Using computational deterministic and Monte Carlo methods, I present an analysis of the gamma and neutron signatures emitted from special nuclear material (SNM) in weapons stockpile storage scenarios. My efforts are focused on 1 year old, 25 year old, 50 year old, and 75 year old highly enriched uranium (HEU), and 1 year old, 22.5 year old, and 50 year old weapons grade plutonium (WGPu). HEU gammas are easily shielded, and when reasonably shielded, do not produce a definable signature at low energies; however, using new methods applied in this work that involve analyzing the higher energy, penetrating gammas from HEU, it can be shown that not only the presence of the HEU can be verified, but also the age since separation of the material can be discerned. Through computational modeling, I am able to verify that the novel methods investigated are both unique and effective for HEU detection. In addition, I also present my investigation of similar methods applied to the detection of WGPu. From this work I determined that WGPu age discrimination is more challenging compared to that of HEU (in spite of more radiation per unit mass) due to the high rate of induced gammas from n-gamma interactions taking place within the Pu metal and container; however, I believe that by combining neutron detection with gamma signature verification of WGPu, the approach identified can be successful. I present the neutron signature of the WGPu and how it would be observed in the detector used for material verification. I calculated the detector response for a pre-determined neutron detector design using adjoint calculations in order to determine whether the detector will perform as designed. In addition to developing a new protocol for WGPu detection, I present in this work, a comprehensive source book as a product of this research, detailing the gamma and neutron signatures for both solid and shell configurations of HEU and WGPu. This can serve as a very beneficial guide for anyone interested in modeling SNM, since the many steps needed to obtain this radiation leakage data will save a significant amount of researcher time. The results from my work have contributed to a collaborative effort supporting funded US department of State research towards designing a mobile detection system that can rapidly validate and verify the presence of SNM in weapons pit containers.

abstract: Splicing of digital images is a powerful form of tampering which transports regions of an image to create a composite image. When used as an artistic tool, this practice is harmless but when these composite images can be used to create political associations or are submitted as evidence in the judicial system they become more impactful. In these cases, distinction between an authentic image and a tampered image can become important. Many proposed approaches to image splicing detection follow the model of extracting features from an authentic and tampered dataset and then classifying them using machine learning with the goal of optimizing classification accuracy. This thesis approaches splicing detection from a slightly different perspective by choosing a modern splicing detection framework and examining a variety of preprocessing techniques along with their effect on classification accuracy. Preprocessing techniques explored include Joint Picture Experts Group (JPEG) file type block line blurring, image level blurring, and image level sharpening. Attention is also paid to preprocessing images adaptively based on the amount of higher frequency content they contain. This thesis also recognizes an identified problem with using a popular tampering evaluation dataset where a mismatch in the number of JPEG processing iterations between the authentic and tampered set creates an unfair statistical bias, leading to higher detection rates. Many modern approaches do not acknowledge this issue but this thesis applies a quality factor equalization technique to reduce this bias. Additionally, this thesis artificially inserts a mismatch in JPEG processing iterations by varying amounts to determine its effect on detection rates. / Dissertation/Thesis / Masters Thesis Computer Science 2015

Le nombre croissant de travaux réalisés ces dernières années a montré que la bioacoustique est particulièrement intéressante pour le suivi d’espèces discrètes. L’émergence de dispositifs d’enregistrement autonomes, associée à de nouvelles méthodes d’analyse, ont récemment participé à l’accroissement des études dans ce domaine. Au cours des 30 dernières années, le Loup gris (Canis lupus), mammifère carnivore aux mœurs discrètes connu pour ses hurlements de longue portée, a fait l’objet de nombreuses études acoustiques. Ces dernières visaient notamment à améliorer son suivi, qui s’avère complexe du fait des grandes capacités de déplacement des loups, de l’étendue de leurs territoires et de la diversité des milieux dans lesquels ils vivent. Cependant, la bioacoustique passive a jusqu’alors très peu été exploitée pour le suivi du Loup. C’est dans ce contexte que la présente thèse s’est organisée autour de trois axes de recherche. Les deux premiers axes portent sur l’apport de la bioacoustique passive pour le suivi du Loup gris en milieu naturel. En combinant des analyses acoustiques, statistiques et cartographiques, le premier objectif a été d’élaborer une méthode pour l’échantillonnage spatial de vastes zones d’étude, afin d’y détecter des hurlements de loups à l’aide de réseaux d’enregistreurs autonomes. Ce même dispositif a ensuite permis, dans un second temps, de tester la possibilité de localiser les loups grâce à leurs hurlements. Les expérimentations conduites en milieu de moyenne montagne (Massif des Vosges) et de plaine (Côtes de Meuse), sur deux zones d’étude de 30 