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The paper describes the design and verification of a self-adaptive system, composed of multiple smart cameras connected to a monitoring station, that determines the allocation of network bandwidth to the cameras. The design of such a system poses significant challenges, since multiple control strategies are active in the system simultaneously. In fact, the cameras adjust the quality of their strea
This contribution reviews theory, algorithms, and validation results for system identification of continuous-time state-space models from finite input- output sequences. The algorithms developed are autoregressive methods, methods of subspace-based model identification and stochastic realization adapted to the continuous-time context. The resulting model can be decomposed into an input-output mode
This paper shows an application of continuous-time system identification methods to Type 1 diabetes. First, a general MISO transfer function structure with individual nominator and denominator polynomials for each input is assumed and a parameter estimation procedure via an iterative prediction error method presented. Then, the proposed identification method is evaluated on a simple simulation exa
This contribution reviews theory, algorithms, and validation results for system identification of continuous-time state-space models from finite input-output sequences. The algorithms developed are autoregressive methods, methods of subspace-based model identification and stochastic realization adapted to the continuous-time context. The resulting model can be decomposed into an input-output model
The focus of this article is to undertake a comparative analysis of multi-step-ahead linear multivariate predictors. The approach considered for the estimation will be based on geometrically reliable linear algebra tools, resorting to subspace identification methods. A crucial issue is quantification of both bias error and variance affecting the estimate of the prediction for increasing values of
In this thesis, a class of linear time-invariant systems is identified for which a particular type of H-infinity optimal control problem can be solved explicitly. It follows that the synthesized controller can be given on a simple explicit form. More specifically, the controller can be written in terms of the matrices of the system’s state-space representation. The result has applications in the c
Despite the creativity of the scientific community and the funding agencies, the underlying model of computation behind IoT, WSN, cloud, edge, fog, and mist is fundamentally the same; Computational nodes which are dynamically interconnected to form a system in where both processing capacity and connectivity may vary over time. On top of such a system, we consider applications that need packets to
A widely embraced approach to mitigate the dynamic degradation in low-inertia power systems is to mimic generation response using grid-connected inverters to restore the stiffness of the grid. In this article, we seek to challenge this approach and advocate for a principled design based on a systematic analysis of the performance trade-offs of inverter-based frequency control. With this aim, we pe
The Scaled Agile Framework (SAFe) is a framework for scaling agile methods in large organizations. We have found several experience reports and white papers describing SAFe adoptions in different banks, which indicates that SAFe is being used in the banking industry. However, there is a lack of academic publications on the topic, the banking industry is missing in the scientific reports analyzing
This paper concerns trajectory tracking control of attitude dynamics configured on SU(2). Inspired by a popular geometric tracking controller on SO(3), differential geometric tools are used to derive both continuous and discontinuous attitude controllers on the SU(2) manifold, relating these to preexisting controllers operating with imaginary quaternion errors. Additionally, a robustness result is
This paper concerns the robustness of attitude controllers for dynamics configured on the SO(3) manifold and poses a set of bilinear matrix inequalities to find an optimal controller tuning with respect to (i) the ultimate bound of the error-state trajectories when perturbed by naturally arising disturbances, and (ii) the worst-case decay rate of the tracking errors. The presented optimization pro
This paper discusses methods to test the performance of the adaptation layer in a self-adaptive system. The problem is notoriously hard, due to the high degree of uncertainty and variability inherent in an adaptive software application. In particular, providing any type of formal guarantee for this problem is extremely difficult. In this paper we propose the use of a rigorous probabilistic approac
Distributed camera surveillance systems typically consist of multiple cameras that need to store some fraction of their video streams in a central storage node. The disk space of this node as well as the network between the cameras and this central node constitute shared resources. In the paper the disk space allocation as well as the network bandwidth reservation are solved using techniques norma
Time-triggered real-time systems achieve deterministic behavior using schedules that are constructed offline, based on scheduling constraints. Their deterministic behavior makes time-triggered systems suitable for usage in safety-critical environments, like avionics. However, this determinism also allows attackers to fine-tune attacks that can be carried out after studying the behavior of the syst
Model Predictive Control (MPC) is an efficient point-to-point trajectory-generation method for robots that can be used in situations that occur under time constraints. The motion plan can be recalculated online to increase the accuracy of the trajectory when getting close to the goal position. We have implemented this strategy in a Franka Emika Panda robot, a redundant collaborative robot, by exte
Semialgebraic proof systems have been studied extensively in proof complexity since the late 1990s to understand the power of Gröbner basis computations, linear and semidefinite programming hierarchies, and other methods. Such proof systems are defined alternately with only the original variables of the problem and with special formal variables for positive and negative literals, but there seems t
Resource management in cloud computing is a difficult problem, as one is often tasked with balancing between adequate service to clients and cost minimization in dynamic environments of many interconnected components. To make correct decisions in these environments, good performance models are necessary. A common modeling methodology is to use networks of queues, but as these are prohibitively exp
Recurrent neural networks (RNNs) are efficient for classification of sequential data such as speech and audio due to their high precision on tasks. However, power efficiency, the required memory capacity and bandwidth requirements make them less suitable for battery powered devices. In this work, we introduce FLoPAD-GRU: a system on a chip (SoC) for efficient processing of gated recurrent unit (GR
There is a large variability between individuals in the response to anesthetic drugs, that seriously limits the achievable performance of closed-loop controlled drug dosing. Full individualization of patient models based on early clinical response data has been suggested as a means to improve performance with maintained robustness (safety). We use estimation theoretic analysis and realization theo
