Eye Fundus Image Analysis for Automatic Detection of Diabetic Retinopathy

Diabetes is a rapidly increasing worldwide problem which is characterised by defective metabolism of glucose that causes long-term dysfunction and failure of various organs. The most common complication of diabetes is diabetic retinopathy (DR), which is one of the primary causes of blindness and visual impairment in adults. The rapid increase of diabetes pushes the limits of the current DR screening capabilities for which the digital imaging of the eye fundus (retinal imaging), and automatic or semi-automatic image analysis algorithms provide a potential solution. In this work, the use of colour in the detection of diabetic retinopathy is statistically studied using a supervised algorithm based on one-class classification and Gaussian mixture model estimation. The presented algorithm distinguishes a certain diabetic lesion type from all other possible objects in eye fundus images by only estimating the probability density function of that certain lesion type. For the training and ground truth estimation, the algorithm combines manual annotations of several experts for which the best practices were experimentally selected. By assessing the algorithm’s performance while conducting experiments with the colour space selection, both illuminance and colour correction, and background class information, the use of colour in the detection of diabetic retinopathy was quantitatively evaluated. Another contribution of this work is the benchmarking framework for eye fundus image analysis algorithms needed for the development of the automatic DR detection algorithms. The benchmarking framework provides guidelines on how to construct a benchmarking database that comprises true patient images, ground truth, and an evaluation protocol. The evaluation is based on the standard receiver operating characteristics analysis and it follows the medical practice in the decision making providing protocols for image- and pixel-based evaluations. During the work, two public medical image databases with ground truth were published: DIARETDB0 and DIARETDB1. The framework, DR databases and the final algorithm, are made public in the web to set the baseline results for automatic detection of diabetic retinopathy. Although deviating from the general context of the thesis, a simple and effective optic disc localisation method is presented. The optic disc localisation is discussed, since normal eye fundus structures are fundamental in the characterisation of DR.

Automated Software Testing in Machine Automation

The problem of software (SW) defaults is becoming more and more topical because of increasing amount of the SW and its complication. The majority of these defaults are founded during the test part that consumes about 40-50% of the development efforts. Test automation allows reducing the cost of this process and increasing testing effectiveness. In the middle of 1980 the first tools for automated testing appeared and the automated process was implemented in different kinds of SW testing. In short time, it became obviously, automated testing can cause many problems such as increasing product cost, decreasing reliability and even project fail. This thesis describes automated testing process, its concept, lists main problems, and gives an algorithm for automated test tools selection. Also this work presents an overview of the main automated test tools for embedded systems.

The Islandora Digital Humanities Platform

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Hydra Europe

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Deep generative models for facial keypoints detection

A new area of machine learning research called deep learning, has moved machine learning closer to one of its original goals: artificial intelligence and general learning algorithm. The key idea is to pretrain models in completely unsupervised way and finally they can be fine-tuned for the task at hand using supervised learning. In this thesis, a general introduction to deep learning models and algorithms are given and these methods are applied to facial keypoints detection. The task is to predict the positions of 15 keypoints on grayscale face images. Each predicted keypoint is specified by an (x,y) real-valued pair in the space of pixel indices. In experiments, we pretrained deep belief networks (DBN) and finally performed a discriminative fine-tuning. We varied the depth and size of an architecture. We tested both deterministic and sampled hidden activations and the effect of additional unlabeled data on pretraining. The experimental results show that our model provides better results than publicly available benchmarks for the dataset.

