Composite materials are usually multi-phase materials, made up from two or more phases, which are combined to provide properties that the individual constituents cannot. This technology represents an economical way to improve product performances avoiding the use of expensive materials. Composite materials can be obtained as films by means of the electrolysis of electroplating solutions in which micrometre- or submicrometre-size particles are suspended: variable amounts of these particles become incorporated in the electrochemically produced solid phase, to which they impart enhanced properties. The main aims of the present work contributing to this thesis are the study of different parameters influencing the electroco-deposition process in order to promote and improve the applicability of such a technology in the high speed electroplating industry. Following a comprehensive review on the electroco-deposition of composite coatings, the phenomena have been analysed moving from a microscopic point of view i. e. the role of the metal ions present in the electrolyte and adsorption on the inert particles and their interactions with the growing metal layer, to a macroscopic point of view i. e. the electrolyte agitation, its influence on particle motion and all the issues related to the presence of particles in an electrolyte during electroplating. In particular the inert particle influence in terms of geometry, dimension and chemical nature (spherical polystyrene particles vs. irregular alumina particles with different dimensions), the metal matrix influence (nickel, copper and zinc), the influence of electrolyte agitation (using a Rotating Cylinder Electrode cell system) and the influence of the coating thickness on particle content in the final coating, using different deposition times, have been examined. The importance of the particle shape has been highlighted showing how incorporating irregular geometries gave higher particle incorporation densities than regular geometries. The influence of the substrate finishing in terms of imperfections has been related to the particle incorporation rate showing how small surface imperfections enhanced the incorporation of particles. Different hydrodynamic regimes have been analysed resulting three different regimes being discerned: laminar, transitional and turbulent. The consequence, in terms of particle incorporation levels, has been found showing how the amount of particles in the coating changed from one regime to another. Different rate-determining steps were related to the hydrodynamics: when the regime is laminar, particles were incorporated as agglomerates and the process was under particle transfer control, whilst in the turbulent zone, the rate determining step was the velocity of reduction of the ions adsorbed on the particle surface.

Lernaeocera branchialis (L., 1767) is a parasitic copepod that parasitises a range of gadoids by anchoring in the proximity of the branchial chamber of its host, deriving nutrition from the blood of its host and causing serious pathogenic effects. This study investigates the taxonomy of the juvenile free-swimming stages and host location behaviour in the pre-metamorphosed adult female. The large size and distinctive appearance of the metamorphosed adult female stage, coupled with the wide exploitation and commercial importance of one of its principle final gadoid hosts, the cod (Gadus morhua L.), means that this species has long been recognised in the scientific literature, and here the extensive literature concerning this potentially important and damaging pathogen is re-examined to provide an up to date overview, which includes both aquaculture and wild fisheries perspectives. Due to disagreements between several descriptions of the L. branchialis juvenile stages, and because the majority of the descriptions are over 60 years old, the juvenile free-swimming stages are re-described, using current terminology and a combination of both light and confocal microscopy. The time of hatching and moults in these stages is also examined. Techniques for the automated creation of taxonomic drawings from confocal images using computer software are investigated and the possibilities and implications of this technique are discussed. The method of host location in L. branchialis is unknown but is likely to involve a variety of mechanisms, possibly including chemo-reception, mechano-reception and the use of physical phenomena in the water column, such as haloclines and thermoclines, to search for fish hosts. In this study the role of host-associated chemical cues in host location by adult female L. branchialis is investigated by analysing the parasites behavioural responses to a range of host-derived cues, in both a choice chamber and a 3D tracking arena. To analyse the data from the experiments, specialised computer software (“Paratrack”) was developed to digitise the paths of the parasites’ movements, and calculate a variety of behavioural parameters, allowing behaviour patterns to be identified and compared. The results show that L. branchialis responds to host-associated chemical cues in a similar way to many copepods in the presence of chemical cues. Of the different cues tested, gadoid conditioned water appears to be most attractive to the parasites, although the wide variation in behavioural responses may indicate that other mechanisms are also required for host location. The different behavioural responses of parasites to whiting (Merlangius merlangus L.) and cod (Gadus morhua) conditioned water, which are both definitive hosts, provide some evidence for sub-speciation in L. branchialis. The role of chemical cues in host location of L. branchialis, and the relative importance of chemical and physical cues in host location are discussed. As well as demonstrating several techniques, which show potential for further development, this work has improved our knowledge of the biology and life-cycle of L. branchialis. Further study of this, and other areas of L. branchialis biology and its host-parasite interactions, should assist the development of contingency plans for the effective management and control of this widespread and potentially devastating pathogen.

