, respectively (n=7). In the third study, an average accuracy of 62.5% was obtained for the MA vs. MS vs. NC problem (n=4). The fourth study demonstrated that the ability to classify mental states (specifically MA vs. NC) remains consistent across multiple sessions (p=0.67), but that there is intersession variability in the spatiotemporal characteristics that best discriminate the states. In the final study, a two-session average accuracy of 71.1% was achieved in the MA vs. NC classification problem for the participant with Duchenne muscular dystrophy.

Apertureless near-field scanning infrared microscopy is a technique in which an impinging infrared beam is scattered by a sharp atomic force microscopy (AFM) tip oscillating at the resonant frequency of the cantilever in close proximity to a sample. Several advantages offered by near-field imaging include nanoscale imaging with high spatial resolution (near-field imaging is not restricted by the diffraction limit of light) and the ability to differentiate between chemical properties of distinct compounds present in the sample under study due to differences in the scattered field. An overview of the assembly, tuning, and implementation of the near-field instrumentation is provided, as well as detailed descriptions about the samples probed and other instrumentation used. A description of the near-field phenomena, a comparison between aperture and apertureless-type near-field microscopy, and the coupled dipoles model explaining the origin of the chemical contrast present in near-field infrared imaging was discussed. Simultaneous topographic and chemical contrast images were collected at different wavelengths for the block copolymer thin film, polystyrene-b-poly(methyl ethacrylate) (PS-b-PMMA) and for amyloid fibrils synthesized from the #21-31 peptide of β2-microglobulin. In both cases it was observed that the experimental scattered field spectrum correlates strongly with that calculated using the far-field absorption spectrum, and using near-field microscopy, nanoscale structural and/or compositional variations were observed, which would not have been possible using ensemble FTIR measurements. Lastly, tip-enhanced Raman spectra of the #21-31 and #16-22 peptide fragments from the β2-microglobulin and Aβ(1-40) peptide were collected, examined, and an outline of the optimization conditions described.

Individuals with limited motor control are often unable to interact with their environment. Recently, near-infrared spectroscopy (NIRS) systems have been investigated as potential brain-computer interfaces (BCI). Previous studies examined data offline, preventing users from understanding how their thoughts triggered the NIRS system. This thesis focused on understanding the short-term effects of feedback on user’s ability to learn how to control BCIs. Data were collected from control and experimental groups over seven sessions, as they performed fast singing imagery or mental arithmetic. Significant differences were observed between the control group’s results in non-feedback sessions and the experimental group’s results in feedback sessions. Qualitative results from 3 of the 10 participants suggested they had control of the feedback system. They performed the task with online accuracies of 61% - 88% in the final 2 sessions with feedback. These results suggest that continued investigation of NIRS feedback systems is warranted.

Near-infrared spectroscopy (NIRS) is an emerging non-invasive brain-computer interface (BCI) modality that measures changes in hemoglobin concentrations in neurocortical tissue. Previous NIRS studies have not employed real-time feedback with online classification, a combination which would allow users to alter their mental strategy on the fly. This thesis reports the results of two online studies. The first study contrasted online classification of prefrontal hemodynamics using an artificial neural network (ANN) and a hidden Markov model-based (HMM) classifier. The second study measured the accuracy of an online linear discriminant classifier. In study 1, only the ANN classifier facilitated online classification rates greater than chance (p=0.0289). In study 2, a new feedback system and experimental protocol led to improved classification rates over those of the first study (p=5.1*10^(-5)). While control over instantaneously generated feedback in online NIRS-BCIs has been demonstrated, factors such as user frustration, mental fatigue, and restrictions on ambient lighting may compromise performance.

Near-infrared spectroscopy (NIRS) is an emerging non-invasive brain-computer interface (BCI) modality that measures changes in hemoglobin concentrations in neurocortical tissue. Previous NIRS studies have not employed real-time feedback with online classification, a combination which would allow users to alter their mental strategy on the fly. This thesis reports the results of two online studies. The first study contrasted online classification of prefrontal hemodynamics using an artificial neural network (ANN) and a hidden Markov model-based (HMM) classifier. The second study measured the accuracy of an online linear discriminant classifier. In study 1, only the ANN classifier facilitated online classification rates greater than chance (p=0.0289). In study 2, a new feedback system and experimental protocol led to improved classification rates over those of the first study (p=5.1*10^(-5)). While control over instantaneously generated feedback in online NIRS-BCIs has been demonstrated, factors such as user frustration, mental fatigue, and restrictions on ambient lighting may compromise performance.

Individuals with limited motor control are often unable to interact with their environment. Recently, near-infrared spectroscopy (NIRS) systems have been investigated as potential brain-computer interfaces (BCI). Previous studies examined data offline, preventing users from understanding how their thoughts triggered the NIRS system. This thesis focused on understanding the short-term effects of feedback on user’s ability to learn how to control BCIs. Data were collected from control and experimental groups over seven sessions, as they performed fast singing imagery or mental arithmetic. Significant differences were observed between the control group’s results in non-feedback sessions and the experimental group’s results in feedback sessions. Qualitative results from 3 of the 10 participants suggested they had control of the feedback system. They performed the task with online accuracies of 61% - 88% in the final 2 sessions with feedback. These results suggest that continued investigation of NIRS feedback systems is warranted.