This thesis deals with curve detection in images. First, current methods used in this area of image processing are summarized and described. Main topic of this thesis is a comparison of methods of parametric curve detection, such as Hough transformation and RANSAC-based methods. These methods are compared according to several criteria which are the most important for precise edge detection.

The application of Image Processing to Autonomous driving has drawn significant attention in recently. However, the demanding nature of the image processing algorithms conveys a considerable burden to any conventional realtime implementation. On the other hand, the emergence of FPGAs has brought numerous facilities toward fast prototyping and implementation of ASICs so that an image processing algorithm can be designed, tested and synthesized in a relatively short period in comparison to traditional approaches. This thesis investigates the best combination of current algorithms to reach an optimum solution to the problem of lane detection and tracking, while aiming to fit the design to a minimal system. The proposed structure realizes three algorithms, namely Edge Detector, Hough Transform, and Kalman filter. For each module, the theoretical background is investigated and a detailed description of the realization is given followed by an analysis of both achievements and shortages of the design. It is concluded by describing the advantages of implementing this architecture and the use of these kinds of systems. / Tillämpningen av bildbehandling inom autonoma fordon har fått stor uppmärksamhet den senaste tiden. Emellertid förmedlar den krävande karaktären hos bildbehandlingsalgoritmerna en stor belastning på vilken konventionell realtidsimplementering som helst. Å andra sidan har framväxten av FPGAer medfört många möjligheter till snabb prototypering och implementering av ASICar så att en bildbehandlingsalgoritm kan utformas, testas och syntetiseras på relativt kort tid jämfört med traditionella tillvägagångssätt. Denna avhandling undersöker den bästa kombinationen av nuvarande algoritmer för att uppnå en optimal lösning på problemet med spårning och fildetektering, med målet att krympa designen till ett minimalt system. Den föreslagna strukturen realiserar tre algoritmer, nämligen Edge Detector, Hough Transform och Kalman filter. För varje modul undersöks den teoretiska bakgrunden och en detaljerad beskrivning av realiseringen ges följd av en analys av både fördelar och brister i konstruktionen. Avhandlingen avslutas med en beskrivning av fördelarna med att implementera lösningen på det sätt den görs och hur dessa system kan användas.

Internet of Things (IoT) is a system of interrelated computing devices with the ability to transfer data over the network and collected by the applications that rely on fresh information, where the freshness of data can be measured by a metric called Age of Information (AoI). Age of Information is the time that is measured by the receiving node from the time the data has generated at the source. It is an important metric for many IoT applications such as, collecting data from temperature sensors, pollution rates in a specific city. However, the bottleneck problem occurs at sensors because they are constrained devices in terms of energy (power via battery), and also have limited memory and computational power. Therefore, they cannot serve many requests at the same time and thus, it will decrease the information quality which means more unnecessary aging. As a result, we suggest as a solution a distributed system that takes into account the AoI transmitted by the sensors so that IoT applications will receive the expected information quality. This thesis describes the three algorithms that can be used tobuild and test three different topologies. The first algorithm builds a Random graph while second and thirds algorithms shapes Clustered and Hybrid graphs respectively. For testing, we use Python based SimPy package which is a process-based discrete-event simulation framework. Finally, we compare Random, Clustered and Hybrid graphs results. Overall, the Hybrid graph delivers more fresh information than other graphs. / Internet of Things (IoT) är ett system med sammanhängande datorenheter med förmågan att överföra data över nätverket och samlas in av applikationer som förlitar sig på ny information, där datorns färskhet kan mätas med en metrisk som kallas Age of Information (AoI ). Age of Information är den tid som mäts av den mottagande noden från den tid datan har genererat vid källan. Det är en viktig metrisk för många IoT-applikationer, till exempel att samla in data från temperatursensorer, föroreningar i en specifik stad. Flaskhalsproblemet uppstår emellertid vid sensorer eftersom de är begränsade enheter i termer av energi (ström via batteri), och också har begränsat minne och beräkningskraft. Därför kan de inte betjäna många förfrågningar samtidigt och det kommer därför att minska informationskvaliteten vilket innebär mer onödigt åldrande. Som ett resultat föreslår vi som en lösning ett distribuerat system som tar hänsyn till AoI som sänds av sensorerna så att IoT-applikationer får den förväntade informationskvaliteten. Den här avhandlingen beskriver de tre algoritmerna som kananvändas för att bygga och testa tre olika topologier. Den första algoritmen bygger ett slumpmässigt diagram medan andra och tredjedels algoritmer formar Clustered respektive hybriddiagram. För testning använder vi ett Python-baserat SimPy-paket somär en processbaserad diskret händelsimuleringsram. Slutligen jämför vi slumpmässiga, klusterade och hybriddiagramresultat. Sammantaget ger hybridgrafen mer ny information än andra grafer.

