Non-Destructive Testing (NDT) is one of the inspection techniques used in industrial tool inspection for quality and safety control. It is performed mainly using X-ray Computed Tomography (CT) to scan the internal structure of the tools and detect the potential defects. In this paper, we propose a new toolbox called the CT-Based Integrity Monitoring System (CTIMS-Toolbox) for automated inspection of CT images and volumes. It contains three main modules: first, the database management module, which handles the database and reads/writes queries to retrieve or save the CT data; second, the pre-processing module for registration and background subtraction; third, the defect inspection module to detect all the potential defects (missing parts, damaged screws, etc.) based on a hybrid system composed of computer vision and deep learning techniques. This paper explores the different features of the CTIMS-Toolbox, exposes the performance of its modules, compares its features to some existing CT inspection toolboxes, and provides some examples of the obtained results.

Driving can be a complicated process, but with sufficient practice, it becomes surprisingly more easier. People tend to forget that even the smallest distractions can have great consequences. Nowadays, experienced drivers are skilled enough to perform multiple tasks like listening to music or texting while simultaneously concentrating on driving. This thesis studies driving under different distractions and how they affect different drivers. The behaviour of individual drivers are also studied to make conclusions on how distractions affect drivers.To understand a driver's behaviour, their driving patterns are studied by constructing Self Organizing Maps and training them on the drivers' datasets. This results in a structure that maps each driver under a particular distraction to their behaviour.The map is then studied by developing labels based on the features of the datasets.These labels serve as test cases to examine different behaviour of each driver, from which conclusions regarding the disruptiveness of each distraction.

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Defect detection is the process of locating defects or anomalies within an object that include changes in textures, features, patterns, missing part, along with other object modifications. The paper discusses some of the main challenges of defect detection including details on sample selection, object orientation, semantic segmentation and image defect classification. This paper focuses on applying modified machine and deep learning models to analyse defects with wide object invariance. We demonstrate algorithms that perform multi-class classification with improvements in the image segmentation process that directly connect to the deep model architecture. Before applying learning algorithms, the paper also demonstrates the value of sample selection together with a more simplified normalised dimension reduction based on image downscaling even before using the convolution operation of the convolutional neural networks (CNN).