Made available in DSpace on 2014-06-11T19:33:23Z (GMT). No. of bitstreams: 0 Previous issue date: 2001-05-28Bitstream added on 2014-06-13T19:44:10Z : No. of bitstreams: 1 terada_hh_dr_arafo.pdf: 1658352 bytes, checksum: 431bfc18fcfd164c8330b1a2453bcd71 (MD5) / Este estudo cefalométrico prospectivo foi desenvolvido com o propósito de descrever os resultados de uma das estratégias de correção da mordida profunda. Foram selecionados 19 indivíduos, com faixa etária entre 11 e 15 anos, apresentando más-oclusões de Classe II, Divisão 1, com mordida profunda de no mínimo 4 milímetros. Desses, 9 indivíduos serviram como grupo controle e os outros 10 foram tratados com a mecânica de intrusão da técnica de Ricketts (arco base). Foram colocados implantes metálicos de referência intra-mandibulares, para sobreposições de traçados, em todos os componentes da amostra. Telerradiografias cefalométricas, em norma lateral, para a avaliação do comportamento dos incisivos inferiores, e em 45 graus, para a avaliação dos primeiros pré-molares e primeiros molares inferiores, foram tomadas no início do tratamento e após o nivelamento da curva de Spee do arco inferior para o grupo experimental, e após aproximadamente 6 meses no grupo controle. Os resultados na região de incisivos inferiores indicaram que houve intrusão dos incisivos inferiores e também um deslocamento horizontal para lingual dos três pontos estudados (borda incisal, centro de resistência e ápice radicular). Não houve deslocamento vertical (extrusivo) nos primeiros pré-molares e nos primeiros molares causados pelo tratamento. Os primeiros pré-molares demonstraram uma inclinação para distal com o fulcro próximo ao ápice, apesar de nenhum acessório ter sido colocado nesses dentes. Na região de molares, houve uma inclinação distal da coroa e mesial de raiz, com o fulcro desse movimento próximo ao centro de resistência. / The purpose of this prospective study was to avaliate the results of treatment strategie for deep overbite correction. Nineteen Class II, Division 1, with deep overbite individuals (age 11 to 15 years) were selected. Nine cases were used as a control group and the others were trated with the bioprogressive technique (Ricketts) for correction of vertical malocclusion. Metallic implants were used for superimpositions. Lateral cephalometric radiographs were used for evaluation of lower incisors. Forty five degrees cephalometric radiographs were used for evaluation of lower first bicuspids and first molars. These radiographs were taken before and immediately after leveling of lower arch and about 6 months later for the control group. The results showed that the technique produced highly significant incisor intrusion and a lingual movement of three points inverstigated (incisal edge, center of resistence and root apex). There was no vertical displacement (extrusion) on lower first bicuspid and first molar. A distal inclination was observed on lower first bicuspid, despite of any bracket has been fixed on it. Lower first molars crowns showed a distal movement and the root showed a mesial movement, with center of rotation near the fulcrum.

Made available in DSpace on 2014-06-11T19:27:55Z (GMT). No. of bitstreams: 0 Previous issue date: 2004-06-29Bitstream added on 2014-06-13T19:36:14Z : No. of bitstreams: 1 nunes_vfm_me_arafo.pdf: 1730181 bytes, checksum: 4ab25e53f6d17c054131c11164a011c6 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Este estudo cefalométrico prospectivo foi desenvolvido com o propósito de descrever os resultados de uma das estratégias de intrusão de caninos. Foram selecionados 19 indivíduos, com faixa etária entre 11 e 15 anos, apresentando más-oclusões de Classe II, Divisão 1, com mordida profunda mínima de 4 milímetros. Desses, 9 indivíduos serviram como grupo controle e os outros 10 foram tratados inicialmente com a mecânica de intrusão da técnica de Ricketts (arco base). Foram colocados implantes intra-mandibulares, para sobreposição de traçados, em todos os componentes da amostra. Teleradiografias cefalométricas, em norma lateral, para a avaliação do comportamento dos incisivos inferiores, e em 45 graus, para avaliação dos caninos inferiores, foram tomadas no início do tratamento e após a intrusão dos caninos no arco inferior para o grupo experimental, e após aproximadamente 6 meses no grupo controle. Os resultados na região dos incisivos inferiores indicaram que houve uma leve vestibularização deste dentes, sem provocar extrusão. Os resultados nos caninos inferiores demonstraram que houve intrusão nos três pontos estudados (ponta de cúspide, centro de resistência e ápice radicular) e uma inclinação para distal do centro de resistência e ápice radicular. / The purpose of this prospective study was to evaluate the results of treatment strategies for canines intrusion. Nineteen Class II, Division 1, with deep overbite individuals (age 11 to 15 years) were selected. Nine cases were used as a control group and the others were treated with the bioprogressive technique (Ricketts) for canine intrusion. Metallic implants were used for superimpositions. Lateral cephalometric radiographs were used for evaluation of lower incisors. Forty-five degrees cephalometric radiographs were used for evaluation of canines. These radiographs were taken after lower incisors intrusion and immediately after canines intrusion and about 6 months later for the control group. The results showed that the technique produced highly significant canines intrusion and a distal movement of center of resistence and root apex. There was no vertical displacement (extrusion) on lower incisors and a vestibular inclination was observed.

