Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The objective of geo-morphological science is to study and understand diverse forms of surfaces in order to grasp the relation that is established between past and present of terrestrial surfaces with human beings, who are modifying agents of the environment. Regarding terrestrial surfaces, slopes are considered markers, even if they are not very steep, which are improper for human dwellings and use since they posit risk of natural mass movements. Thus, if such places are under anthropologic activities, either in terms of agriculture or for human dwellings, the process of mass movements can take place from slow to fast ones. Therefore, the present research consists in a study carried out in the sub-basin of Vacacaí Mirim River close to the DNOS dam with the objective of analyzing and understanding phenomena related to mass gravitational movements that took place in the observed area. Factors such as declivity, hypsometry, lithology and kinds of soil were mapped. According to the mapped data and field studies, episodes of surface movement like very slow landslides, rock dislodgments and bigger landslides were observed. From the patterns of mass movements of occurrences observed in the field, was diagnosed the western sector of the study area, as that was where most of the relief drive event during the study period. Based on the results it appears that the slope of the study area suffers considerable intensification in local (western sector) which occur use and human occupation, a fact that enhances the natural processes of gravitational mass movements. / A ciência geomorfológica tem como objetivo, estudar e compreender as diversas formas do relevo, buscando entender a relação que se estabelece entre o passado e o presente da superfície terrestre com o ser humano, agente modificador do meio. No relevo terrestre, são as vertentes que demarcam, mesmo que de forma não tão visível, áreas impróprias para o uso e ocupação humana, pois se tratam de locais com risco natural de movimentos de massa. Dessa forma, se tais locais contarem com o agravante da ação antrópica, tanto para uso da terra como para a ocupação de moradias, os processos de movimentos de massa podem tornar-se efetivos, variando entre movimentos de massa lentos e/ou rápidos. Nesse sentido, o presente trabalho de pesquisa consiste de um estudo realizado na sub-bacia do Rio Vacacaí Mirim a montante da barragem do DNOS, através do qual se buscou analisar e compreender fenômenos relacionados aos movimentos gravitacionais de massa que ocorreram no local de estudo. Para tal, foram mapeados fatores como a declividade do local, a hipsometria, a litologia e também os solos da área de estudo. De acordo com os presentes dados de mapeamento e os trabalhos de campo, evidenciaram-se episódios de movimentação do relevo, tais como rastejamento, quedas de blocos e também escorregamentos/deslizamentos de terra. A partir dos padrões de ocorrências de movimentos de massa observados em campo, foi diagnosticado o setor oeste da área de estudo, como sendo aquele onde ocorreu a maioria dos eventos de movimentação do relevo no período de estudo. Com base nos resultados obtidos constata-se que a declividade da área de estudo sofre considerável intensificação nos locais (setor oeste) onde ocorrem o uso e a ocupação antrópica, fato esse que potencializa os processos naturais de movimentos gravitacionais de massa.

