Ultra Wideband Preliminaries

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The effect of strong ambient winds on the efficiency of solar updraft power towers: a numerical case study for Orkney.

Solar updraft tower (SUT) is a simple power plant in which ventilation of heated air inside a channel drives a turbine. This system is recognised as suitable for areas with abundant solar radiation. As a result, there is no extensive research on the performance of SUTs under mild solar radiation. Studies show that strong ambient crosswinds can affect the performance of a SUT. In this paper, the efficiency of SUTs in areas which benefit from strong winds, despite low solar radiation, is investigated through numerical modelling. Comparison is made between the efficiency of a commercial-scale SUT in Manzanares (Spain) with intensive solar radiation, and one of the same size potentially located in the windy and mild climate of Orkney Islands in Scotland. The results show that ambient crosswinds can increase internal air speed and efficiency of a SUT by more than 15% and 50%, respectively. Consequently, such a SUT in Orkney can offer more than 70% of the efficiency of the one in Manzanares. The results show that, for a given power capacity, a wind turbine enclosed in a SUT can be considered as an alternative to a number of conventional wind turbines installed at height in the open air

Detection of Rename Local Variable Refactoring Instances in Commit History

Detecting refactoring instances occurred in successive revisions of software systems can provide wealthy information for several purposes, e.g., to facilitate the code review process, to devise more accurate code merging techniques, to help the developers of API clients to ease their adaptation to API changes, and to enable more accurate empirical studies on the refactoring practice. In the literature there are several techniques proposed for refactoring detection, supporting a wide variety of refactoring types. Yet, almost all of them have missed an extensively-applied refactoring type, i.e., Rename Local Variable refactoring. In addition, all these techniques rely on similarity thresholds (which are difficult to tune), or need the systems under analysis to be fully built (which is usually a daunting task), or depend on specific IDEs (which drastically limits their effectiveness and usability). In this thesis, we extend the state-of-the-art refactoring detection tool, RefactoringMiner, by defining necessary rules and extending its core algorithms to tailor it for accurately detecting Rename Local Variable refactoring instances. We have evaluated the proposed technique on two large-scale open-source systems, namely dnsjava and Tomcat. Our comparison with REPENT, the state-of-the-art tool in detecting Rename Local Variable refactoring instances, shows that our approach is superior in terms of precision and recall. Moreover, to automatically create a reference corpus of refactoring instances which is required for the evaluation, we have built a fully-automated infrastructure (called RefBenchmark) that is able to invoke several refactoring detection tools and find the agreements/disagreements between their results

Stochastic Stability of Discrete-time Phase-coupled Oscillators over Uncertain and Random Networks

This paper studies stochastic stability of a class of discrete-time phase-coupled oscillators. We introduce the two new notions of stochastic and ultimate stochastic phase-cohesiveness using the concepts of Harris and positive Harris recurrent Markov chains. Stochastic phase-cohesiveness of oscillators in two types of networks are studied. First, oscillators in a network with an underlying connected topology subject to both multiplicative and additive stochastic uncertainties are considered. Second, we study a special case of the former problem by assuming that the multiplicative uncertainties are governed by the Bernoulli process representing the well known Erd{\H o}s R\'enyi network

Millimetre-wave antennas and systems for the future 5G

Editorial of the special issue on Millimetre-Wave Antennas and Systems for the Future 5