Autoregressive models factorize a multivariate joint probability distribution into a product of one-dimensional conditional distributions. The variables are assigned an ordering, and the conditional distribution of each variable modelled using all variables preceding it in that ordering as predictors. Calculating normalized probabilities and sampling has polynomial computational complexity under autoregressive models. Moreover, binary autoregressive models based on neural networks obtain statistical performances similar to that of some intractable models, like restricted Boltzmann machines, on several datasets. The use of autoregressive probability density estimators based on neural networks to model real-valued data, while proposed before, has never been properly investigated and reported. In this thesis we extend the formulation of neural autoregressive distribution estimators (NADE) to real-valued data; a model we call the real-valued neural autoregressive density estimator (RNADE). Its statistical performance on several datasets, including visual and auditory data, is reported and compared to that of other models. RNADE obtained higher test likelihoods than other tractable models, while retaining all the attractive computational properties of autoregressive models. However, autoregressive models are limited by the ordering of the variables inherent to their formulation. Marginalization and imputation tasks can only be solved analytically if the missing variables are at the end of the ordering. We present a new training technique that obtains a set of parameters that can be used for any ordering of the variables. By choosing a model with a convenient ordering of the dimensions at test time, it is possible to solve any marginalization and imputation tasks analytically. The same training procedure also makes it practical to train NADEs and RNADEs with several hidden layers. The resulting deep and tractable models display higher test likelihoods than the equivalent one-hidden-layer models for all the datasets tested. Ensembles of NADEs or RNADEs can be created inexpensively by combining models that share their parameters but differ in the ordering of the variables. These ensembles of autoregressive models obtain state-of-the-art statistical performances for several datasets. Finally, we demonstrate the application of RNADE to speech synthesis, and confirm that capturing the phone-conditional dependencies of acoustic features improves the quality of synthetic speech. Our model generates synthetic speech that was judged by naive listeners as being of higher quality than that generated by mixture density networks, which are considered a state-of-the-art synthesis technique.

This thesis presents the results of an experimental, numerical and analytical study to develop a design method to calculate shear resistance of flanged ferrocement beams with vertical mesh reinforcements in the web. Two groups of full-scale testing were conducted comprising of three I beams and four U beams. The I beams had the same geometry and reinforcement arrangements, but differed in the matrix strength or shear span to depth ratio. The U beams differed in web and flange thickness, reinforcement arrangements, matrix strength and shear span to depth ratio. The experimental data were used for validation of finite element models which had been developed using the ABAQUS software. The validated models were subsequently employed to conduct a comprehensive parametric study to investigate the effects of a number of design parameters, including the effect of matrix strength, shear span to depth ratio, cross sectional area, length of clear span, volume fraction of meshes and amount of rebar. The main conclusion from the experiments and parametric studies were: shear failure may occur only when the shear span to depth ratio is smaller than 1.5; the shear strength may increase by increasing the matrix strength, volume fraction of meshes, cross sectional area and amount of rebar. The main type of shear failure for I beams was diagonal splitting while for U beams it was shear flexural. Based on the results from the experimental and numerical studies, a shear design guide for ferrocement beams was developed. A set of empirical equations for the two different failure types and an improved strut-and-tie were proposed. By comparison with the procedures currently in practice, it is demonstrated that the methodology proposed in this thesis is likely to give much better predictions for shear capacity of flanged ferrocement beams.

