A doença cancro cítrico é considerada uma das mais importantes doenças da citricultura devido ao seu poder de proliferação nas fazendas, e aos danos causados às plantas e frutos. Os prejuízos causados pela presença da doença são consideravelmente preocupantes, pois as principais medidas de controle pelos órgãos responsáveis envolvem a erradicação de plantas infectadas e demais plantas vizinhas, inviabilizando economicamente grandes áreas produtivas. A legislação brasileira exige um extenso protocolo de atividades que necessita ser realizado antes da confirmação do diagnóstico. Atrasos na confirmação do diagnóstico favorecem a proliferação da doença. Assim, qualquer esforço em acelerar esta detecção deve com certeza ter um grande impacto nesta área. Esta é a motivação de nosso trabalho, onde aplicamos a técnica de espectroscopia por imagens de fluorescência em folhas de culturas cítricas com a intenção de avaliar a capacidade de diagnóstico desta técnica em plantas assintomáticas contaminadas no laboratório com cancro cítrico. O objetivo é determinar o instante de tempo mínimo necessário entre a infecção e o diagnóstico preciso da doença. Este estudo foi aplicado para experimentos envolvendo amostras destrutivas e não-destrutivas. Os resultados mostram a possibilidade de aplicar tal técnica na detecção de cancro cítrico. / The citrus canker disease is considered one of the most important citrus diseases due to its ability to spread on farms, and to damage plants and fruits. The damage, caused by the citrus canker, can be devastating, because the main control actions involve the eradication of infected plants and other plants nearby, causing large economic losses. Brazilian law requires an extensive testing protocol to confirm the diagnosis. Delays in diagnosis tests allows the spread of the disease. Therefore, any effort to accelerate this procedure will have a major impact in this area. This is the motivation of our work, where we apply the fluorescence imaging spectroscopy technique on citrus leaves with the goal to evaluate the diagnostic capability of this technique in asymptomatic plants infected with citrus canker in the laboratory. The goal is to determine the minimum time delay between infection and accurate diagnosis of the disease. This study was applied to experiments involving non-destructive and destructive samples. The results show the possibility of applying this technique in the detection of citrus canker.

