The aim of this thesis is to examine the possibility of a court detection program for sports videos that can identify the court even when some important elements are not visible. The study will also analyze what external factors may impact the program's accuracy in detecting all relevant elements.  These questions are answered through a combination of computer vision techniques and algorithms. The study utilizes Design Science Research (DSR) as its research methodology to develop an artifact. A dataset of padel sports videos are evaluated to measure the artifacts accuracy. The artifact utilizes multiple computer vision techniques from the OpenCV library to detect relevant lines and edges and project them onto the frame using a predetermined court model as reference.  The findings indicated that the developed artifact demonstrated a relatively consistent level of accuracy in court detection across multiple courts, whenever a detection was made. However, the frequency of successful detections exhibited some inconsistency. The research also found that external factors did not significantly influence the accuracy of court detection, yet they posed challenges to the program's overall consistency.

Semilocal density functionals up to the generalized gradient approximation (GGA) level cannot accurately describe band gaps of bulk solids. Meta-GGA density functionals with a dependence on the kinetic energy density ingredient (τ) can potentially give wider band gaps compared with GGAs. The recently developed TASK meta-GGA functional yields excellent band gaps of bulk solids. The accuracy of the TASK functional for band gaps of bulk solids cannot be straightforwardly transferred to low-dimensional materials due to reduced screening in low-dimensional materials. We have developed mTASK from TASK by changing (a) the tight upper-bound for one or two-electron systems (h0X) from 1.174 to 1.29 and (b) the limit of the interpolation function fX(α → ∞) of the TASK functional that interpolates the exchange enhancement factor FX(s,α) from α = 0 to 1, so that mTASK has the screening appropriate for low-dimensional materials. These two conditions guarantee the increased nonlocality within the generalized Kohn-Sham scheme in the mTASK functional and yield a better description of band gaps of low-dimensional materials. We computed the band gaps of bulk solids from mTASK having a wide range of gaps such as Ge, CdO, ZnS, MgO, NiF, Ar. The improvement in the band gaps from mTASK is more consistent than TASK for the large-gaps crystals. We have studied the band structures in two forms of transition metal dichalcogenide (TMD) monolayers, i.e., monolayer hexagonal (1H) and monolayer trigonal (1T) and their nanoribbons. The mTASK functional systematically improves the band gaps and is in close agreement with the experiments or the hybrid level HSE06 density functional for 2D single-layer and nanoribbon systems. In the second part of this assessment, we explore the large tunability of band gaps and optical absorption of phosphorene nanoribbons under mechanical bending from first-principles. Bending can induce an unoccupied edge state in armchair phosphorene nanoribbons. The electronic and optical properties of nanoribbons drastically change because of this edge state. GW-Bethe–Salpeter equation calculations for armchair phosphorene nanoribbons at different bending curvatures show that the absorption peaks generally shift toward the high energy direction with increasing curvature. Our study suggests that bright excitons can also be formed from the transition from the valence bands to the edge state when the edge state completely separates out from the continuum conduction bands. We systematically study the role of the edge state to form bound excitons at large curvatures. Our analysis suggests that the optical absorption peaks of zigzag phosphorene nanoribbons shift toward the low-energy region, and the height of the absorption peaks increases while increasingthe bending curvature. In the third part of this assessment, we extend our study of phosphorene nanoribbons to MoS2 nanoribbons under bending from GW and Bethe-Salpeter equation approaches. We find three critical bending curvatures for armchair MoS2 nanoribbons, and the edge and non-edge band gaps show a non-monotonic trend with bending. The edge band gap shows an oscillating feature with ribbon width n, with a period of ∆n=3. The binding energy and the lowest exciton energy decrease with the curvature. The large tunability of optical properties of bent MoS2 nanoribbon is applicable in tunable optoelectronic nanodevices. / Physics

A star edge coloring is any edge coloring which is both proper and contains no cycles or path of length four which are bicolored, and the star chromatic index of a graph is the smallest number of colors for which that graph can be star edge colored. Star edge coloring is a relatively new field in graph theory, and very little is known regarding upper bounds of the star chromatic index of most graph types, one of these families being multipartite graphs. We investigate a method for obtaining upper bounds on the star chromatic index of complete multipartite graphs. The basic idea is to decompose such graphs into smaller complete bipartite graphs and applying known upper bounds for such graphs.This method has also been implemented and we present a hypothesis based on simulations.

