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On H-infinity Control and Large-Scale Systems

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

Limited Consequences of a Transition From Activity-Based Financing to Budgeting : Four Reasons Why According to Swedish Hospital Managers

Activity-based financing (ABF) and global budgeting are two common reimbursement models in hospital care that embody different incentives for cost containment and quality. The purpose of this study was to explore and describe perceptions from the provider perspective about how and why replacing variable ABF by global budgets affects daily operations and provided services. The study setting is a la

Design of an area efficient crypto processor for 3GPP-LTE NB-IoT devices

Providing information security is crucial for the Internet of Things (IoT) devices, platforms in which the available power budget is very limited. This paper tackles this challenge and presents a cryptographic processor compliant with the security algorithms specified by the 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) NarrowBand IoT (NB-IoT) standard. The proposed processor

Seasonal Cycle of Background in the Tropical Pacific as a Cause of ENSO Spring Persistence Barrier

Statistical model results suggest that the declining growth rate from autumn to spring is the key to cause El Niño-Southern Oscillation (ENSO) spring persistence barrier (SPB). Using a dynamical approach, we develop the physical mechanisms responsible for ENSO SPB in the framework of recharge oscillator by adding a seasonally varying Bjerknes (BJ) stability index and linking it with ENSO growth ra

Five Challenges in Cloud-Enabled Intelligence and Control

The proliferation of connected embedded devices, or the Internet of Things (IoT), together with recent advances in machine intelligence, will change the profile of future cloud services and introduce a variety of new research problems centered around empowering resource-limited edge devices to exhibit intelligent behavior, both in sensing and control. Cloud services will enable learning from data,

A Timely Journey Through the Cloud

This thesis treats the intersection between two of the largest transformations we are seeing within our society today; the cloud and the Internet-of-Things (IoT). The aim of this thesis is to investigate different ways to model and control a network of cloud services so that timing-critical IoT applications can make use of them. Examples of such applications can be autonomous and mobile robots, sm

Large loss of CO2 in winter observed across the northern permafrost region

Recent warming in the Arctic, which has been amplified during the winter1–3, greatly enhances microbial decomposition of soil organic matter and subsequent release of carbon dioxide (CO2)4. However, the amount of CO2 released in winter is not known and has not been well represented by ecosystem models or empirically based estimates5,6. Here we synthesize regional in situ observations of CO2 flux f

Cavity Field Control for Linear Particle Accelerators

High-energy linear particle accelerators enable exploration of the microscopic structure of pharmaceuticals, solar cells, fuel cells, high-temperature superconductors, and the universe itself. These accelerators accelerate charged particles using oscillating magnetic fields that are confined in metal cavities. The amplitudes and phases of the electromagnetic fields need to be accurately controlled

Models for control of intravenous anesthesia

Modeling is fundamental to both feed-forward and feedback control. Within automated anesthesia the two paradigms are usually referred to as target-controlled infusion (TCI) and closed-loop drug delivery, respectively. In both cases, the objective is to control a system with anesthetic drug infusion rate as input, and (measured) clinical effect as output. The input is related to the output through

Self-adaptation of software using automatically generated control-theoretical solutions

Control theory has contributed a set of foundational techniques to handle "change" at runtime in software applications. These techniques however have fundamental limitations as well: (i) they require the development and understanding of mathematical models; (ii) synthesizing solutions is often done on a per-problem basis, discouraging flexibility and generality. Software engineering, as a discipli

Game-Theoretic Network Bandwidth Distribution for Self-Adaptive Cameras

Devices sharing a network compete for bandwidth, being able to transmit only a limited amount of data. This is for example the case with a network of cameras, that should record and transmit video streams to a monitor node for video surveillance. Adaptive cameras can reduce the quality of their video, thereby increasing the frame compression, to limit network congestion. In this paper, we exploit

A Decade Frequency Range CMOS Power Amplifier for Sub-6-GHz Cellular Terminals

A wideband 65-nm CMOS power amplifier (PA) is presented, with a decade frequency range from 600 MHz to 6.0 GHz. In this frequency range, the output power exceeds 26.6 dBm and the power gain and power-added efficiency (PAE) exceed 18.1 dB and 49%, respectively. For a 7.5-dB peak-to-average-power-ratio (PAPR) long term evolution (LTE) signal at 1.9 GHz, the circuit provides an average output power o

A 65-nm CMOS Low-Power Front-End for 3rd Generation DNA Sequencing

A continuous-time, 65-nm CMOS, front-end for processing DNA sequencing measurements from biological nanopore sensors is presented. The measured design has an input referred noise floor of 8.5\;{\text{fA}}/\sqrt {{\text{Hz}}} for 100 pA DC current while consuming 10X less power. The chip also consists of an integrated ADC.

Towards Performance Modeling of Speculative Execution for Cloud Applications

Interesting approaches to counteract performance variability within cloud datacenters include sending multiple request clones, either immediately or after a specified waiting time. In this paper we present a performance model of cloud applications that utilize the latter concept, known as speculative execution. We study the popular Join-Shortest-Queue load-balancing strategy under the processor sh

Minimizing Side-Channel Attack Vulnerability Via Schedule Randomization

Control systems can be vulnerable to security threats where an attacker gathers information about the execution of the system. In particular, side-channel attacks exploit the predictability of real-time control systems and of their schedules. To counteract their action, a scheduler can randomize the temporal execution of tasks and limit the amount of information the attacker can gather. Schedule r

Complexity revealed in the greening of the Arctic

As the Arctic warms, vegetation is responding, and satellite measures indicate widespread greening at high latitudes. This ‘greening of the Arctic’ is among the world’s most important large-scale ecological responses to global climate change. However, a consensus is emerging that the underlying causes and future dynamics of so-called Arctic greening and browning trends are more complex, variable a

Future projections of cyclone activity in the Arctic for the 21st century from regional climate models (Arctic-CORDEX)

Changes in the characteristics of cyclone activity (frequency, depth and size) in the Arctic are analyzed based on simulations with state-of-the-art regional climate models (RCMs) from the Arctic-CORDEX initiative and global climate models (GCMs) from CMIP5 under the Representative Concentration Pathway (RCP) 8.5 scenario. Most of RCMs show an increase of cyclone frequency in winter (DJF) and a de

Large-scale and distributed optimization : An introduction

The recent explosion in size and complexity of datasets and the increased availability of computational resources has led us to what is sometimes called the big data era. In many big data fields, mathematical optimization has over the last decade emerged as a vital tool in extracting information from the data sets and creating predictors for unseen data. The large dimension of these data sets and