Platinum-based catalysts for hydrogen fuel-cell applications have progressed greatly with the addition of a second element in either a mixed-alloy or core-shell structure. Not only do they contain a reduced amount of the more expensive platinum metal but they have been shown to demonstrate a significant improvement in catalytic activity. Further improvement of these systems can only be made by careful investigation of such catalyst panoparticles on an atomic scale. These nanoparticles provide a significant characterisation challenge due to their minute size and beam sensitivity. A new method of quantifying the annular dark-field (ADF) scanning transmission electron microscope (STEM) signal on an absolute scale has been developed to address this problem. Experimental images are scaled to a fraction of the incident beam intensity from a detector map. The integrated intensity of each individual atomic column is multiplied by the pixel area to yield a more robust imaging parameter: a scattering cross section, σ. Using this cross section approach and simulated reference data, I show it is possible to count the number of atoms in individual columns. With some prior knowledge of the sample, this makes it possible to reconstruct the 3-dimensional structures of pure platinum nanoparticles. Such an approach has subsequently been extended to bimetallic particles here the elements are close in atomic number, using the platinum-iridium system as an example. In the same way that the cross section can be calculated from ADF image intensity, it is possible to calculate an energy dispersive x-ray (EDX) partial scattering cross section, beneficial especially because of the simplicity of its implementation. In sufficiently thin samples such that the number of x-ray counts is linearly proportional to sample thickness, we can determine element-specific atom counts. Finally, it is possible to combine EDX and ADF cross sections to provide us with quantitative structural and compositional information.

The estimation of probability density functions (PDF) of intensity values plays an important role in medical image analysis. Non-parametric PDF estimation methods have the advantage of generality in their application. The two most popular estimators in image analysis methods to perform the non-parametric PDF estimation task are the histogram and the kernel density estimator. But these popular estimators crucially need to be ‘tuned’ by setting a number of parameters and may be either computationally inefficient or need a large amount of training data. In this thesis, we critically analyse and further develop a recently proposed non-parametric PDF estimation method for signals, called the NP windows method. We propose three new algorithms to compute PDF estimates using the NP windows method. One of these algorithms, called the log-basis algorithm, provides an easier and faster way to compute the NP windows estimate, and allows us to compare the NP windows method with the two existing popular estimators. Results show that the NP windows method is fast and can estimate PDFs with a significantly smaller amount of training data. Moreover, it does not require any additional parameter settings. To demonstrate utility of the NP windows method in image analysis we consider its application to image segmentation. To do this, we first describe the distribution of intensity values in the image with a mixture of non-parametric distributions. We estimate these distributions using the NP windows method. We then use this novel mixture model to evolve curves with the well-known level set framework for image segmentation. We also take into account the partial volume effect that assumes importance in medical image analysis methods. In the final part of the thesis, we apply our non-parametric mixture model (NPMM) based level set segmentation framework to segment colorectal MR images. The segmentation of colorectal MR images is made challenging due to sparsity and ambiguity of features, presence of various artifacts, and complex anatomy of the region. We propose to use the monogenic signal (local energy, phase, and orientation) to overcome the first difficulty, and the NPMM to overcome the remaining two. Results are improved substantially on those that have been reported previously. We also present various ways to visualise clinically useful information obtained with our segmentations in a 3-dimensional manner.

Due to the risk of complications such as hemorrhage, severe pelvic trauma is associated with a high mortality rate. Prompt medical treatment is therefore vital. However, the complexity of the injuries can make successful diagnosis and treatment challenging. By generating predictions and recommendations based on patient data, computer-aided decision support systems have the potential to assist physicians in improving outcomes. However, no current system considers features automatically extracted from medical images. This dissertation describes a system to extract diagnostic features from pelvic X-ray images that can be used as input to the prediction process; specifically, the presence of fracture and quantitative measures of displacement. Feature extraction requires prior identification of separate structures of interest within the pelvis. The proposed system therefore incorporates a hierarchical segmentation algorithm which is able to automatically extract multiple structures in a single pass, using a combination of anatomical knowledge and computational techniques such as directed Hough Transform. This algorithm also applies a novel Spline/ASM segmentation method which combines cubic spline interpolation with a deformable model approach which maintains curved contours and provides local control over segmentation. In order for the proposed system to be used as a component in a computerized decision support system, segmentation is designed to be entirely automatic. Furthermore, Spline/ASM is suitable for many other segmentation applications where the objects of interest show curved contours. After successful segmentation, fracture detection is performed on the pelvic ring and pubis structures, using an algorithm based on wavelet transform, anatomical information and boundary tracing. A method is also developed to calculate quantitative measures of symphysis pubis displacement that may indicate pelvic instability and prove useful in identifying fracture patterns. Finally, X-ray features are combined with patient demographics and physiological scores for generation of predictive rules for injury severity, with promising current results. This indicates the potential diagnostic value of the extracted features, and in turn the usefulness of the proposed radiograph analysis component in a larger decision support system.