As the understanding of the workings of the biological world expands, biomimetic designs increasingly move into the focus of engineering research studies. For this thesis, two studiesinvolving leading edge vortex generation for lift production as observed in nature were explored intheir respective flow regimes. The first study focused on the steady state analysis of streamwise vortices generated byleading edge tubercles of an adult humpback whale flipper. A realistic scaled model of a humpbackflipper was fabricated based on the 3D reconstruction from a sequence of 18 images taken whilecircumscribing an excised flipper of a beached humpback whale. Two complementary modelswith smooth leading edges were transformed from this original digitized model and fabricatedfor testing to further understand the effect of the leading edge tubercles. Experimentally-obtainedforce and qualitative flow measurements were used to study the influence of the leading edgetubercles. The presence of leading edge tubercles are shown to decrease maximum lift coefficient(Cl ), but increase Cl production in the post-stall region. By evaluating a measure of hydrodynamicefficiency, humpback whale flipper geometry is shown to be more efficient in the pre-stall regionand less efficient in the post-stall region as compared to a comparable model with a smooth leadingedge. With respect to a humpback whale, if the decrease in efficiency during post-stall angles ofattack was only required during short periods of time (turning), then this decrease in efficiencymay not have a significant impact on the lift production and energy needs. For the pursuit ofbiomimetic designs, this decrease in efficiency could have potential significance and should beinvestigated further. Qualitative flow measurements further demonstrate that these force results aredue to a delay of separation resulting from the presence of tubercles.The second study investigated explored the effects of flapping frequency on the passive flowcontrol of a flapping wing with a sinusoidal leading edge profile. At a flapping frequency of f =0.05 Hz, an alternating streamwise vortical formation was observed for the sinusoidal leading edge,while a single pair of vortices were present for the straight leading edge. A sinusoidal leading edgecan be used to minimize spanwise flow by the generation of the observed alternating streamwisevortices. An increase in flapping frequency results in these streamwise vortices becoming stretchedin the path of the wing. The streamwise vortices are shown to minimize spanwise flow even afterbeing stretched. Once instabilities are formed at f ≥ 0:1 Hz due to velocity shearing generatedby the increase in cross-radial velocity, the alternating streamwise vortices begin to break downresulting in a increase of spanwise flow.

Um Systeme der fünften Generation zellularer Kommunikationsnetze (5G) zu ermöglichen, sind Energie effiziente Architekturen erforderlich, die eine zuverlässige Serviceplattform für die Bereitstellung von 5G-Diensten und darüber hinaus bieten können. Device Enhanced Edge Computing ist eine Ableitung des Multi-Access Edge Computing (MEC), das Rechen- und Speicherressourcen direkt auf den Endgeräten bereitstellt. Die Bedeutung dieses Konzepts wird durch die steigenden Anforderungen von rechenintensiven Anwendungen mit extrem niedriger Latenzzeit belegt, die den MEC-Server allein und den drahtlosen Kanal überfordern. Diese Dissertation stellt ein Berechnungs-Auslagerungsframework mit Berücksichtigung von Energie, Mobilität und Anreizen in einem gerätegestützten MEC-System mit mehreren Benutzern und mehreren Aufgaben vor, das die gegenseitige Abhängigkeit der Aufgaben sowie die Latenzanforderungen der Anwendungen berücksichtigt. / To enable fifth generation cellular communication network (5G) systems, energy efficient architectures are required that can provide a reliable service platform for the delivery of 5G services and beyond. Device Enhanced Edge Computing is a derivative of Multi-Access Edge Computing (MEC), which provides computing and storage resources directly on the end devices. The importance of this concept is evidenced by the increasing demands of ultra-low latency computationally intensive applications that overwhelm the MEC server alone and the wireless channel. This dissertation presents a computational offloading framework considering energy, mobility and incentives in a multi-user, multi-task device-based MEC system that takes into account task interdependence and application latency requirements.

Knowledge about forest measurements is essential for efficient and sustainableforestry. One important measurement is wood volume, both from an economicand an environmental perspective. Therefore it is crucial to measure wood vol-ume accurately and reliably. With airborne laser scanners, wood volume can beestimated at a large scale, more time efficiently than traditional, manual measure-ments. By utilising deep neural networks, we present methods to predict woodvolume on point clouds efficiently and accurately. Different network structuresfor point cloud regression are devised using field measurements from wood har-vesters and manual field measurements. To achieve more data, techniques tosplit up areas into smaller subareas and data augmentation methods were imple-mented. Our version of GDANet adapted for regression provided the best resultswith a RMSE of 62.68, MAPE of 24.7%, and relative RMSE of 28.0%. Furthermore,the final model produces more accurate wood volume predictions than more shal-low machine learning methods and predictions from Skoglig Grunddata.

