Nuclear physics experiments typically involve the collection and analysis of detector signals produced by the interaction of subatomic particles with matter to deduce various quantities. When heavy ions are involved, Si Detector signals are distorted by the formation of a plasma-like cloud from the interaction between the heavy ions and the detector material. The signal amplitude is reduced and delayed, two effects known as Pulse Height Defect (PHD) and Plasma Delay Time (PDT). A recent experiment was performed at the Institut Laue-Langevin(ILL) experimental nuclear reactor facility in Grenoble, using the LOHENGRIN mass spectrometer, to study these walk effects. The purpose of this project is to use a subset of the data to perform pulse shape analysis and develop a parametrization of the pulse waveform in order to better understand the PDT and PHD and how the pulses are affected. Initially, the PDT and PHD are estimated for masses 90, 100, 130 and 143 u using already established methods. The pulse waveforms are then investigated and a suitable parametrization of the pulse waveform is developed. The region around the pulse onset, which is important in extracting the timing characteristics of the pulse, is found to be described rather well by the Landau function. The Landau function parameters are further investigated and correlations with pulse shape characteristics are discussed. Finally, this novel parametrization is used as an alternative approach to estimate the PDT for the same masses as initially. Comparisons between the two methods indicate that the PDT is actually a combined effect of the physical plasma delay and the walk effects introduced by the underlying triggering routine that is used during offline analysis.

The application of pulse shape analysis (PSA) and γ-ray tracking techniques has attracted a great deal of interest in the recent years in fields ranging from nuclear structure studies to medical imaging. These new data analysis methods add position sensitivity as well as directional information for the detected γ-rays to the excellent energy resolution of germanium detectors. This thesis focuses on the application of PSA on planar segmented germanium detectors, divided into three separate studies. The pulse shape analysis technique known as the matrix method was chosen due to its ability to treat events with arbitrary number and combinations of interactions within a single detector. It has been applied in two experiments with the 25-fold segmented planar pixel detector -- imaging and polarization measurements -- as well as in a simulation of upcoming detectors for DESPEC at NuSTAR/FAIR. In the first experiment, a point source of 137Cs was imaged. Events where the 662 keV γ-rays scattered once and were then absorbed in a different segment were treated by the PSA algorithm in order to find the locations of these interactions. The Compton scattering formula was then used to determine the direction to the source. The experiment has provided a robust test of the performance of the PSA algorithm on multiple interaction events, in particular those with interactions in adjacent segments, as well as allowed to estimate the realistically attainable position resolution. In the second experiment, the response of the detector to polarized photons of 288 keV was studied. The polarization of photons can be measured through the observation of the angular distribution of Compton-scattered photons, Hence the ability to resolve the interaction locations had once again proven useful. The third study is focused on the performance of the proposed planar germanium detectors for the DESPEC array. As these detectors have not yet been manufactured at the time of this writing, a set of data simulated in GEANT4 was used. The detector response was calculated for two of the possible segmentation patterns -- that with a single pixelated contact and one where both contacts are segmented into mutually orthogonal strips. In both cases, PSA was applied in order to reconstruct the interaction locations from this response. It was found that the double-sided strip detector can achieve an over-all better position resolution with a given number of readout channels. However, this comes at the expense of a small number of complex events where the reconstruction fails. These results have also been compared to the performance of the 25-fold pixelated detector. / QC 20101110

Cognitive deficits in first-episode psychosis (FEP) are well documented including deficits in cognitive control, but how sex may influence or impact these cognitive deficits is not well known. Cognitive deficits may impact multiple neural networks, including the fronto-parietal network (FPN). How sex may influence the structural integrity of regions in the FPN is also an important area of research in FEP that may provide further insight into the beginnings of the disease. The current study aimed to examine sexual dimorphisms in structural integrity of the frontoparietal network (FPN) and its role in cognitive control in FEP. A total of 111 FEP patients (68 male, 43 female) and 55 healthy control participants (35 male, 20 female) from the Human Connectome Project for Early Psychosis who underwent T1-weighted magnetic resonance imaging and neuropsychological testing were included in the study. Regions of interest (ROIs) included: left and right superior frontal gyrus, left and right middle frontal gyrus, left inferior frontal gyrus, left and right inferior parietal gyrus, right caudate and left thalamus. Using high-dimensional brain mapping procedures, surface shape of the right caudate and left thalamus was characterized using Large Deformation Diffeomorphic Metric Mapping, and cortical thickness of frontal and parietal regions was estimated using the FreeSurfer toolkit. Cognitive control was assessed using the Fluid Cognition Composite score from the NIH Toolbox Cognition Battery. Multivariate ANOVA models tested group differences, separated by sex, in cortical thickness ROIs, in addition to a whole-brain vertex-wise analysis. Vertex-wise statistical surface t-maps evaluated differences in subcortical surface shape, and Pearson correlations tested relationships between brain regions and Fluid Cognition performance. Results of deep brain region comparisons between schizophrenia males (SCZM) and schizophrenia females (SCZF) groups revealed significant outward deformation at the tail of the right caudate and significant inward deformation along the dorsal aspects of the right caudate. Additionally, significant inward deformation in multiple nuclei of the left thalamus were revealed. Significant negative relationships between Fluid Cognition and the left superior/middle frontal gyrus (r = -0.24, p = 0.05) in the male FEP group were observed. Additionally, significant positive relationships between Fluid Cognition and left inferior frontal gyrus (r = 0.35, p = 0.02) and left inferior parietal gyrus (r = 0.35, p = 0.02) in the female FEP group were found. Support vector machine models were trained using measures of cortical thickness and subcortical shape deformation values in all cohorts to classify participants based on diagnosis. Classification accuracy in all testing models ranged from 75-81%. Overall, findings revealed significant differences of subcortical structures, including smaller caudal and thalamic volume, in male FEP compared to female FEP, providing evidence of the importance to examine sex differences at the first episode. Increased consideration for the role of deep-brain structures in male and female FEP can aid in the clinical characterization of the early stages of the disease.

