Dissertations / Theses on the topic 'Attributes data'

Thesis (M.S.)--University of Missouri-Columbia, 2007.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on November 9, 2007) Includes bibliographical references.

International Telemetering Conference Proceedings / October 25-28, 1993 / Riviera Hotel and Convention Center, Las Vegas, Nevada
An automated method is described for binding attributes to extracted data from a telemetry stream. These attributes can be used by post processing utilities to facilitate efficient analysis. A practical implementation of such a scheme is described.

PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
Atributos de curvatura são uma importante ferramenta para visualização e interpretação de feições estruturais em dados sísmicos. Tais medidas podem realçar falhas e fraturas sutis que não estavam evidentes no dado de amplitude, fornecendo ao intérprete informações importantes para a construção do modelo geológico da área de interesse. Neste trabalho é apresentado um método para estimar atributos de curvatura volumérica em dados sísmicos empilhados. A partir do dado de amplitude, é computado um atributo identificador de horizonte, o qual permite que horizontes sísmicos sejam representados como superfícies de nível. Dessa maneira, o gradiente desse atributo fornece uma representação coerente do campo de normais do volume. Fórmulas para o cálculo de curvatura em superfícies implícitas são usadas para obter vários atributos de curvatura úteis na delineação e predição de importantes feições estratigráficas. Testes realizados com dados sintéticos e reais mostram que o método proposto é capaz de fornecer estimativas coerentes de atributos de curvatura a um baixo custo de processamento. São avaliados três atributos identificadores de horizontes: fase instantânea, derivada vertical e atributo de ridges.
Curvature attributes are powerful tools for visualization and interpretation of structural features in seismic data. Such measures may highlight faults and subtle fractures that were not evident in amplitude data, providing important information to the interpreter to build the geological model of the area of interest. This paper presents a method for estimating volumetric curvature attributes in post-stack seismic data. Using amplitude volume, an horizon identifier attribute is computed, in order to represent seismic horizons as level surfaces. Thus, the gradient of this attribute provides a coherent estimate of volumetric normal field. Formulas for the calculation of curvature in implicit surfaces are used to compute several curvature attributes useful in the delineation and prediction of important stratigraphic features. Tests with synthetic and real data show that the proposed method is able to provide consistent estimates of attributes of curvature at low cost processing. Three horizon identifer attributes are evaluated: instantaneous phase, vertical derivative and ridge attribute.

Online databases containing geographic and related tabular data for maps and attributes often require continuous updates from widely distributed sources afield. For some applications, these data are dynamic, and thus are of little value if they do not reflect the latest information or changes. A status map that depicts graphically temporal data affecting accountability is an example of this type of data. How can accommodations be made collectively for the perpetual data updates in the database and the need to deliver online information in real time without making concessions? The goal of the dissertation was to analyze and evaluate techniques and technology for data collection and storage, online data delivery, and real-time upload. The result of this analysis culminated in the design and prototype of a system that allowed real-time delivery of up-to-date maps and attributes information. A literature review revealed that an ample amount of research material existed on the theory and practice of developing dynamic update techniques. Despite that fact, no research literature was available that specifically dealt with dynamic update techniques that provide for real-time delivery of up-to-date maps while allowing online update of attributes information. This dissertation was the first attempt at providing research material in this important area. The procedure consisted of five major steps encompassing a number of small steps, and culminated in the development of a prototype. The steps included gathering data collection and storage information, investigating technological advances in data delivery and access, studying dynamic update techniques, assessing the feasibility of an implementation solution, and developing a prototype. The results revealed that the dynamic update technique as implemented in the prototype met the need for timely delivery of accountability, geospatial, and metadata information within an infrastructure.

PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE SUPORTE À PÓS-GRADUAÇÃO DE INSTS. DE ENSINO
Esta dissertação aborda o tema do discurso persuasivo nos infográficos, um dos produtos do Design da Informação. Os infográficos são amplamente usados como ferramenta de comunicação pela mídia, com o intuito de transmitir informações de modo sintético, rápido e atraente por meio de representações visuais diagramáticas. Examina e analisa os atributos visuais dos gráficos e infográficos sob o ponto de vista da retórica visual. Investiga o uso do ferramental disponível para a elaboração de visualizações de dados persuasivas, que comuniquem de forma eloquente e eficiente o discurso desejado, obtendo a adesão do leitor. Os aportes teóricos da pesquisa se encontram na proposta de uma Retórica do Design, de Almeida Junior, fundamentada na Nova Retórica, de Chaïm Perelman e Lucie Olbrechts-Tyteca, nas investigações sobre a Percepção Visual voltada à visualização de dados, nas pesquisas de Colin Ware e Stephen Few, e na Semiótica de Charles Sanders Peirce, como elemento transdisciplinar, perpassando pelos diferentes tópicos como forma de integrá-los. Foram tomados, como casos exemplares, infográficos da seção Jornais da Sexta Mostra Nacional de Infografia de 2012, o Infolide. Os infográficos analisados mostram a intensa presença de recursos e ferramentas de persuasão na infografia impressa. Como resultado, chegou-se à conclusão de que é possível o designer intensificar o poder persuasivo dos seus infográficos, aprofundando-se acerca dos sistemas cognitivos da linguagem que regem a compreensão do leitor, ou seja, do seu auditório.
This dissertation addresses the topic of persuasive speech in infographics, one of the products of the Information Design. The infographics are widely used as a communication tool by the media, in order to transmit information in a synthetic, fast and attractive way using visual diagrammatic representations. Examines and analyzes the visual attributes of the data graphs and infographics from the point of view of visual rhetoric. Investigates the use of the tools available for developing compelling data visualizations that communicate eloquently and efficiently the desired speech, with the adherence of the reader. The theoretical references of the research are the proposal of a Rhetoric of Design, by Almeida Junior, based on the New Rhetoric of Chaïm Perelman and Lucie Olbrechts- Tyteca, the investigations on Visual Perception oriented to data visualization, in surveys of Colin Ware and Stephen Few, and the Semiotics of Charles Sanders Peirce, as a transdisciplinary element, passing through the different topics, integrating them. Were taken, as special cases, infographics from the Newspapers section of the 6th National Exhibition Infographics 2012, Infolide. The analyzed infographics show the intense presence of persuasive tools in printed infographics. As a result, the conclusion that the designer can enhance the persuasive power of their infographics deepening his knowledge about cognitive and language systems that govern the reader s understanding, ie, of his audience.

Thesis (M.S.) -- Worcester Polytechnic Institute.
Keywords: mining complex data; temporal association rules; computer system performance; stock market analysis; sleep disorder data. Includes bibliographical references (p. 79-85).