Cette thèse s’inscrit dans la recherche d’images basée sur leur contenu. La recherche opère sur des images eprésentéesdans un domaine transformé et où sont construits directement les vecteurs de caractéristiques ou indices. Deux types detransformations sont explorés : la transformée en cosinus discrète ou Discrete Cosine Transform (DCT) et la transforméen ondelettes discrète ou Discrete Wavelet Transform (DWT), utilisés dans les normes de compression JPEG et JPEG2000. Basés sur les propriétés des coefficients de la transformation, différents vecteurs de caractéristiquessont proposés. Ces vecteurs sont mis en oeuvre dans la reconnaissance de visages et de textures couleur.Dans le domaine DCT, sont proposés quatre types de vecteurs de caractéristiques dénommés «patterns» : Zigzag-Pattern,Sum-Pattern, Texture-Pattern et Color-Pattern. Le premier type est l’amélioration d’une approche existante. Les trois derniers intègrent la capacité de compactage des coefficients DCT, sachant que certains coefficients représentent une information de directionnalité. L’histogramme de ces vecteurs est retenu comme descripteur de l’image. Pour une réduction de la dimension du descripteur lors de la construction de l’histogramme il est défini, soit une adjacence sur des patterns proches puis leur fusion, soit une sélection des patterns les plus fréquents. Ces approches sont évaluées sur des bases de données d’images de visages ou de textures couramment utilisées. Dans le domaine DWT, deux types d’approches sont proposés. Dans le premier, un vecteur-couleur et un vecteur–texture multirésolution sont élaborés. Cette approche se classe dans le cadre d’une caractérisation séparée de la couleur et de la texture. La seconde approche se situe dans le contexte d’une caractérisation conjointe de la couleur et de la texture. Comme précédemment, l’histogramme des vecteurs est choisi comme descripteur en utilisant l’algorithme K-means pour construire l’histogramme à partir de deux méthodes. La première est le procédé classique de regroupement des vecteurs par partition. La seconde est un histogramme basé sur une représentation parcimonieuse dans laquelle la valeur des bins représente le poids total des vecteurs de base de la représentation. / This thesis comes within content-based image retrieval for images by constructing feature vectors directly fromtransform domain. In particular, two kinds of transforms are concerned: Discrete Cosine Transform (DCT) andDiscrete Wavelet Transform (DWT), which are used in JPEG and JPEG2000 compression standards. Based onthe properties of transform coefficients, various feature vectors in DCT domain and DWT domain are proposedand applied in face recognition and color texture retrieval. The thesis proposes four kinds of feature vectors in DCTdomain: Zigzag-Pattern, Sum-Pattern, Texture-Pattern and Color-Pattern. The first one is an improved method based onan existing approach. The last three ones are based on the capability of DCT coefficients for compacting energy and thefact that some coefficients hold the directional information of images. The histogram of these patterns is chosen as descriptor of images. While constructing the histogram, with the objective to reduce the dimension of the descriptor, either adjacent patterns are defined and merged or a selection of the more frequent patterns is done. These approaches are evaluated on widely used face databases and texture databases. In the aspect of DWT domain, two kinds of approaches for color texture retrieval are proposed. In the first one, color-vector and multiresolution texture-vector are constructed, which categorize this approach into the context of extracting color and texture features separately. In contrast, the second approachis in the context of extracting color and texture features jointly: multiresolution feature vectors are extracted from luminance and chrominance components of color texture. Histogram of vectors is again chosen as descriptor and using k-means algorithm to divide feature vectors into partitions corresponding to the bins of histogram. For histogram generation, two methods are used. The first one is the classical method, in which the number of vectors that fall into the corresponding partition is counted. The second one is the proposition of a sparse representation based histogram in which a bin value represents the total weight of corresponding basis vector in the sparse representation.

