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  • Disputas: 2019-12-06 13:30 Delta, Västerås
    Hellström, Per Anders Rickard
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Wearable Pedobarography System for Monitoring of Walk Related Parameters2019Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Health care costs have increased over the last decades due to an ageing population. Therefore, research in personal health monitoring (PHM) has increased in response to this. PHM has advantages such as mobility (monitoring of health at work or at home), early detection of health problems enabling preventive health measures and a reduction of health care cost. Human motion analysis, using for example pedobarography (PBG), is an important subcategory of PHM. PBG is used to study the force fields acting between the plantar surface of the foot and a supporting surface. Gait and posture analysis, prosthetics evaluation and monitoring of recovery from injury or disease are examples of PBG applications. Portable PBG can be performed using force sensing resistors built into the insole inside the shoe.

     In accordance with this, the research aim for this thesis is to design, build and evaluate a wireless wearable measurement system based on PBG for monitoring of walk related parameters. Monitoring of carried weight and walking speed were chosen as the applications for validation of the system. Motivations for choosing these applications are that there is a lack of a wearable system for monitoring of weight while walking and a possible combination with accelerometers to improve the estimation of walking speed. Both walking speed and weight are important factors for estimating energy expenditure. A portable system, that estimates weight while walking, enables monitoring of heavy working conditions.

    The main research contributions include design of a PBG measurement system with a sensor implementation resulting in good sensor durability, several novel methods for weight estimation during walk and a novel method for analysing walking intensity and relating it to walking speed. The research results show that the new PBG system, in combination with the novel analysing methods, are suitable for use in wearable systems for monitoring of health related walk parameters.

  • Disputas: 2019-12-18 13:15 Delta, Västerås
    Leon, Miguel
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. Mälardalens Högskola.
    IMPROVING DIFFERENTIAL EVOLUTION WITH ADAPTIVE AND LOCAL SEARCH METHODS2019Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Differential Evolution (DE) is a population-based algorithm that belongs to the Evolutionary algorithm family. During recent years, DE has become a popular algorithm in optimization due to its strength solving different types of optimization problems and due to its easy usage and implementation.

    However, how to choose proper mutation strategy and control parameters for DE presents a major difficulty in many real applications. Since both mutation strategy and DE control parameters are highly problem dependent, they have to be adapted to suite different search spaces and different problems. Failure in proper assignment for them will cause slow convergence in search or stagnation with a local optimum.

    Many researches have been conducted to tackle the above issues. The major efforts have been made in the following three directions. First, some works have been proposed that adapt the selection between various mutation strategies. But the choice of strategies in these methods has not considered the difference of quality of individuals in the population, which means that all individuals will acquire the same probability to select a mutation strategy from the candidates. This does not seem a very desired practice since solutions of large difference would require different mutation operators to reach improvement. Second, many works have been focusing on the adaptation of the control parameters of DE (mutation factor (F) and crossover rate (CR)). They mainly rely on previous successful F and CR values to update the probability functions that are used to generate new F and CR values. By doing this, they ignore the stochastic nature of the operators in DE such that weak F and CR values can also get success in producing better trial solutions. The use of such imprecise experiences of success would prevent the DE parameters from being adapted towards the most effective values in coming generations. Third, various local search methods have been incorporated into DE to enhance exploitation in promising regions so as to speed up the convergence to optima. It is important to properly adjust the characteristics of the local search in DE to achieve well balanced exploratory/exploitative behavior to solve complex optimization problems.

    This thesis aims to further improve the performance of DE by new adaptation and local search methods. The main results can be summarized in the following three aspects:

    1) Proposal of a new rank-based mutation adaptation method, which takes into account the quality of solutions in the population when adapting the selection probabilities of mutation strategies. This makes possible to treat solutions with distinct ranks (in quality) differently by using different selection probabilities for mutation operators.

    2) Development of improved parameter adaptation methods for DE, which emphasizes more reliable and fair evaluation of candidates (F and CR assignments) during the search process. It is suggested that greedy search being used as a fast and cheap technique to look for better parameter assignment for F and CR respectively in the neighborhood of a current candidate. Further, a joint parameter adaptation method is proposed that enables continuous update of the selection probabilities for F and CR pairs based on feedback acquired during the search.