Thermal modelling of commercial lithium-ion batteries

The accelerating adoption of electrical technologies in vehicles over the recent years has led to an increase in the research on electrochemical energy storage systems, which are among the key elements in these technologies. The application of electrochemical energy storage systems for instance in hybrid electrical vehicles (HEVs) or hybrid mobile working machines allows tolerating high power peaks, leading to an opportunity to downsize the internal combustion engine and reduce fuel consumption, and therefore, CO2 and other emissions. Further, the application of electrochemical energy storage systems provides an option of kinetic and potential energy recuperation. Presently, the lithium-ion (Li-ion) battery is considered the most suitable electrochemical energy storage type in HEVs and hybrid mobile working machines. However, the intensive operating cycle produces high heat losses in the Li-ion battery, which increase its operating temperature. The Li-ion battery operation at high temperatures accelerates the ageing of the battery, and in the worst case, may lead to a thermal runaway and fire. Therefore, an appropriate Li-ion battery cooling system should be provided for the temperature control in applications such as HEVs and mobile working machines. In this doctoral dissertation, methods are presented to set up a thermal model of a single Li-ion cell and a more complex battery module, which can be used if full information about the battery chemistry is not available. In addition, a non-destructive method is developed for the cell thermal characterization, which allows to measure the thermal parameters at different states of charge and in different points of cell surface. The proposed models and the cell thermal characterization method have been verified by experimental measurements. The minimization of high thermal non-uniformity, which was detected in the pouch cell during its operation with a high C-rate current, was analysed by applying a simplified pouch cell 3D thermal model. In the analysis, heat pipes were incorporated into the pouch cell cooling system, and an optimization algorithm was generated for the estimation of the optimalplacement of heat pipes in the pouch cell cooling system. An analysis of the application of heat pipes to the pouch cell cooling system shows that heat pipes significantly decrease the temperature non-uniformity on the cell surface, and therefore, heat pipes were recommended for the enhancement of the pouch cell cooling system.

Dynamic ecosystems under anthropogenic stress : how does macrotidal sandy fauna respond to green tides?

Highly dynamic systems, often considered as resilient systems, are characterised by abiotic and biotic processes under continuous and strong changes in space and time. Because of this variability, the detection of overlapping anthropogenic stress is challenging. Coastal areas harbour dynamic ecosystems in the form of open sandy beaches, which cover the vast majority of the world’s ice-free coastline. These ecosystems are currently threatened by increasing human-induced pressure, among which mass-development of opportunistic macroalgae (mainly composed of Chlorophyta, so called green tides), resulting from the eutrophication of coastal waters. The ecological impact of opportunistic macroalgal blooms (green tides, and blooms formed by other opportunistic taxa), has long been evaluated within sheltered and non-tidal ecosystems. Little is known, however, on how more dynamic ecosystems, such as open macrotidal sandy beaches, respond to such stress. This thesis assesses the effects of anthropogenic stress on the structure and the functioning of highly dynamic ecosystems using sandy beaches impacted by green tides as a study case. The thesis is based on four field studies, which analyse natural sandy sediment benthic community dynamics over several temporal (from month to multi-year) and spatial (from local to regional) scales. In this thesis, I report long-lasting responses of sandy beach benthic invertebrate communities to green tides, across thousands of kilometres and over seven years; and highlight more pronounced responses of zoobenthos living in exposed sandy beaches compared to semi-exposed sands. Within exposed sandy sediments, and across a vertical scale (from inshore to nearshore sandy habitats), I also demonstrate that the effects of the presence of algal mats on intertidal benthic invertebrate communities is more pronounced than that on subtidal benthic invertebrate assemblages, but also than on flatfish communities. Focussing on small-scale variations in the most affected faunal group (i.e. benthic invertebrates living at low shore), this thesis reveals a decrease in overall beta-diversity along a eutrophication-gradient manifested in the form of green tides, as well as the increasing importance of biological variables in explaining ecological variability of sandy beach macrobenthic assemblages along the same gradient. To illustrate the processes associated with the structural shifts observed where green tides occurred, I investigated the effects of high biomasses of opportunistic macroalgae (Ulva spp.) on the trophic structure and functioning of sandy beaches. This work reveals a progressive simplification of sandy beach food web structure and a modification of energy pathways over time, through direct and indirect effects of Ulva mats on several trophic levels. Through this thesis I demonstrate that highly dynamic systems respond differently (e.g. shift in δ13C, not in δ15N) and more subtly (e.g. no mass-mortality in benthos was found) to anthropogenic stress compared to what has been previously shown within more sheltered and non-tidal systems. Obtaining these results would not have been possible without the approach used through this work; I thus present a framework coupling field investigations with analytical approaches to describe shifts in highly variable ecosystems under human-induced stress.