Abstract
In this paper, a novel Bayesian model fusion (BMF) method is proposed for test cost reduction based on wafer-level spatial variation modeling. BMF relies on the assumption that a large number of wafers of the same circuit design (e.g., all wafers from the same lot) share a similar spatial pattern. Hence, the measurement data from one wafer can be borrowed to model the spatial variation of other wafers via Bayesian inference. By applying the Sherman-Morrison-Woodbury formula, a fast numerical algorithm is derived to reduce the computational cost of BMF for practical test applications. Furthermore, a new test methodology is developed based on BMF and it closely monitors the escape rate and yield loss. As is demonstrated by the wafer probe measurement data of an industrial RF transceiver, BMF achieves 1.125x reduction in test cost and 2.6x reduction in yield loss, compared to the conventional approach based on virtual probe (VP).

Abstract
A review in fiber post-processing for sensing applications is presented. The review is divided in three parts. Tapers devices, chemical etching for Fabry-Perot cavities and focused ion beam (FIB) as post-processing applied in optical fibers are considered. The most recent results as sensing elements are shown. © 2014 OSA.

Abstract
As Wireless Sensor Networks have been employed to support critical monitoring applications, network availability has become a major design concern. In these networks, redundancy can be exploited to enhance the attainable availability level, where redundant sensors can replace faulty nodes. When camera-enabled sensors are deployed to retrieve visual information, the perception of redundancy changes considerably, since the redundancy of visual sensors depends on the monitoring requirements of the applications. In such context, characteristics as deployment density, viewing angle and sensing range are relevant when planning wireless sensor network applications, directly impacting in the number of redundant nodes. We propose an algorithm to select redundant nodes in Wireless Visual Sensor Networks, according to the application requirements. Moreover, we discuss how parameters of the deployed network can influence on the number of redundant nodes.

Abstract
Supply Function Equilibrium (SFE) and Conjectured Supply Function Equilibrium (CSFE) are some of the approaches most used to model electricity markets in the medium and long term. SFE represents the generators' strategies with functions that link prices and quantities, but leads to systems of differential equations hard to solve, unless linearity is assumed (Linear Supply Function Equilibrium, LSFE). CSFE also assumes linearity of the supply functions but only around the equilibrium point, also avoiding the system of differential equations. This paper analyzes the existence and uniqueness of G-CSFE (a CSFE previously proposed by the Authors) for both elastic and inelastic demands. In addition, it also proves that the iterative algorithm proposed to compute G-CSFE has a fixed point structure and is convergent, and that LSFE is a particular case of G-CSFE when demand and marginal costs are linear. Selected examples show the performance of G-CSFE and how it can be applied to market power analysis with meaningful results.

Abstract
During the last years, the increased penetration of distributed generation (DG) has started to affect also the transmission system. The increasing presence of weak networks, together with unpredictable power produced by the DG units, has a considerable impact on the behaviour of the whole power system, starting from the DG units and passing to distribution/transmission levels. Voltage control, frequency control and stability issues of the network are changing considerably due to the presence of DG. In particular, the problems appear at the connection of DG units with the distribution network. This paper presents several aspects regarding the connection problems between low inertia DG's and the electrical grid, reviewing the state-of-the-art with the objective of identifying the main concepts and operational aspects referring to dynamic analysis.

Abstract
Set plays are predefined collaborative coordinate actions that players from any sport can use to gain advantage over its adversaries. Recently, a complete framework for creation and execution of this kind of coordinate behavior by teams composed of multiple independent agents was launched as free software (the Set play Framework). In this paper, an approach based on Reinforcement Learning(RL) is proposed, that allows the use of experience to devise the better course of action in set plays with multiple choices. Simulations results show that the proposed approach allows a team of simulated agents to improve its performance against a known adversary team, achieving better results than previously proposed approaches using RL.