The work presented in this dissertation involves the development of parametric magnetic resonance imaging (MRI) techniques that can be used in a clinical set up. In the first chapter an introduction of basic magnetic resonance physics is given. The introduction covers the source to tissue magnetization, the origin of the detectable signal, the relaxation mechanisms, and the imaging principles. In the second chapter T₂ estimation - the main parametric MRI technique addressed in this work - is introduced and the problem associated with T₂ estimation from highly undersampled fast spin-echo (FSE) data is presented. In Chapter 3, a novel model-based algorithm with linearization by principal component analysis (REPCOM) is described. Based on simulations, physical phantom and in vivo data, the proposed algorithm is shown to produce accurate and stable T₂ estimates. In Chapter 4, the concept of indirect echoes associated with the acquisition of FSE data is introduced. Indirect echo correction using the extended phase graph approach is then studied for standard sampled data. A novel reconstruction algorithm (SERENADE) is presented for the reconstruction of decay curves with indirect echoes from highly undersampled data. The technique is evaluated using simulations, physical phantom and in vivo data; decay curves with indirect echoes are shown to be accurately recovered by this technique. Chapter 5 is dedicated to correcting the partial volume effect (PVE) in T₂ estimation. For the case of small lesions within a background tissue, PVE affects T₂ estimation which in turn affects lesion classification. A novel joint fitting algorithm is proposed and compared to conventional fitting algorithms using fully sampled spin-echo (SE) images. It is shown that the proposed algorithm is more accurate, robust, and insensitive to region of interest drawing than the conventional fitting algorithms. Because the acquisition of fully sampled SE images is long, the technique is combined with a thick refocusing slice approach in order to be able to use undersampled FSE data and reduce the acquisition time to a breath hold (~ 20 s). The final chapter summarizes the results presented in the dissertations and discusses areas for future work.

In this thesis appropriate statistical methods to overcome two types of problems that occur during parameter estimation in chemical engineering systems are studied. The first problem is having too many parameters to estimate from limited available data, assuming that the model structure is correct, while the second problem involves estimating unmeasured disturbances, assuming that enough data are available for parameter estimation. In the first part of this thesis, a model is developed to predict rates of undesirable reactions during the finishing stage of nylon 66 production. This model has too many parameters to estimate (56 unknown parameters) and not having enough data to reliably estimating all of the parameters. Statistical techniques are used to determine that 43 of 56 parameters should be estimated. The proposed model matches the data well. In the second part of this thesis, techniques are proposed for estimating parameters in Stochastic Differential Equations (SDEs). SDEs are fundamental dynamic models that take into account process disturbances and model mismatch. Three new approximate maximum likelihood methods are developed for estimating parameters in SDE models. First, an Approximate Expectation Maximization (AEM) algorithm is developed for estimating model parameters and process disturbance intensities when measurement noise variance is known. Then, a Fully-Laplace Approximation Expectation Maximization (FLAEM) algorithm is proposed for simultaneous estimation of model parameters, process disturbance intensities and measurement noise variances in nonlinear SDEs. Finally, a Laplace Approximation Maximum Likelihood Estimation (LAMLE) algorithm is developed for estimating measurement noise variances along with model parameters and disturbance intensities in nonlinear SDEs. The effectiveness of the proposed algorithms is compared with a maximum-likelihood based method. For the CSTR examples studied, the proposed algorithms provide more accurate estimates for the parameters. Additionally, it is shown that the performance of LAMLE is superior to the performance of FLAEM. SDE models and associated parameter estimates obtained using the proposed techniques will help engineers who implement on-line state estimation and process monitoring schemes. / Thesis (Ph.D, Chemical Engineering) -- Queen's University, 2013-12-23 15:12:35.738