Three-dimensional analysis of mandibular growth provides the potential for pedodontists, orthodontists and surgeons to prescribe treatment that works in harmony with the individual growth of the patient. Despite efforts by 3D pioneers, the visualization of growth and development remains reminiscent of the landmark-based cephalometric analyses used in two-dimensional studies. The objective of this study was to identify 3D topographical changes of the mandible during growth and development of adolescent orthodontic patients. Nine pairs of pre- and post-orthodontic cone-beam computed tomography (CBCT) scans were used to generate mandibular surfaces. Surfaces were superimposed on trabecular bone in the anterior mandible using a mutual information algorithm, and topographical changes were visualized and quantified. The intra- and inter-rater intraclass correlation coefficients for surface generation (0.94 and 0.93, respectively) and superimposition (0.96 and 0.82, respectively) demonstrate the reliability of the techniques. The findings of this study support the theories of bone remodeling reported in histological, implant-based, and landmark studies of mandibular growth. / Oral Biology

Autonomous vehicles are the subject of intense research because they are a safe and convenient alternative to present-day vehicles. Human drivers base their navigational decisions primarily on visual information and researchers have been attempting to use computers to do the same. The current challenge in using computer vision lies not in the collection or transmission of visual data, but in the perception of visual data to extract from it useful information. The focus of this thesis is on the use of computer vision to navigate an autonomous vehicle that will participate in the Intelligent Ground Vehicle Competition (IGVC.) This document starts with a description of the IGVC and the software design of an autonomous vehicle. This thesis then focuses on the weakest link in the system - the computer vision module. Vehicles at the IGVC are expected to autonomously navigate an obstacle course. Competing vehicles need to recognize and stay between lines painted on grass or pavement. The research presented in this document describes two methods used for boundary line extraction: color-based object extraction, and shape analysis for line recognition. This is the first time a combination of these methods is being applied to the problem of line recognition in the context of the IGVC. The most significant contribution of this work is a method for extracting lines in a binary image even when the line is attached to a shape that is not a line. Novel methods have been used to simplify camera calibration, and for perspective correction of the image. The results give promise of vastly improved autonomous vehicle performance. / Master of Science

