Real-time scheduling policies have been widely studied, with many known schedulability and feasibility analysis techniques for different task models, that have advanced the state-of-the-art. Most of these techniques are typically derived under the assumption of negligible runtime overheads which may not be realistic for modern embedded real-time systems, and hence potentially compromises the guarantees on their correct behaviors. This calls for methods to reason about the functioning of the system under the presence of such overheads as well as to predictably control them. Controlling these overheads may place additional performance demands, consequently requiring more resources such as faster processors. At the same time, the need for energy efficiency in these class of systems further complicates the problem and necessitates a holistic approach. In this thesis, we apply resource augmentation, viz., processor speed-up, to guarantee desired real-time properties even under the presence of runtime overheads. We specifically consider preemptions and faults that, at runtime, manifest as overheads in the system in various ways. Our aim is to provide specified non-preemption and fault tolerance feasibility guarantees in a real-time system. We first propose offline and online methods, that uses CPU frequency scaling, to control the number of preemptions in periodic and sporadic task systems, under a preemptive Fixed Priority Scheduling (FPS) policy. Furthermore, we derive the resource augmentation bound, specifically the upper-bound on the lowest processor speed, that guarantees the feasibility of a specified non-preemption behavior for any real-time task. We show that, for any task Ti , the resource augmentation bound that guarantees a non- reemptive execution for a specified duration Li , is given by 4Li/Dmin, where Dmin  is the shortest deadline in the task set. Consequently, we show that the upper-bound on the lowest processor speed that guarantees the feasibility of a non-preemptive schedule for the task set is 4Cmax/Dmin, where Cmax  is the largest execution time in the task set. We then propose a method to guarantee specified upper-bounds on the preemption related overheads in the schedule. We first translate the requirements of meeting specified upper-bounds on the preemption related overheads to a set of non-preemption requirements for the task set. The resource augmentation bound in conjunction with a sensitivity analysis is used to calculate the optimal processor speed that guarantees the derived non-preemption requirements, achieving the specified bounds on the preemption related costs. Finally, we derive the resource augmentation bound that guarantees the fault tolerance feasibility of a set of real-time tasks under an error burst of known length. We show that if the error burst length is no longer than half the shortest deadline in the task set, the resource augmentation bound that guarantees fault tolerance feasibility is 6.  Our contribution bounds the extra resources, specifically the required processor speed-up, that provides specified non-preemption and fault tolerance feasibility guarantees in a real-time system. It allows us to quantify the 'goodness' of non-preemptive scheduling, referred to as its sub-optimality, as compared to an optimal uni-processor scheduling algorithm, in terms of the required processor speed-up that guarantees a non-preemptive schedule for any uni-processor feasible task set. We intend to extend this work to provide non-preemption and fault tolerance feasibility guarantees in multi-processor systems.

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A High-order accurate finite difference scheme is derived for a non-linear soliton model of nerve signal propagation in axons. Boundary conditions yielding well-posed problems are suggested and included in the scheme using a penalty technique. Stability is shown using the summation-by-parts framework for a frozen parameter version of the non-linear problem.

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Luftstridsskolan upplever problem med deras digitala resurser eftersom de saknar överblick och vet inte hur studerande använder dessa. Infor- mationsarkitektur är ett område som bemöter dessa problem och dess metodiska ramverk kommer i detta arbete att användas för att kartlägga resurser, se hur dessa används och slutligen skapa en ny struktur att pre- sentera resurserna i.

This thesis develops a Gaussian processes model for bankruptcy risk classification and prediction in a Bayesian framework. Gaussian processes and linear logistic models are discriminative methods used for classification and prediction purposes. The Gaussian processes model is a much more flexible model than the linear logistic model with smoothness encoded in the kernel with the potential to improve the modeling of the highly nonlinear relationships between accounting ratios and bankruptcy risk. We compare the linear logistic regression with the Gaussian process classification model in the context of bankruptcy prediction. The posterior distributions of the GPs are non-Gaussian, and we investigate the effectiveness of the Laplace approximation and the expectation propagation approximation across several different kernels for the Gaussian process. The approximate methods are compared to the gold standard of Markov Chain Monte Carlo (MCMC) sampling from the posterior. The dataset is an unbalanced panel consisting of 21846 yearly observations for about 2000 corporate firms in Sweden recorded between 1991−2008. We used 5000 observations to train the models and the rest for evaluating the predictions. We find that the choice of covariance kernel affects the GP model’s performance and we find support for the squared exponential covariance function (SEXP) as an optimal kernel. The empirical evidence suggests that a multivariate Gaussian processes classifier with squared exponential kernel can effectively improve bankruptcy risk prediction with high accuracy (90.19 percent) compared to the linear logistic model (83.25 percent).

The purpose of the project was to make a system that could extract data from a mobile eye tracker and make it comparable with data from visual stimuli. The produced system was programmed in Java and provided all the necessary parts that were required to achieve the purpose. This provides a foundation for further research to determine whether the eye tracker is sufficiently accurate to diagnose Parkinson’s disease.

Automation of model building enables new predictive models to be generated in a faster, easier and more straightforward way once new data is available to predict on. Automation can also reduce the demand for tedious bookkeeping that is generally needed in manual workflows (e.g. intermediate files needed to be passed between steps in a workflow). The applicability of the Spark framework related to the creation of pipelines for predictive drug discovery was here evaluated and resulted in the implementation of two pipelines that serves as a proof of concept. Spark is considered to provide good means of creating pipelines for pharmaceutical purposes and its high level approach to distributed computing reduces the effort put on the developer compared to a regular HPC implementation.

Parallel computing is on the rise and the applications are steadily growing. This thesis will consider one such application, namely computational electromagnetic modeling (CEM). The methods that are being used as of today are usually computationally heavy which makes them time-consuming and not considered as viable options for some applications. In this thesis a parallel solution of the FDTD method has been presented. In the results it is shown to be faster than the sequential solutions upon which it is based, and alterations are suggested which could improve it further.

During the last two decades the interest about computer vision raised steadily with multiple applications in fields like medical care, automotive, entertainment, retail, industrial, and security. Objectdetection is part of the recognition problem, which is the most important scope of the computervision environment.The target of this thesis work is to analyse and propose a solution for object detection in a real timedynamic environment. RoboCup@Home will be the benchmarking event for this system, which willbe equipped on a robot competing in the 2018 event. The system has to be robust and fast enoughto allow the robot to react to each environment change in a reasonable amount of time.The input hardware used to achieve such system comprise of a Microsoft Kinect, which providesan high definition camera and fast and reliable 3D scanner. Through the study and analysis ofstate-of-the-art algorithms regarding machine vision and object recognition, the more suitable oneshave been tested to optimise the execution on the targeted hardware. Porting of the application toan embedded platform is discussed.