Digital Elevation Models (DEMs) are fundamental datasets for environmental modelling. They provide the basic data from which terrain indices that represent or influence environmental phenomena are derived, for example slope gradient and hydrological contributing area, and also the source from which specific morphometric features are quantified and characterised, for example mountains and drainage basins. This thesis focuses on the latter, with the aim being to develop an algorithm to characterise the landscape in terms of five morphometric features (peaks, passes, pits, ridges and valleys) and to assess its validity and effectiveness for characterising landform from DEMs. The research in this thesis is divided into two parts. First, an algorithm of morphometric characterisation of landform from OEMs is developed based on a locally fitted quadratic surface and its positional relationship with the analysis window. Five requirements are taken into account within the algorithm: (1) the ideal cases of different morphometric features are simply and clearly defined; (2) the output is spatially continuous to reflect the inherent fuzziness of landform features; (3) the output is easily combined into a multi-scale index across a range of operational scales; (4) the standard general morphometric parameters can be easily quantified due to the easy calculation of first and second order derivatives from the quadratic surface; and (5) the algorithm is applicable to the different data structures used to represent DEMs. An additional benefit of the quadratic surface is the derivation of the R?? goodness-of-fit statistic, which allows both an assessment of the reliability of the results and the complexity of the terrain. Of the five morphometric features identified using the algorithm, valleys are perhaps the most commonly used. Therefore the second part of this thesis is a more detailed comparison between the Multi-Scale Valleyness (MSV) and three existing algorithms (D8, D∞ and MrVBF). D8 and D∞ are global flow accumulation algorithms, and perform well when characterising valley centre lines. However, they do not identify the valley areas themselves, although this is to be expected given their formulation. MrVBF focuses on characterising valley bottoms and so performs well when characterising valleys in broad and topographically flat areas. It does not identify valleys in the steeper upland parts of a catchment, although this too is something to be expected given its formulation. MSV directly characterises valley areas from a geomorphometric point of view, and performs well for both upland and lowland catchments, irrespective of their width. Overall, the results show that the single- and multi-scale terrain indices developed in this research perform well when characterising the five morphometric features. The approach has considerable potential for use in environmental modelling and terrain analysis.

Thesis (M.A.)--Brandeis University, 2009. / Title from PDF title page (viewed on June 29, 2009). Includes bibliographical references.

Bibliography: p. [217]-221.

We consider the problem of detecting a large number of different classes of objects in cluttered scenes. Traditional approaches require applying a battery of different classifiers to the image, at multiple locations and scales. This can be slow and can require a lot of training data, since each classifier requires the computation of many different image features. In particular, for independently trained detectors, the (run-time) computational complexity, and the (training-time) sample complexity, scales linearly with the number of classes to be detected. It seems unlikely that such an approach will scale up to allow recognition of hundreds or thousands of objects.We present a multi-class boosting procedure (joint boosting) that reduces the computational and sample complexity, by finding common features that can be shared across the classes (and/or views). The detectors for each class are trained jointly, rather than independently. For a given performance level, the total number of features required, and therefore the computational cost, is observed to scale approximately logarithmically with the number of classes. The features selected jointly are closer to edges and generic features typical of many natural structures instead of finding specific object parts. Those generic features generalize better and reduce considerably the computational cost of an algorithm for multi-class object detection.

Στην παρούσα διπλωματική εργασία γίνεται μελέτη των στατιστικών παραμέτρων ηχητικών σημάτων. Μελετάται η δυνατότητα αυτόματης ταξινόμησης μουσικής ανά είδος, η οποία βασίζεται στην εξαγωγή αυτών των παραμέτρων. Επίσης, μελετάται η μεταβολή αυτών σε συνθήκες αντήχησης, δίνοντας έμφαση στην παράμετρο φασματικής ασυμμετρίας ηχητικού σήματος. Σε αυτό το πλαίσιο, προτείνεται μέθοδος κατασκευής μοντέλου πρόβλεψης της συμπεριφοράς της συγκεκριμένης παραμέτρου σε συνθήκες αντήχησης, που στόχο έχει την εκτίμηση της απόστασης ηχητικής πηγής – δέκτη σε κλειστό χώρο, καθώς και την πρόβλεψη της ανωτέρω παραμέτρου ανηχωικού σήματος από σήματα με αντήχηση. / In this thesis we study the audio features and their applications, such as automatic music genre classification. It is also studied the behavior of these features under reverberant conditions, emphasizing on spectral skewness. In this framework, it is suggested a method of predicting the behavior of this feature under reverberant conditions, which could have many applications such as source - receiver distance estimation and prediction of the spectral skewness of anechoic audio signals.

