Remediation of unexploded ordnance (UXO) and prioritization of excavation procedures for archaeological artifacts using electromagnetic (EM) induction are studied in this dissertation. Lowering of the false alarm rates that require excavation and artifact excavation prioritization can reduce the costs associated with unnecessary procedures. Data were taken over 5 areas at the San Jacinto Battleground near Houston, Texas, using an EM-63 metal detection instrument. The areas were selected using the archaeological concepts of cultural and natural formation processes applied to what is thought to be areas that were involved in the 1836 Battle of San Jacinto. Innovative use of a Statistical Point Pattern Analysis (PPA) is employed to identify clustering of EM anomalies. The K-function uses point {x,y} data to look for possible clusters in relation to other points in the data set. The clusters once identified using K-function will be further examined for classification and prioritization using the Weighted K-function. The Weighted K-function uses a third variable such as millivolt values or time decay to aid in segregation and prioritization of anomalies present. Once the anomalies of interest are identified, their locations are determined using the Gi-Statistics Technique. The Gi*-Statistic uses the individual Cartesian{x, y} points as origin locations to establish a range of distances to other cluster points in the data set. The segregation and location of anomalies supplied by this analysis will have several benefits. Prioritization of excavations will narrow down what areas should be excavated first. Anomalies of interest can be located to guide excavation procedures within the areas surveyed. Knowing what anomalies are of greater importance than others will help to lower false alarm rates for UXO remediation or for archaeological artifact selection. Knowing significant anomaly location will reduce the number of excavations which will subsequently save time and money. The procedures and analyses presented here are an interdisciplinary compilation of geophysics, archaeology and statistical analysis brought together for the first time to examine problems associated with UXO remediation as well as archaeological artifact selection at historic battlegrounds using electromagnetic data.

Multiphoton laser scanning microscopy (MPLSM) is an advanced fluorescence imaging technology which can produce a less noisy microscope image and minimize the damage in living tissue. The MPLSM image in this research is the dehydroergosterol (DHE, a fluorescent sterol which closely mimics those of cholesterol in lipoproteins and membranes) on living cell's plasma membrane area. The objective is to use a statistical image analysis method to describe how cholesterol is distributed on a living cell's membrane. Statistical image analysis methods applied in this research include image segmentation/classification and spatial analysis. In image segmentation analysis, we design a supervised learning method by using smoothing technique with rank statistics. This approach is especially useful in a situation where we have only very limited information of classes we want to segment. We also apply unsupervised leaning methods on the image data. In image data spatial analysis, we explore the spatial correlation of segmented data by a Monte Carlo test. Our research shows that the distributions of DHE exhibit a spatially aggregated pattern. We fit two aggregated point pattern models, an area-interaction process model and a Poisson cluster process model, to the data. For the area interaction process model, we design algorithms for maximum pseudo-likelihood estimator and Monte Carlo maximum likelihood estimator under lattice data setting. For the Poisson Cluster process parameter estimation, the method for implicit statistical model parameter estimate is used. A group of simulation studies shows that the Monte Carlo maximum estimation method produces consistent parameter estimates. The goodness-of-fit tests show that we cannot reject both models. We propose to use the area interaction process model in further research.

With increasing levels of motor vehicle ownership, automobile crashes have become a serious public issue in the U.S. and around the world. Knowing when, where, and how traffic accidents happen is critical in order to ensure road safety and to plan for adequate road infrastructure. There is a rich body of literature pertaining to time-related fatal crashes, most of which focuses on non-spatial factors such as a driver’s visibility at night, drinking and drug use, and road conditions. These studies provide a theoretical basis for understanding the causes of crashes from a non-spatial perspective, and a number of traffic laws and policies consequently have been enacted to minimize the impacts of non-spatial factors. Over the past few years, advances in Geographic Information Systems (GIS) have greatly enhanced our ability to analyze traffic accidents from a spatial perspective. This study aims to fill a void in traffic safety studies by comparing and analyzing the differences in the spatial distribution of fatal crashes based on temporal factors, specifically in three periods: 1) day and night; 2) A.M. rush hours and P.M. rush hours; and 3) weekdays and weekends. With the Nashville Metropolitan Area as the study area, the research utilized a number of spatial point-pattern analysis (SPPA) methods, including planar KDE, planar global auto K function, network global cross K functions, and network local cross K functions. All fatal crashes in the Nashville area were found to be clustered and generally follow the patterns of average daily traffic flow. All time-based subtypes of fatal crashes also were found to be concentrated within the central urban area of Nashville, mostly along major roads, and especially near major road intersections and highway interchanges. No notable spatial differences were detected among the subtypes of fatal crashes when applying network global cross K function. However, with the help of the network local cross K function, some localized spatial differences were identified. Some specific locations of hotspots of nighttime and P.M. rush hour fatal crashes were found not to be at the same locations as those at of daytime and A.M. rush hour fatal crashes, respectively. The approach adopted in this study not only provides a new way to analyze spatial distribution of spatial point events such as fatal crashes, but it also can be applied readily to real-world applications. A good understanding of where these spatial differences are should help various agencies practice effective measures and policies in order to improve road conditions, reduce traffic accidents, and ensure road safety.