Parameterised analytical models that describe the trimmed inflight behaviour of classical aircraft have been studied and are widely accepted by the flight dynamics community. Therefore, the primary role of aircraft parameter estimation is to quantify the parameter values which make up the models and define the physical relationship of the air vehicle with respect to its local environment. Nevertheless, a priori empirical predictions dependent on aircraft design parameters also exist, and these provide a useful means of generating preliminary values predicting the aircraft behaviour at the design stage. However, at present the only feasible means that exist to actually prove and validate these parameter values remains to extract them through physical experimentation either in a wind-tunnel or from a flight test. With the advancement of UAVs, and in particular smaller UAVs (less than 1m span) the ability to fly the full scale vehicle and generate flight test data presents an exciting opportunity. Furthermore, UAV testing lends itself well to the ability to perform rapid prototyping with the use of COTS equipment. Real-time system identification was first used to monitor highly unstable aircraft behaviour in non-linear flight regimes, while expanding the operational flight envelope. Recent development has focused on creating self-healing control systems, such as adaptive re-configurable control laws to provide robustness against airframe damage, control surface failures or inflight icing. In the case of UAVs real-time identification, would facilitate rapid prototyping especially in low-cost projects with their constrained development time. In a small UAV scenario, flight trials could potentialy be focused towards dynamic model validation, with the prior verification step done using the simulation environment. Furthermore, the ability to check the estimated derivatives while the aircraft is flying would enable detection of poor data readings due to deficient excitation manoeuvres or atmospheric turbulence. Subsequently, appropriate action could then be taken while all the equipment and personnel are in place. This thesis describes the development of algorithms in order to perform online system identification for UAVs which require minimal analyst intervention. Issues pertinent to UAV applications were: the type of excitation manoeuvers needed and the necessary instrumentation required to record air-data. Throughout the research, algorithm development was undertaken using an in-house Simulink© model of the Aerosonde UAV which provided a rapid and flexible means of generating simulated data for analysis. In addition, the algorithms were further tested with real flight test data that was acquired from the Cranfield University Jestream-31 aircraft G-NFLA during its routine operation as a flying classroom. Two estimation methods were principally considered, the maximum likelihood and least squares estimators, with the aforementioned found to be best suited to the proposed requirements. In time-domain analysis reconstruction of the velocity state derivatives ˙W and ˙V needed for the SPPO and DR modes respectively, provided more statistically reliable parameter estimates without the need of a α- or β- vane. By formulating the least squares method in the frequency domain, data issues regarding the removal of bias and trim offsets could be more easily addressed while obtaining timely and reliable parameter estimates. Finally, the importance of using an appropriate input to excite the UAV dynamics allowing the vehicle to show its characteristics must be stressed.

Cette thèse porte sur la segmentation d'images OCT cutanées. Cette modalité d'imagerie permet de visualiser les structures superficielles avec une profondeur de l'ordre du millimètre. En dermatologie, elle permet d'explorer l'épiderme et sa jonction avec le derme. Cependant, les images OCT sont sévèrement affectées par le bruit speckle. Ce phénomène conjugué à la complexité inhérente aux structures de la peau rend l'interprétation des images difficile même pour des experts. L'approche classique consiste à filtrer le speckle avant de faire des traitements de segmentation. A l'inverse, dans cette thèse nous exploitons exclusivement le bruit comme information pour segmenter. Notre approche repose sur la modélisation statistique du speckle. La segmentation se fait par classification des paramètres de ce modèle probabiliste. Ainsi, - On montre que le speckle ne suit pas une loi Rayleigh, comme cela est établi analytiquement. - On ajuste plusieurs lois de probabilité à l'amplitude OCT; et on montre que celle-ci est distribuée selon la loi Gamma généralisée. - On établit que les paramètres de cette loi discriminent statistiquement les couches de la peau. - On conçoit une méthode de segmentation par classification des paramètres locaux du speckle. Les nombreuses expérimentations faites sur plusieurs corpus d'images in-vivo confirment la validité de notre approche. / This thesis deals with the segmentation of skin OCT images. This modality provides the means to visualize superficial structures down to a millimeter depth. In dermatology, it is used to examine the epidermis and its junction with the dermis. However, OCT images are severely affected by the speckle noise. This random phenomenon added to the complexity of human skin structures makes the visual interpretation of images very difficult. Classical image processing techniques filter this noise prior to any segmentation step. In this thesis, we rely exclusively on the properties of the speckle to perform segmentation. Our approach is based on the statistical modeling of the speckle. Images are segmented by classifying parameters of the statistical model. Therefore, - We show that speckle does not follow the Rayleigh distribution, as developed analytically in the literature. - We fit various probability laws to model OCT signal amplitude ; we show that Generalized Gamma has the best goodness of fit. - We establish that statistical parameters of this distribution discriminate skin layers with good variability. - We develop a segmentation method based on the classification of local statistical parameters. The various experimental results with a number of in-vivo images reported in the thesis confirm the validity of our approach

Discrete-time survival analysis as an educational research technique has focused on analysing and interpretating parameter estimates. The purpose of this study was to examine the effects of certain data characteristics on the hazard estimates and goodness of fit statistics. Fifty-four simulated data sets were crossed with four conditions in a 2 (time period) by 3 (distribution of Y = 1) by 3 (distribution of Y = 0) by 3 (sample size) design.

