Thesis (MTech (Electrical Engineering))--Cape Peninsula University of Technology, 2016. / Tomography is the ability to internally visualise an opaque medium or a body, using different imaging techniques. Electrical Resistance Tomography (ERT) technique is a method commonly used in process tomography. It uses a non-intrusive resistance measurement between a set of electrodes attached on the circumference of a fixed cross-section with a given conductivity and permittivity distribution. ERT appears to be simple, low cost, safe and non-invasive. Despite the advantages of ERT, the reconstruction of the internal conductivity of the pipe still face a crucial challenges such as noise, a relatively low spatial resolution, as well as ill-posedness of the inverse problem when doing the image reconstruction using reconstruction algorithms. Although previous work showed the potential of various algorithms for the reconstruction of ERT tomograms, no full characterisation and comparison of different algorithms could be found for real flow situations. The ERT system was tested in the identification of different objects and fluid beds in a real time situation. The data collected from the measurements were then used for the image reconstruction using an algorithm developed by Time Long (One-step algorithm) and four EIDORS-based algorithms namely: Gauss-Newton algorithm with Laplace Prior (LP) and Gaussian prior (Automatic Hyper Parameter Selection (AHSP)), the Total Variation (TV) algorithm and the Conjugate Gradient (CG) algorithm. The performance of each algorithm was tested in different scenarios. The results obtained were then compared based on the quality and the accuracy of the images as well as the computational time of each algorithm. Firstly, reconstructed images were obtained using objects placed inside the ERT pipe test. Secondly, the algorithm performances were put to test in a level bed setup experiment and finally, the algorithm reconstructions were applied to the real flow situation, where different flow rates were applied. The results obtained were then analysed and compared.

To fully optimize the value of material produced from a log requires information about the log's internal defects prior to log breakdown. Studies have shown that a 7 to 21 percent improvement in log value recovery can be achieved if the location and identity of internal defects are known. Recent developments in advanced nondestructive testing methods such as CT and MRI offer, for the first time, the possibility of finding internal defects in logs prior to breakdown. Our ability to detect and recognize defects using this data depends Critically on our understanding of wood structure and our ability to devise reliable method for automated image interpretation. While a lot of work has gone into demonstrating that certain types of defects manifest themselves in such sensor imagery, there has not been a systematic approach toward making the automatic inspection of logs a practical reality. This dissertation describes work aimed at creating a viable automated technology for locating and identifying log defects. The imaging modality used in this dissertation is CT. An important first step is to establish a data base of imagery and the ground truth information to determine how the various defects manifest themselves in this imagery. The second step is to study defect characterization and determine exactly which defects are detectable. The final step is to develop a basic method of approach to automated image analysis. A data base has been created from two hardwood species. It is representative of hardwood logs in the sense that it contains almost all the major defects. Visual inspection and analysis of these CT images have shown that most defects manifest themselves in CT imagery. These defects can be detected by features such as intensity, 3-d shape, and texture. As a means of automated image analysis, a knowledge-based vision system has been developed. It consists of three components: a data acquisition unit, an image segmentation module, and scene analysis module. A 3-d adaptive LS filter has been developed in the segmentation module that is efficient in removing annual rings while preserving other needed high frequency detail. Images are segmented using a multiple threshold scheme and regions are grouped using a 3-d connected volume growing algorithm. To represent the 3-d nature of wood defects, a set of basic features have been defined and used to design a set of hypothesis tests. These features seem to be adequate for defect recognition. To cope with imprecision and ambiguity the Dempster-Shaffer model for knowledge representation is used in the vision system. As a viable alternative to Bayesian-based theory, the Dempster's method of evidential reasoning is employed that uses previously unavailable information such as the amount of ignorance and ambiguity a hypothesis exhibits. As such, the proposed vision system seems to be able to recognize a number of hardwood defects. This dissertation also explores wood texture as an additional feature in defect recognition, and contributes the first application of robust Spatial AutoRegressive modeling to wood texture analysis. Based on a correlation measure, two simple but efficient texture discrimination schemes are proposed. Incorporating a texture test in the scene analysis should improve the vision system's recognition power. As a pilot research, this dissertation has explored a number of important issues in creating a vision system for automated log inspection. Clearly, more work is needed to make the system more robust with additional species. Nevertheless, preliminary results seem to indicate that a machine vision system for automated hardwood log inspection can be developed. / Ph. D.