Made available in DSpace on 2014-06-11T19:23:31Z (GMT). No. of bitstreams: 0 Previous issue date: 2003Bitstream added on 2014-06-13T18:09:43Z : No. of bitstreams: 1 maia_jl_me_prud.pdf: 3722466 bytes, checksum: 4b39a6e2afdd3917058650acc98046cf (MD5) / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / A avaliação de feições cartográficas extraídas (semi-) automaticamente a partir de imagens digitais é de grande importância no contexto de validação de algoritmos computacionais de extração de feições. O procedimento geral para a avaliação da qualidade geométrica de feições cartográficas baseia-se na comparação entre as entidades extraídas por algoritmos computacionais e as correspondentes extraídas através da visão natural, sendo estas últimas denominadas de feições de referência. A comparação entre os dois grupos de feições é realizada através das seguintes etapas: (1) cálculo de pontos correspondentes entre as feições extraídas e as de referência; (2) cálculo de parâmetros de qualidade (completeza, correção, qualidade, erro médio (EM) e erro médio quadrático (EMQ)) com base nos pontos correspondentes; e (3) análise envolvendo os parâmetros de qualidade obtidos na etapa 2. A metodologia de avaliação foi testada numa variedade de situações, envolvendo tanto imagens com características diferentes quanto diferentes metodologias de extração. Os resultados comprovam que a metodologia permite uma avaliação bastante detalhada dos resultados dos métodos de extração. / The evaluation of cartographic features that were (semi-) automatically extracted from digital images is of great importance in the context of validation of computational algorithms of feature extraction. The general procedure to evaluate the geometrical quality of cartographic features is based on the comparison between the entities extracted via computational algorithms and the corresponding ones extracted through natural vision, being the latter called reference features. Such comparison between the two groups of features is performed in three steps, as follows: (1) calculation of corresponding points between the extracted features and the reference features; (2) calculation of quality parameters (completeness, correcteness, quality, mean error (RM) and root mean squared (RMSE) based on the corresponding points; and (3) analysis involving the quality parameters obtained in step 2. The evaluation methodology was tested in many situations, involving different images as well as extraction methodologies with different characteristics. The results proved that the methodology enables a very detailed evaluation of the results regarding the extraction methods.

A presente tese consta da criação de traços de sentido e de um grupo de peças composto para este trabalho. Os traços de sentido são utilizados na definição do sentido de materiais composicionais. Há três categorias principais nas quais os traços estão inseridos: traços sonoros, expressivos e estilísticos. Por meio de um conjunto de traços é possível definir questões de sonoridade, conteúdo expressivo e pressupostos estilísticos que estão associados a cada material composicional de cada peça. Este grupo de elementos sonoros, expressivos e estilísticos é o sentido que procuro definir neste trabalho. / This work presents the creation of sense features and a group of musical pieces composed to this work. The sense features have the purpose of defining the sense of compositional materials. There is three categories of sense features: sonorous features, expressive features and stylistic features. It’s possible to define matters of sound, expressive content and stylistic agenda associated to each compositional material in one piece. This group of sonic, expressive and stylistic elements are the sense that I intend to define in this work.

Fluid flow in porous media is important for a number of fields including nuclear waste disposal, oil and gas, fuel cells, water treatment and civil engineering. The aim of this work is to improve the current understanding of how the pore space governs the fluid flow in porous media in the context of nuclear waste disposal. The effects of biofilm formation on flow are also investigated. The thesis begins with a review of the current porous media characterisation techniques and the means for converting the pore space into pore network models and their existing applications. Further, I review the current understanding of biofilm lifecycle in the context of porous media and its interactions with fluid flow. The model porous media used in this project is Hollington sandstone. The pore space of the material is characterised by X-ray CT and the equivalent pore networks from two popular pore network extraction algorithms are compared comprehensively. The results indicate that different pore network extraction algorithms could interpret the same pore space rather differently. Despite these differences, the single-phase flow properties of the extracted networks are in good agreement with the estimates from a direct approach. However, it is recommended that any flow or transport study using pore network modelling should entail a sensitivity study aiming to determine if the model results are extraction method specific. Following these results, a pore merging algorithm is introduced aimed to improve the over segmentation of long throats and hence improve the quality of the extracted statistics. The improved model is used to study quantitatively the pore space evolution of shale rock during pyrolysis. Next, the extracted statistics from one of the algorithms is used to explore the potential of regular pore network models for up-scaling the flow properties of porous materials. Analysis showed that the anisotropic flow properties observed in the irregular models are due to the different number of red (critical) features present along the flow direction. This observation is used to construct large regular models that can mimic that behaviour and to discuss the potential of estimating the flow properties of porous media based on their isotropic and anisotropic properties. Finally, a long-term flow-through column experiment is conducted aiming to understand the effects of bacterial colonisation on flow in Hollington sandstone. The results show that such systems are quite complex and are susceptible to perturbations. The flow properties of the sandstone were reduced significantly during the course of the experiment. The possible mechanisms responsible for the observed reductions in permeability are discussed and the need for developing new imaging techniques that can allow examining biofilm development in-situ is underlined as necessary for drawing more definitive conclusions.