A identificação de unia planta exige, pelos padrões de taxionomia vegetal, a análise de folhas, flores e frutos. O projeto TreeVis surge com uma proposta de auxiliar na identificação de espécies vegetais, por meio do uso de métodos biométricos, a partir da análise de alguns atributos de uma folha. A contribuição inicial deste trabalho de mestrado, para o projeto TreeVis, está obtenção de classificadores por meio do uso de assinaturas de contorno, sob o domínio da frequência, possibilitando a composição de diversos tipos de assinaturas e classificadores para uma mesma espécie. Devido à baixa eficiência obtida por métodos de classificação como distância mínima, optou-se pelo uso de redes neurais. Essa abordagem evidenciou a necessidade de solução de dois problemas: o grande número de possibilidades de composição de sinais o que ocasionaria um grande esforço computacional para a obtenção de todas respectivas redes neurais; e o reduzido número das amostras utilizadas no trabalho - o qual comprometeria as etapas de treinamento e teste de uma rede neural. Para a solução desses problemas, foram desenvolvidos dois métodos: o primeiro método identifica e seleciona as assinaturas que apresentam um maior potencial de sucesso em obter um classificador por meio de redes neurais, solucionando o problema e desperdício de esforço computacional; o segundo método possibilita a geração de amostras artificiais de folhas através da combinação dos espectros de frequência do contorno das amostras reais por meio operadores genéticos de cross-over e mutação. Solucionadas as duas questões, foram obtidas diversas redes neurais, através da indicação das assinaturas de melhor potencial e treinadas com amostras artificiais. Do total de 31 classes, 7 foram descartadas da tentativa de obtenção de classificadores por não apresentarem nenhuma assinatura com potencial de classificação - conforme indicação do método desenvolvido. Das 24 espécies restantes, foram obtidos classificadores para 18 espécies (75%) com taxas médias de 85% de acerto. A execução deste trabalho necessitou do desenvolvimento de um arcabouço para a automatização da geração, treinamento e teste das redes neurais. / The vegetable identifieation is done, in vegetal taxonomy standards, by fiower, fruits and leaves analyses. The TreeVis project proposes to identifv vegetal speeiniens by biometric methods using only same leaf features. The contribution of this work for to TreeVis project is the generation of classifiers by the contour signatures, under frequency domain, niaking be able the coniposition of several types of signatures and classifiers for the same speeimen. Because of poor efficiency results from methods like minimal distance, was chosen to use neural networks. This approach showed the need to solve two probleins: the numerous composition possibilities of signatures - that would be need a big computational effort to obtained ali possible neural networks; and the small number of speeimen samples - that would compromise the training and test. of neural networks. To solve these two probleins was developed two methods: The first identify and select the signatures that have a good pattern recognition potential, before of the network will be done, solving the waste unneeded effort problem. The second method proposed produces artificial leaf sliapes by combination of contour spectrum frequency speeiniens of real leaves, using genetic operators like cross-over and mutation. Solved these probleins several networks was obtained by appointed potential signature methods and trained and tested with artificial leaves. From 31 speeiniens class, 07 were discarded because tliey had not signatures with classification potential - indicated by developed method. From 24 classes remaining were obtained classifiers for 18 classes (75%) with médium rates 85% of set riglit. The execution of this work demanded the construction of a framework to automatize the generation, training and test of the neural networks.

As pragas e doenças apresentam-se como um desafio para a citricultura brasileira em razão do impacto económico que elas causam à produção. Neste trabalho é dado destaque à doença da mancha preta (MPC), causada pelo fungo Guignardia citricarpa. Essa doença provoca lesões no fruto, depreciando-o no mercado de frutas in natura, além de causar amadurecimento e queda precoce. Um dos principais agravantes da doença é a demora no aparecimento dos sintomas, sendo muito importante detectar a presença dos esporos do fungo no pomar, antes que os sintomas apareçam. Dessa maneira, há a possibilidade de se controlar a doença de forma eficaz, aplicando-se quantidades menores de fungicidas e, consequentemente, reduzindo os custos da produção e os efeitos deletérios ao meio-ambiente. Atualmente, a detecção desses esporos é realizada por meio da análise de amostras coletadas nos pomares. Essa análise é efetuada por especialistas que realizam a identificação e a contagem dos ascósporos manualmente. Com o objetivo de automatizar esse processo, um conjunto de técnicas para a análise das imagens e a caracterização dos ascósporos do fungo a partir da forma foi estudado e comparado. Dentre as técnicas, a curvatura e os descritores de Fourier apresentaram resultados bastante satisfatórios e foram utilizados na implementação do protótipo de um sistema de visão computacional - o CITRUSVIS, que analisa e identifica os ascósporos existentes nas imagens dos discos de coleta. / The pest and disease management is one of the significant factors in the citrus culture. This work focuses on the black spot disease ( C B S ) , a fungai disease caused by Guignardia citricarpa that occasions sunken lesions in the rind of fruits causing precocious maturation, accented fali, depreciation for in natura fruit market and increase of the production costs for disease controlling. One of the main problems to control the CBS disease is the delay to appearance of symptom (when the orchard is already infected), and the fungai presence identification is necessary as soon as possible, allowing the appliance of procedures to control it. Nowadays, spores identification, particularly the ascospores (sexual spores), is made by collecting suspended particles in orchards blown on discs, which are analyzed by specialists using the microscope. The use of a computer aided vision system to assist the spores identification is one of the strategies to speed up this process. In this work, methods to analyze and characterize the spores, based on its shape, were studied and compared. Among them, the shape curvature method and the Fourier descriptors, chosen for presenting the best result, were implemented in a system - the CITRUS Vis - to analyze the images and identify the ascospores.

