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This article discusses theoretical and practical aspects of real-time brain computer interface control methods based on Bayesian statistics. The theoretical aspects include how the data from the brain computer interface can be translated into a Gaussian mixture model that is used in the Bayesian statistics-based control methods. The practical aspects include how the control methods improve the per
This article discusses practical and theoretical aspects of real-time brain computer interface control methods based on Bayesian statistics. We investigate and improve the performance of automatic control and feedback algorithms of a reactive brain computer interface based on a visual oddball paradigm for faster statistical convergence. We introduce transfer learning using Gaussian mixture models,
Understanding the evolution of multipath components (MPCs) in real radio channels is crucial to enhancing channel modeling and multipath-assisted positioning. This paper provides an experimental analysis of the behavior of MPCs originating from a standard building facade at millimeter wave (mmWave) frequencies. Utilizing a high-resolution channel parameter estimation method alongside a joint clust
We study dynamics in a network of interacting agents updating their binary states according to a time-varying threshold rule. Specifically, agents revise their state asynchronously by comparing the weighted average of the current states of their neighbors in the interaction network with possibly heterogeneous time-varying threshold values. Such thresholds are determined by an exogenous signal repr
Background: Predicting future Alzheimer’s disease (AD)-related cognitive decline among individuals with subjective cognitive decline (SCD) or mild cognitive impairment (MCI) is an important task for healthcare. Structural brain imaging as measured by magnetic resonance imaging (MRI) could potentially contribute when making such predictions. It is unclear if the predictive performance of MRI can be
This thesis embarks on a journey in the advancement of urban traffic management, centering around the innovative integration of Autonomous Intersection Management (AIM) systems. The research encompasses a comprehensive exploration of various facets of AIM implementation, significantly contributing to the evolution of a more efficient and safer urban transport system.The research investigates the d
In the last couple of decades, developments in SAT-based optimization have led to highly efficient maximum satisfiability (MaxSAT) solvers, but in contrast to the SAT solvers on which MaxSAT solving rests, there has been little parallel development of techniques to prove the correctness of MaxSAT results. We show how pseudo-Boolean proof logging can be used to certify state-of-the-art core-guided
Safety measures need to be systemically investigated to what extent they evaluate the intended performance of Deep Neural Networks (DNNs) for critical applications. Due to a lack of verification methods for high-dimensional DNNs, a trade-off is needed between accepted performance and handling of out-of-distribution (OOD) samples.This work evaluates rejecting outputs from semantic segmentation DNNs
There are many pedagogical methods to engage students in active learning. This paper presents Discussion Seminars, which is one such method that is much appreciated by students. This paper explains how to structure seminars and discusses their benefits and what requirements they put on the teacher. The paper aims to inspire other teachers to try something similar.
A Brain-Computer Interface (BCI) is a system that, in real-time, translates the user's brain activity into commands that can be used to control applications, such as moving a cursor on the screen. The translation is made possible by machine learning methods and other algorithms. The thesis focuses on EEG-based BCIs which are the most common type of BCIs due to EEG measurements being non-invasive,
A recent singleshot multiplane image (MPI) generation enables to copy an observed reality within a camera frame into other reality domains via view synthesis. While the scene scale is unknown due to the nature of singleshot MPI processing, camera tracking algorithms can estimate depth within the application world coordinate system. Given such depth information, we propose to adjust the scale of si
We propose a variation of the forward–backward splitting method for solving structured monotone inclusions. Our method integrates past iterates and two deviation vectors into the update equations. These deviation vectors bring flexibility to the algorithm and can be chosen arbitrarily as long as they together satisfy a norm condition. We present special cases where the deviation vectors, selected
The geometry-based stochastic channel models (GSCM), which can describe realistic channel impulse responses, often rely on the existence of both local and far scatterers. However, their visibility from both the base station (BS) and mobile station (MS) depends on their relative heights and positions. For example, the condition of visibility of a scatterer from the perspective of a BS is different
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
This article considers state estimation of rigid-body dynamics where the positions q are available to measurement but where the angular velocities ̇or accelerations are not available to measurement. Using a stability-oriented approach to model-based design of state estimation for Euler–Lagrange systems and rigid-body dynamics, state estimation based on position measurement is shown to guarantee se
After the COVID-19 pandemic, during which teachers developed a lot of online course material, teachers worldwide are challenged with how to combine online teaching with classroom teaching in an efficient way. This paper presents Discussion seminars, a teaching activity that structures discussions clearly, making it easy for students and teachers to follow the discussion. The paper puts Discussion
Effective preprocessing of electroencephalography (EEG) data is fundamental for deriving meaningful insights. Independent component analysis (ICA) serves as an important step in this process by aiming to eliminate undesirable artifacts from EEG data. However, the decision on which and how many components to be removed remains somewhat arbitrary, despite the availability of both automatic and manua
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
In this thesis, we provide a number of novel algebraic means of certifying stability and performance for linear systems constrained in various ways by cones. The purpose is mainly threefold: to provide mathematical statements with applicative potential, to unify seemingly dissimilar results in the literature and thereby increase understanding, and to advance the state of the art on dynamical syste
The analysis of interconnected systems is a large and growing field, with successful applications in a wide range of natural and synthesized systems. Biomolecular networks, power grids and human social dynamics have all been the subject of study through the lens of network dynamics, with impressive results. The work presented in this thesis takes the form of four research papers all focusing in di