Bridge edge beam system is an increasing concern in Sweden. Because it is the mostvisible part of the structure which is subjected to harsh weather. The edge beamcontributes to the stiffness of overhang slab and helps to distribute the concentratedload. The design of edge beam is not only affected by the structural members, but it isalso affected by non-structural members.The aim of the thesis is to investigate the influence of edge beam on the structuralbehavior of reinforced concrete overhang slab. A three-dimensional (3D) non-linearfinite element model is developed by using the commercial software ABAQUS version6.1.14. The load displacement curves and failure modes were observed. The bendingmoment and shear capacity of the cantilever slab is studied.The validated model from non-linear analysis of reinforced concrete slab gives morestiffer result and leads to the high value of load capacity when comparing with theexperimental test. The presence of the edge beam in the overhang slab of length 2.4 mslightly increases the load capacity and shows ductile behavior due to the self-weightof the edge beam. The non-linear FE-analysis of overhang slab of length 10 m leads tomuch higher load capacity and gives stiffer response as compare to the overhang slabof 2.4 m. The presence of the edge beam in the overhang slab of length 10 m giveshigher load capacity and shows stiffer response when comparing with the overhangslab of length 10 m. This might be due to the self-weight of the edge beam and theoverhang slab is restrained at the right side of the slab.

[pt] A importância da Internet das Coisas (IoT) tem aumentado significativamente nos últimos anos, e dispositivos IoT têm sido usados em diferentes indústrias e tipos de aplicação, como casas inteligentes, sensores indutriais, veículos autonomos, wearables, etc. Apesar deste cenário trazer inovações tecnológicas, novas experiências para usuários, e novas soluções de negócio, também levanta preocupações relevantes relacionadas a segurança da informação e privacidade. Neste trabalho nós apresentamos o EdgeSec Framework, um novo framework de segurança para IoT desenvolvido como uma solução de segurança para os middlewares ContextNet e Mobile-Hub. O seu objetivo principal é estender e melhorar uma arquitetura e uma implementação já existentes para estes middlewares, criando uma solução mais genérica, robusta e flexível,e garantindo autenticação, autorização, integridade e confidencialidade de dados. O framework foi elaborado com foco na total extensiblidade através da introdução de interfaces de protocolos, que podem ser implementadas por plugins, tornando-o compatível com uma variedade de algoritmos de segurança e dispositivos IoT. Uma implementação completa foi realizada como prova de conceito, e testes de desempenho e experimentos foram realizados para avaliar a viabilidade da solução. Os resultados mostram que o EdgeSec Framework pode melhorar significativamente a segurança do Mobile-Hub e diversos tipos de aplicações IoT através de uma maior compatibilidade e flexibilidade, e garantindo todas as proteções básicas de segurança. / [en] The importance of the Internet of Things (IoT) has increased significantly in recent years, and IoT devices are being used in many different industries and types of applications, such as smart homes, industrial sensors, autonomous vehicles, personal wearables, and more. While this brings technology innovation, new user experiences, and new business solutions, it also raises important concerns related to information security and privacy. In this work we present EdgeSec Framework, a new IoT security framework, made concrete as a security solution for ContextNet and Mobile-Hub middlewares. Its main goal is to extend and improve on an existing security architecture and implementation, creating a more generic, robust, and flexible solution that ensures authentication, authorization, data integrity and confidentiality. The framework was designed with full extensibility in mind by introducing protocol interfaces that can be implemented by external plugins, making it compatible to a variety of security algorithms and edge devices. A complete implementation was developed as proof-of-concept, and performance tests and experiments were made to evaluate the feasibility of the solution. Results show that EdgeSec framework can greatly improve the security of Mobile-Hub and similar IoT middlewares by increasing its compatibility and flexibility, and ensuring all the basic security protections.