Visual surveillance of human crowds in a dynamic environment has attracted a great amount of computer vision research efforts in recent years. Moving object detection, which conventionally includes motion segmentation and optionally, object classification, is the first major task for any visual surveillance application. After detecting the targets, estimation of their geo-locations is needed to create the same reference coordinate system for them for higher-level decision-making. Depending on the required fidelity of decision, multi-target data association may be also needed at higher levels to differentiate multiple targets in a series of frames. Applying all these vision-based algorithms to a crowd surveillance system (a major application studied in this dissertation) using a team of cooperative unmanned vehicles (UVs), introduces new challenges to the problem. Since the visual sensors move with the UVs, and thus the targets and the environment are dynamic, it adds to the complexity and uncertainty of the video processing. Moreover, the limited onboard computation resources require more efficient algorithms to be proposed. Responding to these challenges, the goal of this dissertation is to design and develop an effective and efficient visual surveillance system based on dynamic data driven application system (DDDAS) paradigm to be used by the cooperative UVs for autonomous crowd control and border patrol. The proposed visual surveillance system includes different modules: 1) a motion detection module, in which a new method for detecting multiple moving objects, based on sliding window is proposed to segment the moving foreground using the moving camera onboard the unmanned aerial vehicle (UAV); 2) a target recognition module, in which a customized method based on histogram-of-oriented-gradients is applied to classify the human targets using the onboard camera of unmanned ground vehicle (UGV); 3) a target geo-localization module, in which a new moving-landmark-based method is proposed for estimating the geo-location of the detected crowd from the UAV, while a heuristic method based on triangulation is applied for geo-locating the detected individuals via the UGV; and 4) a multi-target data association module, in which the affinity score is dynamically adjusted to comply with the changing dispersion of the detected targets over successive frames. In this dissertation, a cooperative team of one UAV and multiple UGVs with onboard visual sensors is used to take advantage of the complementary characteristics (e.g. different fidelities and view perspectives) of these UVs for crowd surveillance. The DDDAS paradigm is also applied toward these vision-based modules, where the computational and instrumentation aspects of the application system are unified for more accurate or efficient analysis according to the scenario. To illustrate and demonstrate the proposed visual surveillance system, aerial and ground video sequences from the UVs, as well as simulation models are developed, and experiments are conducted using them. The experimental results on both developed videos and literature datasets reveal the effectiveness and efficiency of the proposed modules and their promising performance in the considered crowd surveillance application.

Number of lanes is a basic roadway attribute that is widely used in many transportation applications. Traditionally, number of lanes is collected and updated through field surveys, which is expensive especially for large coverage areas with a high volume of road segments. One alternative is through manual data extraction from high-resolution aerial images. However, this is feasible only for smaller areas. For large areas that may involve tens of thousands of aerial images and millions of road segments, an automatic extraction is a more feasible approach. This dissertation aims to improve the existing process of extracting number of lanes from aerial images automatically by making improvements in three specific areas: (1) performance of lane model, (2) automatic acquisition of external knowledge, and (3) automatic lane location identification and reliability estimation. In this dissertation, a framework was developed to automatically recognize and extract number of lanes from geo-rectified aerial images. In order to address the external knowledge acquisition problem in this framework, a mapping technique was developed to automatically estimate the approximate pixel locations of road segments and the travel direction of the target roads in aerial images. A lane model was developed based on the typical appearance features of travel lanes in color aerial images. It provides more resistance to “noise” such as presence of vehicle occlusions and sidewalks. Multi-class classification test results based on the K-nearest neighbor, logistic regression, and Support Vector Machine (SVM) classification algorithms showed that the new model provides a high level of prediction accuracy. Two optimization algorithms based on fixed and flexible lane widths, respectively, were then developed to extract number of lanes from the lane model output. The flexible lane-width approach was recommended because it solved the problems of error-tolerant pixel mapping and reliability estimation. The approach was tested using a lane model with two SVM classifiers, i.e., the Polynomial kernel and the Radial Basis Function (RBF) kernel. The results showed that the framework yielded good performance in a general test scenario with mixed types of road segments and another test scenario with heavy plant occlusions.

The main focus of this thesis was to gain a better understanding about the dynamics of risk perception and its influence on people’s evacuation behavior. Another major focus was to improve our knowledge regarding geo-spatial and temporal variations of risk perception and hurricane evacuation behavior. A longitudinal dataset of more than eight hundred households were collected following two major hurricane events, Ivan and Katrina. The longitudinal survey data was geocoded and a geo-spatial database was integrated to it. The geospatial database was composed of distance, elevation and hazard parameters with respect to the respondent’s household location. A set of Bivariate Probit (BP) model suggests that geospatial variables have had significant influences in explaining hurricane risk perception and evacuation behavior during both hurricanes. The findings also indicated that people made their evacuation decision in coherence with their risk perception. In addition, people updated their hurricane evacuation decision in a subsequent similar event.