Nesta tese foi estudado um grupo de 28 compostos não-imidazólicos antagonistas do receptor H3 através de cálculos de orbitais moleculares utilizando os métodos de química quântica Austin Model 1, Hartree-Fock-Roothaan e Teoria do Funcional da Densidade com o objetivo de investigar possíveis relações entre descritores eletrônicos teóricos e as afinidades ligantes experimentais desses compostos (pKi). Observou-se nos resultados obtidos que as energias dos orbitais FERMOs (Frontier Effective-for-Reaction Molecular Orbitals) apresentam melhor correlação com os valores de pKi do que as energias dos orbitais de fronteira HOMO (Highest Occupied Molecular Orbital) e LUMO (Lowest Unoccupied Molecular Orbital). Além disso, verificou-se pelas análises de métodos multivariados PCA (Principal Componente Analysis) e HCA (Hierarchical Cluster Analysis) que um conjunto de quatro descritores foi capaz de separar os compostos em dois grupos distintos, o primeiro que apresenta valores de afinidades ligantes maiores e o segundo com menores valores de pKi. Esta separação foi possível com o uso dos seguintes descritores teóricos: energia do FERMO (εFERMO), carga derivada do potencial eletrostático no átomo de nitrogênio N1, índice de densidade eletrônica no átomo N1 (Σ(FERMO) ci2) e eletrofilicidade (ω\'). Estes descritores foram utilizados, posteriormente, para a construção de três equações de regressão pelo método PLS (Partial Least Squares). O melhor modelo de regressão gerou os seguintes parâmetros estatísticos Q2 = 0,88 e R2 = 0,927, obtidos com um conjunto treino e de validação externa de 23 e 5 moléculas, respectivamente. Logo após a avaliação da equação de regressão, juntamente com os valores dos descritores selecionados e outros não selecionados, foi sugerido que altos valores de energias dos FERMOs e de Σ(FERMO) ci2 em conjunto com baixos valores de eletrofilicidades e cargas extremamente negativas no átomo N1 são parâmetros relevantes para potencializar as afinidades ligantes de outros compostos a serem sintetizados, que apresentem estruturas químicas semelhantes às moléculas estudadas neste trabalho. Além disso, esses compostos podem ser considerados como doadores de elétrons e, logo, há uma grande probabilidade que tais moléculas interajam com o receptor histamínico H3 a partir de um processo de transferência de carga. / In this thesis, molecular orbital calculations were carried out on a set of 28 non-imidazole H3 antihistamine compounds using Austin Moldel 1, Hartree-Fock-Roothaan, and Density Functional Theory methods in order to investigate the possible relationships between electronic descriptors and binding affinity for H3 receptors (pKi). It was observed that the frontier effective-for-reaction molecular orbital (FERMO) energies were better correlated with pKi values than HOMO (Highest Occupied Molecular Orbital) and LUMO (Lowest Unoccupied Molecular Orbital) energy values. Exploratory data analysis through hierarchical cluster (HCA) and principal component analysis (PCA) showed a separation of the compounds into two sets by using four descriptors, one grouping the molecules with high pKi values, the other gathering low pKi value compounds. This separation was obtained with the use of the following descriptors: FERMO energies (εFERMO), charges derived from the electrostatic potential on the nitrogen atom (N1), electronic density indexes for FERMO on the N1 atom (Σ(FERMO) ci2), and electrophilicity (ω\'). These electronic descriptors were used to construct three quantitative structure-activity relationship (QSAR) models through the Partial Least Squares Method (PLS). The best model generated Q2 = 0.88 and R2 = 0.927 values obtained from a training set and external validation of 23 and 5 molecules, respectively. After the analysis of the PLS regression equation, the values for the selected electronic descriptors and other descriptors, it is suggested that high values of FERMO energies and of Σ(FERMO) ci2, together with low values of electrophilicity and pronounced negative charges on N1 appear as desirable properties for the conception of new molecules which might have high binding affinity. Moreover, these molecules can be classified as electron donating compounds and have a great probability of interacting through a charge transfer process with the biological receptor H3.

This diploma work was done at Seco Tools AB (SECO) in Fagersta and aimed to evaluate the possibility to model the relationship between deposition data, deposition properties and, cutting performance of a (Ti,Al)N coating on cutting inserts by applying the Multivariate Data Analysis (MVDA) modeling technique Partial Least Squares Projection to Latent Structures Modeling (PLS). Cathodic Arc Deposition (Arc-PVD) was the PVD technique focused on this study. The deposition technique that was focused on in this study was Cathodic Arc Deposition (Arc-PVD). For this purpose, two series of Arc-PVD coatings were manufactured. The first series aimed to generate a supervised explorative model for the deposition process. The second manufactured series was aimed to generate a batch-to-batch variation model of a deposition process. In the first supervised explorative model, the deposition parameters were set by a Design of Experiment (DOE) setup using a quarter factorial design with resolution III. In the second batch-to-batch model, the non-fixed deposition parameters and the cathode wear were monitored, and all other parameters were kept the same for every run. The results demonstrate good possibilities to model Arc-PVD coating properties and its performance in metal cutting with respect to the applied deposition parameters. The supervised explorative model confirmed previously established relationships, while the batch-to-batch model shows that variations between batches could be related to the wear of the cathode. This wear was shown to have a negative influence on the properties of the deposited coating.

Master's thesis focuses on processing fMRI data, which are mapping blood oxygenation level dependence in a state of brain activity. Usable and necessarily preprocessing tech- niques of the data, together with two main analysis approaches are introduced. The area of univariate methods, especially general linear model and multivariate principal or independent component analysis is explained. Practical application of the methods involved on the real fMRI data set is implemented. Relevant results as well as theirs mutual possible comparison is presented.