The field of autonomous robots have been explored more and more over the last decade. The combination of machine learning advances and increases in computational power have created possibilities to explore the usage of machine learning models on edge devices. The usage of object detection on edge devices is bottlenecked by the edge devices' limited computational power and they therefore have constraints when compared to the usage of machine learning models on other devices. This project explored the possibility to use real time object detection on a Raspberry Pi as input in different control systems. The Raspberry with the help of a coral USB accelerator was able to find a specified object and drive to it, and it did so successfully with all the control systems tested. As the robot was able to navigate to the specified object with all control systems, the possibility of using real time object detection in faster paced situations can be explored. / Ämnet autonoma robotar har blivit mer och mer undersökt under de senaste årtiondet. Kombinationen av maskin inlärnings förbättringar och ökade beräknings möjligheter hos datorer och chip har gjort det möjligt att undersöka användningen av maskin inlärningsmodeller på edge enheter. Användandet av bildigenkänning på edge enheter är begränsad av edge enheten begränsade datorkraft, och har därför mer begränsningar i jämförelse med om man använder bildigenkänning på en annan typ av enhet. Det här projektet har undersökt möjligheten att använda bildigenkänning i realtid som input för kontrollsystem på en Raspberry Pi. Raspberry Pien med hjälp av en Coral USB accelerator lyckades att lokalisera och köra till ett specificerat objekt, Raspberryn gjorde detta med alla kontrollsystem som testades på den. Eftersom roboten lyckades med detta, så öppnas möjligheten att använda bildigenkänning på edge enheter i snabbare situationer.

A Linear Program is a problem where the goal is to maximize a linear function subject to a set of linear inequalities. Geometrically, this can be rephrased as finding the highest point on a polyhedron. The Simplex method is a commonly used algorithm to solve Linear Programs. It traverses the vertices of the polyhedron, and in each step, it selects one adjacent better vertex and moves there. There can be multiple vertices to choose from, and therefore the Simplex method has different variants deciding how the next vertex is selected. One of the most natural variants is Random Edge, which in each step of the Simplex method uniformly at random selects one of the better adjacent vertices. It is interesting and non-trivial to study the complexity of variants of the Simplex method in the number of variables, d, and inequalities, N. In 2011, Friedmann, Hansen, and Zwick found a class of Linear Programs for which the Random Edge algorithm is subexponential with complexity 2^Ω(N^(1/4)), where d=Θ(N). Previously all known lower bounds were polynomial. We give an improved lower bound of 2^Ω(N^(1/2)), for Random Edge on Linear Programs where d=Θ(N). Another well studied variant of the Simplex method is Random Facet. It is upper bounded by 2^O(N^(1/2)) when d=Θ(N). Thus we prove that Random Edge is not faster than Random Facet on Linear Programs where d=Θ(N). Our construction is very similar to the previous construction of Friedmann, Hansen and Zwick. We construct a Markov Decision Process which behaves like a binary counter with linearly many levels and linearly many nodes on each level. The new idea is a new type of delay gadget which can switch quickly from 0 to 1 in some circumstances, leading to fewer nodes needed on each level of the construction. The key idea is that it is worth taking a large risk of getting a small negative reward if the potential positive reward is large enough in comparison. / Ett linjärt program är ett problem där målet är att maximiera en linjär funktion givet en mängd linjära olikheter. Geometriskt kan detta omformuleras som att hitta den högsta punkten på en polyeder. Simplexmetoden är en algoritm som ofta används för att lösa linjära program. Den besöker hörnen i polyedern, och i varje steg väljer den ett närliggande bättre hörn och flyttar dit. Det kan finnas flera hörn att välja mellan, och därför finns det olika varianter av simplexmetoden som bestämmer hur nästa hörn ska väljas. En av de mest naturliga varianterna är Random Edge, som i varje steg av simplexmetoden, uniformt slumpmässigt väljer ett av de närliggande bättre hörnen. Det är intressant och icke-trivialt att studera komplexiteten av olika varianter av simplexmetoden i antalet variabler, d, och olikheter N. År 2011 hittade Friedmann, Hansen och Zwick en familj av linjära program där Random Edge är subexponentiell med komplexitet 2^Ω(N^(1/4)), där d=Θ(N). Innan dess var alla kända undre gränser polynomiska. Vi ger en förbättrad undre gräns på 2^Ω(N^(1/2)), för Random Edge på linjära program där d=Θ(N). En annan välstuderad variant av simplexmetoden är Random Facet. Dess komplexitet har en övre gräns på 2^O(N^(1/2)) när d=Θ(N). Alltså bevisar vi att Random Edge inte är snabbare än Random Facet på linjära program där d=Θ(N). Vår konstruktion är väldigt lik den tidigare konstruktionen av Friedmann, Hansen och Zwick. Vi konstruerar en Markov-beslutsprocess som beter sig som en binär räknare med linjärt många nivåer och linjärt många noder på varje nivå. Den nya idén är en ny typ av försenings-multinod som kan byta snabbt från 0 till 1 i vissa fall, vilket leder till att det behövs färre noder på varje nivå av konstruktionen. Nyckelidén är att det är värt att ta en stor risk att få en liten negativ poäng om den potentiella positiva poängen är stor nog i jämförelse.