As a result of the rapid evolution of smart mobile devices and the wide application of satellite-based positioning devices, the moving object database (MOD) has become a hot research topic in recent years. The moving objects generate a large amount of geo-referenced data in different types, such as videos, audios, images and sensor logs. In order to better analyze and utilize the data, it is useful and necessary to visualize the data on a map. With the rise of web mapping, visualizing the moving object and geo-referenced data has never been so easy. While displaying the trajectory of a moving object is a mature technology, there is little research on visualizing both the location and data of the moving objects in a synchronized manner. This dissertation proposes a general moving object visualization model to address the above problem. This model divides the spatial data visualization systems into four categories. Another contribution of this dissertation is to provide a framework, which deals with all these visualization tasks with synchronization control in mind. This platform relies on the TerraFly web mapping system. To evaluate the universality and effectiveness of the proposed framework, this dissertation presents four visualization systems to deal with a variety of situations and different data types.

The thesis deals with the theoretical and practical aspects of implementation of Business Intelligence in a field of geo-location data with emphasis on the design and implementation of data architecture and the development of dynamic dashboards. The whole issue is described on the development of a tool for the analysis of geo-location data, on which the author participated with other members of the Competence Center Retail Analytics - Ondrej Funiok, Vilem Kalus and Martin Matejka. The first part of the thesis describes the theoretical bases of Business and Location Intelligence, which is followed by a description of the basic tasks of working with geo-location data - data storage in relational database management systems, data processing by map servers and data presentation. Individual tasks are described with practical examples and use software tools to support these tasks. The next section presents the project of development tool for the analysis of geo-location data that is created in the Retail Analytics Competence Center at the University of Economics, Prague. The second part of the thesis describes the design and implementation of the data architecture of developed tool, which is followed by a description of the frontend portion of the tool consisting of the implementation of a library for creating dynamic Dashboards to present geo-location data.

The random deployment of Wireless Sensor and Actuator Network (WSAN) nodes in areas often inaccessible, results in so-called coverage holes – i.e. areas in the network that are not adequately covered by nodes to suit the requirements of the network. Various coverage protocol algorithms have been designed to reduce or eliminate coverage holes within WSANs by indicating how to move the nodes. The effectiveness of such coverage protocols could be jeopardised by inaccuracy in the initial node location data that is broadcast by the respective nodes. This study examines the effects of location inaccuracies on five sensor deployment and reconfiguration algorithms – They include two algorithms which assume that mobile nodes are deployed (referred to as the VEC and VOR algorithms); two that assume static nodes are deployed (referred to as the CNPSS and OGDC algorithms); and a single algorithm (based on a bidding protocol) that assumes a hybrid scenario in which both static and mobile nodes are deployed. Two variations of this latter algorithm are studied. A location simulation tool was built using the GE Smallworld GIS application and the Magik programming language. The simulation results are based on three above-mentioned deployment scenarios; mobile, hybrid and static. The simulation results suggest the VOR algorithm is reasonably robust if the location inaccuracies are somewhat lower than the sensing distance and also if a high degree of inaccuracy is limited to a relatively small percentage of the nodes. The VEC algorithm is considerably less robust, but prevents nodes from drifting beyond the boundaries in the case of large inaccuracies. The bidding protocol used by the hybrid algorithm appears to be robust only when the static nodes are accurate and there is a low degree of inaccuracy within the mobile nodes. Finally the static algorithms are shown to be the most robust; the CPNSS algorithm appears to be immune to location inaccuracies whilst the OGDC algorithm was shown to reduce the number of active nodes in the network to a better extent than that of the CPNSS algorithm. Copyright / Dissertation (MSc)--University of Pretoria, 2010. / Computer Science / unrestricted

Several scholars have studied the Prison-Industrial Complex (PIC) since the late 1990s. However, there is a lack of research on how private actors profiting from the PIC, such as private prisons, are maintaining and sustaining it. This study explores how private prisons utilise different forms of power to maintain the PIC by shedding light on which real, structural, and soft powers CoreCivic and The GEO Group (the two largest private prison companies in America) are utilising to sustain the PIC. This will be done by analysing CoreCivic and The GEO group through the lens of Steven Lukes’ framework - Three Dimensions of Power. The study has been conducted as a qualitative desk and case study following abductive reasoning. The data have been analysed employing both text and discourse analysis. While the companies unquestionably use structural power, primarily through lobbying, to maintain and sustain the PIC, it can only be suggested how they have employed real and soft power. The companies use real power mainly to ensure high-profit margins. It can thus, be suggested that the companies are indirectly maintaining the PIC by simply ensuring continued profitability. Finally, the study suggests how the companies are employing soft power to upkeep the PIC. By portraying themselves to the American society in a more positive way, contradictory to how they operate, the companies ensure continued support by society, which is vital for their continued existence.