The usage of titanium alloys in many industries has increased significantly over the years due to their superior properties. However, they are extremely difficult to machine because of their distinctive characteristics such as their high temperature strength, low thermal conductivity, and high chemical affinity for tool materials. Hence, despite their increased usage, they are still expensive to machine when compared to other metals. The current research aims to address the machinability issues of titanium alloys by developing novel compositions of a new generation of self-adaptive Physical Vapor Deposition (PVD) coatings that function by forming beneficial tribo-films through their interaction with the environment. These tribo-films form during cutting and provide enhanced lubricity, hardness, strength, and thermal barrier characteristics to the cutting tool. It was found that during Ti6Al4V machining, significant BUE and crater wear formation occurs; however, one is dominant over the other depending on the cutting conditions. Therefore, the coatings investigated were designed by taking into consideration the dominant tool wear mechanisms and the complex tribological phenomena that occur in the cutting zone. The current research investigated monolayer TiB2 and CrN self-adaptive PVD coatings for the rough (cutting speed - 45 m/min, feed -0.15 mm/rev, and depth of cut – 2 mm) and finish (cutting speed - 150 m/min, feed -0.1225 mm/rev, and depth of cut – 0.25 mm) turning of Ti6Al4V alloy. Detailed experimental studies were performed to study the effectiveness of the coatings during machining. Micro-mechanical characteristics of the coatings were also studied to understand how coating properties affect the coatings performance in machining and tribo-film formation. The results obtained show that both the TiB2 and CrN coatings significantly improve tool performance during the rough turning of Ti6Al4V alloy compared to the current industrial standard, which is due to certain micro-mechanical coating properties and the beneficial tribo-films formed. A coating of CrN coating was found to increase tool life during finish turning. It was also established that for machining applications where intensive adhesive interaction occurs at the tool-chip interface, coatings with lower hardness values perform significantly better than harder ones. / Thesis / Doctor of Philosophy (PhD) / Titanium alloys are increasingly becoming the material of choice for many industrial applications due to their superior properties. However, they are very difficult to machine since they have high chemical affinity towards tool materials, low thermal conductivity, and high temperature strength. These properties cause rapid failure of the tool. The objective of the current research is to address machinability issues during Ti6Al4V machining and improve tool performance. One effective strategy to minimize tool wear is to apply self-adaptive PVD tool coatings that can form beneficial tribo-films through their interaction with the environment and provide enhanced lubricity, hardness, strength, and thermal barrier characteristics to the cutting tool. In the current research, two self-adaptive PVD coatings were developed that offset the dominant tool wear mechanisms prevalent during the rough and finish turning of Ti6Al4V alloy and reduced the tool wear rate by more than 60% compared to the current industrial standard.

UAVs have gained in popularity and the number of applications has soared over the past years, ranging from leisure to commercial activities. This thesis is discussing specifically railroad applications, which is a domain rarely explored. Two different aspects are analysed. While developing a new application or migrating a ground-based system to UAV platform, the different challenges encountered are often unknown. Therefore, this thesis highlights the most important ones to take into consideration during the development process. From a more technical aspect, the implementation of autonomous guidance for UAVs over railroads using vanishing point extraction is studied. Two different algorithms are presented and compared, the first one is using line extraction method whereas the second uses joint activities of Gabor filters. The results demonstrate that the applied methodologies provide good results and that a significant difference exists between both algorithms in terms of computation time. A second implementation tackling the detection of railway topologies to enable the use on multiple rail road configurations is discussed. A first technique is presented using exclusively vanishing points for the detection, however, the results for complex images are not satisfactory. Therefore, a second method is studied using line characteristics on top of the previous algorithm. This second implementation has proven to give good results.

