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Due to their wide adaptability to different application fields spanning from opinion dynamics to biology, the analysis of evolutionary dynamics is a compelling problem in the science of networks and systems. In this paper, we deal with controlled evolutionary dynamics in networks. We discuss a novel approach to model these phenomena, which enables us to estimate the duration of the process dependi
With the ever-growing traffic demands, the transportation networks are getting more and more congested. While expanding these networks with more roads is both costly and in many cities not even feasible, the rapid development of new sensing and communication techniques has made it possible to perform control of transportation networks in real-time. With the right usage of such technologies, existi
This work considers the assignment of vehicle traffic consisting of both individual, opportunistic vehicles and a cooperative fleet of vehicles. The first set of vehicles seek a user-optimal policy and the second set seeks a fleet-optimal policy. We provide explicit sufficient conditions for the existence and uniqueness of a Nash equilibrium at which both policies are satisfied.We also propose two
Time-dependent ensemble averages, i.e., trajectory-based averages of some observable, are of importance in many fields of science. A crucial objective when interpreting such data is to fit these averages (for instance, squared displacements) with a function and extract parameters (such as diffusion constants). A commonly overlooked challenge in such function fitting procedures is that fluctuations
Driven by the increasing demands on data rate from applications, the wireless communication standard has for decades been evolving approximately at a pace of one generation per ten years. Following this trend, the ambitious plan to replace the current cellular mobile network standard (4G) with the next generation standard (5G) is going through the standardization phase and is getting close to its
The purpose of this paper is to analyse transient dynamics in adaptive control using statistical concentration bounds. For maximal clarity, the study is limited to a linear first order system with a single uncertain parameter. Two types of bounds are given: First we prove probabilistic bounds on the parameter estimation error as a function of time. In particular, we prove that the estimation error
We analyze the local convergence of proximal splitting algorithms to solve optimization problems that are convex besides a rank constraint. For this, we show conditions under which the proximal operator of a function involving the rank constraint is locally identical to the proximal operator of its convex envelope, hence implying local convergence. The conditions imply that the non-convex algorith
Cloud computing infrastructures are powering most of the web hosting services that we use at all times. A recent failure in the Amazon cloud infrastructure made many of the website that we use on a hourly basis unavailable1. This illustrates the importance of cloud applications being able to absorb peaks in workload, and at the same time to tune their power requirements to the power and energy cap
In this paper we consider L1 optimal and H-infinity optimal control problems for a particular class of Positive Bilinear Systems that arise in drug dosage design for HIV treatment. Starting from existent characterizations of the L1-norm for positive systems, a convex formulation for the first problem is provided. As for the H-infinity case, we propose an algorithm based on the iterative solution o
In the framework of robotics, Reinforcement Learning (RL) deals with the learning of a task by the robot itself. This paper presents a hierarchical planning approach in which the robot learns the optimal behavior for different levels. For high-level discrete actions, Q-learning was chosen, whereas for the low level we utilize Policy Improvement with Path Integrals (PI^2) algorithm to learn the par
This work presents a 2.0mm2 128×16 massive MIMO detector IC that provides 21dB array gain and 16x multiplexing gain at the system level. The detector implements iterative expectation-propagation detection (EPD) for up to 256-QAM modulation. Tested with measured channel data [1], the detector achieves 4.3dB processing gain over state-of-the-art massive MlMo detectors [2, 3], enabling 2.7x reduction
We reconsider the variational problem of finding the shape of a vertically hanging rope such that its elongation, due to the rope’s own weight and that of a load attached at its lower end, is minimum. The known solution is recalled and the missing proof of optimality is supplied.
Sudden cardiac arrest is the second most common cause of death in Sweden, following tumors. Annually, 10 000 people are subject to sudden cardiac arrest outside of hospital in the country.Following sudden cardiac arrest, blood circulation in the body ceases, and the brain is subject to irreversible damage within minutes. The treatment consists mainly of mechanical chest compressions to circulate b
The objective of this work is to provide governmental agencies and the society a condensed overview of the current state of knowledge on the efficiency of applied restoration and remediation of areas affected by radioactive fallout. The most actual real-world case is the Fukushima accident in Japan in 2011: the accident and the following applied decontamination methods are in focus in this review
Algorithms for Massive MIMO uplink detection typically rely on a centralized approach, by which baseband data from all antennas modules are routed to a central node in order to be processed. In case of Massive MIMO, where hundreds or thousands of antennas are expected in the base-station, this architecture leads to a bottleneck, with critical limitations in terms of interconnection bandwidth requi
We consider model order reduction of stable linear systems which leave ellipsoidal cones invariant. We show how balanced truncation can be modified to preserve cone- invariance. Additionally, this implies a method to perform external positivity preserving model reduction for a large class of systems.
Industrial robots typically require detailed programming and carefully configured work cells to perform well. The large engineering effort implicates high cost and long preparation time, and this is the major obstruction when mediating tasks to robots. The research in this thesis therefore aims to make robot programming faster and more accessible. Methods that allow for programmers to mediate and
In the wake of the arrival of cloud computing, future applications are poised to become more resilient and adaptive by embracing elasticity in an osmotic manner. Although cloud computing is a strong attractor for application developers, thereare still unconquered performance frontiers. Latency-sensitive and missioncritical applications make up a significant portion of all software systems, and the
A tropical maritime case of deep convective clouds was studied using a state-of-the-art aerosol-cloud model in order to evaluate the microphysical mechanisms of aerosol indirect effects (AIE). The aerosol-cloud scheme used is a hybrid bin/bulk model, which treats all phases of clouds and precipitation allowing a detailed analysis of process-level aerosol indirect effects on targeted cloud types.