Based on technological advances made within the past decades, ground-penetrating radar (GPR) has become a well-established, non-destructive subsurface imaging technique. Catalyzed by recent demands for high-resolution, near-surface imaging (e.g., the detection of unexploded ordnances and subsurface utilities, or hydrological investigations), the quality of today's GPR-based, near-surface images has significantly matured. At the same time, the analysis of oil and gas related reflection seismic data sets has experienced significant advances. Considering the sensitivity of attribute analysis with respect to data positioning in general, and multi-trace attributes in particular, trace positioning accuracy is of major importance for the success of attribute-based analysis flows. Therefore, to study the feasibility of GPR-based attribute analyses, I first developed and evaluated a real-time GPR surveying setup based on a modern tracking total station (TTS). The combination of current GPR systems capability of fusing global positioning system (GPS) and geophysical data in real-time, the ability of modern TTS systems to generate a GPS-like positional output and wireless data transmission using radio modems results in a flexible and robust surveying setup. To elaborate the feasibility of this setup, I studied the major limitations of such an approach: system cross-talk and data delays known as latencies. Experimental studies have shown that when a minimal distance of ~5 m between the GPR and the TTS system is considered, the signal-to-noise ratio of the acquired GPR data using radio communication equals the one without radio communication. To address the limitations imposed by system latencies, inherent to all real-time data fusion approaches, I developed a novel correction (calibration) strategy to assess the gross system latency and to correct for it. This resulted in the centimeter trace accuracy required by high-frequency and/or three-dimensional (3D) GPR surveys. Having introduced this flexible high-precision surveying setup, I successfully demonstrated the application of attribute-based processing to GPR specific problems, which may differ significantly from the geological ones typically addressed by the oil and gas industry using seismic data. In this thesis, I concentrated on archaeological and subsurface utility problems, as they represent typical near-surface geophysical targets. Enhancing 3D archaeological GPR data sets using a dip-steered filtering approach, followed by calculation of coherency and similarity, allowed me to conduct subsurface interpretations far beyond those obtained by classical time-slice analyses. I could show that the incorporation of additional data sets (magnetic and topographic) and attributes derived from these data sets can further improve the interpretation. In a case study, such an approach revealed the complementary nature of the individual data sets and, for example, allowed conclusions about the source location of magnetic anomalies by concurrently analyzing GPR time/depth slices to be made. In addition to archaeological targets, subsurface utility detection and characterization is a steadily growing field of application for GPR. I developed a novel attribute called depolarization. Incorporation of geometrical and physical feature characteristics into the depolarization attribute allowed me to display the observed polarization phenomena efficiently. Geometrical enhancement makes use of an improved symmetry extraction algorithm based on Laplacian high-boosting, followed by a phase-based symmetry calculation using a two-dimensional (2D) log-Gabor filterbank decomposition of the data volume. To extract the physical information from the dual-component data set, I employed a sliding-window principle component analysis. The combination of the geometrically derived feature angle and the physically derived polarization angle allowed me to enhance the polarization characteristics of subsurface features. Ground-truth information obtained by excavations confirmed this interpretation. In the future, inclusion of cross-polarized antennae configurations into the processing scheme may further improve the quality of the depolarization attribute. In addition to polarization phenomena, the time-dependent frequency evolution of GPR signals might hold further information on the subsurface architecture and/or material properties. High-resolution, sparsity promoting decomposition approaches have recently had a significant impact on the image and signal processing community. In this thesis, I introduced a modified tree-based matching pursuit approach. Based on different synthetic examples, I showed that the modified tree-based pursuit approach clearly outperforms other commonly used time-frequency decomposition approaches with respect to both time and frequency resolutions. Apart from the investigation of tuning effects in GPR data, I also demonstrated the potential of high-resolution sparse decompositions for advanced data processing. Frequency modulation of individual atoms themselves allows to efficiently correct frequency attenuation effects and improve resolution based on shifting the average frequency level. GPR-based attribute analysis is still in its infancy. Considering the growing widespread realization of 3D GPR studies there will certainly be an increasing demand towards improved subsurface interpretations in the future. Similar to the assessment of quantitative reservoir properties through the combination of 3D seismic attribute volumes with sparse well-log information, parameter estimation in a combined manner represents another step in emphasizing the potential of attribute-driven GPR data analyses.
Geophysikalische Erkundungsmethoden haben in den vergangenen Jahrzehnten eine weite Verbreitung bei der zerstörungsfreien beziehungsweise zerstörungsarmen Erkundung des oberflächennahen Untergrundes gefunden. Im Vergleich zur Vielzahl anderer existierender Verfahrenstypen ermöglicht das Georadar (auch als Ground Penetrating Radar bezeichnet) unter günstigen Standortbedingungen Untersuchungen mit der höchsten räumlichen Auflösung. Georadar zählt zu den elektromagnetischen (EM) Verfahren und beruht als Wellenverfahren auf der Ausbreitung von hochfrequenten EM-Wellen, das heisst deren Reflektion, Refraktion und Transmission im Untergrund. Während zweidimensionale Messstrategien bereits weit verbreitet sind, steigt gegenwärtig das Interesse an hochauflösenden, flächenhaften Messstrategien, die es erlauben, Untergrundstrukturen dreidimensional abzubilden. Ein dem Georadar prinzipiell ähnliches Verfahren ist die Reflexionsseismik, deren Hauptanwendung in der Lagerstättenerkundung liegt. Im Laufe des letzten Jahrzehnts führte der zunehmende Bedarf an neuen Öl- und Gaslagerstätten sowie die Notwendigkeit zur optimalen Nutzung existierender Reservoirs zu einer verstärkten Anwendung und Entwicklung sogenannter seismischer Attribute. Attribute repräsentieren ein Datenmaß, welches zu einer verbesserten visuellen Darstellung oder Quantifizierung von Dateneigenschaften führt die von Relevanz für die jeweilige Fragestellung sind. Trotz des Erfolgs von Attributanalysen bei reservoirbezogenen Anwendungen und der grundlegenden Ähnlichkeit von reflexionsseismischen und durch Georadar erhobenen Datensätzen haben attributbasierte Ansätze bisher nur eine geringe Verbreitung in der Georadargemeinschaft gefunden. Das Ziel dieser Arbeit ist es, das Potential von Attributanalysen zur verbesserten Interpretation von Georadardaten zu untersuchen. Dabei liegt der Schwerpunkt auf Anwendungen aus der Archäologie und dem Ingenieurwesen. Der Erfolg von Attributen im Allgemeinen und von solchen mit Berücksichtigung von Nachbarschaftsbeziehungen im Speziellen steht in engem Zusammenhang mit der Genauigkeit, mit welcher die gemessenen Daten räumlich lokalisiert werden können. Vor der eigentlichen Attributuntersuchung wurden deshalb die Möglichkeiten zur kinematischen Positionierung in Echtzeit beim Georadarverfahren untersucht. Ich konnte zeigen, dass die Kombination von modernen selbstverfolgenden Totalstationen mit Georadarinstrumenten unter Verwendung von leistungsfähigen Funkmodems eine zentimetergenaue Positionierung ermöglicht. Experimentelle Studien haben gezeigt, dass die beiden potentiell limitierenden Faktoren - systeminduzierte Signalstöreffekte und Datenverzögerung (sogenannte Latenzzeiten) - vernachlässigt beziehungsweise korrigiert werden können. In der Archäologie ist die Untersuchung oberflächennaher Strukturen und deren räumlicher Gestalt wichtig zur Optimierung geplanter Grabungen. Das Georadar hat sich hierbei zu einem der wohl am meisten genutzten zerstörungsfreien geophysikalischen Verfahren entwickelt. Archäologische Georadardatensätze zeichnen sich jedoch oft durch eine hohe Komplexität aus, was mit der wiederholten anthropogenen Nutzung des oberflächennahen Untergrundes in Verbindung gebracht werden kann. In dieser Arbeit konnte gezeigt werden, dass die Verwendung zweier unterschiedlicher Attribute zur Beschreibung der Variabilität zwischen benachbarten Datenspuren eine deutlich verbesserte Interpretation in Bezug auf die Fragestellung ermöglicht. Des Weiteren konnte ich zeigen, dass eine integrative Auswertung von mehreren Datensätzen (methodisch sowie bearbeitungstechnisch) zu einer fundierteren Interpretation führen kann, zum Beispiel bei komplementären Informationen der Datensätze. Im Ingenieurwesen stellen Beschädigungen oder Zerstörungen von Versorgungsleitungen im Untergrund eine große finanzielle Schadensquelle dar. Polarisationseffekte, das heisst Änderungen der Signalamplitude in Abhängigkeit von Akquisitions- sowie physikalischen Parametern stellen ein bekanntes Phänomen dar, welches in der Anwendung bisher jedoch kaum genutzt wird. In dieser Arbeit wurde gezeigt, wie Polarisationseffekte zu einer verbesserten Interpretation verwendet werden können. Die Überführung von geometrischen und physikalischen Attributen in ein neues, so genanntes Depolarisationsattribut hat gezeigt, wie unterschiedliche Leitungstypen extrahiert und anhand ihrer Polarisationscharakteristika klassifiziert werden können. Weitere wichtige physikalische Charakteristika des Georadarwellenfeldes können mit dem Matching Pursuit-Verfahren untersucht werden. Dieses Verfahren hatte in den letzten Jahren einen großen Einfluss auf moderne Signal- und Bildverarbeitungsansätze. Matching Pursuit wurde in der Geophysik bis jetzt hauptsächlich zur hochauflösenden Zeit-Frequenzanalyse verwendet. Anhand eines modifizierten Tree-based Matching Pursuit Algorithmus habe ich demonstriert, welche weiterführenden Möglichkeiten solche Datenzerlegungen für die Bearbeitung und Interpretation von Georadardaten eröffnen. Insgesamt zeigt diese Arbeit, wie moderne Vermessungstechniken und attributbasierte Analysestrategien genutzt werden können um dreidimensionale Daten effektiv und genau zu akquirieren beziehungsweise die resultierenden Datensätze effizient und verlässlich zu interpretieren.

