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We propose a method for sensor array self-localization using a set of sources at unknown locations. The sources produce signals whose times of arrival are registered at the sensors. We look at the general case where neither the emission times of the sources nor the reference time frames of the receivers are known. Unlike previous work, our method directly recovers the array geometry, instead of fi
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
Motivated by the novel coronavirus disease (COVID-19) pandemic, this paper aims to apply Gunter Stein's cautionary message of respecting the unstable to the problem of controlling the spread of an infectious disease. With this goal, we study the effect that delays and capacity constraints have in the test, trace, and isolate (TeTrIs) process, and how they impact its ability to prevent exponential
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
This paper presents a four-stage algorithm for the realization of multi-input/multi-output (MIMO) switched linear systems (SLSs) from Markov parameters. In the first stage, a linear time-varying (LTV) realization that is topologically equivalent to the true SLS is derived from the Markov parameters assuming that the discrete states have a common MacMillan degree and a mild condition on their dwell
When creating models of a system, there is always a tradeoff between the ease of modelling a part and the increased value it brings to the model. Learning a model using machine learning instead, we are able to capture all kinds of things we don't necessarily understand, but can need large amounts of data to learn even the things we find simple. Using universal differential equations we can combine
District heating systems make an important puzzle piece in the energy system of both today and tomorrow. When designing, simulating and controlling these systems, hydraulics play a vital role. The pressure generated by pumps has to drive sufficient flow throughout the system to satisfy the requirements of customers. Ensuring that the system is sufficiently pressurized is a challenging task already
Variance-reduced stochastic gradient methods have gained popularity in recent times. Several variants exist with different strategies for storing and sampling gradients, and this work concerns the interactions between these two aspects. We present a general proximal variance-reduced gradient method and analyze it under strong convexity assumptions. Special cases of the algorithm include SAGA, L-SV
In this technical note, we introduce a novel approach to studying consensus of continuous-time nonlinear systems with varying topology based on Hilbert metric. We demonstrate that this metric offers significant flexibility in analyzing consensus properties, while effectively handling nonlinearities and time dependencies. Notably, our approach relaxes key technical assumptions from some standard re
This thesis, comprising four research papers, contributes to the field of systematic and computer-aided analyses and design of first-order methods. The first two papers focus on developing new methodologies, while the remaining two apply these techniques to refine complexity and convergence results, as well as to design new methods. A central theme of the work is the use of Lyapunov-type analyses,
With the increase of devices connected to 5G services, the importance of tuning User Equipment (UE) to their assigned frequency slot becomes increasingly vital. This paper approached this issue by securing the control signals sent through a Serial Peripheral Interface (SPI) in digital beamforming radios. This was done by securing three distinct components: the transmitting end of the channel, the
When patients experience peripheral vestibular or certain central disorders causing dizziness or vertigo, this is sometimes associated with experiencing also disrupted spatial orientation, which may be accentuated by exposure to a distorted visual environment. This study explores the impact of sensorimotor factors and sex on spatial orientation in sitting and standing positions. In the Rod-and-Fra
Lattice-based cryptography is in the process of being standardized. Several proposals to deal with side-channel information using lattice reduction exist. However, it has been shown that algorithms based on Bayesian updating are often more favorable in practice. In this work, we define distribution hints; a type of hint that allows modelling probabilistic information. These hints generalize most p
