Masters Degrees (Computer Science)

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Browsing Masters Degrees (Computer Science) by browse.metadata.advisor "Grobler, Trienko"

Now showing 1 - 5 of 5
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    Application of statistical pattern recognition and deep learning for morphological classification in radio astronomy
    (Stellenbosch : Stellenbosch University, 2022-04) Becker, Adolf Burger; Grobler, Trienko; Stellenbosch University. Faculty of Science. Dept. of Computer Science.
    ENGLISH ABSTRACT: The morphological classification of radio sources is important to gain a full under standing of galaxy evolution processes and their relation with local environmental properties. Furthermore, the complex nature of the problem, its appeal for citi zen scientists and the large data rates generated by existing and upcoming radio telescopes combine to make the morphological classification of radio sources an ideal test case for the application of machine learning techniques. One approach that has shown great promise recently is Convolutional Neural Networks (CNNs). Literature, however, lacks two major things when it comes to CNNs and radio galaxy morphological classification. Firstly, a proper analysis to identify whether overfitting occurs when training CNNs to perform radio galaxy morphological clas sification is needed. Secondly, a comparative study regarding the practical appli cability of the CNN architectures in literature is required. Both of these short comings are addressed in this thesis. Multiple performance metrics are used for the latter comparative study, such as inference time, model complexity, compu tational complexity and mean per class accuracy. A ranking system based upon recognition and computational performance is proposed. MCRGNet, ATLAS and ConvXpress (novel classifier) are the architectures that best balance computational requirements with recognition performance.
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    Building identification in aerial imagery using deep learning
    (Stellenbosch : Stellenbosch University, 2024-03) Nakiranda, Proscovia; Grobler, Trienko; Stellenbosch University. Faculty of Science. Dept. of Computer Science.
    ENGLISH ABSTRACT: Advancements in the field of remote sensing have facilitated the effortless re- trieval of information about any location on Earth at any given time. This has resulted in a variety of developments, including the identification of economic activities taking place in a particular area. The task of identifying build- ings is one significant application of remote sensing imagery as it is crucial for assessing resource distribution, especially in low-resource or data-scarce areas. Advances in machine learning and computation resources allow for au- tomatic analysis of collected remote sensing data, eliminating the need for human intervention. The task of building identification falls under computer vision and is an example of a task that can be automated. Several machine learning architectures (models) have been proposed for building identification. However, choosing the appropriate one can be challenging due to limitations such as difficulty in accurately identifying boundary or near boundary pixels, resource requirements, and overall model accuracy. Therefore, conducting a comparative study is necessary to evaluate the performance of the building identification models. In this thesis, we carry out a comparative study of four state-of-the-art models used for the building identification task. We eval- uate their performance both qualitatively and quantitatively. Furthermore, we investigate the effect of multitask learning on the models’ performance in building identification. The thesis concludes by providing our research find- ings and outlining prospective future research avenues. Moreover, it provides a thorough overview of the fundamental theory underpinning remote sensing and machine learning.
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    The generation of longest optimal box repetition-free words
    (Stellenbosch : Stellenbosch University, 2022-04) Habeck, Manfred; Grobler, Trienko; Van Zijl, Lynette; Geldenhuys, Jaco; Stellenbosch University. Faculty of Science. Dept. of Computer Science.
    ENGLISH ABSTRACT: This thesis focuses on a specific problem within the field of combinatorial generation, namely, the generation of box repetition-free words. A box is a word over a given alphabet, where the first symbol in the word is the same as the last symbol. For example, the word abaca is a box. A box can contain other boxes. The box abaca contains boxes aba and aca. Boxes can overlap, such as aba and aca in abaca. This work investigates the generation of the longest possible sequence of symbols, over a given alphabet, which does not contain any repeating boxes. We show that an exhaustive enumeration based on a brute force approach with backtracking is not feasible. That is, we checked if adding a symbol to a word would create a repeating box; if not, recursively add another symbol. This method will eventually find all valid words, but takes an unreasonable amount of time for larger alphabets. As a non-enumerative attempt to find individual valid words, the Monte Carlo tree search is used. The search is based on the assumption that prefixes with good past results will also give good results in the future. Based on an analysis of the properties of box repetition-free words, a new search is devised. Factors of words are mapped onto a graph, and all non-optimal edges removed. It is then shown that any Hamiltonian path on this graph will result in a longest optimal word. The results of this work show that backtracking fails to generate longest optimal words within a reasonable time for any alphabet with more than three symbols. The Monte Carlo tree search performs better than backtracking, finding optimal words for an alphabet size of four, but failing for larger alphabets. The new method outperforms both, and with a small optimization, is shown to generate longest optimal words up to an alphabet size of six.
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    Implementation of the Cavalieri Integral
    (2023-02) van Zyl, Christoff; Grobler, Trienko; Stellenbosch University. Faculty of Science. Dept. of Computer Science.
    ENGLISH ABSTRACT: Cavalieri Integration in R n presents a novel visualization mechanism for weighted integration and challenges the notion of strictly rectangular integration strips. It does so by concealing the integrator inside the boundary curves of the integral. This paper investigates the Cavalieri integral as a superset of Riemann-integration in R n−1 , whereby the integral is defined by a translational region in R n−1 , which uniquely defines the integrand, integrator and integration region. In R 2 , this refined translational region definition allows for the visualization of Riemann-Stieltjes integrals along with other forms of weighted integration such as the Riemann–Liouville fractional integral and convolution operator. Programmatic implementation of such visualizations and computation of integral values are also investigated and relies on knowledge relating to numeric integration, algorithmic differentiation and numeric root finding. For the R 3 case, such visualizations over polygonal regions requires a mechanism for the triangulation of a set of nested polygons and transformations which allow for the use of repeated integration to solve the integration value over the produced triangular regions using standard 1-dimensional integration routines.
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    Solidifying what is known about calibration artefacts and the development of an educational tool to assist in the teaching of interferometric imaging
    (Stellenbosch : Stellenbosch University, 2023-03) Jackson, Jason Peter; Grobler, Trienko; Ludick, Danie; Stellenbosch University. Faculty of Science. Dept. of Computer Science.
    ENGLISH ABSTRACT: Radio interferometers are arrays of radio antennas that work together to capture celestial radio emission. Imaging involves transforming the raw measurements made by these so-called interferometers into images of the radio sky. The first contribution of this thesis is the creation of an educational tool that utilizes the Transient Array Radio Telescope (TART). This tool can be used to teach radio interferometric imaging to undergraduate and postgraduate students. Calibration is the act of trying to correct for the effects that may have interfered with the celestial radio emission that an interferometer receives. Calibration artefacts or systematics are inadvertently created when we calibrate our instrument. Calibrating with an incomplete sky model in particular can create artefacts called ghosts. Ghosts are spurious sources that do not truly exist. A second contribution of the thesis is the creation of a scientific tool with which calibration artefacts can be studied. This tool is then used to investigate what artefacts form when a single extended source is only partially modelled (with a point source model). The results of this study show that for the aforementioned extended use-case ghosts become extended sources themselves. They also alter the original extended source in various ways. The original source takes on the same flux scale as the source in the calibration model and its profile changes; it becomes more point-like. The shorter baselines are also more severely affected than the longer baselines are and in contrast to previous studies for this particular setup the number of antennas does not impact the severity of the artefacts which are created.