In his paper introducing fuzzy sets, L.A. Zadeh describes the difficulty of assigning some real-world objects to a particular class when the notion of class membership is ambiguous. If exact classification is not obvious, most people approximate using intuition and may reach agreement by placing an object in more than one class. Numbers or ‘degrees of membership’ within these classes are used to provide an approximation that supports this intuitive process. This results in a ‘fuzzy set’. This fuzzy set consists any number of ordered pairs to represent both the class and the class’s degree of membership to provide a formal representation that can be used to model this process. Although the fuzzy approach to reasoning and classification makes sense, it does not comply with two of the basic principles of classical logic. These principles are the laws of contradiction and excluded middle. While they play a significant role in logic, it is the violation of these principles that gives fuzzy logic its useful characteristics. The problem of this representation within a database system, however, is that the class and its degree of membership are represented by two separate, but indivisible attributes. Further, this representation may contain any number of such pairs of attributes. While the data for class and membership are maintained in individual attributes, neither of these attributes may exist without the other without sacrificing meaning. And, to maintain a variable number of such pairs within the representation is problematic. C. J. Date suggested a relation valued attribute (RVA) which can not only encapsulate the attributes associated with the fuzzy set and impose constraints on their use, but also provide a relation which may contain any number of such pairs. The goal of this dissertation is to establish a context in which the relational database model can be extended through the implementation of an RVA to support of fuzzy data on an actual system. This goal represents an opportunity to study through application and observation, the use of fuzzy sets to support imprecise and uncertain data using database queries which appropriately adhere to the relational model. The intent is to create a pathway that may extend the support of database applications that need fuzzy logic and/or fuzzy data.

International Telemetering Conference Proceedings / October 21, 2002 / Town & Country Hotel and Conference Center, San Diego, California
As stated in IRIG 106-93/96/99/00, the purpose of the Telemetry Attributes Transfer Standard (TMATS) is; “... provides a common format for the transfer of information between the user and a test range or between ranges. This format will minimize the 'station unique' activities that are necessary to support any test item. In addition, it is intended to relieve the labor intensive process currently required to reformat the information by providing the information on computer compatible media, thus reducing errors and requiring less preparation time for test support.” However, it is well known that TMATS does not support “Instrumentation” data. Also, TMATS does not include many current data conversion formats, or have a way to easily include new formats as they are adopted. We believe that such changes will help TMATS reach its full potential and become more closely aligned with its stated objectives. It is the hope of the authors that this paper will generate support for IRIG to revise TMATS to include these important amendments.

Context: - Machine learning is a part of artificial intelligence, this area is now continuously growing day by day. Most internet related services such as Social media service, Email Spam, E-commerce sites, Search engines are now using machine learning. The Quality of machine learning output relies on the input data, so the input data is crucial for machine learning and good quality of input data can give a better outcome to the machine learning system. In order to achieve quality data, a data scientist can use a data quality model on data of machine learning. Data quality model can help data scientists to monitor and control the input data of machine learning. But there is no considerable amount of research done on data quality attributes and data quality model for machine learning. Objectives: - The primary objectives of this paper are to find and understand the state-of-art and state-of-practice on data quality attributes for machine learning, and to develop a data quality model for machine learning in collaboration with data scientists. Methods: - This paper mainly consists of two studies: - 1) Conducted a literature review in the different database in order to identify literature on data quality attributes and data quality model for machine learning. 2) An in-depth interview study was conducted to allow a better understanding and verifying of data quality attributes that we identified from our literature review study, this process is carried out with the collaboration of data scientists from multiple locations. Totally of 15 interviews were performed and based on the results we proposed a data quality model based on these interviewees perspective. Result: - We identified 16 data quality attributes as important from our study which is based on the perspective of experienced data scientists who were interviewed in this study. With these selected data quality attributes, we proposed a data quality model with which quality of data for machine learning can be monitored and improved by data scientists, and effects of these data quality attributes on machine learning have also been stated. Conclusion: - This study signifies the importance of quality of data, for which we proposed a data quality model for machine learning based on the industrial experiences of a data scientist. This research gap is a benefit to all machine learning practitioners and data scientists who intended to identify quality data for machine learning. In order to prove that data quality attributes in the data quality model are important, a further experiment can be conducted, which is proposed in future work.

With the rapid growing marketing business, Data Mining technology is playing a more and more important role in the demands of analyzing and utilizing the large scale information gathered from customers. To predict the consequent business strategy by using Data Mining, the Customer Relationship Management (CRM) nowadays is required to evaluate the customer performance, discover the trends or patterns in customer behavior, and understand the factual value of their customers to their company. In this paper, we present an effective model to apply Data Mining to the CRM problem of categorizing the customers in marketing to search for potential clients based on their properties by (1) computing Distance in Cluster Analysis and Life in Association Rules according to the Attributes of Customer Relationship (ACR) including Self-Reliance Index, Impact Index and Matrix for customer value, and (2) in the Data Mining modeling theory, constructing the Regression Model in the ACR and implementing the corresponding algorithm to mine the most profitable customer group.

In questa tesi si vuole stabilire se un motociclista è riconoscibile dal suo stile di guida. Si fanno delle considerazioni iniziali, come il metodo da adottare e gli strumenti da utilizzare, per capire come affrontare il problema per poi passare al lato pratico. Viene descritto come effettivamente si è riusciti a creare l'applicazione Android che utilizza i sensori integrati nello smartphone per registrare i dati relativi allo stile di guida del motociclista. Viene anche descritto come i dati vengono poi processati e utilizzati nell'algoritmo di machine learning con lo scopo di dare una risposta al quesito iniziale.