    3) Proposal of new methods for better incorporation of local search into a DE algorithm. The Eager Random Search method is investigated as local search inside DE, which exhibits different exploratory-exploitative characteristics by using different probability density functions. More importantly, we propose a novel memetic framework in which Alopex local search (ALS) is performed in collaboration with a DE algorithm. The framework favors seamless connection between exploration and exploitation in the sense that the behavior of exploitation by ALS can be controlled by the status of global exploration by DE.

    The proposed methods and algorithms have been tested in a number of benchmark problems, obtaining competitive results compared with the state-of-the-art algorithms. Additionally, the Greedy Adaptive DE (GADE) algorithm (developed based on greedy search for DE parameters) has been tested in a real industrial problem, i.e., finding best component parameters to optimize the performance of harmonic filters for power transmission. GADE is shown to produce better harmonic filter systems with lower harmonic distortion than the standard DE.

  • Disputas: 2020-01-10 13:15 Beta, Västerås
    Källberg, Linus
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Minimum Enclosing Balls and Ellipsoids in General Dimensions2019Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    In this doctoral thesis, we study the problem of computing the ball of smallest radius enclosing a given set of points in any number of dimensions. Variations of this problem arise in several branches of computer science, such as computer graphics, artificial intelligence, and operations research. Applications range from collision detection for three-dimensional models in video games and computer-aided design, to high-dimensional clustering and classification in machine learning and data mining. We also consider the related and more challenging problem of finding the enclosing ellipsoid of minimum volume. Such ellipsoids can provide more descriptive data representations in the aforementioned applications, and they find further utility in, for example, optimal design of experiments and trimming of outliers in statistics.

    The contributions of this thesis consist of practical methods for the efficient solution of these two problems, with a primary focus on problem instances involving a large number of points. We introduce new algorithms to compute arbitrarily fine approximations of the minimum enclosing ball or ellipsoid in general dimensions. In our experimental evaluations, these algorithms exhibit running times that are highly competitive with, and often markedly superior to, those of earlier algorithms from the literature. Moreover, we present a new out-of-core algorithm to compute the exact minimum enclosing ball for massive, low-dimensional point sets residing in secondary storage. In addition to these solution methods, we develop acceleration techniques that can further improve their performance, either by using pruning heuristics to reduce the amount of work performed in each iteration, or by utilizing parallel hardware features of modern processors and graphics processing units. These techniques are also applicable to several existing algorithms.

    Fulltekst tilgjengelig fra 2019-12-20 08:00
  • Disputas: 2020-01-28 09:15 Kappa, Västerås
    Ahlberg, Carl
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Embedded high-resolution stereo-vision of high frame-rate and low latency through FPGA-acceleration2020Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Autonomous agents rely on information from the surrounding environment to act upon. In the array of sensors available, the image sensor is perhaps the most versatile, allowing for detection of colour, size, shape, and depth. For the latter, in a dynamic environment, assuming no a priori knowledge, stereo vision is a commonly adopted technique. How to interpret images, and extract relevant information, is referred to as computer vision. Computer vision, and specifically stereo-vision algorithms, are complex and computationally expensive, already considering a single stereo pair, with results that are, in terms of accuracy, qualitatively difficult to compare. Adding to the challenge is a continuous stream of images, of a high frame rate, and the race of ever increasing image resolutions. In the context of autonomous agents, considerations regarding real-time requirements, embedded/resource limited processing platforms, power consumption, and physical size, further add up to an unarguably challenging problem.

    This thesis aims to achieve embedded high-resolution stereo-vision of high frame-rate and low latency, by approaching the problem from two different angles, hardware and algorithmic development, in a symbiotic relationship. The first contributions of the thesis are the GIMME and GIMME2 embedded vision platforms, which offer hardware accelerated processing through FGPAs, specifically targeting stereo vision, contrary to available COTS systems at the time. The second contribution, toward stereo vision algorithms, is twofold. Firstly, the problem of scalability and the associated disparity range is addressed by proposing a segment-based stereo algorithm. In segment space, matching is independent of image scale, and similarly, disparity range is measured in terms of segments, indicating relatively few hypotheses to cover the entire range of the scene. Secondly, more in line with the conventional stereo correspondence for FPGAs, the Census Transform (CT) has been identified as a recurring cost metric. This thesis proposes an optimisation of the CT through a Genetic Algorithm (GA) - the Genetic Algorithm Census Transform (GACT). The GACT shows promising results for benchmark datasets, compared to established CT methods, while being resource efficient.