km² et avec un réseau de 20 enregistreurs autonomes, ont permis de démontrer l’intérêt de la bioacoustique passive pour le suivi du Loup gris. En effet, près de 70% des émissions sonores (son synthétique aux propriétés similaires à celles de hurlements de loups) ont été détectés par au moins un enregistreur autonome en milieu de moyenne montagne et plus de 80% en milieu de plaine, pour des distances enregistreurs– source sonore atteignant respectivement plus de 2.7 km et plus de 3.5 km. Grâce à un modèle statistique et à un Système d’Information Géographique, la probabilité de détection des hurlements a pu être cartographiée sur les deux zones. En moyenne montagne, elle était forte à très forte (>0.5) sur 5.72 km² de la zone d’étude, contre 21.43 km² en milieu de plaine. Les sites d’émission ont été localisés avec une précision moyenne de 315 ± 617 (SD) m, réduite à 167 ± 308 (SD) m après l’application d’un seuil d’erreur temporelle défini d’après la distribution des données. Le troisième axe de travail porte quant à lui sur l’application d’indices de diversité acoustique pour estimer le nombre d’individus participant à un chorus et ainsi contribuer au suivi de l’effectif des meutes. Les valeurs obtenues pour les six indices (H, Ht, Hf, AR, M et ACI) étaient corrélées avec le nombre de loups hurlant dans les chorus artificiels testés. De bonnes prédictions de l’effectif ont été obtenues sur des chorus réels avec l’un de ces indices (ACI). L’influence de plusieurs biais sur la précision des prédictions de chacun des six indices a ensuite pu être étudiée, montrant que trois d’entre eux y étaient relativement peu sensibles (Hf, AR et ACI). Finalement, les résultats obtenus avec les enregistreurs autonomes montrent le potentiel des méthodes acoustiques passives pour la détection de la présence de loups mais aussi pour les localiser avec une bonne précision, dans des milieux contrastés et à de larges échelles spatiale et temporelle. L’utilisation des indices de diversité acoustique ouvre également de nouvelles perspectives pour l’estimation de l’effectif des meutes. Prometteuses, l’ensemble des méthodes émergeant de ce travail nécessite à présent quelques investigations complémentaires avant d’envisager une application concrète pour le suivi du Loup gris dans son milieu naturel / The growing number of studies carried out in recent years has shown that bioacoustics is particularly interesting for the monitoring of secretive species. The emergence of autonomous recording devices, combined with new methods of analysis, have recently contributed to the increase of studies in this field. Over the last 30 years, many bioacoustic studies have been developed for the Grey wolf (Canis lupus), a secretive large carnivore known for its howls spreading over distances up to several kilometers. These researches notably aimed to improve its monitoring, which is complex because of the strong wolf dispersal capacities over long distances, the large extent of their territories and the various natural contexts in which they live. In this context, this PhD thesis was organized around three research axes. The first two axes focused on the contribution of passive bioacoustics for the Grey wolf monitoring in the field. By combining acoustic, statistical and cartographic analysis, the first objective was to develop a spatial sampling method adapted to large study areas for the detection of wolf howls by using autonomous recorders. Then, the same protocol was used to investigate the possibility to localize wolves thanks to their howls. Field experimentations, conducted in mid-mountain (Massif des Vosges) and lowland (Côtes de Meuse) environments, in two study areas of 30 km² and with an array of 20 autonomous recorders, demonstrated the high potential of passive bioacoustics for the Grey wolf monitoring. Indeed, nearly 70% of broadcasts (synthetic sound with similar acoustic properties to howls) were detected by at least one autonomous recorder in mid-mountain environment and more than 80% in lowland environment, for sound source-recorders distances of up to 2.7 km and 3.5 km respectively. By using statistical model and Geographic Information System, the detection probability of wolf howls was modeled in both study areas. In the mid-mountain environment, this detection probability was high or very high (greater than 0.5) in 5.72 km² of the study area, compared with 21.43 km² in lowland environment. The broadcast sites were localized with an overall mean accuracy of 315 ± 617 (SD) m, reducing until 167 ± 308 (SD) m after setting a temporal error threshold defined from the data distribution. The third axe focused on the application of acoustic diversity indices to estimate the number of howling wolves in choruses and thus to contribute to pack size monitoring. Index values of the six indices (H, Ht, Hf, AR, M, and ACI) were positively correlated with the number of howling wolves in the artificial tested choruses. Interesting size predictions based on real choruses were obtained with one of the indices (ACI). The effects of several biases on the reference values for the acoustic indices were then explored, showing that three of them were relatively insensitive (Hf, AR and, ACI). Finally, results obtained with autonomous recorders confirm the real potential of passive acoustic methods for detecting the presence of wolves but also for localizing individuals with high precision, in contrasting natural environments, at large spatial and temporal scales. The use of acoustic diversity indices also opens new perspectives for estimating pack sizes. All of the promising methods emerging from this thesis require now further investigations before considering a concrete application for monitoring the Grey wolf in its natural environment