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In this thesis work, we evaluate an object recognition system for an autonomous wheel loader, to detect objects in its vicinity, in particular an articulated hauler truck, by using an interest point extraction method that explicitly considers object borders information, combined with a feature descriptor known as Normal Aligned Radial Features (NARF) in 3D point cloud data. The object recognition technique relies on extraction of NARF from range images (computed from point clouds) for both model(hauler) and the scene. The technique used is robust feature matching where the extracted model features are mapped on to the scene containing the model and then seeking for a best transformation that aligns the model with respect to the scene. In this context we conducted several experiments with many number of 3D scans obtained from the laser scanner mounted on the top of an autonomous wheel loader to analyze the accuracy of the object recognition system. Finally we demonstrated the results, as the system is able to recognize the hauler from any view point. vii

Feature selection is the task of selecting a small subset from original features that can achieve maximum classification accuracy. This subset of features has some very important benefits like, it reduces computational complexity of learning algorithms, saves time, improve accuracy and the selected features can be insightful for the people involved in problem domain. This makes feature selection as an indispensable task in classification task. This dissertation presents a two phase approach for feature selection. In the first phase a filter method is used with “correlation coefficient” and “mutual information” as statistical measure of similarity. This phase helps in improving the classification performance by removing redundant and unimportant features. A wrapper method is used in the second phase with the sequential forward selection and sequential backward elimination. This phase helps in selecting relevant feature subset that produce maximum accuracy according to the underlying classifier. The Support Vector Machine (SVM) classifier (linear and nonlinear) is used to evaluate the classification accuracy of our approach. This empirical results of commonly used data sets from the University of California, Irvine repository and microarray data sets showed that the proposed method performs better in terms of classification accuracy, number of selected features, and computational efficiency. 7

Quadrocopter is an aerial vehicle platform which has become very popular among researchers in the recent past due to the advantages it offers over conventional helicopters. Quadrocopter is very simple and unique, but it is inherently unstable from aerodynamics point of view. In recent past researchers have proposed many control schemes for quadrocopter. In this thesis we present fuzzy logic controller for quadrocopter. After brief introduction brief hardware details are given that is used in this thesis. After that design procedure for the fuzzy controller is presented. Then the designed fuzzy controller is tested in Hardware In Loop (HIL) setup. The experimentation to validate the functionality and applicability of the designed controller were performed in contrained setup due to some technical problems. The results of the experiments were satisfactory and it is concluded that it is possible to stabilize quadrocopter with fuzzy logic controller.

To date, many industrial embedded systems are very large, flexible, and highly configurable software systems, containing millions of lines of code and consisting of hundreds of tasks, many with real-time constraints, being triggered in complex, nested patterns. Furthermore, the temporal dependencies between tasks in such systems are difficult to determine analytically, and they vary the execution time and response time of tasks greatly. We refer to such systems as Complex Real-Time Embedded Systems (CRTES). To maintain, analyze and reuse such CRTES is very difficult and expensive, which, nevertheless, offers high business value in response to great concern in industry. Moreover, in such context, not only the functional behavior of systems has to be assured, but also non-functional properties such as the temporal behavior, i.e., Worst-Case Response Time (WCRT) of the adhering tasks in systems has to be known. However, due to high complexity of such systems and the nature of the problem, the exact WCRT of tasks is impossible to find in practice, but may only be bounded. In addition, the existing relatively well-developed theories for modeling and analysis of real-time systems are having problems, which limit their application in the context. In this thesis, we address this challenge, and present a framework for approximate timing analysis of CRTES that provides a tight interval of WCRT estimates of tasks by the usage of three novel contributions. The first contribution is a novel statistical approach to WCRT analysis of CRTES. The proposed algorithm combines Extreme Value Theory (EVT) with other statistical methods in order to produce a probabilistic WCRT estimate, using response time data from either Monte Carlo simulations of a detailed model of the system, or time-stamped traces of the real system execution. The focus of the method is to give a WCRT prediction with a given probability of being exceeded, which potentially could be considered as an upper bound on the WCRT estimate in systems, especially in the case where conventional timing analysis methods cannot be applied. The second contribution is to introduce a concrete process of formally obtaining the exact value of both Worst-Case Execution Time (WCET) and WCRT of tasks in the system model by using upper-part binary search algorithms together with a timed model checker, after a semantic-preserving model transformation. The underline premise is that the size and complexity of CRTES have to be reduced such that they can be manageable by the model checking tool. The third contribution is to apply an optimization algorithm, in this case a meta-heuristic search algorithm, on top of the traditional Monte Carlo simula-tion, which yields substantially better results with respect to tight lower bounds on WCRT estimates of tasks in CRTES. In addition, a number of tools have been implemented and used for the evaluation of the research results. These evaluations, using four simulation models depicting two fictive but representative industrial control applications, give clear indication that the proposed methods have the potential to be both applicable and useful in practice.

The Hierarchical Scheduling Framework (HSF) has been introduced to enable compositional schedulability analysis and execution of embedded software systems with real-time constraints. In this thesis, we consider a system consisting of a number of semi-independent components called subsystems, and these subsystems are allowed to share logical resources. The HSF provides CPU-time to the subsystems and it guarantees that the individual subsystems respect their allocated CPU budgets. However, if subsystems are allowed to share logical resources, extra complexity with respect to analysis and run-time mechanisms is introduced. In this thesis we address three issues related to hierarchical scheduling of semi-independent subsystems. In the first part, we investigate the feasibility of implementing the hierarchical scheduling framework in a commercial operating system, and we present the detailed figures of various key properties with respect to the overhead of the implementation. In the second part, we studied the problem of supporting shared resources in a hierarchical scheduling framework and we propose two different solutions to support resource sharing. The first proposed solution is called SIRAP, a synchronization protocol for resource sharing in hierarchically scheduled open real-time systems, and the second solution is an enhanced overrun mechanism. In the third part, we present a resource efficient approach to minimize system load (i.e., the collective CPU requirements to guarantee the schedulability of hierarchically scheduled subsystems). Our work is motivated from a tradeoff between reducing resource locking times and reducing system load. We formulate an optimization problem that determines the resource locking times of each individual subsystem with the goal of minimizing the system load subject to system schedulability. We present linear complexity algorithms to find an optimal solution to the problem, and we prove their correctness

Since the 90ies a bunch of problems and flaws in the old Internet Protocol version four has occurred.The biggest problem is the extinction of addresses which will come soon, soon in the matter of theclassic “wolf is coming” matter, but as in the fairy tale the wolf will finally come. When this will happenis unknown, but not many believes it won’t. Carl-Henrik Swanberg CEO for Ericsson once said that year2020 there will be 50 billion mobile units connected to internet. Considering there is maximum 4 billionaddresses in IPv4 the needs for more addresses will be significant. The solution of this is the version sixof Internet Protocol released in middle of the 90ies. This thesis mentions a lot of the problems with theold version and tries to make a good explanation of the benefits and possibilities that lies within thenew. It also contains a larger amount of information on the protocols that lies within this, like newversion of OSPF, the extensions of BGPv4 called multiprotocol BGP (MP-BGP) and new things likenetwork discovery protocol (NDP). The later chapter is about the planning of an implementation atMälarenergi City Network and the implementation at the internet service provider MDFnet whichnowadays runs IPv6 in their core and office network.

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