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Navigating complex environments requires Unmanned Aerial Vehicles (UAVs) and autonomous systems to perform trajectory tracking and obstacle avoidance in realtime. While many control strategies have effectively utilized linear approximations, addressing the non-linear dynamics of UAV, especially in obstacle-dense environments, remains a key challenge that requires further research. This paper intro
In this paper, we unify two already published results on state feedback H-infinity optimality. Previously, optimality has been shown for a particular controller structure in the case that the open-loop state matrix is symmetric, as well as in the case that the closed-loop system is internally positive. By contrast, the main result of the present paper gives optimality based on neither of these two
We study optimal seeding problems for binary super-modular network games. The system planner's objective is to design a minimal cost seeding guaranteeing that at least a predefined fraction of the players adopt a certain action in every Nash equilibrium. Since the problem is known to be NP-hard and its exact solution would require full knowledge of the network structure, we focus on approximate so
We study stochastic interaction network models whereby a finite population of agents, identified with the nodes of a graph, update their states in response to pairwise interactions with their neighbors as well as spontaneous mutations. These include the main epidemic models, such as the Susceptible-Infected -Susceptible, the Susceptible-Infected-Recovered, and the Susceptible-Infected-Recovered-Su
The wide spread of on-line social networks poses new challenges in information environment and cybersecurity. A key issue is detecting stubborn behaviors to identify leaders and influencers for marketing purposes, or extremists and automatic bots as potential threats. Existing literature typically relies on known network topology and extensive centrality measures computation. However, the size of
Cloud computing has quickly grown to become an essential component in many modern-day software applications. It allows consumers, such as a provider of some web service, to quickly and on demand obtain the necessary computational resources to run their applications. It is desirable for these service providers to keep the running cost of their cloud application low while adhering to various perform
This thesis studies two families of methods for finding zeros of finite sums of monotone operators, the first being variance-reduced stochastic gradient (VRSG) methods. This is a large family of algorithms that use random sampling to improve the convergence rate compared to more traditional approaches. We examine the optimal sampling distributions and their interaction with the epoch length. Speci
Glycolipids such as gangliosides affect the properties of lipid membranes and in extension the interactions between membranes and other biomolecules like proteins. To better understand how the properties of individual lipid molecules can contribute to shape the functional aspects of a membrane, the spatial restriction and dynamics of C–H bond segments can be measured using nuclear magnetic re
Self-Adaptive Systems (SAS) and Cyber-Physical Systems (CPS) have received significant attention in recent computer engineering research. This is due to their ability to improve the level of autonomy of engineering artefacts. In both cases, this autonomy increase is achieved through feedback. Feedback is the iteration of sens- ing and actuation to respectively acquire knowledge about the current s
Microservice applications consist of a set of smaller services interacting in a graph structure to deliver the full application. Jobs will traverse this graph in different paths, both depending on the type of job, but also on the current load of different service replicas. Different paths will incur different scenario-specific costs, dependent on, e.g., deployment and the underlying cloud system.
In this paper, we explore the use of Reinforcement Learning (RL) to improve the control of cooling equipment in Data Centers (DCs). DCs are inherently complex systems, and thus challenging to model from first principles. Machine learning offers a way to address this by instead training a model to capture the thermal dynamics of a DC. In RL, an agent learns to control a system through trial-and-err
Optimization problems occur in many areas in science and engineering. When the optimization problem at hand is of large-scale, the computational cost of the optimization algorithm is a main concern. First-order optimization algorithms—in which updates are performed using only gradient or subgradient of the objective function—have low per-iteration computational cost, which make them suitable for t
In this survey, we present the current status on robots performing manipulation tasks that require varying contact with the environment, such that the robot must either implicitly or explicitly control the contact force with the environment to complete the task. Robots can perform more and more manipulation tasks that are still done by humans, and there is a growing number of publications on the t
Engineering, in general, is a non-diverse profession around the globe. Women are one group of minorities in engineering. Despite the fact that control engineering is heavily based on mathematics, which has a larger number of female students, it has the same proportion of women as other engineering disciplines. To address the issue of low female participation in engineering disciplines both at univ
We present a new world-coordinate tracking algorithm for road users seen from static surveillance cameras, denoted GUTS. It is based upon the previously published UTS method but simplifies and replaces parts allowing association logic to work in world coordinates, by using a novel convolutional neural network denoted SAMHNet to convert every detection into world coordinates. Experimental evaluatio
Anticipatory and predictive models are becoming very important features of robot systems. This thesis investigates some aspects of predictive modeling. Is prediction always a good thing? How important is it to anticipate what will happen in the future? Is it better to anticipate far into the future or to focus on the next few seconds? What are the requirements for predictive models? Predictive mod
Popular Abstract in Swedish Det övergripande området för min studie är lärararbetet. Initialt riktar jag blickarna mot lärare som verkar i den svenska grundskolan. Den övergripande forskningsfrågan lyder: Hur är lärararbetet? Även om det i grundad teori framstår som önskvärt att forskaren ska ge sig ut på fältet utan förutfattade idéer och perspektiv som tvingar data in i förutbestämda analyser, gThe aim of this study is to generate a grounded theory about the work done by teachers. The initial area of data collection was the work done by teachers in their non-regulated hours, in which the teachers are expected to carry out professional activities like planning, preparation, correcting and spontaneous contacts with students, parents and colleagues. Data was collected and analysed according
