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Robust and reliable formulas for predicting bed load and suspended load were developed for application in the nearshore zone where waves and currents may transport sediment separately or in combination. Also, a routine was included to determine the sediment transport in the swash zone, both in the longshore and cross-shore directions. An important objective of the development was to arrive at gene
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
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
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
The ability of state-of-the-art regional climate models (RCMs) to simulate the trends of intense cyclone activity in the Arctic is assessed based on an ensemble of 13 simulations from 11 models from the Arctic-CORDEX initiative. Some models employ large-scale spectral nudging techniques. Cyclone characteristics simulated by the ensemble in winter and summer are compared with the results from four
This paper presents an injection-locked 65-nm CMOS circuit that upconverts and power amplifies baseband signals to RF. The circuit delivers an RF output power of 28.7 dBm, with a power gain and maximum power added efficiency (PAE) of 20.6 dB and 68.1%, respectively. Both AM–AM-conversion and AM–PM-conversion are low, less than 1 dB and 1°, respectively, resulting in an EVM of 4.7% for Long Term Ev
We present two sensitivity function trade-offs that apply to a class of networks with a string topology. In particular we show that a lower bound on the H-infinity norm and a Bode sensitivity relation hold for an entire family of sensitivity functions associated with growing the network. The trade-offs we identify are a direct consequence of growing the network, and can be used to explain why poor
We consider least-squares approximation of a function of one variable by a continuous, piecewise-linear approximand that has a small number of breakpoints. This problem was notably considered by Bellman who proposed an approximate algorithm based on dynamic programming. Many suboptimal approaches have been suggested, but so far, the only exact methods resort to mixed integer programming with super
