Image Automatic Categorisation using Selected Features Attained from Integrated Non-Subsampled Contourlet with Multiphase Level Sets

A framework of automatic detection and categorization of Breast Cancer (BC) biopsy images utilizing significant interpretable features is initially considered in discussed work. Appropriate efficient techniques are engaged in layout steps of the discussed framework. Different steps include 1.To emphasize the edge particulars of tissue structure; the distinguished Non-Subsampled Contourlet (NSC) transform is implemented. 2. For the demarcation of cells from background, k-means, Adaptive Size Marker Controlled Watershed, two proposed integrated methodologies were discussed. Proposed Method-II, an integrated approach of NSC and Multiphase Level Sets is preferred to other segmentation practices as it proves better performance 3. In feature extraction phase, extracted 13 shape morphology, 33 textural (includes 6 histogram, 22 Haralick’s, 3 Tamura’s, 2 Graylevel Run-Length Matrix,) and 2 intensity features from partitioned tissue images for 96 trained image

Electron Crystallography of Organic Pigments

The principle aim of this thesis is the detailing of the development and subsequent use of electron crystallographic techniques which employ the maximum entropy approach. An account is given of the electron microscope as a crystallographic instrument, along with the necessary theory involved. Also, an overview of the development of electron crystallography, as a whole, is given. This progresses to a description of the maximum entropy methodology and how use can be made of electron diffraction data in ab initio phasing techniques. Details are also given of the utilisation of image derived phases in the determination of structural information. Extensive examples are given of the use of the maximum entropy program MICE, as applied to a variety of structural problems. A particular area of interest covered by this thesis is regarding the solid state structure of organic pigments. A detailed structure review of both beta-naphthol and acetoacetanilide pigments was undertaken. Information gained from this review was used as a starting point for the attempted structural elucidation of a related pigment. Barium Lake Red C. Details are given of the synthesis, electron microscope studies and subsequent ab initio phasing procedures applied in the determination of structural information on Barium Lake Red C. The final sections of this thesis detail electron crystallographic analyses of three quite different structures. Common to all was the use of maximum entropy methods, both for ab initio phasing and use of image derived phases. Overall, it is shown that electron crystallographic structure analyses using maximum entropy methods are successful using electron diffraction data and do provide distinct structural information even when significant perturbations to the data exist

Adaptive algorithms for history matching and uncertainty quantification

Numerical reservoir simulation models are the basis for many decisions in regard to predicting, optimising, and improving production performance of oil and gas reservoirs. History matching is required to calibrate models to the dynamic behaviour of the reservoir, due to the existence of uncertainty in model parameters. Finally a set of history matched models are used for reservoir performance prediction and economic and risk assessment of different development scenarios. Various algorithms are employed to search and sample parameter space in history matching and uncertainty quantification problems. The algorithm choice and implementation, as done through a number of control parameters, have a significant impact on effectiveness and efficiency of the algorithm and thus, the quality of results and the speed of the process. This thesis is concerned with investigation, development, and implementation of improved and adaptive algorithms for reservoir history matching and uncertainty quantification problems. A set of evolutionary algorithms are considered and applied to history matching. The shared characteristic of applied algorithms is adaptation by balancing exploration and exploitation of the search space, which can lead to improved convergence and diversity. This includes the use of estimation of distribution algorithms, which implicitly adapt their search mechanism to the characteristics of the problem. Hybridising them with genetic algorithms, multiobjective sorting algorithms, and real-coded, multi-model and multivariate Gaussian-based models can help these algorithms to adapt even more and improve their performance. Finally diversity measures are used to develop an explicit, adaptive algorithm and control the algorithm’s performance, based on the structure of the problem. Uncertainty quantification in a Bayesian framework can be carried out by resampling of the search space using Markov chain Monte-Carlo sampling algorithms. Common critiques of these are low efficiency and their need for control parameter tuning. A Metropolis-Hastings sampling algorithm with an adaptive multivariate Gaussian proposal distribution and a K-nearest neighbour approximation has been developed and applied