84 research outputs found
Eddy current defect response analysis using sum of Gaussian methods
This dissertation is a study of methods to automatedly detect and produce approximations of eddy current differential coil defect signatures in terms of a summed collection of Gaussian functions (SoG). Datasets consisting of varying material, defect size, inspection frequency, and coil diameter were investigated. Dimensionally reduced representations of the defect responses were obtained utilizing common existing reduction methods and novel enhancements to them utilizing SoG Representations. Efficacy of the SoG enhanced representations were studied utilizing common Machine Learning (ML) interpretable classifier designs with the SoG representations indicating significant improvement of common analysis metrics
Computer tools to assist the diagnosis of dyspraxia
Dyspraxia is a condition which relates to an impairment in the development of motor coordination, and which has been estimated to affect around 6% of children. It is a highly debilitating condition, seriously undermining both academic and social development. It is, however, a condition which can be difficult to diagnose, and one which can be reflected in different aspects of behaviour.
This thesis discusses an approach to the assessment of Dyspraxia in which computer tools can be used to support the detailed monitoring and analysis of a childâs behaviour during standardised testing procedures. Current methods of diagnosis require specially trained clinical staff and can be very time consuming. We have taken one of the standard assessment tests, the Beery Developmental Test of Visual-Motor Integration (the VMI Test), and investigated improvements which can be achieved using computer software. The proposed improvements relate both to supporting more objective and automated support for test analysis, but also to the provision of options to extract characteristics of behaviour which are traditionally difficult to extract using conventional procedures. We have developed software to provide support to a clinician scoring the VMI. We have produced a generalised assessment system for any figure copying task. This has been adapted to score the VMI test. Novel approaches to diagnosing Dyspraxia have also been discussed. On-line data capture provides an assortment of measures which are unavailable to a scoring system based solely on a static image.
Overall, the work reported describes and evaluates a package of tools which can be used to support diagnostic procedures in routine clinical practice
Applications of Physically Accurate Deep Learning for Processing Digital Rock Images
Digital rock analysis aims to improve our understanding of the fluid flow properties of reservoir rocks, which are important for enhanced oil recovery, hydrogen storage, carbonate dioxide storage, and groundwater management. X-ray microcomputed tomography (micro-CT) is the primary approach to capturing the structure of porous rock samples for digital rock analysis. Initially, the obtained micro-CT images are processed using image-based techniques, such as registration, denoising, and segmentation depending on various requirements. Numerical simulations are then conducted on the digital models for petrophysical prediction. The accuracy of the numerical simulation highly depends on the quality of the micro-CT images. Therefore, image processing is a critical step for digital rock analysis.
Recent advances in deep learning have surpassed conventional methods for image processing. Herein, the utility of convolutional neural networks (CNN) and generative adversarial networks (GAN) are assessed in regard to various applications in digital rock image processing, such as segmentation, super-resolution, and denoising. To obtain training data, different sandstone and carbonate samples were scanned using various micro-CT facilities. After that, validation images previously unseen by the trained neural networks are utilised to evaluate the performance and robustness of the proposed deep learning techniques.
Various threshold scenarios are applied to segment the reconstructed digital rock images for sensitivity analyses. Then, quantitative petrophysical analyses, such as porosity, absolute/relative permeability, and pore size distribution, are implemented to estimate the physical accuracy of the digital rock data with the corresponding ground truth data. The results show that both CNN and GAN deep learning methods can provide physically accurate digital rock images with less user bias than traditional approaches. These results unlock new pathways for various applications related to the reservoir characterisation of porous reservoir rocks
A study of the effects of ageing on the characteristics of handwriting and signatures
The work presented in this thesis is focused on the understanding of factors that are unique to the elderly and their use of biometric systems. In particular, an investigation is carried out with a focus on the handwritten signature as the biometric modality of choice. This followed on from an in-depth analysis of various biometric modalities such as voice, fingerprint and face. This analysis aimed at investigating the inclusivity of and the policy guiding the use of biometrics by the elderly. Knowledge gained from extracted features of the handwritten signatures of the elderly shed more light on and exposed the uniqueness of some of these features in their ability to separate the elderly from the young. Consideration is also given to a comparative analysis of another handwriting task, that of copying text both in cursive and block capitals. It was discovered that there are features that are unique to each task. Insight into the human perceptual capability in inspecting signatures, in assessing complexity and in judging imitations was gained by analysing responses to practical scenarios that applied human perceptual judgement. Features extracted from a newly created database containing handwritten signatures donated by elderly subjects allowed the possibility of analysing the intra-class variations that exist within the elderly population
Deep Learning Methods for Remote Sensing
Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing
Proceedings of the 19th Sound and Music Computing Conference
Proceedings of the 19th Sound and Music Computing Conference - June 5-12, 2022 - Saint-Ătienne (France).
https://smc22.grame.f
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