Orientador: Elizabeth Moreira dos Santos Schmidt / Resumo: Os geo-helmintos são um grupo de parasitas em que parte do ciclo evolutivo ocorre necessariamente no solo, que desempenha o papel de fonte de infecção. O solo pode ser responsável pela transmissão das geo-helmintíases, principalmente quando o local é compartilhado por animais e seres humanos, como em parques e/ou praças públicas. Em relação aos geo-hemintos de potencial zoonótico, destacam-se os gêneros Toxocara e Ancylostoma. Entretanto, outros parasitos intestinais, como Trichuris spp. e Strongyloides spp., que possuem parte de seu ciclo biológico no solo, também desempenham importante papel na contaminação de ambientes públicos. Desta forma o objetivo deste trabalho foi investigar a contaminação do solo por geo-helmintos em praças do município de Botucatu-SP, identificando a prevalência dos principais gêneros e sua ocorrência em praças de diferentes áreas do município. As praças foram catalogadas e distribuídas entre as regiões administrativas (norte, sul, leste, oeste e central). As coletas foram realizadas em dois momentos: momento I (de outubro a dezembro de 2017) e momento II (de fevereiro a março de 2018). No presente trabalho foram coletadas amostras de solo e avaliadas por meio das técnicas de centrífugo-sedimentação, centrífugo-flutuação com sulfato de zinco, e o método de Baermann-Moraes na busca por ovos e larvas de geo-helmintos. A partir das análises das amostras do solo obtidas nas praças estudadas, foram observados ovos do gênero Toxocara e ovos de ancilostom... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Geohelminths are a group of parasites in which part of the life cycle necessarily occurs in the soil, playing a role as a source of infection. Soil may be responsible for the transmission of geohelminthiasis, especially when the site is shared by animals and humans, such as in parks and/or public squares. Regarding geohelminths of zoonotic potential, we highlight the genera Toxocara and Ancylostoma. However, other intestinal parasites, such as Trichuris spp. and Strongyloides spp., which also have part of their biological cycle in the soil, also play an important role in the contamination of public environments. This study aimed to investigate the soil contamination by geohelminths in squares of the municipality of Botucatu-SP, identifying the prevalence of the main genera and their occurrence in squares of different areas of the municipality. The squares were cataloged and distributed among administrative regions (north, south, east, west, and central). The samples were carried out in two time point: I (from October to December 2017) and II (from February to March 2018). In this study soil samples were collected and evaluated by means of centrifugalsedimentation techniques, zinc sulfate centrifugal flotation , and the BaermannMoraes method searching for geohelminths eggs and larvaes. From the soil samples obtained in the studied squares, eggs of the genus Toxocara and eggs of Ancylostomatoidea were observed, being the latter only found in the samples obtained at time point I... (Complete abstract click electronic access below) / Mestre

The goal of this master thesis is creation of an online system serving as a performing application for presentation results of visual geo-localization in nature and mountain environment. The system offers the users to choose one of the pre-defined photographs or~to~upload one's own photography while choosing a file or inserting an URL address. The~system will localizate a camera of a given image based on a visual geo-localization. The~geo-localization uses the mountain horizon as a key characteristic when searching for similar horizons. The~curve line of the horizon is extracted by a fully automatic algorithm based on supervised learning and dynamic programming. Visual geo-localization running on the server which using new inversed index with cache politic. This allows further scaling of the system. The server processing detected horizon curve and respond with set of the best candidates on results. Results are visualised to the user in form of classic map, detailed sattelite view and rendering of found panorama.