During July 1974, two reversed deep seismic sounding (DSS) profiles extending about 75 km were recorded in the Explorer Ridge region of the northeastern Pacific, one parallel and the other perpendicular to the ridge. A two-ship operation was used to record near-vertical incidence to wide-angle reflected waves and refracted waves with penetration from the ocean bottom to the upper mantle. Signals from six individual hydrophones suspended at 45 m depth from a 600 m cable trailed behind the receiving ship were recorded in digital form. The shooting ship detonated charges ranging from 2.3 kg to 280 kg and recorded the direct arrival plus the WWVB time code. Processing of the data recorded at distances beyond 4 km included demultiplexing, stacking, and filtering. Before the data were presented in record section form, traveltime corrections were made for topography and shot distance, and amplitude corrections were made for amplifier gain, charge size, and spherical spreading. The interpretation procedure consisted of two steps. A homogeneous, layered velocity-depth model was initially constructed from first arrival traveltime data. The p-A curve corresponding to this model was then altered until an amplitude fit was obtained using synthetic seismograms. Weichert-Herglotz integration of the resultant p-A curve produced the final velocity-depth model. This traveltime and amplitude interpretation required the introduction of velocity gradients into the model. The profile run across the ridge showed no anomalous behaviour as the ridge was crossed; the profile on the Juan de Fuca plate, paralleling the ridge, exhibited traveltime branch offsets and delays. These have been interpreted as due to faulting with a. vertical component of offset of about 5 km. The reversed upper mantle velocities are 7.8 and 7.3 km/s in directions perpendicular and parallel to the ridge. Anisotropy is proposed to explain these different velocities. Compared with crustal sections from other ridge areas, the data require a thick "layer 3" (up to 7 km) near the ridge crest. The total depth to the base of the oceanic crust varies between 10 and 12 km except in the faulted region. The results of this study favor the hypothesis that Explorer Ridge is presently an inactive spreading center. / Science, Faculty of / Earth, Ocean and Atmospheric Sciences, Department of / Graduate

This dissertation examines the idea that ethnographically reported relationships between artifact classes and faunal food resource remains can be detected in an archaeological context. A detailed site report is presented for Deep Bay (DiSe 7), including analyses of the artifact and faunal assemblages, and quantitative techniques are employed to search for associations between faunal and artifact variables in this site. The results of four analyses are compared, and the recurring associations of variable pairs are interpreted in the light of ethnographic and ecological data. The various lines of evidence relevant to the most likely season of site occupation are also examined. It is concluded that some of the ethnographically reported food resource procurement patterns can successfully be detected in the archaeological record. Evidence is presented that suggests the existence of food resource procurement systems centered around herring, deer, sea mammal, and migratory waterfowl. The site was most likely occupied during the late winter and early spring, primarily for deer hunting and herring fishing, and secondarily for sea mammal and waterfowl hunting. The acquisition of molluscs is considered to be a given. This subsistence pattern appears to have varied little over the past 2000 years. It is also concluded that the same techniques could be used profitably for similar studies in the future. / Arts, Faculty of / Anthropology, Department of / Graduate

Magister Scientiae (Biodiversity and Conservation Biology) - MSc (Biodiv and Cons Biol) / This thesis explores the diversity, spatial patterns and community structure for the sponges (Porifera) along the west and south coasts of South Africa. Species collected were identified to the lowest level of lowest taxonomic unit possible (either species or genus). The study site was divided into areas and in each of these we documented the spatial diversity and in so doing were able to assess the variation of sponge communities between the west and south coasts. The total number of species recorded for this deep-water region was eighty-three of which nineteen are described. The south coast was more diverse than the west coast and eleven species were found to be common to both coasts. The analysis based on location and depth showed that both coasts are significantly different to each other. We determined that these areas are biogeographically separated. Species contributing toward the dissimilarity between both coasts include Suberites carnosus, Myxilla (Burtonanchora) sp 1, Rossella antarctica, Tetilla capillosa and Haliclona sp. Patterns of species richness showed an increase in diversity from the west to south. It was found that species richness increases with depth for both coasts but only up to 350 m for the west coast and 200 m for the south coast. However, the sampling effort was determined to possibly have not been enough to gain a full understanding of species richness for the entire study area as the number of species was correlated with sampling effort. Estimated richness found that higher richness of sponges could still be found within most of the best bins and for each coast. An estimate of samples needed both each depth bin per coast showed that more samples would be needed on the south coast and this is possibly due to the greater variety and variability of the species found on the coast. The sponge community on the south coast was found to have no significant difference in pattern with some of the depth bins, whereas depth plays a role in sponge community on the west coast. Species of Suberites were dominant at depths lower than 200 m while Hamacantha (Vomerula) esperioides was dominant between 200 and 350 m with Tetilla capillosa dominated depths lower than 350 m. The thesis is concluded with an overview of what is now known and what still needs to be discovered and determined to further enhance biodiversity knowledge in the country.