[pt] Detecção e reconhecimento de objetos são uma questão importante na área de Visão Computacional, onde a sua realização em tempo real e com taxas baixas de falsos positivos tem se tornado o objetivo principal de inúmeras pesquisas, inclusive daquelas relacionadas às novas formas de interatividade na TV Digital. Esta dissertação propõe um sistema de software baseado em aprendizado de máquina que permite um treinamento eficiente para novos objetos e realiza o subseqüente reconhecimento destes objetos em tempo real, tanto para imagens estáticas como para vídeos digitais. O sistema é baseado no uso de características Haar do objeto, que requerem um baixo tempo de computação para o seu cálculo, e na utilização de classificadores em cascata, que permitem tanto um rápido descarte de áreas da imagem que não possuem o objeto de interesse, quanto uma baixa ocorrência de falsos positivos. Por meio do uso de técnicas de segmentação de imagem, o sistema torna a busca por objetos uma operação extremamente rápida em vídeos de alta resolução. Além disto, com a utilização de técnicas de paralelismo, pode-se detectar vários objetos simultaneamente sem perda de desempenho. / [en] Object detection and recognition are an important issue in the field of Computer Vision, where its accomplishment in both real time and low false positives rates has became the main goal of various research works, including the ones related to new interactivity forms in Digital TV. This dissertation proposes a software system based on machine learning that allows an efficient training for new objects and performs their subsequent recognition in real time, for both static images and digital videos. The proposed system is based on the use of Haar features of the object, which require a low computation time for their calculation, and on the usage of a cascade of classifiers, which allows a quick discard of image areas that does not contain the desired object while having a low occurrence of false positives. Through the use of image segmentation techniques, the system turns the search for objects into an extremely fast operation in high-resolution videos. Furthermore, through the use of parallelism techniques, one can simultaneously detect various objects without losing performance.

[pt] O objetivo deste trabalho foi desenvolver uma metodologia para quantificação, por microscopia, do microconstituinte Austenita-Martensita (AM) em um aço de alta resistência e baixa liga (ABRL) da classe API5LX80. Foram obtidas imagens de Microscopia Ótica (MO) em campo claro e Eletrônica de Varredura (MEV), nos modos de elétrons secundários (SE) e retroespalhados (BSE). As imagens foram quantificadas por Processamento e Análise Digital de Imagens (PADI) e os resultados dos dois tipos de microscopia foram comparados. O principal desafio foi discriminar AM em meio a uma microestrutura multifásica complexa, com frações variadas de ferrita, bainita e do próprio AM. Para revelar a presença de AM foram testadas diferentes sequências de ataques químicos e eletrolíticos. Os resultados demostraram que o ataque químico misto, acrescentando um passo Lepera modificado gerou uma melhora acentuada do contraste em imagens de MO e de MEV no modo BSE. Imagens de MEV no modo SE apresentaram problemas de borda devido à posição do detector de elétrons, o que impediu a correta discriminação das regiões de AM. No modo BSE, a tensão de aceleração foi reduzida para 5 kV, para reduzir a penetração do feixe de elétrons e aumentar o contraste devido à fina camada de AM. Estas imagens foram filtradas para reduzir ruído e segmentadas por limiar simples para quantificar o AM. Nas imagens coloridas de MO, o AM foi segmentado por limiares nos espaços de cor RGB e HSB e em seguida foi quantificado. Utilizando Microscopia Co-Localizada (MCL) foram obtidas imagens de campos idênticos no MEV e no MO, permitindo comparar as técnicas. Mostrou-se que, no mesmo aumento, a microscopia ótica tende a subestimar a fração de AM quando comparada à microscopia eletrônica de varredura, no modo BSE. / [en] This dissertation proposed the development of a quantification method, by microscopy, of the microconstituent Martensite-Austenite (MA) in a High Strength Low Alloy (HSLA) steel of the API5LX80 class. Images were obtained by Optical Microscopy (OM) and Scanning Electron Microscopy (SEM), in secondary electron (SE) and backscattered electron (BSE) modes. Digital Image Processing and Analysis (IA) was employed to process and quantify the acquired images and compare the results of the two types of microscopy. The main challenge was to discriminate the MA amidst a complex multiphase microstructure with varying fractions of ferrite, bainite and MA itself. To reveal the MA different chemical and electrolytic etching sequences were tested. The results showed that a mixed combination with an extra step of modified LePera etchant issued the best contrast for both OM and BSE mode SEM. SEM images in SE mode showed edge problems due to the location of the electron detector, what prevented the correct discrimination of MA regions. The accelerating voltage in the BSE mode was reduced to 5 kV which in turn decreased beam penetration and increase contrast due to the thin MA layer. These images were filtered to reduce noise and segmented by a simple threshold to quantify MA. In the color OM images MA was segmented by thresholds in the RGB or HSB color spaces and subsequently quantified. Employing Co-Site Microscopy images of identical fields acquired by OM and SEM, a direct comparison of the techniques was allowed. It was show that, for the same magnification, optical microscopy tends to underestimate the MA fraction when compared to electron microscopy in BSE mode.