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L'un des problèmes les plus complexes dans l'analyse des images histologiques est l'évaluation de l¿organisation spatiale des structures histologiques dans le tissu. En fait, les sections histologiques peuvent contenir un très grand nombre de cellules de différents types et irrégulièrement réparties dans le tissu, ce qui rend leur contenu spatial indescriptible d'une manière simple. Les méthodes fondées sur la théorie des graphes ont été largement explorées dans cette direction, car elles sont des outils de représentation efficaces ayant la capacité expressive de décrire les caractéristiques spatiales et les relations de voisinage qui sont interprétées visuellement par le pathologiste. On peut distinguer trois familles principales de méthodes des graphes utilisées à cette fin: analyse de structure syntaxique, analyse de réseau et analyse spectrale. Cependant, un autre ensemble distinctif de méthodes basées sur la morphologie mathématique sur les graphes peut être développé et adapté pour ce problème. L'objectif principal de cette thèse est le développement d'un outil capable de fournir une évaluation quantitative des arrangements spatiaux des structures histologiques en utilisant la morphologie mathématique basée sur les graphes. / One of the most challenging problems in histological image analysis is the evaluation of the spatial organizations of histological structures in the tissue. In fact, histological sections may contain a very large number of cells of different types and irregularly distributed, which makes their spatial content indescribable in a simple manner. Graph-based methods have been widely explored in this direction, as they are effective representation tools having the expressive ability to describe spatial characteristics and neighborhood relationships that are visually interpreted by the pathologist. We can distinguish three main families of graph-based methods used for this purpose: syntactic structure analysis, network analysis and spectral analysis. However, another distinctive set of methods based on mathematical morphology on graphs can be additionally developed for this issue. The main goal of this dissertation is the development of a framework able to provide quantitative evaluation of the spatial arrangements of histological structures using graph-based mathematical morphology.

La compréhension des processus écologiques qui sous-tendent l’assemblage des communautés végétales et la coexistence des espèces est un objectif central en écologie. Ces processus sont potentiellement nombreux et de natures contrastées. Ainsi, la composition d’une communauté de plantes dépend de processus déterministes liés aux conditions environnementales abiotiques (climat, conditions physiques et chimiques du sol, lumière) et d’interactions biotiques complexes, positives (facilitation, symbioses) comme négatives (compétition, prédation, pathogènes). En outre, les communautés sont influencées par des processus stochastiques (capacité de dispersion limitée, dérive écologique). Si les mécanismes à l’origine de ces processus sont très différents, ils ont néanmoins en commun la génération de motifs (patterns) spatiaux de distribution d’espèces dans les communautés. L’analyse de la structure spatiale des communautés permet ainsi une étude indirecte des processus régissant les communautés. La nature complexe de ces patterns spatiaux a mené au développement de nombreuses méthodes statistiques de détection et de description de patterns. Les méthodes basées sur des vecteurs propres spatiaux sont parmi les plus puissantes et précises pour détecter des patterns complexes et multi-échelles. Ces vecteurs propres, utilisés comme prédicteurs spatiaux, peuvent être combinés à un ensemble de variables environnementales dans un cadre de partition de variation. Celui-ci permet, en théorie, de démêler les effets uniques et l’effet conjoint des variables environnementales et spatiales sur la variation de composition d’une communauté. Il mène ainsi à une quantification de l’action des processus déterministes et des processus stochastiques sur l’assemblage de la communauté. Néanmoins, je montre dans cette thèse qu’un certain flou méthodologique concernant deux étapes déterminantes des analyses basées sur les vecteurs propres spatiaux a mené une proportion élevée d’études à utiliser ces méthodes de manière sous-optimale, voire fortement biaisée. Ceci compromet la fiabilité des patterns spatiaux détectés et des processus écologiques inférés. Une autre limitation de ce cadre d’analyse concerne la fraction de la partition de variation décrivant l’effet environnemental spatialement structurés qu’aucune méthode ne permet de tester.Cette thèse présente des solutions non biaisées, puissantes et précises à ces différentes limitations méthodologiques et permet d’élargir le cadre de l’inférence de processus écologique à partir de patterns spatiaux de communautés. Les différentes étapes d’amélioration de ces méthodes ont également été illustrées dans la thèse au travers de trois cas d’études fournis par deux communautés d’arbres tropicale et tempérée et une communauté de champignons symbiotiques des arbres. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

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