Techniques for data pattern selection and abstraction

This thesis concerns the problem of prototype reduction in instance-based learning. In order to deal with problems such as storage requirements, sensitivity to noise and computational complexity, various algorithms have been presented that condense the number of stored prototypes, while maintaining competent classification accuracy. Instance selection, which recovers a smaller subset of the original training set, is the most widely used technique for instance reduction. But, prototype abstraction that generates new prototypes to replace the initial ones has also gained a lot of interest recently. The major contribution of this work is the proposal of four novel frameworks for performing prototype reduction, the Class Boundary Preserving algorithm (CBP), a hybrid method that uses both selection and generation of prototypes, Instance Seriation for Prototype Abstraction (ISPA), which is an abstraction algorithm, and two selective techniques, Spectral Instance Reduction (SIR) and Direct Weight Optimization (DWO). CBP is a multi-stage method based on a simple heuristic that is very effective in identifying samples close to class borders. Using a noise filter harmful instances are removed, while the powerful heuristic determines the geometrical distribution of patterns around every instance. Together with the concepts of nearest enemy pairs and mean shift clustering this algorithm decides on the final set of retained prototypes. DWO is a selection model whose output set of prototypes is decided by a set of binary weights. These weights are computed according to an objective function composed of the ratio between the nearest friend and nearest enemy of every sample. In order to obtain good quality results DWO is optimized using a genetic algorithm. ISPA is an abstraction technique that employs the concept of data seriation to organize instances in an arrangement that favours merging between them. As a result, a new set of prototypes is created. Results show that CBP, SIR and DWO, the three major algorithms presented in this thesis, are competent and efficient in terms of at least one of the two basic objectives, classification accuracy and condensation ratio. The comparison against other successful condensation algorithms illustrates the competitiveness of the proposed models. The SIR algorithm presents a set of border discriminating features (BDFs) that depicts the local distribution of friends and enemies of all samples. These are then used along with spectral graph theory to partition the training set in to border and internal instances

Simultaneous drone localisation and wind turbine model fitting during autonomous surface inspection

We present a method for simultaneous localisation and wind turbine model fitting for a drone performing an automated surface inspection. We use a skeletal parameterisation of the turbine that can be easily integrated into a non-linear least squares optimiser, combined with a pose graph representation of the drone's 3-D trajectory, allowing us to optimise both sets of parameters simultaneously. Given images from an onboard camera, we use a CNN to infer projections of the skeletal model, enabling correspondence constraints to be established through a cost function. This is then coupled with GPS/IMU measurements taken at key frames in the graph to allow successive optimisation as the drone navigates around the turbine. We present two variants of the cost function, one based on traditional 2D point correspondences and the other on direct image interpolation within the inferred projections. Results from experiments on simulated and real-world data show that simultaneous optimisation provides improvements to localisation over only optimising the pose and that combined use of both cost functions proves most effective.Comment: Submitted to IROS201

Foxp3 Expression in Liver Correlates with the Degree but Not the Cause of Inflammation

Patients with chronic viral hepatitis display increased expression of Foxp3 in liver, suggesting that Tregs expansion contributes to persistent infection. The purpose of this study was to elucidate whether the expression of Foxp3 relates not to the viral infection but to the resulting liver inflammation. Liver biopsies obtained from 69 individuals (26 chronic HBV hepatitis, 14 chronic HCV hepatitis, 11 nonalcoholic fatty liver disease, 8 autoimmune diseases, 2 methotrexate-related toxicity, and 8 controls) were examined, by qRT-PCR, for the mRNA expression of Foxp3, IL-10, TGF-β1, Fas, FasL, TRAIL, caspase-3, TNF-α, IFN-γ, and IL-1β. Significant increase of Foxp3 was observed in all disease groups compared to controls, which was positively correlated with the intensity of inflammation. The expression of the apoptosis mediators Fas, FasL, and TRAIL, but not of IL-10 and TGF-β1, was also significantly elevated. Our findings indicate that, independently of the initial inducer, liver inflammation is correlated with elevated expression of apoptosis mediators and is followed by local Treg accumulation. Further research towards the elucidation of the underlying casual relationships is required, in order to clarify whether our results signify the existence of a uniform Treg-mediated regulatory mechanism of apoptosis-induced inflammation

Improving drone localisation around wind turbines using monocular model-based tracking

We present a novel method of integrating image-based measurements into a drone navigation system for the automated inspection of wind turbines. We take a model-based tracking approach, where a 3D skeleton representation of the turbine is matched to the image data. Matching is based on comparing the projection of the representation to that inferred from images using a convolutional neural network. This enables us to find image correspondences using a generic turbine model that can be applied to a wide range of turbine shapes and sizes. To estimate 3D pose of the drone, we fuse the network output with GPS and IMU measurements using a pose graph optimiser. Results illustrate that the use of the image measurements significantly improves the accuracy of the localisation over that obtained using GPS and IMU alone.Comment: Accepted at for the International Conference on Robotics and Automatio