Complex trace attribute analysis has been applied to 24-fold VIBROSEIS reflection data acquired on the Atlantic Coastal Plain near Charleston, S. C., to yield an expanded interpretation of a Mesozoic basin concealed beneath Coastal Plain sediments. Complex trace attributes express the seismic trace in terms of a complex variable and emphasize different components of the original seismogram. Attributes derived from synthetic seismograms of thin beds are used to interpret the patterns observed on the real data. Complex trace attributes derived from the original seismic trace complement the interpretation of a Mesozoic basin originally imaged by conventional data. The combination of single-sweep recording and use of complex trace attributes is believed to support an interpretation of a transition from basin border conglomerates into finer-grained siltstones nearer to the center of the basin.
Master of Science
incomplete_metadata

This thesis makes an original contribution to knowledge in the fi eld of data objects' comparison where the objects are described by attributes of fuzzy or heterogeneous (numeric and symbolic) data types. Many real world database systems and applications require information management components that provide support for managing such imperfect and heterogeneous data objects. For example, with new online information made available from various sources, in semi-structured, structured or unstructured representations, new information usage and search algorithms must consider where such data collections may contain objects/records with di fferent types of data: fuzzy, numerical and categorical for the same attributes. New approaches of similarity have been presented in this research to support such data comparison. A generalisation of both geometric and set theoretical similarity models has enabled propose new similarity measures presented in this thesis, to handle the vagueness (fuzzy data type) within data objects. A framework of new and unif ied similarity measures for comparing heterogeneous objects described by numerical, categorical and fuzzy attributes has also been introduced. Examples are used to illustrate, compare and discuss the applications and e fficiency of the proposed approaches to heterogeneous data comparison.
Libyan Embassy

The nasal turbinates of ungulates are complex bony scrolls within the nasal cavity. These intricate bony plates, covered by moist epithelium, provide a large surface area that facilitates a countercurrent exchange of both water and heat between turbinal lining and respired air. Given their functional importance, maxilloturbinate size and structural dimensions may vary among species of different body sizes, activity levels and from different habitats, and may also serve as a predictor of the ability of the species to cope with high temperatures or limiting water resources. This is the first study to measure nasal turbinate surface area in ungulates using high-resolution computed tomography (CT) scanning. Heads of eleven South African bovid species and one European bovid species were successfully scanned and surface area measurements made using stereological techniques. These species included Cape grysbok, springbuck, bushbuck, blesbok, impala, mountain reedbuck, fallow deer, kudu, nyala, gemsbok and blue wildebeest; and represent species of different body sizes, from a range of habitats and with different water dependencies and predator avoidance strategies. The total maxilloturbinate surface area increased with body size for all study species. The surface areas of the nasal turbinates varied rostrocaudally, with the highest surface area occurring approximately midway along the length of the maxilloturbinate bones. The Cape grysbok stood out as having a nasal turbinate surface area of 12.77 cm2/kg, which was lower than the observed trend, the reason for this not being clear from these data. Phylogenetic independent analyses showed that log body mass and water dependence had a significant effect on nasal turbinate surface area, with habitat, distributional range and anti-predator behaviour having no effect. Subsequent phylogenetic species comparisons showed that structural variations in nasal turbinate surface area were phylogeny based due to the close genetic relatedness of the study species, and not associated with any environmental factors. The environmentally linked results for water dependency need further investigation in future studies of larger sample sizes and a broader range of species. Changes in climatic conditions may impact on a species‟ activity patterns, with individuals being forced to make behavioural modifications rather than physiological or anatomical adjustments. However, there is no clear evidence to indicate large differences in nasal turbinate surface area in relation to water dependence. This subsequently rules out the use of nasal turbinate surface area as a predictor to which and how ungulate species will respond to increasing global temperatures.

This thesis makes an original contribution to knowledge in the fi eld of data objects' comparison where the objects are described by attributes of fuzzy or heterogeneous (numeric and symbolic) data types. Many real world database systems and applications require information management components that provide support for managing such imperfect and heterogeneous data objects. For example, with new online information made available from various sources, in semi-structured, structured or unstructured representations, new information usage and search algorithms must consider where such data collections may contain objects/records with di fferent types of data: fuzzy, numerical and categorical for the same attributes. New approaches of similarity have been presented in this research to support such data comparison. A generalisation of both geometric and set theoretical similarity models has enabled propose new similarity measures presented in this thesis, to handle the vagueness (fuzzy data type) within data objects. A framework of new and unif ied similarity measures for comparing heterogeneous objects described by numerical, categorical and fuzzy attributes has also been introduced. Examples are used to illustrate, compare and discuss the applications and e fficiency of the proposed approaches to heterogeneous data comparison.

An important problem that appears often when analyzing data involves identifying irregular or abnormal data points called outliers. This problem broadly arises under two scenarios: when outliers are to be removed from the data before analysis, and when useful information or knowledge can be extracted by the outliers themselves. Outlier Detection in the context of the second scenario is a research field that has attracted significant attention in a broad range of useful applications. For example, in credit card transaction data, outliers might indicate potential fraud; in network traffic data, outliers might represent potential intrusion attempts. The basis of deciding if a data point is an outlier is often some measure or notion of dissimilarity between the data point under consideration and the rest. Traditional outlier detection methods assume numerical or ordinal data, and compute pair-wise distances between data points. However, the notion of distance or similarity for categorical data is more difficult to define. Moreover, the size of currently available data sets dictates the need for fast and scalable outlier detection methods, thus precluding distance computations. Additionally, these methods must be applicable to data which might be distributed among different locations. In this work, we propose novel strategies to efficiently deal with large distributed data containing mixed-type attributes. Specifically, we first propose a fast and scalable algorithm for categorical data (AVF), and its parallel version based on MapReduce (MR-AVF). We extend AVF and introduce a fast outlier detection algorithm for large distributed data with mixed-type attributes (ODMAD). Finally, we modify ODMAD in order to deal with very high-dimensional categorical data. Experiments with large real-world and synthetic data show that the proposed methods exhibit large performance gains and high scalability compared to the state-of-the-art, while achieving similar accuracy detection rates.
Ph.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Computer Engineering PhD

Ionospheric scintillation is a rapid variation in the amplitude and/or phase of radio signals traveling through the ionosphere. This spatial and time-varying phenomenon is of interest because its occurrence may affect the reception quality of satellite signals. Specialized receivers at strategic regions can track multiple variables related to the phenomenon, generating a database of historical observations on the regional behavior of ionospheric scintillation. The analysis of such data is very challenging, since it consists of time-varying measurements of many variables which are heterogeneous in nature and with possibly many missing values, recorded over extensive time periods. There is a need to introduce alternative intuitive strategies that contribute to experts acquiring further knowledge from the ionospheric scintillation data. Such challenges motivated a study on the applicability of visualization techniques to support tasks of identification of relevant attributes in the study of the behavior of phenomena described by multiple time-varying variables, of which the ionospheric scintillation is a good example. In particular, this thesis introduces a visual analytics framework, named TV-MV Analytics, that supports exploratory tasks on time-varying multivariate data and was developed following the requirements of experts on ionospheric scintillation from the Faculty of Science and Technology of UNESP at Presidente Prudente, Brazil. TV-MV Analytics provides an interactive visual exploration loop to analysts inspecting the behavior of multiple variables at different temporal scales, through temporal representations associated with clustering and multidimensional projection techniques. Analysts can also assess how different feature sub-spaces contribute to characterizing a certain behavior, where they may direct the analysis process and include their domain knowledge in the exploratory analysis. We also illustrate the application of TV-MV Analytics on multivariate time-varying data sets from three alternative application domains. Experimental results indicate the proposed solutions show good potential on assisting time-varying multivariate data mining tasks, since it reduces the effort required from experts to gain deeper insight into the historical behavior of the variables describing a phenomenon or domain.
A cintilação ionosférica é uma variação rápida na amplitude e/ou na fase dos sinais de rádio que viajam através da ionosfera. Este fenômeno espacial e variante no tempo é de grande interesse, pois pode afetar a qualidade de recepção dos sinais de satélite. Receptores especializados em regiões estratégicas podem rastrear múltiplas variáveis relacionadas ao fenômeno, gerando um banco de dados de observações históricas sobre o comportamento regional da cintilação. O estudo do comportamento da cintilação é desafiador, uma vez que requer a análise extensiva de dados multivariados e variantes no tempo, coletados por longos períodos. Medições são registradas continuamente, e são de natureza heterogênea, compreendendo múltiplas variáveis de diferentes categorias e possivelmente com muitos valores faltantes. Portanto, existe a necessidade de introduzir estratégias alternativas, eficientes e intuitivas, que contribuam para a adquisição de conhecimento, a partir dos dados, por especialistas que estudam a cintilação ionosférica. Tais desafios motivaram o estudo da aplicabilidade de técnicas de visualização para apoiar tarefas de identificação de atributos relevantes no estudo do comportamento de fenômenos ou domínios que envolvem múltiplas variáveis, como a cintilação. Em particular, esta tese introduz um arcabouço visual, o qual foi denominado TV-MV Analytics, que apoia tarefas de análise exploratória sobre dados multivariados e variáveis no tempo, inspirado em requisitos de especialistas no estudo da cintilação, vinculados à Faculdade de Ciências e Tecnologia da UNESP de Presidente Prudente, Brasil. O TV-MV Analytics fornece aos analistas um ciclo de interativo de exploração que apoia a inspeção do comportamento temporal de múltiplas variáveis, em diferentes escalas temporais, por meio de representações visuais temporais associadas a técnicas de agrupamento e de projeção multidimensional. Também permite avaliar como diferentes sub-espaços de atributos caracterizam um determinado comportamento, podendo direcionar o processo de análise e inserir seu conhecimento do domínio no processo de análise exploratória. As funcionalidades do TV-MV Analytics também são ilustradas em dados variantes no tempo oriundos de outros três domínios de aplicação. Os resultados experimentais indicaram que as soluções propostas têm bom potencial em tarefas de mineração de dados multivariados e variantes no tempo, uma vez que reduz o esforço e contribui para os especialistas obterem informações detalhadas sobre o comportamento histórico das variáveis que descrevem um determinado fenômeno ou domínio.