In radiation therapy, it is imperative to deliver high doses of radiation to the tumor while reducing radiation to the healthy tissue. Respiratory motion is the most significant source of errors during treatment. Therefore, it is essential to accurately model respiratory motion for precise and effective radiation delivery. Many approaches exist to account for respiratory motion, such as controlled breath hold and respiratory gating, and they have been relatively successful. They still present many drawbacks. Thus, research has been expanded to tumor tracking. The overall goal of 4D-CT is to predict tumor motion in real time, and this work attempts to move in that direction. The following work addresses both the temporal and the spatial aspects of four-dimensional CT reconstruction. The aims of the paper are to (1) estimate the temporal parameters of 4D models for anatomy deformation using a novel neural network approach and (2) to use intelligently chosen non-uniform, non-separable splines to improve the spatial resolution of the deformation models in image registration.

This thesis deals with the estimation of model parameters of the interacting segments process in plane. The motivation is application on the system of stress fibers in human mesenchymal stem cells, which are detected by fluorescent microscopy. The model of segments is defined as a spatial Gibbs point process with marks. We use two methods for parameter estimation: moment method and Takacs-Fiksel method. Further, we implement algorithm for these estimation methods in software Mathematica. Also we are able to simulate the model structure by Markov Chain Monte Carlo, using birth-death process. Numerical results are presented for real and simulated data. Match of model and data is considered by descriptive statistics. Powered by TCPDF (www.tcpdf.org)

Induction motors (IM) find widespread use in modern industry and for this reason they have been subject to a significant amount of research interest in recent times. One particular aspect of this research is the fault detection and diagnosis (FDD) of induction motors for use in a condition based maintenance (CBM) strategy; by effectively tracking the condition of the motor, maintenance action need only be carried out when necessary. This type of maintenance strategy minimises maintenance costs and unplanned downtime. The benefits of an effective FDD for IM is clear and there have been numerous studies in this area but few which consider the problem in a practical sense with the aim of developing a single system that can be used to monitor motor condition under a range of different conditions, with different motor specifications and loads. This thesis aims to address some of these problems by developing a general FDD system for induction motor. The solution of this problem involved the development and testing of a new approach; the adaptive mixed-residual approach (AMRA). The main aim of the AMRA system is to avoid the vast majority of unplanned failures of the machine and therefore as opposed to tackling a single induction motor fault, the system is developed to detect all four of the most statistically prevalent induction motor fault types; rotor fault, stator fault, air-gap fault and bearing fault. The mixed-residual fault detection algorithm is used to detect these fault types which includes a combination of spectral and model-based techniques coupled with particle swarm optimisation (PSO) for automatic identification of motor parameters. The AMRA residuals are analysed by a fuzzy-logic classifier and the system requires only current and voltage inputs to operate. Validation results indicate that the system performs well under a range of load torques and different coupling methods proving it to have significant potential for use in industrial applications.

Orientador: Sandra Aidar de Queiroz / Coorientador: William Koury Filho / Coorientador: Marcos Eli Buzanskas / Banca: Roberto Carvalheiro / Banca: Patrícia Tholon / Resumo: Neste estudo, foram estimadas herdabilidades e correlações genéticas, fenotípicas e ambientais para as características estrutura, precocidade e musculosidade à desmama (ED, PD, MD) e ao sobreano (ES, PS e MS), peso padronizado aos 210 e 450 dias de idade (P210 e P450, respectivamente), idade ao primeiro parto (IPP), probabilidade de parto precoce (3P) e habilidade de permanência no rebanho (STAY) em bovinos da raça Nelore participantes de programa de melhoramento genético. O conjunto de dados utilizado foi composto por registros de 37.826 animais e pedigree contendo 88.213 animais. O método dos quadrados mínimos foi utilizado para definição de efeitos fixos considerados nos modelos de estimação de parâmetros. Foram testados quatro modelos de estimação, em análises uni-característica por meio do método da máxima verossimilhança restrita, sob modelo animal. Para as características mensuradas ao desmame, verificou-se a necessidade de inclusão dos efeitos genético materno e de ambiente permanente materno no modelo de estimação. Para as características medidas ao sobreano e características reprodutivas, o modelo foi composto pelo efeito aleatório genético direto de animal e residual. As estimativas dos parâmetros genéticos, ambientais e fenotípicos foram obtidas em análises uni e bi-características, por meio de análise bayesiana. Estimativas de herdabilidade média, obtidas por análises bi-característica, foram iguais a 0,28, 0,30, 0,27 e 0,28, para ED, PD, MD e P210, respectivamente. Para as características mensuradas ao sobreano, as estimativas médias de herdabilidade variaram entre 0,40 e 0,37 (ES), 0,44 e 0,42 (PS), 0,39 e 0,37 (MS) e 0,50 e 0,48 (P450). As características medidas em diferentes idades (desmame e sobreano) apresentaram altas correlações genéticas, sendo iguais a 0,96 entre ED e ES, PD e PS e P210 e P450; e 0,94 entre MD e MS. As herdabilidades das características reprodutivas foram... / Abstract: The aim of this study was to estimate the heritability and the genetic, phenotypic and environmental correlations for visual scores (structure, precocity and musculature at weaning and yearling), body weight at 210 and 450 days of age, age at first calving, heifer pregnancy and stayability in Nelore beef cattle participating in a breeding program. The data set consisted on information of 37,826 animals and 88,213 animals in the pedigree. The least square method was used to define the fixed effects for parameters estimation. For traits measured at weaning, four estimation models were tested using single-trait analysis, by means of the restricted maximum likelihood method, using the animal model. Was considered additive and residual random effects (Model 1); additive, maternal and residual random effects (Model 2); additive, maternal permanent environment and residual random effects (Model 3); and additive, maternal, maternal permanent environment and residual random effects (Model 4). Model 1 was used for yearling and reproductive traits. The genetic parameters were determined by single and two-trait analyses using Bayesian methodology. Estimates of average heritability obtained in two-trait analyses, were equal to 0.28, 0.30, 0.27, and 0.28, for structure, precocity and musculature at weaning and body weight at 210 days of age. For yearling traits, the average heritability estimates varied from 0.40 to 0.37 (structure), 0.44 to 0.42 (precocity), 0.39 to 0.37 (musculature), and 0.50 to 0.48 (body weight at 450 days of age). The traits measured at different ages (weaning and yearling) had high genetic correlation: 0.96 for structure, precocity and weight, and 0.94 for musculature. The mean heritability estimate for IPP, 3P and STAY were equal to 0.18, 0.38 and 0.20, respectively. The genetic correlations between visual scores and reproductive traits were favorable, indicating that when selecting animals of greater weight and higher ... / Mestre

