Filetype
We propose a distributed optimization algorithm for mixed L_1/L_2-norm optimization based on accelerated gradient methods using dual decomposition. The algorithm achieves convergence rate O(1/k^2), where k is the iteration number, which significantly improves the convergence rates of existing duality-based distributed optimization algorithms that achieve O(1/k). The performance of the developed al
Low-complex and low power non-coherent energy detector (ED) is interesting for low data rate impulse radio (IR) ultra wideband (UWB) systems but, compared to coherent receivers, it suffers from a loss in performance due to low signal-to-noise ratio (SNR) at the detector. In addition, the performance of an ED strongly depends on the integration interval (window size) of the integrator and the window
In manipulation tasks, motion trajectories are characterized by a set of key phases (i.e., motion primitives). It is therefore important to learn the motion primitives embedded in such tasks from a complete demonstration. In this paper, we propose a core framework that autonomously segments motion trajectories to support the learning of motion primitives. For this purpose, a set of segmentation po
Robots operating in real-world settings must manage task variability, environmental uncertainty, and failures during execution. This thesis presents a unified framework for building self-reliant robotic systems by integrating symbolic planning, reinforcement learning, behavior trees (BTs), and vision-language models (VLMs).At the core of the approach is an interpretable policy representation based
This dissertation presents the design and implementation of a new block diagram programming language, Bloqqi, for building control systems with focus on variability. The language has been developed in collaboration with industry with the goal of reducing engineering time and improving reuse of functionality.When building a control system for a plant, there are typically different variants of the s
In multipath routing, maximization of the cardinality K of the disjoint-path set for a given source and destination assuming an upper bound on the differential delay D is one of the key factors enabling its practical applications. In the paper we study such an optimization problem for multipath routing involving maximization of K under the D constraint as the primary objective, and then minimizati
Optimization of channel utilization in wireless networks is typically based on transmission parallelization under signal-to-interference and noise constraint assuming minimum frame-length scheduling. In application scenarios like sensor networks this approach may not be suitable since it does not explicitly consider end-to-end packet delay nor energy consumption. In the paper we propose a mixed-in
A method for optimization of PID controller parameters and measurement filter time constant is presented. The method differs from the traditional approach in that the controller and filter parameters are simultaneously optimized, as opposed to standard, sequential, design. Control performance is maximized through minimization of the integrated absolute error (IAE) caused by a unit step load distur
Changes in soil moisture are likely to contribute to future changes in latent heat flux and various characteristics of daily precipitation. Such contributions during the second half of the twenty-first century are assessed using the simulations from the GLACE-CMIP5 experiment, applying a linear regression analysis to determine the magnitude of these contributions. As characteristics of daily preci
Change Impact Analysis (CIA) during software evolution of safety-critical systems is a labor-intensive task. Several authors have proposed tool support for CIA, but very few tools were evaluated in industry. We present a case study on ImpRec, a recommendation System for Software Engineering (RSSE), tailored for CIA at a process automation company. ImpRec builds on assisted tracing, using informati
Quantitative data comes with enormous possibilities for presenting key characteristics of the data in a very compressed form. Basic descriptive statistics, like mean and standard deviation, comprise thousands or millions of data points into single numbers. In contrast, qualitative data, with its focus on descriptions, words, and phrases does not come with such powerful tools, leading to wordy desc
Data assimilation methods provide a rigorous statistical framework for constraining parametric uncertainty in land surface models (LSMs), which in turn helps to improve their predictive capability and to identify areas in which the representation of physical processes is inadequate. The increase in the number of available datasets in recent years allows us to address different aspects of the model
A fundamental problem in robotics is the generation of motion for a task. How to translate a task to a set of movements is a non-trivial problem. The complexity of the task, the capabilities of the robot, and the desired performance, affect all aspects of the trajectory; the sequence of movements, the path, and the course of motion as a function of time.This thesis is about trajectory generation a
We study a two compartment, nonlinear, age structured model for the cell cycle. The phases of the cell cycle G1, S, G2 and M are grouped into two phases, which we call Phase 1 and Phase 2, where Phase 1 consists of the phase G1 and Phase 2 consists of the phases S, G2 and M. It is assumed that Phase 1 has a variable duration while the duration of Phase 2 is fixed. The model consists of a system of
The field of robotics offers a wide array of estimation problems, ranging from kinematic and dynamic calibration to pose estimation and computer vision. This thesis presents a set of methods to solve estimation problems encountered in robotics, with an emphasis on industrial robotics. The researched topics are all practically motivated and have found immediate use in applications.Industrial roboti
Although surface integral equations (SIEs) have been extensively used in solving electromagnetic problems of penetrable objects, there are still open issues relating to their application to the Theory of Characteristic Modes. This work demonstrates that when an SIE is used to solve for the characteristic modes (CMs) of a dielectric or magnetic object, the resulting eigenvalues are unrelated to the
The focus of this contribution is the estimation of multi-step-ahead linear multivariate predictors of the output making use of finite input-output data sequences. Different strategies will be presented, the common factor being the exploitations of geometric operations on appropriate subspaces spanned by the data. In order to test the capabilities of the proposed methods in predicting new data, a