Peer-to-Peer (P2P) is an internet application that allows a group of internet users to share their files and computing resources. P2P traffic was tremendously increased to an estimated value of 70% of broadband traffic with a special nature that directly impacts the Telecom industry. Accordingly, the Telecom business has become very interested in finding solutions to identify and control P2P traffic. This research focuses on developing a practical P2P traffic classification using data mining techniques and the information available in the TCP/IP header. We captured internet traffic, pre-processed and labeled them, and built several models using a combination of different attributes for various sizes of record files. We built the models based on neural network and decision tree techniques. Successful models were then subjected to a more stressful test using different ratios of P2P/Non-P2P in the training data set. We observed that the accuracy of the classification increases significantly when we take into account the source and destination IP addresses. We concluded that source and destination IP addresses depict information about the "community of peers". Based on this observation, we recommended that the classifier needs to be implemented within the administrative domain of the individual service provider's network, and continuously updated to ensure that new communities of peers are detected, while old communities of peers are not penalized after they stop using P2P applications. The proposed classification is based only on information in the IP layer, eliminating the privacy issues associated with deep packet inspection.

Sweden is known for being at the forefront of becoming a cashless society.However, cash continues to be an important part of the payment ecosystem butthere are limited studies and data regarding the preference for holding cash. Previousstudies have shown that the use of cash is declining with regards to comers and thatthere are behavior differences between cultures and sociodemographic groups inregards to characteristics and preferences for payment methods. The aim of thisstudy was to investigate the informational attributes that drives the preference forholding cash by studying two extreme cases, Sweden which is a pioneer of cashlesssociety, and Germany which is one of the most conservative cash-intensivecountries in the Western world. Primary quantitative data was derived fromstandardized questionnaires issued in both countries by Loomis AB; the data wasdescribed and analyzed with applicable statistical procedures. A multivariate analysiswas performed on a specific segment of the collected data were the respondents hadbeen asked to rate cash associated statements based on their agreeableness towardsthem. These segments were then examined through a Principal component analysisto determine the underlying dimensions regarding preferences for cash. Our resultssuggest that Germans withdrew cash more frequently, from a wider spectrum ofdenominations and carried it to a larger extend than Swedes. Swedes made limitedwithdrawals, of small denominations and preferred to carry smaller amounts of cash.The results also show that there were differences in the perception and preferencesfor cash between sociodemographic groups, with e.g. older age groups being used to itand the youngest using it due to ‘status quo bias’. The main conclusions include thatthe informational attributes such as security, anonymity, ease of use and paymentinfrastructure was the main drives for the preferences of holding cash.
Sverige är känt för att vara i framkanten gällande transformationen till att bli ettkontantlöst samhälle. Kontanter fortsätter dock att vara en viktig del avbetalningsekosystemet, men det finns begränsade studier och data om preferensenför att hålla kontanter. Tidigare studier har visat att användningen av kontanterminskat i dagligvaru- och sällanköpshandeln och att det finns beteendeskillnadermellan kulturer och sociodemografiska grupper med avseende på egenskaper ochpreferenser för betalningsmetoder. Syftet med denna studie var att undersöka deinformativa attribut som driver preferensen för att hålla pengar genom att studeratvå extrema fall, Sverige som är en pionjär gällande det kontantlösa samhället ochTyskland som är ett av de mest konservativa kontantanvändande länderna ivästvärlden. Kvantitativa data härleddes från standardiserade frågeformulärutfärdade i de båda länderna av Loomis AB; data beskrevs och analyserades medtillämpliga statistiska förfaranden, mer specifikt en multivariantanalys som gjordespå ett visst segment av de insamlade uppgifterna, där de svarande blev ombedda attbedöma kontantanvändningen genom associerade uttalanden baserat på derasöverensstämmelse med dem. Dessa segment undersöktes sedan genom enhuvudkomponentanalys för att bestämma de underliggande dimensionerna gällandepreferenser för kontanter. Våra resultat tyder på att tyskar tar ut pengar merfrekvent, från ett bredare spektrum av valörer och bär i större utsträckning merkontanter än svenskar. Svenskar har en begränsad uttagsfrekvens, främst av småvalörer samt att de föredrar att bära kontanter i en liten utsträckning. Fortsatt visarResultaten att det fanns skillnader i perception och preferenser för kontanter mellansociodemografiska grupper, exempelvis är äldre åldersgrupper vana vidkontantanvändning och att de yngre åldersgrupperna använder kontanter på grundav "status quo bias". De viktigaste slutsatserna är att de informativa attribut somsäkerhet, anonymitet, användarvänlighet och betalningsinfrastruktur är de viktigastedrivkrafterna gällande preferensen att hålla kontanter.

The accurate estimation of forest attributes at many different spatial scales is a critical problem. Forest landowners may be interested in estimating timber volume, forest biomass, and forest structure to determine their forest's condition and value. Counties and states may be interested to learn about their forests to develop sustainable management plans and policies related to forests, wildlife, and climate change. Countries and consortiums of countries need information about their forests to set global and national targets to deal with issues of climate change and deforestation as well as to set national targets and understand the state of their forest at a given point in time.

This dissertation approaches these questions from two perspectives. The first perspective uses the process model Biome-BGC paired with inventory and remote sensing data to make inferences about a current forest state given known climate and site variables. Using a model of this type, future climate data can be used to make predictions about future forest states as well. An example of this work applied to a forest in northern California is presented. The second perspective of estimating forest attributes uses high resolution aerial imagery paired with light detection and ranging (LiDAR) remote sensing data to develop statistical estimates of forest structure. Two approaches within this perspective are presented: a pixel based approach and an object based approach. Both approaches can serve as the platform on which models (either empirical growth and yield models or process models) can be run to generate inferences about future forest state and current forest biogeochemical cycling.