Este trabalho apresenta novos métodos para análise de formas tridimensionais dentro do contexto de visão computacional, destacando-se o uso das transformadas wavelets 1D, 2D e 3D, as quais proporcionam uma análise multi-escala das formas estudadas. As formas analisadas se dividem em três tipos diferentes, dependendo da sua representação matemática: f(t)=(x(t),y(t),z(t)), f(x,y)=z e f(x,y,z)=w. Cada tipo de forma é analisado por um método melhor adaptado. Primeiramente, tais formas passam por uma rotina de pré-processamento e, em seguida, pela caracterização por meio da aplicação das transformadas wavelet 1D, 2D e 3D para as respectivas formas. Esta aplicação nos permite extrair características que sejam invariantes à rotação e translação, levando em consideração alguns conceitos matemáticos da geometria diferencial. Destaca-se também neste trabalho a não obrigatoriedade de parametrização das formas. Os resultados obtidos a partir de formas extraídas de imagens médicas e dados biológicos, que justificam este trabalho, são apresentados. / This work presents new methods for three-dimensional shape analysis in the context of computational vision, being emphasized the use of 1D, 2D and 3D wavelet transforms, which provide a multiscale analysis of the studied shapes. The analyzed shapes are divided in three different types depending on their representation: f(t)=(x(t),y(t),z(t)), f(x,y)=z and f(x,y,z)=w. Each type of shape is analyzed by a more suitable method. Firstly, such shapes undergo a pre-processing procedure followed by the characterization using the 1D, 2D or 3D wavelet transform, depending on its representation. This application allows to extract features that are rotation- and translation-invariant, based on some mathematical concepts of differential geometry. In this work, we emphasize that it is not necessary to use the parameterized version of the 2D and 3D shapes. The experimental results obtained from shapes extracted from medical and biological images, that corroborate the introduced methods, are presented.

The optic disc is the region of the retina where the optic nerve exits the back of the eye. A number of conditions can cause the optic disc to swell. Papilledema, optic disc swelling caused by raised intracranial pressure (ICP), and nonarteritic anterior ischemic optic neuropathy (NAION), swelling caused by reduced blood flow to the back of the eye, are two such conditions. Rapid, accurate diagnosis of the cause of disc swelling is important, as with papilledema the underlying cause of raised ICP could potentially be life-threatening and may require immediate intervention. The current clinical standard for diagnosing and assessing papilledema is a subjective measure based on qualitative inferences drawn from fundus images. Even with the expert training required to properly perform the assessment, measurements and results can vary significantly between clinicians. As such, the need for a rapid, accurate diagnostic tool for optic disc swelling is clear. Shape analysis of the structures of the retina has emerged as a promising quantitative tool for distinguishing between causes of optic disc swelling. Optic disc swelling can cause the retinal surfaces to distort, taking on shapes that differ from their normal arrangement. Recent work has examined how changes in the shape of one of these surfaces, Bruch's membrane (BM), varies between different types of optic disc swelling, containing clinically-relevant information. The inner limiting membrane (ILM), the most anterior retinal surface and furthest from BM, can take on shapes that are distinct from the more posterior layers when the optic disc becomes swollen. These unique shape characteristics have yet to be explored for their potential clinical utility. This thesis develops new shape models of the ILM. The ultimate goal of this work is to develop noninvasive, automated diagnostic tools for clinical use. To that end, a necessary first step in establishing clinical relevance is demonstrating the utility of retinal shape information in a machine learning classifier. Retinal layer shape information and regional volume measurements acquired from spectral-domain optical coherence tomography scans from 78 patients (39 papilledema, 39 NAION) was used to train random forest classifiers to distinguish between cases of papilledema and NAION. On average, the classifiers were able to correctly distinguish between papilledema and NAION 85.7±2.0% of the time, confirming the usefulness of retinal layer shapes for determining the cause of optic disc swelling. The results of this experiment are encouraging for future studies that will include more patients and attempt to differentiate between additional causes of optic disc edema.

In this work, Neutron Depth Profiling (NDP) analysis capability has been added to the Oregon State University TRIGA�� Reactor Prompt Gamma Neutron Activation Analysis Facility (PGNAA). This system has been implemented with an advanced digital spectroscopy system and is capable of rise time pulse shape analysis as well as coincidence measurements from multiple detectors. The digital spectroscopy system utilizes a high-speed multichannel digitizer with speeds up to 200 Megasamples/second (MS/s) with advanced hardware trigger and time stamping capabilities. These additions allow the facility to conduct simultaneous NDP and PGNAA combined measurements, which also enables cross calibration. The digital pulse processing is implemented with software programmed rise time pulse shape analysis capabilities for the analysis of the detector responses on a pulse-by-pulse basis to distinguish between different interactions in the detector. The advanced trigger capabilities of the digitizer were configured to accurately measure and correct for dead time effects from pulse pile up and preamplifier decay time. / Graduation date: 2013