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / A Tomografia de Processos é uma importante ferramenta para diversos setores da indústria. Tal importância vem da necessidade de obter informações sobre determinada propriedade física em regiões de complicado acesso, por exemplo, o interior de um duto. A tomografia é uma ferramenta muito versátil, podendo ser adaptada para investigar diversas propriedades físicas. Entre as diversas modalidades tomográficas está a elétrica, conhecida como Tomografia de Impedância Elétrica (EIT). A EIT pode ainda ser dividida em duas partes: Tomografia de Resistência Elétrica (ERT) e Tomografia de Capacitância Elétrica (ECT). Enquanto a ERT é capaz de distinguir materiais condutivos de não-condutivos, a ECT é capaz de diferenciar dois materiais não-condutivos pela sua permissividade elétrica. A modalidade de tomografia elétrica possui vantagens como: baixo tempo de aquisição, baixo custo e não-radioatividade. Os principais desafios enfrentados na tomografia elétrica são: a dependência da trajetória do campo em relação ao meio (efeito de campo mole) e a pouca quantidade de eletrodos disponı́veis para medições devido às dimensões dos mesmos. Em decorrência do efeito de campo mole, a soma da contribuição individual de cada pixel em uma região é diferente da contribuição real da região, em outras palavras, é um problema não-linear. Devido a pequena quantidade de eletrodos, em geral 8 ou 12, reconstruir uma imagem com resolução prática é um problema mal-posto. Muitos métodos foram propostos para contornar essas dificuldades, grande parte se baseia em um modelo linearizado do sistema e na resolução de um problema inverso. Neste trabalho é proposto um método de reconstrução de imagens com representação esparsa, no qual busca-se reconstruir uma imagem composta de poucos elementos de uma base redundante. Esses elementos são aprendidos a partir de sinais de treinamento e usados como entrada para um modelo de ECT. As respostas, em capacitância, desse modelo formam uma matriz de sensibilidade redundante. Tal matriz pode ser interpretada como uma linearização por partes do problema direto. Para validação desse algoritmo foram realizados experimentos em escoamentos bifásicos ar-água. Os sinais de treinamento foram obtidos com o uso de um sensor de ECT em conjunto com um sensor wire-mesh capacitivo. Os resultados obtidos demonstram a capacidade do método proposto em reconstruir imagens a partir de 8 medições de capacitâncias. As imagens reconstruı́das apresentam melhores resultados, segundo diferentes métricas, quando comparados a outros métodos com representações esparsas. / Process Tomography is an important tool for many sectors of industry. Such importance comes from the necessity of obtaining knowledge of physical properties from hard reaching places, as the interior of a solid object or pipe. Tomography is a very versatile tool, it can be adapted for investigating different physical properties. Among the many tomographic modalities is the electrical, know as Electrical Impedance Tomography (EIT). The EIT can also be divided in two: Electrical Resistance Tomography (ERT) and Electrical Capacitance Tomography (ECT). While the ERT is capable of distinguishing conducting materials from non-conducting ones, the ECT is capable of distinguishing two non-conducting materials by their electrical permittivity. The electrical modality has advantages such as: low acquisition time, low cost and non-radioactive. The main challenges of electrical tomography are: dependency of the trajectory of the field in the medium (effect know as soft-field) and the low number of electrodes available for measurement due to their sizes. As a result of the soft-field effect, the sum of individual contributions of small discrete segments in a given region is different from the contribution of the entire region as one. In other words, the relation between the electrical property and the electrical measurements are non-linear. Due to the small number of measuring electrodes, commonly 8 or 12, reconstructing images with practical resolution is an ill-posed problem. In order to overcome these obstacles, many methods were proposed and the majority are based on the resolution of an inverse problem of a linear model. This work proposes a method of image reconstruction with sparse inducing regularization that seeks to obtain an image representation with only few elements of a redundant basis. The elements of this basis are obtained from training images and used as input of an ECT simulation. The output capacitances of the model make up the columns of a redundant sensitivity matrix. Such matrix can be viewed as a piecewise linearization of the direct problem. For validation purposes, experimental tests were conducted on two-phase flows (air-water). The training signals were obtained from an experiment with a capacitive wire-mesh sensor along with an ECT sensor. The results obtained show that the proposed method is capable of reconstructing images from a set of only 8 capacitance measurements. The reconstructed images show better results, according to different metrics, when compared to other methods that also use sparse representations.