Spatial databases are becoming more and more important. One type of spatial database is a database of measurements, which combine positions with alphanumerical attributes. While these databases are important, little research has been done on them. There is therefore need for research on how to store this type of data most effectively.In this thesis a read performance benchmark and an execution plan analysis of three different database schemas using two different datasets of different size are presented. The three schemas investigated are the existing schema of the dataset, a schema where the spatial data have been split into a separate table, and a star schema. Additionally, these benchmarks were performed in two database management systems, Oracle Spatial and PostGIS.The results show that the choice of database schema has a real impact on performance, but none of the designs performs best in all cases. Star schema seems promising and may perform better than the existing schema in some cases, but more research is required to investigate further the specific cases where one schema outperforms the other. Finally, the results also show that PostGIS might perform better than Oracle Spatial for this type of data.
Spatiala databaser blir mer och mer populära. En typ av spatial databas är en databas med mätningar som kombinerar positioner med alfanumeriska attribut. Även om dessa databaser är viktiga, så har det skett ytterst lite forskning om dessa databaser. Därför finns det behov av mer forskning om hur man lagrar denna typ av data mest effektivt.I denna examensrapport presenteras en prestandamätning av läsprestanda och en exekveringsplansanalys för tre olika databasscheman på två olika datamängder av olika storlek. De tre scheman som undersöks är det existerande schemat för datamängden, ett schema där spatialdata har delats upp i en separat tabell, samt ett stjärnschema. Dessa prestandamätningar utfördes dessutom i två databashanteringssystem, Oracle Spatial och PostGIS.Resultaten visar att valet av schema påverkar prestandan, men inget schema presterar bäst i samtliga fall. Stjärnschema verkar lovande och kan i vissa fall prestera bättre än det befintliga schemat, men mer forskning krävs för att ytterligare undersöka de specifika fallen där så sker. Slutligen visar resultaten också att PostGIS kan fungera bättre än Oracle Spatial för denna typ av data.

Orientador: Martin Tygel
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica e Instituto de Geociências
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Resumo: Dados sísmicos dispostos em grades densas e regulares são essenciais em vários objetivos de processamento, imageamento e inversão. Exemplos de tais objetivos incluem a eliminação de múltiplas de superfície (SRME) e migração baseada na equação da onda (WEM). Dados de sísmica de reflexão são geralmente sub-amostrados e as razões para isso incluem limitações físicas (por exemplo, comprimento finito dos cabos, dificuldades topográficas e ambientais), falhas instrumentais (por exemplo, traços ruins ou inexistentes) e, acima de tudo, a amostragem espacial densa é cara. Para superar o problema de subamostragem, bem como os deslocamentos dos dados da malha regular, é necessário reconstruir os dados faltantes a partir dos dados adquiridos. Em outras palavras, é preciso realizar interpolação e/ou extrapolação dos dados existentes. Este trabalho discute e compara métodos de interpolação e extrapolação com base na abordagem da superfície de reflexão comum (CRS), que se baseia num certo número de atributos cinemáticas das ondas de propagação, obtidos a partir dos dados de multicobertura. A análise é realizada em três metodologias CRS, a saber: (a) empilhamento parcial (PS) CRS; (b) regularização orientada à amostra alvo (TO) CRS e (c) regularização orientada ao operador (OO) CRS. Uma breve descrição das três metodologias é realizada e, por meio de exemplos ilustrativos, examinamos suas vantagens e desvantagens
Abstract: Dense and regularly sampled seismic data are essential to a number of processing, imaging and inversion purposes. Processes such as Surface-Related Multiple Elimination (SRME) and wave-equation migration (WEM) are good examples that required data displayed on a regular grid. Seismic reflection data are usually under or irregularly sampled. Reasons for that include physical limitations (e.g., finite-length spread, topographic or environmental difficulties), instrumental failures (e.g., bad or dead traces). Above all, dense spatial sampling is expensive. To overcome the under sampling problem, as well of having the data displaced on regular grids, it is necessary to simulate the missing data from the acquired data. In other words, one needs to interpolate and/or extrapolate the given data to a user defined regular grid. This work discusses and compares interpolation and/or extrapolation methods based on the common-reflection-surface (CRS) approach, which is based on a number of kinematic wavefield attributes, extracted from the multicoverage data. The analysis will be carried out on three CRS methodologies, namely (a) the partial-stack (PS) CRS; (b) the target-oriented (TO) CRS and (c) the operator-oriented (OO) CRS. We provide a brief description of the three methodologies and, by means of illustrative examples, examine their advantages and disadvantages
Mestrado
Reservatórios e Gestão
Mestre em Ciências e Engenharia de Petróleo

PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
FUNDAÇÃO DE APOIO À PESQUISA DO ESTADO DO RIO DE JANEIRO
BOLSA NOTA 10
A interpretação de falhas sísmicas é uma tarefa complexa e trabalhosa, que está sujeita à experiência do geólogo. Normalmente ela é auxiliada pela análise de atributos sísmicos, que podem não ser suficientes para uma clara visualização das falhas. Este trabalho realiza uma análise dos métodos atuais que utilizam ACO para o realce de atributos de falha, de forma a entender a contribuição de cada etapa para o resultado. Com base nessa análise, um novo método é proposto, o qual elimina as fraquezas encontradas de forma a buscar uma convergência mais estável e rápida ao resultado.
The interpretation of seismic faults is a complex and labourious task, which is dependent on the experience of the geologist. The interpretation is normally aided by seismic attributes. However, they may not be enough for a clear visualization nor to be used in automatic extraction methods. This dissertation accomplishes an examination of the state of the art ACO algorithms for fault enhancement. This study reveals the importance, contributions and weaknesses of each step of these methods. From there, a new method is proposed, which eliminates some of the problems found, acquiring a more stable and quick convergence of the end result.

Human auditory perception is suited to receiving and interpreting information from the environment but this knowledge has not been used extensively in designing computer-based information exploration tools. It is not known which aspects of sound are useful for accurately conveying information in an auditory display. An auditory display was created using PD, a graphical programming language used primarily to manipulate digital sound. The interface for the auditory display was a blank window. When the cursor is moved around in this window, the sound generated would changed based on the underlying data value at any given point. An experiment was conducted to determine which attribute of sound most accurately represents data values in an auditory display. The four attributes of sound tested were frequency-sine waveform, frequency-sawtooth waveform, loudness and tempo. 24 subjects were given the task of finding the highest data point using sound alone using each of the four sound treatments. Three dependent variables were measured: distance accuracy, numeric accuracy, and time on task. Repeated measures ANOVA procedures conducted on these variables did not rise to the level of statistical significance (α=.05). None of the sound treatments was more accurate than the other as representing the underlying data values. 52% of the trials were accurate within 50 pixels of the highest data point (target). An interesting finding was the tendency for the frequency-sin waveform to be used in the least accurate trial attempts (38%). Loudness, on the other hand, accounted for very few (12.5%) of the least accurate trial attempts. In completing the experimental task, four different search techniques were employed by the subjects: perimeter, parallel sweep, sector, and quadrant. The perimeter technique was the most commonly used.

