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The telecommunication industry has been successful in turning the Internet into a mobile service and stimulating the creation of a new set of networked, remote services. Most of these services now run or are supported by cloud computing platforms. Embracing cloud computing solutions is fundamental for the telecommunication industry to remain competitive. However, there are many legal, regulatory,
Bioaerosols include bacterial cells and spores, viruses, pollen, fungi, algae, detritus, allergens and cell fragments. Bioaerosol particles are usually a small fraction of all aerosol particles in our surroundings, but their impact can be critical. They are a means for transmission of disease, they cause allergic reactions and they have effects on the global climate, ecology and biodiversity. This
The purpose of risk management in the development of safety-critical software is to eliminate or reduce harmful behaviour. In health-care it is essential to manage risk related to software due to its increased use in medical devices and other computer systems. This paper presents some of the experiences gained from an ongoing case study at a large hospital in Sweden. The study focuses on identific
This manuscript proposes a novel viewpoint on computing systems' modelling. The classical approach is to consider fully functional systems and model them, aiming at closing some external loops to optimise their behaviour. On the contrary, we only model strictly physical phenomena, and realise the rest of the system as a set of controllers. Such an approach permits rigorous assessment of the obtain
Adaptive reservation is a real-time scheduling technique in which each application is associated a fraction of the computational resource (a reservation) that can be dynamically adapted to the varying requirements of the application by using appropriate feedback control algorithms. An adaptive reservation is typically implemented by using an aperiodic server (e.g. sporadic server) algorithm with f
A parametrized model in addition to the control and state-space variables depends on time-independent design parameters, which essentially define a family of models. The goal of parametrized model reduction is to approximate this family of models. In this paper, a reduction method for linear time-invariant (LTI) parametrized models is presented, which constitutes the development of a recently prop
We consider model predictive control (MPC) problems with linear dynamics, polytopic constraints, and quadratic objective. The resulting optimization problem is solved by applying an accelerated gradient method to the dual problem. The focus of this paper is to provide bounds on the number of iterations needed in the algorithm to guarantee a prespecified accuracy of the dual function value and the
The development of models that provide accurate spatio-temporal predictions of ambient air pollution at small spatial scales is of great importance for the assessment of potential health effects of air pollution. Here we present a spatio-temporal framework that predicts ambient air pollution by combining data from several different monitoring networks and deterministic air pollution model(s) with
Safe coexistence of industrial robots and human operators in the same workspace is one of the long standing goals of robotics research. One way to enforce safety is to endow the robotic system with additional sensors that can to some extent monitor the environment surrounding the robot and allow fast reaction to unexpected obstacles. This paper discusses the design of one such anti-collision syste
Background: Cohort studies of the relationship between air pollution exposure and chronic health effects require predictions of exposure over long periods of time. Objectives: We developed a unified modeling approach for predicting fine particulate matter, nitrogen dioxide, oxides of nitrogen, and black carbon (as measured by light absorption coefficient) in six U.S. metropolitan regions from 1999
It has recently been shown that several classical open problems in linear system theory, such as optimization of decentralized output feedback controllers, can be readily solved for positive systems using linear programming. In particular, optimal solutions can be verified for large-scale systems using computations that scale linearly with the number of interconnections. Hence two fundamental adva
In high throughput applications, such as in multimedia, it is preferable to fully utilize computing resources, even at the price of some (bounded) delay. However, in real-time systems, where the maximum admissible delay is modeled by a deadline, most of the theory is developed with the assumption of a task deadline smaller than or equal to the task period. The reason of this limitation is in the i
We propose an energy-based framework for approximating surfaces from a cloud of point measurements corrupted by noise and outliers. Our energy assigns a tangent plane to each (noisy) data point by minimizing the squared distances to the points and the irregularity of the surface implicitly defined by the tangent planes. In order to avoid the well-known "shrinking" bias associated with first-order
Cloud drop condensation nuclei (CCN) and ice nuclei (IN) particles determine to a large extent cloud microstructure and, consequently, cloud albedo and the dynamic response of clouds to aerosol-induced changes to precipitation. This can modify the reflected solar radiation and the thermal radiation emitted to space. Measurements of tropospheric CCN and IN over large areas have not been possible an
A fundamental problem in robotics is generating the motion for a task. How to translate a task to motion or a series of movements is a non-trivial problem. The complexity of the task, the structure of the robot, and the desired performance determine the sequence of movements, the path, and the course of motion as a function of time, namely the trajectory. As we discuss in this thesis, a trajectory