Submitted by Diogo Barreiros (diogo.barreiros@ufba.br) on 2017-06-02T17:34:16Z No. of bitstreams: 1 template-msc.pdf: 7779839 bytes, checksum: 3727636ba3903e44e4de17aefcf68481 (MD5) / Approved for entry into archive by Uillis de Assis Santos (uillis.assis@ufba.br) on 2017-06-07T18:25:00Z (GMT) No. of bitstreams: 1 template-msc.pdf: 7779839 bytes, checksum: 3727636ba3903e44e4de17aefcf68481 (MD5) / Made available in DSpace on 2017-06-07T18:25:00Z (GMT). No. of bitstreams: 1 template-msc.pdf: 7779839 bytes, checksum: 3727636ba3903e44e4de17aefcf68481 (MD5) / O câncer de mama é considerado a segunda neoplasia responsável por mais mortes em mulheres no mundo. Para a prevenção e redução desse número, a mamografia de screening é o exame mais utilizado para detecção de nódulos em estágios iniciais. A partir desse exame, o radiologista pode analisar as anomalias e a partir disso desenvolver um diagnóstico. Para aumentar a acurácia dos resultados obtidos a partir das imagens de mamografia, estão sendo desenvolvidos softwares de auxílio à diagnóstico computer-aided diagnosis capazes de automatizar o processo de análise da imagem e extrair informações relevantes para a classificação dos nódulos presentes nos exames. Esse trabalho apresenta duas novas metodologias para extração de features e classificação de massas e não-massas,s a partir da Entropia de Tsallis extraídas através da matriz de co-ocorrência (GLCM) e através da matriz de valores singulares (SVD) da imagem de mamografia, alcançando uma acurácia máxima de 91.3% / Breast cancer has been considered the second neoplasia responsible for women’s death in the last few years. To prevent and to reduce these statistics, screening mammography has been used as the most important exam to detect nodules on initial stages. From this exam, the radiologist can analyze anomalies and to provide some diagnostic. To improve the results accuracy rate from mammography images, computer-aided diagnosis softwares have been developed with the ability to automate the image analyses processing and to extract relevant information for mass classifications on screening exams. This work presents two new methodologies for feature extraction for mass and non-mass classification, based on Tsallis entropy calculated from gray level cooccurrence matrix (GLCM) and from singular value decomposition (SVD), reaching the best accuracy rate of 91.3%.

This dissertation presents a novel features selection wrapper method based on neural networks, named the Binary Wrapper for Features Selection Technique. The major aim of this method is to reduce the computation time that is consumed during the implementation of the process of features selection and classifier optimization in the Heuristic for Features Selection (HVS) method. The HVS technique is a neural network based features selection technique that uses the weights of a well-trained neural network as relevance index for each input feature with respect to the target. The HVS technique consumes long computation time because it follows a sequential approach to discard irrelevant, low relevance, and redundant features. Hence, the HVS technique discards a single feature only at each training session of the classifier. In order to reduce the computation time of the HVS technique, a threshold was produced and used to implement the features selection process. In this dissertation, a new technique, named the replacement technique, was designed and implemented to produce an appropriate threshold that can be used in discarding a group of features instead of discarding a single feature only, which is currently the case with HVS technique. Since the distribution of the candidate features (i.e. relevant, low relevance, redundant and irrelevant features) with respect to the target in a dataset is unknown, the replacement technique produces low relevance features (i.e. probes) to generate a low relevance threshold that is compared to the candidate features and used to detect low relevance, irrelevant and redundant features. Moreover, the replacement technique is considered to be a novel technique that overcomes the limitation of another similar technique that is known as: random shuffling technique. The goal of the random shuffling technique is to produce low relevance features (i.e. probes) in comparison with the relevance of the candidate features with respect to the target. However, using the random shuffling technique, it is not guaranteed to produce such features, whereas this is guaranteed when using the replacement technique. The binary wrapper for features selection technique was evaluated by implementing it over a number of experiments. In those experiments, three different datasets were used, which are: Congressional Voting Records, Wave Forms, and Multiple Features. The numbers of features in the datasets are: 16, 40, and 649 respectively. The results of those experiments were compared to the results of the HVS method and other similar methods to evaluate the performance of the binary wrapper for features selection technique. The technique showed a critical improvement in the consumed time for features selection and classifier optimization, since the consumed computation time using this method was largely less than the time consumed by the HVS method and other methods. The binary wrapper technique was able to save 0.889, 0.931, and 0.993 of the time that is consumed by the HVS method to produce results identical to those produced by the binary wrapper technique over the former three datasets. This implies that the amount of the saved computation time by the binary wrapper technique in comparison with the HVS method increases as the number of features in a dataset increases as well. Regarding the classification accuracy, the results showed that the binary wrapper technique was able to enhance the classification accuracy after discarding features, which is considered as an advantage in comparison with the HVS which did not enhance the classification accuracy after discarding features.