Newly available industry well data and seismic attribute analysis reveal that late Ordovician-early Devonian Hunton Group strata are more widespread (i.e., not removed by mid-Devonian erosion) in the central and southern portions of the Arkoma Basin in eastern Oklahoma than previously thought. This study demonstrates the value of applying seismic attribute analysis to problems of quantifying and mapping stratigraphic features caused by erosions and/or karstification. Well and seismic isochron data in the Red Oak petroleum field for the Viola-Woodford interval (the units that lie stratigraphically beneath and above, respectively, the Huton Group) show isolated ~40-m thick lenses of Hunton rocks, on average measuring 3 km in diameter, with a surrounding halo of karsted rock. This distribution can be explained in two different ways: 1) Hunton occurrences could represent isolated erosional remnants reflecting incomplete removal of the Hunton Group during Middle Devonian time (pre-Woodford unconformity) or 2) due to karsting and collapse of stratigraphically lower units (Viola or Bromide carbonates), lenses of Hunton rocks would have sagged into sinkholes where they were preserved beneath regional base level. Using formation tops from a well data set correlated with attribute and structure maps from a proprietary 3-D seismic data set, we identify three seismic characteristics in the middle Paleozoic interval that correlate well with: 1) absent Hunton seismic markers, indicating that Hunton rocks were completely removed, 2) the Hunton contacts, indicating where a seismically visible section of Hunton rocks remains, 3) absent Hunton but with a thin horizon included within lower carbonate strata that is interpreted to be an incipient karst zone, which is consistently adjacent to areas containing Hunton rocks. The base of the Sylvan Shale and the top of the Woodford Shale, the respective lower and upper adjoining units, form significant chronostratigraphic surfaces. As such, anomalous thicknesses of these units are depositionally related; thick Woodford sections often correlate to thin or absent Hunton rocks, possibly indicating back-filled pre-Woodford channels eroded into or through the Hunton Group. Conversely, when there is little or no Woodford thickening over Hunton lenses and when adjacent areas show thinning and partially karsted Viola rocks, we propose that karstic collapse of Viola strata was responsible for the Hunton rocks preservation. A combination of these models may be necessary to account for areas where we see thinning both in the Woodford and Viola, suggesting that a Hunton lens is structurally lowered due to karsting, but due to its erosionally resistive nature, the lens forms a depositional high, causing the Woodford to thin over it. The 3-D approach is absolutely necessary to reveal the subtle waveform details that illustrate the karstic and erosional processes involved in the preservation of the Hunton wedges. These findings were interpolated, constrained by well data, over the entire Oklahoma portion of the Arkoma basin in order to produce a new Hunton isopach map and 20 separate cross-sections (two shown herein). These show a broad linear region of absent Hunton. Eustatic sea levels rose throughout the middle and late Devonian, so this large area of eroded Hunton is interpreted as a post-Hunton, pre-Woodford structural uplift. Other Hunton wedges, similar in size and extant to that seismically imaged in this study, were also found in the well data. The karstic collapse of the Viola and subsequent preservation of Hunton rocks occurred on both limbs of the arch.

There is an increasing demand for urban vegetation mapping, and airborne laser scanning (ALS) has the unique ability to provide geo-referenced three-dimensional data useful for mapping of surface features. This thesis examines the ability of full-waveform and discrete return ALS point data to distinguish urban surface features, and represent the three-dimensional attributes of vegetation at different scales in a vector-based GIS environment. Two full-waveform datasets, at a wavelength of 1550 nm, and a discrete return dataset, at 1064 nm, are used. Points extracted from the first full-waveform dataset are classified with k-means clustering and decision tree into vegetation, buildings and roads, based on the attributes of individual points and the relationships between neighbouring points. A decision tree is shown to perform significantly better (74.62%) than k-means clustering (51.59%) based on the overall accuracies. Grass and paved areas could be distinguished better using intensity from discrete return data than amplitude from full-waveform data, both values proportional to the strength of the return signal. The differences in the signatures of surfaces could be related to the wavelengths of the lasers, and need to be explored further. Calibration of intensity is currently possible only with full-waveform data. When the decision tree is applied on the second full-waveform dataset, the backscatter coefficient proves to be a more useful attribute than amplitude, pointing to the need for calibration if a classification method using intensity is to be applied on datasets with different scanning geometries. A vector-based approach for delineating tree crowns is developed and implemented at three scales. The first scale provides a good estimation of the tree crown area and structure, suitable for estimating biomass and canopy gaps. The third scale identifies the number of trees and their locations and can be used for modelling individual trees.

Context. Most games make use of graphics to create an environment that fits the mood they wish to convey. To use a game's graphical attributes such as colour, shape and texture to their utmost ability, knowing how these are perceived could help. Objective. This paper tries to determine how graphical attributes such as colour, texture, and shapes affect the perceived safety of a path inside a 3d environment. Method. To reach the objective, an experiment was conducted with 20 participants. The experiment was a two-alternative forced-choice (2AFC) test of 38 pairs of images, where each pair contained two versions of a tunnel entrance scene rendered using different graphical attributes. Each difference was based around either colour (warm and cold colour schemes), shape (round, wide, angular and thin), or texture (rugged, neutral and sterile). Results. The experiment generated results that varied compared to the expected results. For instance, the wider shapes were seen as safer compared to the thinner shapes, as was the same result with rounder shapes being perceived safer than angular shapes. Although a few preferred the cold colour scheme, the warmer colour scheme was seen as safer by the majority. While expected to be perceived as less safe than neutral textures but more than the rugged ones, the sterile texture was actually most commonly seen as safe. Conclusions. The main conclusion that was made is that colour, texture and shape can be applied to change the perception of safety in a scene. However, when opposing attributes are used in combination, the result might be based on how dominant the attribute is. The dominance of the graphical attributes could be an interesting topic for future work.

The overall objectives of this dissertation were to (1) determine whether leaf area index (LAI) (Chapter 2), as well as stem density and height to live crown (Chapter 3) can be estimated accurately in intensively managed pine plantations using small-footprint, multiple-return airborne laser scanner (lidar) data, and (2) ascertain whether leaf area index in temperate mixed forests is best estimated using multiple-return airborne laser scanning (lidar) data or dual-band, single-pass interferometric synthetic aperture radar data (from GeoSAR) alone or both in combination (Chapter 4). In situ measurements of LAI, mean height, height to live crown, and stem density were made on 109 (LAI) or 110 plots (all other variables) under a variety of stand conditions. Lidar distributional metrics were calculated for each plot as a whole as well as for crown density slices (newly introduced in this dissertation). These metrics were used as independent variables in best subsets regressions with LAI, number of trees, mean height to live crown, and mean height (measured in situ) as the dependent variables. The best resulting model for LAI in pine plantations had an R2 of 0.83 and a cross-validation (CV) RMSE of 0.5. The CV-RMSE for estimating number of trees on all 110 plots was 11.8 with an R2 of 0.92. Mean height to live crown was also well-predicted (R2 = 0.96, CV-RMSE = 0.8 m) with a one-variable model. In situ measurements of temperate mixed forest LAI were made on 61 plots (21 hardwood, 36 pine, 4 mixed pine hardwood). GeoSAR metrics were calculated from the X-band backscatter coefficients (four looks) as well as both X- and P-band interferometric heights and magnitudes. Both lidar and GeoSAR metrics were used as independent variables in best subsets regressions with LAI (measured in situ) as the dependent variable. Lidar metrics alone explained 69% of the variability in temperate mixed forest LAI, while GeoSAR metrics alone explained 52%. However, combining the LAI and GeoSAR metrics increased the R2 to 0.77 with a CV-RMSE of 0.42. Analysis of data from active sensors shows strong potential for eventual operational estimation of biophysical parameters essential to silviculture.
Ph. D.