abstract: Over the past decade, advancements in neural networks have been instrumental in achieving remarkable breakthroughs in the field of computer vision. One of the applications is in creating assistive technology to improve the lives of visually impaired people by making the world around them more accessible. A lot of research in convolutional neural networks has led to human-level performance in different vision tasks including image classification, object detection, instance segmentation, semantic segmentation, panoptic segmentation and scene text recognition. All the before mentioned tasks, individually or in combination, have been used to create assistive technologies to improve accessibility for the blind. This dissertation outlines various applications to improve accessibility and independence for visually impaired people during shopping by helping them identify products in retail stores. The dissertation includes the following contributions; (i) A dataset containing images of breakfast-cereal products and a classifier using a deep neural (ResNet) network; (ii) A dataset for training a text detection and scene-text recognition model; (iii) A model for text detection and scene-text recognition to identify product images using a user-controlled camera; (iv) A dataset of twenty thousand products with product information and related images that can be used to train and test a system designed to identify products. / Dissertation/Thesis / Masters Thesis Computer Science 2020

AN ABSTRACT OF THE DISSERTATION OFSherri Houmadi, for the Doctor of Philosophy degree in Engineering Science, presented on March 27, 2020, at Southern Illinois University Carbondale. TITLE: THE APPLICATION OF CONVOLUTIONAL NEURAL NETWORKS TO CLASSIFY PAINT DEFECTSMAJOR PROFESSOR: Dr. Julie DunstonDespite all of the technological advancements in computer vision, many companies still utilize human visual inspection to determine whether parts are good or bad. It is particularly challenging for humans to inspect parts in a fast-moving manufacturing environment. Such is the case at Aisin Manufacturing Illinois where this study will be testing the use of convolutional neural networks (CNNs) to classify paint defects on painted outside door handles and caps for automobiles. Widespread implementation of vision systems has resulted in advancements in machine learning. As the field of artificial intelligence (AI) evolves and improvement are made, diverse industries are adopting AI models for use in their applications. Medical imaging classification using neural networks has exploded in recent years. Convolutional neural networks have proven to scale very well for image classification models by extracting various features from the images. A goal of this study is to create a low-cost machine learning model that is able to quickly classify paint defects in order to identify rework parts that can be repaired and shipped. The central thesis of this doctoral work is to test a machine learning model that can classify the paint defects based on a very small dataset of images, where the images are taken with a smartphone camera in a manufacturing setting. The end goal is to train the model for an overall accuracy rate of at least 80%. By using transfer learning and balancing the class datasets, the model was trained to achieve an overall accuracy rate of 82%.

Surveillance cameras are a great support in crime investigation and proximity alarms and play a vital role in public safety. However current surveillance systems require continuous human supervision for monitoring. The primary goal of the thesis is to prevent firearm-related violence and injuries. Automatic firearm detection enhances security and safety among people. Therefore, introducing a Deep Learning Object Detection model to detect Firearms and alert the corresponding police department is the main motivation. Visual Object Detection is a fundamental recognition problem in computer vision that aims to find objects of certain target classes with precise localization of input image and assign it to the corresponding label. However, there are some challenges arising to the wide variations in shape, size, appearance, and occlusions by the weapon carrier. There are other objections to the selection of best object detection model. So, three deep learning models are selected, explained and shown the differences in detecting the firearms. The dataset in this thesis is the customized selection of different categories of firearm collection like the pistol, revolver, handgun, bullet, rifle along with human detection. The entire dataset is annotated manually in pascalvoc format. Date augmentation technique has been used to enlarge our dataset and facilitate in detecting firearms that re deformed and having occlusion properties.. To detect firearms this thesis developed and practiced unified networks like SSD and two-stage object detectors like faster RCNN. SSD is easy to understand and detect objects however it fails to detect smaller objects. Faster RCNN are efficient and able to detect smaller firearms in the scene. Each class has attained more than 90% of confidence score.