Abstract
This paper focuses on the design of high quality spiral resonators for maximising wireless power transfer efficiency between an AUV and an underwater docking station. By using 3D electromagnetic simulations and numerical analysis, the relevant parameters for quality factor computation are extracted. The impact of different variables on a spiral resonator's quality factor is assessed, allowing to conclude on the optimum design parameters to achieve optimum efficiency on the power transmission through magnetic coupling. This work will contribute to enable the development future AUV wireless charging systems, which will allow for an improvement of AUV's range and endurance while ensuring lower operational costs. © 2016 IEEE.

Abstract
This paper presents a proposal for the use of the Hybrid Fuzzy Inference System algorithm (HyFIS) as solar intensity forecast mechanism. Fuzzy Inference Systems (FIS) are used to solve regression problems in various contexts. The HyFIS is a method based on FIS with the particular advantage of combining fuzzy concepts with Artificial Neural Networks (ANN), thus optimizing the learning process. This algorithm is part of several other FIS algorithms implemented in the Fuzzy Rule-Based Systems (FRBS) package of R. The ANN algorithms and Support Vector Machine (SVM), both widely used for solving regression problems, are also used in this study to allow the comparison of results. Results show that HyFIS presents higher performance when compared to the ANN and SVM, when applied to real data of Florianopolis, Brazil, which helps to reinforce the potential of this algorithm in solving the solar intensity forecasting problems. © 2016 IEEE.

Abstract
The design, manufacture and operational characteristics (e.g., yield, performance, and reliability) of modern electronic integrated systems exhibit extreme levels of complexity that cannot be easily modelled or predicted. Different mathematical methodologies have been explored to address this issue. Monte Carlo simulation is the most widely employed and straightforward approach to evaluate the circuits' performance statistics. However, the high number of trial cases and the long simulations times required to obtain results for complex circuits with a ppm resolution, lead to very long analysis times. The present work addresses the evaluation of alternative statistical inference methodologies which allow obtaining similar results departing from a smaller dimension data set of Monte Carlo simulations from which the overall population is estimated. These methodologies include the use of Bayesian inference, Expectation-inimization, and Kolmogorov-Smirnov tests. Results are presented which show the validity of these approaches.

Abstract

Abstract
In this work, we tackle the problem of predicting entity popularity on Twitter based on the news cycle. We apply a supervised learning approach and extract four types of features: (i) signal, (ii) textual, (iii) sentiment and (iv) semantic, which we use to predict whether the popularity of a given entity will be high or low in the following hours. We run several experiments on six different entities in a dataset of over 150M tweets and 5M news and obtained F1 scores over 0.70. Error analysis indicates that news perform better on predicting entity popularity on Twitter when they are the primary information source of the event, in opposition to events such as live TV broadcasts, political debates or football matches.

Abstract