Image captioning is a crucial field across computer vision and natural language processing. It could be widely applied to high-volume web images, such as conveying image content to visually impaired users. Many methods are adopted in this area such as attention-based methods, semantic-concept based models. These achieve excellent performance on general image datasets such as the MS COCO dataset. However, it is still left unexplored on single-object images.In this paper, we propose a new attribute-information-combined attention- based network (AIC-AB Net). At each time step, attribute information is added as a supplementary of visual information. For sequential word generation, spatial attention determines specific regions of images to pass the decoder. The sentinel gate decides whether to attend to the image or to the visual sentinel (what the decoder already knows, including the attribute information). Text attribute information is synchronously fed in to help image recognition and reduce uncertainty.We build a new fashion dataset consisting of fashion images to establish a benchmark for single-object images. This fashion dataset consists of 144,422 images from 24,649 fashion products, with one description sentence for each image. Our method is tested on the MS COCO dataset and the proposed Fashion dataset. The results show the superior performance of the proposed model on both multi-object images and single-object images. Our AIC-AB net outperforms the state-of-the-art network, Adaptive Attention Network by 0.017, 0.095, and 0.095 (CIDEr Score) on the COCO dataset, Fashion dataset (Bestsellers), and Fashion dataset (all vendors), respectively. The results also reveal the complement of attention architecture and attribute information.
Bildtextning är ett avgörande fält för datorsyn och behandling av naturligt språk. Det kan tillämpas i stor utsträckning på högvolyms webbbilder, som att överföra bildinnehåll till synskadade användare. Många metoder antas inom detta område såsom uppmärksamhetsbaserade metoder, semantiska konceptbaserade modeller. Dessa uppnår utmärkt prestanda på allmänna bilddatamängder som MS COCO-dataset. Det lämnas dock fortfarande outforskat på bilder med ett objekt.I denna uppsats föreslår vi ett nytt attribut-information-kombinerat uppmärksamhetsbaserat nätverk (AIC-AB Net). I varje tidsteg läggs attributinformation till som ett komplement till visuell information. För sekventiell ordgenerering bestämmer rumslig uppmärksamhet specifika regioner av bilder som ska passera avkodaren. Sentinelgrinden bestämmer om den ska ta hand om bilden eller den visuella vaktposten (vad avkodaren redan vet, inklusive attributinformation). Text attributinformation matas synkront för att hjälpa bildigenkänning och minska osäkerheten.Vi bygger en ny modedataset bestående av modebilder för att skapa ett riktmärke för bilder med en objekt. Denna modedataset består av 144 422 bilder från 24 649 modeprodukter, med en beskrivningsmening för varje bild. Vår metod testas på MS COCO dataset och den föreslagna Fashion dataset. Resultaten visar den överlägsna prestandan hos den föreslagna modellen på både bilder med flera objekt och enbildsbilder. Vårt AIC-AB-nät överträffar det senaste nätverket Adaptive Attention Network med 0,017, 0,095 och 0,095 (CIDEr Score) i COCO-datasetet, modedataset (bästsäljare) respektive modedatasetet (alla leverantörer). Resultaten avslöjar också komplementet till uppmärksamhetsarkitektur och attributinformation.

This study evaluates the ability of LiDAR, IKONOS and Landsat ETM+ data to provide estimates of forest structure in British upland conifer plantations. Little use has so far been made of these technologies in the UK, whereas in some other countries remote sensing has become integral to forest management systems. The aim of this thesis is to demonstrate the application of the selected remote sensing systems to provide up-to- date and accurate information on key forest variables such as tree height, volume and density. Two upland conifer areas, located in south-west Scotland and north-east England, were used to develop and validate the regression models used to estimate these forest variables. The ability of LiDAR to provide an accurate measurement of the ground and canopy surfaces was investigated in densely stocked plantations, typical for commercial forestry in the U.K. The results show that, despite the dense nature of the forest canopy, sufficient laser pulses penetrate through to the ground to generate an accurate Digital Terrain Model (DTM). Provided that the ground surface is accurately defined, a point density of 2 returns/m(^2) will enable measurement of tree height to be made. LiDAR-derived top heights were found to be as accurate as field-based measurements (RMSE of 0.57 m). LiDAR-derived top height is easily integrated with established Forestry Commission models to provide volume estimations. Tree density is not accurately estimated using LiDAR data (RMSE of 434 trees/ha). Results strongly suggest that predictive equations developed for top height can be transferred to other conifer forests. Furthermore, the relationship between field-measured top height and laser-derived top height appears to be stable across different conifer species. LiDAR data can be used to identify tree species in pure and mixed stands. Two methods were developed: the first used summary measures based on the laser height distribution and the second the near infrared intensity. These measures when mapped spatially can be used to classify areas by species and to identify areas of anomalous growth and wind damage. At a larger spatial scale. Landsat ETM+ and IKONOS data can provide height estimates up to the point of canopy closure (approximately 10 m). LiDAR-derived height can be used in place of field-based measurements to drive reflectance-based models to estimate height from optical satellite data. The methods developed are transferable to other conifer forests that are managed in a similar way. The results from this thesis show that LİDAR, IKONOS and Landsat ETM+ data provide valuable and complementary information at a_ range of scales and can assist managers to make more informed resource management decisions.

Biological diversity is a key component in assessing ecosystem health. Alteration, degradation and loss of habitat due to human influence is currently the primary stressor resulting in decreases in diversity. Reliable assessment of large areas in terms of biological integrity are needed for conservation and preservation efforts. Remotely sensed data provide an integrated view of reflected electromagnetic energy over large areas of the earth. These energy patterns provide unique spectral signatures which can be correlated to land cover and habitat. This research sought relationships between traditional ecological measures and information gathered from satellite digital imagery. Reliable interpretation of earth surface characteristics relies largely on accurate rectification to a map projection and subsequent thematic classification. Use of the Global Positioning System (GPS) for rectification was superior than digitizing topographical maps. Differentially corrected GPS locations provided optimum rectification with SPOT satellite imagery while marginally better rectifications were obtained for Landsat MSS imagery using uncorrected GPS positions. SPOT imagery provided more accurate land cover classifications than did MSS. Detection of temporal land cover change using MSS imagery was hampered by confusion among intermediate successional classes. Confusion between upland and bottomland forest classes occurred with both SPOT and MSS. Landscape analyses using thematic maps produced from the previously discussed endeavors suggested that terrestrial habitat in the Ray Roberts Lake area became more fragmented and complex in shape. Habitat patches became smaller but more numerous. Forested areas were most effected and conservation efforts should focus on management strategies that promote vegetation succession and forest maturation. Remotely sensed SPOT data were successfully used to predict tree basal area. There were no significant relationships found with other in situ measures or between MSS data and any vegetation measures. Remote sensing provided information suitable for large scale projects concerning landscape-level ecological issues. Rectification and classification accuracies were the primary factors influencing meaningful interpretation. Project goals should determine the scale of remotely sensed data and acceptable level of accuracy.

This research has been focused on the characterisation and analysis of the deposits of large-scale mass movement events that shaped the North Sea Fan since the Mid-Pleistocene. Located at the mouth of the cross-shelf trough Norwegian Channel, the North Sea Fan is one of the largest through-mouth fans in the glaciated european margin with an area of approximately 142,000 km2. Submarine mass movement processed have occurred intermittenrly throughout the Quarternary history of the North Sea Fan, related to recurrent climate-related episodes of growth and retreat of the ice sheets. These processes can transport large amounts of sediment from the upper shelf up to the abyssal basins, playing an important role on the evolution of continental margins and can also reporesnet major geological hazards. This thesis uses mainly 3D seismic data to investigate the external geometry and internal structure of large-scale mass movement deposits. The high spatial resolution provided by the 3D seismic data has allowed a detailed geomorpholocial analysis of these deposits, This study involved the interpretation of the seismic data and the detailed pickling of key reflectors followed by tge extraction of both horizon and window-based seismic attributes. Digital elevation models of the key reflectors and their seismic attribute maps were then transferred to a geographical information system (GIS) where they were interactively interpreted using spatial analysis tools and the full visualisation potential of the software. The outcomes of this study highlight the importance of detailed horizon pickling and interactice interpretation followed by spatial analysis and visualisation in GIS environment. The identification of acoustic patterns within deposits that are normally described from 2D seismic as chaotic or acoustically transparent emphasizes the potential of detailed analysis of 3D seismic data. It gives an example of how this type of data can provide new insights into the mechanisms and processes associated with mass movements. In particular, amplitude and RMS amplitude maps provide remarkable detailed information of internal deformation structures whereas slope, shaded-relief and thickness maps allowed detailed characterisation of the external geometry. Various types of kinematic indicators can be recognized within the mass movement deposits through combined seismic analysis and detaield morphological mapping.