[pt] O presente trabalho visa o desenvolvimento de um sistema automático de identificação do Grau de Maturação de pelotas de minério de ferro, envolvendo técnicas de microscopia digital e processamento e análise digital de imagens. As pelotas de minério de ferro, juntamente com o sínter e o minério granulado, constituem uma das matérias-primas básicas no processo de produção do ferro primário. Numa das etapas de sua produção, as pelotas adquirem diferentes características microestruturais, os chamados Graus de Maturação, que influenciam diretamente seu comportamento nos processos de redução. Sendo assim, a caracterização microestrutural constitui-se em uma importante etapa de controle de qualidade do material. A evolução da microestrutura de pelotas específicas para altos-fornos foi dividida em quatro classes: A, B, C e D. A partir das micrografias obtidas por microscópio ótico, foram extraídos atributos capazes de descrever características intrínsecas de cada classe (ou Grau de Maturação), posteriormente utilizados para treinar um classificador automático. Para tentativas de otimização do processo, foram utilizadas 3 técnicas para redução de dimensionalidade (Busca exaustiva do conjunto com a melhor taxa de acerto, Análise de Componentes Principais e Análise Discriminante Linear de Fisher), e testados 3 tipos de classificadores (Quadrático, Mahalanobis e Linear). A maior taxa de acerto global obtida foi acima de 90 por cento. Este resultado indica que a metodologia desenvolvida mostrou-se efetiva para a classificação automática do Grau de Maturação de pelotas de minério de ferro de alto-forno. / [en] The present work aims to develop an automatic system to identify the maturation degree of iron ore pellets, involving techniques of microscopy and digital image processing. Iron ore pellets, as well as sinter and lump ore, are the basic iron-bearing burden used in ironmaking. In one of the steps of their production, the pellets acquire different microstructural characteristics, the socalled Maturation Degree, which influences directly in the reduction processes. Thus, microstructural characterization is an important step in quality control of the material. The microstructure evolution of pellets for blast furnaces was divided into four classes: A, B, C and D. Attributes that describe intrinsic characteristics of each class were extracted from optical microscope images and were used to train an automatic classifier. Three dimensionality reduction techniques were tested to optimize the classification system: exhaustive search, Principal Component Analysis and Fisher’s Linear Discriminant Analysis. Three types of classifiers were tested (Quadrático, Mahalanobis and Linear). The highest global success rate was above 90 per cent. This indicates that this methodology is effective for automatic classification of the Maturation Degree in blast furnace pellets.

<div><div><div><p>Head injuries in collision sports have been linked to long-term neurological disorders. High school collision sport athletes, a population vulnerable to head injuries, are at a greater risk of chronic damage. Various studies have indicated significant deviations in brain function due to the accumulation of repetitive low-level subconcussive impacts to the head without externally observable cognitive symptoms. The aim of this study was to investigate metabolic changes in asymptomatic collision sport athletes across time within their competition season and as a function of mechanical force to their head. For this purpose, Proton Magnetic Resonance Spectroscopy (MRS) was used as a tool to detect altered brain metabolism in high school collision sport athletes (football and soccer) without diagnosed concussion. Also, sensors were attached to each athletes head to collect the count and magnitude of head impacts during their games and practices. Transient neurometabolic alterations along with prolonged recovery were observed in collision sport athletes.</p><div><div><div><p><br></p><p>Multi-site studies are becoming important to increase statistical power, enhance generalizability, and to improve the likelihood of pooling relevant subgroups together activities which are otherwise limited by the availability of patients or funds at a single site. Even with harmonized imaging sequences, site-dependent variability can mask the advantages of these multi-site studies. The aim of this study was to assess multi-site reproducibility in resting-state functional connectivity fingerprints, and to improve identifiability of obtained functional connectomes. We evaluated individual fingerprints in test- retest visit pairs within and across two sites and present a generalized framework based on principal component analysis (PCA) to improve identifiability. The optimally reconstructed functional connectomes using PCA showed a substantial improvement in individual fingerprinting of the subjects within and across the two sites and test-retest visit pairs relative to the original data. Results demonstrate that the data-driven method presented in the study can improve identifiability in resting-state functional connectomes in multi-site studies.</p></div></div></div></div></div></div>