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This paper combines the Metropolis-Hastings Improved Particle Smoother (MHIPS) with marginalized models. It demonstrates the effectiveness of the combination by looking at two examples; a degenerate model of a double integrator and a fifth order mixed linear/nonlinear Gaussian (MLNLG) model. For the MLNLG model two different methods are compared with the non-marginalized case; the first marginaliz
Sufficient conditions for stability of feedback interconnections of negative imaginary systems are derived via an integral quadratic constraint (IQC) approach. These extend existing results in the literature by exploiting the flexibility present at the static and infinite frequencies to reduce conservatism. Negative imaginary transfer functions with poles on the imaginary axis are accommodated usi
We focus on the problem of semantic segmentation based on RGB-D data, with emphasis on analyzing cluttered indoor scenes containing many visual categories and instances. Our approach is based on a parametric figureground intensity and depth-constrained proposal process that generates spatial layout hypotheses at multiple locations and scales in the image followed by a sequential inference algorith
We provide a framework for 6 DOF pose estimation in seam-tracking applications using particle filtering. The particle filter algorithm developed incorporates measurements from both a 2 DOF laser seam tracker and the robot forward kinematics under an assumed external force. Special attention is paid to modeling of disturbances in the respective measurements, and methods are developed to assist the
This paper presents a Bayesian indoor positioningsystem for smartphones based on the strengths of WiFi andBluetooth signals. A framework for improving the performanceof existing positioning methods with the help informationsharing between users is proposed and evaluated. Bluetoothsignals are sent between users, and the signal strengths containinformation about their relative distances, which is us
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
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
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
The concept of dynamical movement primitives (DMPs) has become popular for modeling of motion, commonly applied to robots. This paper presents a framework that allows a robot operator to adjust DMPs in an intuitive way. Given a generated trajectory with a faulty last part, the operator can use lead-through programming to demonstrate a corrective trajectory. A modified DMP is formed, based on the f
Recently, several authors have shown local and global convergence rate results for Douglas–Rachford splitting under strong monotonicity, Lipschitz continuity, and cocoercivity assumptions. Most of these focus on the convex optimization setting. In the more general monotone inclusion setting, Lions and Mercier showed a linear convergence rate bound under the assumption that one of the two operators
Online 3D reconstruction has been an active research area for a long time. Since the release of the Microsoft Kinect Camera and publication of KinectFusion [11] attention has been drawn how to acquire dense models in real-time. In this paper we present a method to make online 3D reconstruction which increases robustness for scenes with little structure information and little texture information. I
We introduce the 'security index' of a discrete-time, linear time-invariant system under sensor attacks as a quantitative representation-free measure on the vulnerability of an observable system. Ideas from linear coding theory are employed in providing conditions for attack detection and correction in terms of a system's security index, along with methods for its computation based on different re
The pervasiveness and growing complexity of software systems are challenging software engineering to design systems that can adapt their behavior to withstand unpredictable, uncertain, and continuously changing execution environments. Control theoretical adaptation mechanisms have received growing interest from the software engineering community in the last few years for their mathematical groundi
In the near future, the number of connected mobile devices and data-rates are expected to dramatically increase. Demands exceed the capability of the currently deployed (4G) wireless communication systems. Development of 5G systems is aiming for higher data-rates, better coverage, backward compatibility, and conforming with “green communication” to lower energy consumption. Massive Multiple-Input
The purpose of this study is to investigate the interaction between small-scale turbulence and aerosol and cloud microphysical properties using direct numerical simulations (DNS). We consider the domain located at the height of about 2000 m from the sea level, experiencing transient high supersaturation due to atmospheric fluctuations of temperature and humidity. To study the effect of total numbe
A simple form for the optimal H-infinity state feedback of linear time-invariant infinite-dimensional systems is derived. It is applicable to systems with bounded input and output operators and a closed, densely defined, self-adjoint and strictly negative state operator. However, unlike other state-space algorithms, the optimal control is calculated in one step. Furthermore, a closed-form expressi