This thesis is a comparative study where the question is whether a neural network approach can outperform the principal component analysis (PCA) approach for predicting changes of interest rate curves. Today PCA is the industry standard model for predicting interest rate curves. Specifically the goal is to better understand the correlation structure between Swedish and European swap rates. The disadvantage with the PCA approach is that only the information contained in the covariance matrix can be used and not for example whether or not the curve might behave different depending on the current state. In other words, some information that might be quite important to the curve dynamic is lost in the PCA approach. This raises the question whether the lost information is important for prediction accuracy or not. As previously been shown by Alexei Kondratyev in the paper "Learning Curve Dynamics with Artificial Neural Networks", the neural network approach is able to use more information in the data and therefore has potential to outperform the PCA approach. Our thesis shows that the neural network approach is able to achieve the same or higher accuracy than PCA when performing long term predictions. The results show that the neural network model has potential to replace the PCA model, however, it is a more time consuming model. Higher accuracy can probably be achieved if the network is more optimized. / Det här är en jämförande studie där syftet är att undersöka hurvida noggrannare prediktioner kan uppnås genom att använda sig av artificiella neurala nätverk (ANN) istället för principalkomponentanalys (PCA) för att förutspå swapräntekurvor. PCA är idag industristandard för att förutspå räntekurvor. Specifikt är målet att bättre kunna förstå korrelationsstrukturen mellan de Svenska swapräntorna och de Europiska swapräntorna. En nackdel med PCA är att den enda tillgängliga informationen sparas i kovariansmatrisen. Det kan till exempel vara fallet att kurvan beter sig väldigt annorlunda beroende på om de nuvarande räntenivåerna är höga eller låga. Eftersom att sådan information går förlorad i PCA-modellen ligger intresset i att undersöka hur mycket noggrannare prediktionerna kan bli om man tar tillvara på ännu mer av informationen i datan. Som Alexei Kondratyev visar i rapporten "Learning Curve Dynamics with Artificial Neural Networks", så har ANN-modellen potential att ersätta PCA-modellen för att förutspå räntekurvor. I denna studie framgår det att ANN-modellen uppnår samma eller bättre resultat jämfört med PCA-modellen vid längre prediktioner.

We introduce new approaches of modeling and synthesizing realistic-looking 2D fire animations using video-based techniques and statistical analysis. Our approaches are based on real footage of various small-scale fire samples with customized motions that we captured for this research, and the final results can be utilized as a sequence of images in video games, motion graphics and cinematic visual effects. Instead of conventional physically-based simulation, we utilize example-based principal component analysis (PCA) and take it to a new level by introducing “Eigenfires”, as a new way to represent the main features of various real fire samples. The visualization of Eigenfires helps animators to design the fire interactively through a more meaningful and convenient way in comparison to known procedural approaches or other video-based synthesis models. Our system enables artists to control real-life fire videos through motion transitions and loops by selecting any desired ranges of any video clips and then the system takes care of the remaining part that best represent a smooth transition. Instead of tricking the eyes with a basic blending only between similar shapes, our flexible fire transitions are capable of connecting various fire styles. Our techniques are also effective for data compressions, they can deliver real-time interactive recognition for high resolution images, very easy to implement, and requires little parameter tuning.