A big challenge regarding the development of autonomous vehicles is how to handle complex situations. If an autonomous vehicle ends up in a situation where it cannot make a decision on its own it will cause the car to stop, unable to continue driving. For these situations, human intervention is required. By making it possible to control the car remotely there is no need for an actual human in the car. Instead, a human operator can remotely control one or several cars from a distance. The purpose of this project was to identify such complex situations, evaluate remote control options and implement one of these controllers to drive the SVEA cars in the Smart Mobility Lab. After evaluation of possible remote control options, the PlayStation controller was chosen to be the simplest and most intuitive steering option. The controller was successfully implemented first in simulation and then on the SVEA cars in the Smart Mobility Lab. A test track was designed to measure the performance of the implemented controller and to be able to measure user-friendliness through a survey. It was concluded that a majority of the participants would not feel comfortable steering a real car using the PlayStation controller. However, a more extensive evaluation would be required to draw any major conclusions.

Autonomous driving technology is now evolving at an unprecedented speed. HD maps, which are embedded with highly precise and detailed road spatial and object information, play an important role in supporting autonomous vehicles. This thesis presents the development of a semi-automated HD map creation and updating method that is capable of extracting basic road feature information to HD maps by employing raw MLS point cloud data. The proposed HD map creation method consists of four steps: Road edge extraction, road surface extraction, road marking extraction and driving line generation. First, an existing curb-based road edge detection method is applied to extract road edge candidate points according to the elevation difference and slope between points. This thesis develops an edge vectorization algorithm based on the point's distance-to-trajectory. Then, the road surface is extracted by filtering the points inside fitted edges on the XY plane within a range of the ground elevation. In the next step, instead of using intensity to detect road markings used by most studies, this thesis fuses point clouds and images to assign each point with an RGB value to segment marking points. Marking objects are extracted by conditional Euclidean clustering and classified according to a manually defined decision tree. Finally, driving lines are generated based on the vectorized road edge and lane markings. The HD map update method varies depending on which data source is updated for the road segments, including updating images only, updating point clouds only and updating both images and point clouds. The method is evaluated by six point clouds and image datasets collected from different types of roads. The extracted road edges are assessed by both length- and buffer-based assessment methods. The results indicate that the road edge extraction algorithm performs well in all three dimensions. The road surface extraction results confirm the high accuracy of extracted edges. In addition, the quantitative evaluations of road markings demonstrate that the proposed road marking extraction method achieves an average recall, precision, and F1-score of 94.50%, 81.65% and 87.09%.

With the increased focus on reducing carbon emissions in today’s society, several industries have to overcome new challenges, where especially the automotive industry is under a lot of scrutiny to deliver improved and more environmentally friendly products. To meet the demands from customers and optimize vehicles aerodynamically, new cars often contain complex body geometries, together with advanced materials that are introduced to reduce the total vehicle weight. With the introduction of the complex body components and advanced materials,one area in the automotive industry that has to overcome these challenges is manufacturing engineering, and in particular the departments working with the sheet metal forming process. In this process complex body component geometries can lead to non-linear strain paths and stretch bending load cases, and newly introduced advanced materials can be prone to exhibit behaviour of edge cracks not observed in conventional sheet metals. This thesis takes it onset in the challenges seen in industry today with predicting failure of the three complex load cases: Non-Linear Strain Paths, Stretch-Bending,and Edge Cracks. Through Finite Element simulation attempts are made to accurately predict failure caused by aforementioned load cases in industrial components or experimental setups in an effort to develop post-processing methods that are applicable to all cases.

IoT enables profitable communication between sensor/actuator devices and the cloud. Slow network causing Edge data to lack Cloud analytics hinders real-time analytics adoption. VRebalance solves priority-based workload performance for stream processing at the Edge. BO is used in VRebalance to prioritize workloads and find optimal resource configurations for efficient resource management. Apache Storm platform was used with RIoTBench IoT benchmark tool for real-time stream processing. Tools were used to evaluate VRebalance. Study shows VRebalance is more effective than traditional methods, meeting SLO targets despite system changes. VRebalance decreased SLO violation rates by almost 30% for static priority-based workloads and 52.2% for dynamic priority-based workloads compared to hill climbing algorithm. Using VRebalance decreased SLO violations by 66.1% compared to Apache Storm's default allocation.

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