With the introduction of machine learning algorithms in industry came a new improvement to excising production line. Image recognisance can now be used to detect defects of the product made by these production lines. However these kinds of flaw detection can be fooled by mirror-like specular light effect on these products, which can reduce the reliability of these detection methods. In this thesis two approaches are taken to
minimize these spurious specular light features. Here I look at images of welding spots taken from a car-door production line of Fiat Chrysler Automotive Italy. First a median filter is used on multiple images of welding spots, which each have a different illumination. Secondly the same images were fed into a modified algorithm developed by Antonello et al,2018 , originally used to split fore-and background of videos, to remove these spurious features. Then I tried to improve the output of this algorithm so that oversaturated pixels in the output were replaced by a combination of pixel values of the input images that were not oversaturated.
Here is found that the median filter does not satisfy the desired goal of this thesis. The algorithm however gives a good filtered output of the input images and therefore are suitable to possibly be used for computer vision applications. The improvements made to the output of the algorithm do not correct the oversaturated pixels the right way, however the method I propose here seems to have some promising results if some improvement to this method are made, mainly concerning the normalisation of these pixels.

Significant work has been done in the field of computer vision focusing on learning and clustering methods. The use of improved learning methods has paved a way forward for researches to explore various theories to improve existing methods. One among various learning methods is Hierarchical learning which has showed impressive benefits and performance over traditional sequential learning approaches. In general, machine learning models require a lot of data for every new scenario which is not always possible and if so, is very expensive. Transfer learning, which focuses on transferring knowledge across trained machine learning models, is a promising machine learning methodology for solving the above problem. In this thesis, we propose an end-to-end neural network architecture on the NM500 neuromorphic chip using an incremental hierarchical learning approach. We first design a hierarchical representation of a taxonomy, develop a batch of pre-classifiers and use their output to construct a custom feature vector that is the input to the front-end network which learns the taxonomy. In other words, the taxonomy is embedded in the clustering method and not trained by a backpropagation algorithm. The custom feature vector has been structured to accurately incorporate the taxonomy based on the Manhattan distance norm. The structure has been proven mathematically and validated using experiments. A Radial Basis Function (RBF) is used for learning and a combination of RBF and K-Nearest Neighbors (KNN) for classification. The applicability of the proposed framework has been demonstrated on a road sign classification problem which is represented as a taxonomy. The ability of the framework to incrementally learn new categories and update the taxonomy online has also been shown. Lastly, we show a case of transfer learning where the entire back- end networks is used as a starting point to learn new features without significantly forgetting prior knowledge. This transfer learning framework showed comparable performance to the standard learning method in terms of accuracy while using significantly less labelled data. This work paves a way forward for researchers to develop transfer learning frameworks and more importantly explore neuromorphic hardware for machine learning tasks.

Schistosomiasis is a neglected tropical disease (NTD) that affects around a quarter-billion people worldwide. Most of the infected people live in tropical regions, especially in sub-Saharan Africa, where there is limited access to diagnostic and other relevant medical facilities. The current reference standard diagnostic procedure - conventional microscopy - is a relatively inexpensive procedure to use on a large scale, but it still requires trained operators and an initial financial investment which are hard to procure and maintain in remote areas with inadequate facilities. Moreover, the diagnostic sensitivity of the procedure is modest and varies over a wide range. In this work, we present the development of an inexpensive diagnostic instrument for urinary schistosomiasis that is automated to scan, analyse and diagnose the disease, and quantify the level of infection. We explore the design of the device with an open-source philosophy in mind, to enable makerspaces and other interested parties to reproduce the device locally. The device is manufacturable for as little as €200. It adheres to the standard sample preparation and diagnosis procedure established by the World Health Organisation (WHO), and images the relevant biomarkers to an adequate resolution for automated detection, diagnosis, and quantification for epidemiological surveillance. We believe this device can be an essential means for point-of-care diagnosis in resource-limited settings.

For large-scale system with tens of thousands of states and outputs the computation in the conventional Kalman filter becomes time-consuming such that Kalman filtering in large-scale real-time application is practically infeasible. A possible mathematical framework to lift the curse of dimensionality is to lift the problem in higher dimensions with the use of tensors and then decompose it. The tensor-train decomposition is chosen due to its computational advantages for systems with low tensor-train rank. Within this thesis two main limitations of the existing tensor Kalman filter are solved. First, a method is developed based on tensor-train rank truncation of the covariances to increase the computational speed for more general systems. Second, a MIMO tensor Kalman filter is developed for a specific class of systems. The power of the developed methods is shown on the example of adaptive optics which fits into the framework. A comparison with state-of-the-art large-scale estimation algorithms shows the computational advantage of the tensor Kalman filter at the cost of approximation errors.