High value added products is the only segment of the electronics sector in which the UK is likely to remain competitive and where manufacturing will be retained within this country. Even though UK companies have a competitive advantage in this market, they also face a range of new challenges including demanding customer requirements, constantly changes conditions and highly complex products and technologies. Consequently, effective product and process (re-) design that encourages continuous improvement and innovation to satisfy highly demanding customers has become vital. Additionally, support to undertake design in an agile manner while managing complexity at the same time is required. The research described in this thesis addresses this problem by developing a software tool (i.e. INMOST - INtegrated MOdelling and Simulation Tool) that support agile design. This support is provided through modelling, simulation and root cause analysis (i.e. the functional modules within the tool). The functionality of the software is enabled through two novel concepts proposed. The first one is an integrated modelling framework that combines different modelling techniques in a single structure to enable more complete and realistic models. The second is a Hierarchical Object Oriented Simulation Structure (HOOSS) that unifies generalisation and customisation ideas to facilitate the utilisation of INMOST in an industrial context. The functionality of INMOST was tested wit three case studies. The case studies proves the capability of the software to be easily adopted in an industrial context, to provide predictive feedback to identify potential problems, and to complete the design cycle by providing decision support to solve identified problems. In this way, the compliance of the software with the domain requirements and needs is demonstrated. The research is completed by providing recommendations for the adoption of INMOST in industry and clear establishing clear directions for future work.

In this paper a resilient sensor network is built in order to lessen the effects of a small portion of corrupted sensors when an aggregated result such as the average needs to be obtained. By examining the variance in sensor readings, a change in the pattern can be spotted and minimized in order to maintain a stable aggregated reading. Offset in sensors readings are also analyzed and compensated to help reduce a bias change in average. These two analytical techniques are later combined in Kalman filter to produce a smooth and resilient average given by the readings of individual sensors. In addition, principal components analysis is used to detect variations in the sensor network. Experiments are held using real sensors called MICAz, which are use to gather light measurements in a small area and display the light average generated in that area.

Domsjö Fabriker has earlier installed a Near Infrared (NIR) spectrometer after one of their recovery boilers. The purpose is to monitor the reduction efficiency of the boiler and later do process optimization. In this work calibration models for the instrument have been created. 108 green liquor samples have been extracted. A NIR spectrum was recorded for each sample and the samples were subsequently analyzed in laboratory for total alkali, sulfide and total sulfur. Several calibration models were created with multivariate data analysis and their performance and robustness were compared. The best model was able to predict reduction efficiency with a RMSEP of 2.75 percent units. Moreover, models were created for prediction of total alkali with a RMSEP of 0.108 mol/l, sulfides with a RMSEP of 1.95 g/l, total sulfur with a RMSEP of 2.83 g/l and S/Na2 ratio with a RMSEP of 0.022. The result is good enough that the instrument could be used to optimize the process and monitor process disturbances.

In traversing the diverse field of biometric security and face recognition techniques, this investigation explores a rather rare comparative study of three of the most popular Appearance-based Face Recognition projection classes, these being the methodologies of Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA). Both the linear and kernel alternatives are investigated along with the four most widely accepted similarity measures of City Block (L1), Euclidean (L2), Cosine and the Mahalanobis metrics. Although comparisons between these classes can become fairly complex given the different task natures, the algorithm architectures and the distance metrics that must be taken into account, an important aspect of this study is the completely equal working conditions that are provided in order to facilitate fair and proper comparative levels of evaluation. In doing so, one is able to realise an independent study that significantly contributes to prior literary findings, either by verifying previous results, offering further insight into why certain conclusions were made or by providing a better understanding as to why certain claims should be disputed and under which conditions they may hold true. The experimental procedure examines ten algorithms in the categories of expression, illumination, occlusion and temporal delay; the results are then evaluated based on a sequential combination of assessment tools that facilitate both intuitive and statistical decisiveness among the intra and inter-class comparisons. In a bid to boost the overall efficiency and accuracy levels of the identification system, the ‘best’ categorical algorithms are then incorporated into a hybrid methodology, where the advantageous effects of fusion strategies are considered. This investigation explores the weighted-sum approach, which by fusion at a matching score level, effectively harnesses the complimentary strengths of the component algorithms and in doing so highlights the improved performance levels that can be provided by hybrid implementations. In the process, by firstly exploring previous literature with respect to each other and secondly by relating the important findings of this paper to previous works one is also able to meet the primary objective in providing an amateur with a very insightful understanding of publicly available subspace techniques and their comparable application status within the environment of face recognition.