Orientador: Lucia Galvão de Albuquerque / Coorientador: Gerardo Alves Fernandes Júnior / Banca: Henrique Nunes de Oliveira / Banca: Raphael Bermal Costa / Resumo: A habilidade de permanência (HP), que pode ser definida como a probabilidade de uma vaca parir numa determinada idade, dado que ela teve esta oportunidade, é uma característica reprodutiva de grande importância em bovinos de corte, estando diretamente relacionada à produtividade do rebanho. O objetivo deste trabalho foi estimar parâmetros genéticos e identificar possíveis regiões do genoma que estejam associadas com a expressão fenotípica da HP de vacas da raça Nelore. Os componentes de variância foram estimados por inferência bayesiana utilizando um modelo de limiar no qual foram considerados os efeitos sistemáticos de grupos de contemporâneos e precocidade sexual, e os efeitos aleatórios de animal e resíduo.Os efeitos dos SNPs foram estimados por meio da metodologia ssGBLUP, sendo utilizados na análise 2838 animais genotipados com o painel de alta densidade da Illumina (Bovine HD Assay Illumina, San Diego, CA, USA). A variância explicada por janelas formadas por 200 SNPs consecutivos foi utilizada na identificação das regiões de maior efeito sobre a expressão da característica HP. A herdabilidade encontrada utilizando a matriz A (pedigree) foi de 0,11±0,01 e utilizando a matriz H( matriz de parentesco que combina a informação de pedigree e dos SNPs) de 0,14±0,01. Foram encontrados 147 genes candidatos para a característica habilidade de permanência em regiões dos cromossomos 1, 2, 5, 6, 9, 20 e no cromossomo sexual X. Novas regiões candidatas para a característica habilida... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Stayability, which can be defined as the probability of a cow calving to a certain age since it has this opportunity, is a reproduction trait of great importance in beef cattle and is directly related to the productivity of the herd. The objective of this study was to estimate variance components through single-step G-BLUP methodology, and use the solutions of the SNPs effects to identify possible regions of the genome that are associated with the phenotypic expression of stayability in Nellore cows. The variance components were estimated by Bayesian inference using a threshold model in which were considered the fixed effects of precocity and contemporary groups, and the random effects of residue and animal. The effects of SNP were estimated by ssGBLUP methodology being used for the analysis 2838 animals genotyped with the Illumina panel of high density ( HD Assay Bovine Illumina, San Diego, CA, USA ). A Manhattan plot containing the variance explained by the windows formed by 200 SNPs consecutive was used to identify regions of greatest effect on the expression of stayability trait. The heritability found using the A matrix was 0.11 ± 0.01 and using the H matrix was 0.14 ± 0.01. 147 potential genes for stayability were found in regions of chromosomes 1,2,5,6,9,20 and sexual chromosome X. New candidate regions for stayability were detected and most of them are related to reproductive, immune and nervous system. / Mestre