Abstract
This paper describes a new current control method that enhances the dynamic performance of a singlephase bidirectional AC-DC battery charger to provide a high-frequency link between the grid and electric vehicle. The single-stage structure and the bidirectional power flow make the matrix converter an attractive solution for electric vehicle (EV) battery charging applications in the context of smart grids. The operating principles and modulation method are analyzed and discussed in detail. Furthermore, a current controller improved with a Smith predictor is proposed to decrease the phase delay in the measurement of the average current in the battery pack. The SP reduces the rise time to around a third and the settling time to half when compared with a PI controller. Simulations and experimental results from a laboratory prototype are shown to verify the feasibility of the proposed control scheme.

Abstract
The original version of this article unfortunately contained an error in the author affiliation. The corresponding author, “Dr. Borislav Nikoli´c” is currently affiliated in “Technische Universität Braunschweig”, but the work of this paper was performed and funded by CISTER/INESC-TEC, ISEP, IPP. Therefore, the corresponding author is linked to both the affiliations. This has been corrected with this erratum.

Abstract
In this work, we investigate the superfluidic properties of light propagating in a four-level coherent atomic medium. The model is derived under the paraxial approximation in the form of a generalized nonlinear Schrodinger equation and features spatially controllable and quantum-enhanced optical properties, which can offer new possibilities in the field of optical analogue systems. In particular, we use this versatility to study the dynamics of an optical vortex beam confined in a nontrivial connected geometry, finding numerical evidence of another superfluidic signature analogue: the persistent current of light. (C) 2017 Optical Society of America

Abstract
BackgroundUltrasound is an effective tool for breast cancer diagnosis. However, its relatively low image quality makes small lesion analysis challenging. This promotes the development of tools to help clinicians in the diagnosis. MethodsWe propose a method for segmentation and three-dimensional (3D) reconstruction of lesions from ultrasound images acquired using the automated breast volume scanner (ABVS). Segmentation and reconstruction algorithms are applied to obtain the lesion's 3D geometry. A total of 140 artificial lesions with different sizes and shapes are reconstructed in gelatin-based phantoms and biological tissue. Dice similarity coefficient (DSC) is used to evaluate the reconstructed shapes. The algorithm is tested using a human breast phantom and clinical data from six patients. ResultsDSC values are 0.860.06 and 0.86 +/- 0.05 for gelatin-based phantoms and biological tissue, respectively. The results are validated by a specialized clinician. ConclusionsEvaluation metrics show that the algorithm accurately segments and reconstructs various lesions. Copyright (c) 2016 John Wiley & Sons, Ltd.

Abstract
Nowadays most learning problems demand adaptive solutions. Current challenges include temporal data streams, drift and non-stationary scenarios, often with text data, whether in social networks or in business systems. Various efforts have been pursued in machine learning settings to learn in such environments, specially because of their non-trivial nature, since changes occur between the distribution data used to define the model and the current environment. In this work we present the Drift Adaptive Retain Knowledge (DARK) framework to tackle adaptive learning in dynamic environments based on recent and retained knowledge. DARK handles an ensemble of multiple Support Vector Machine (SVM) models that are dynamically weighted and have distinct training window sizes. A comparative study with benchmark solutions in the field, namely the Learn + +.NSE algorithm, is also presented. Experimental results revealed that DARK outperforms Learn + +.NSE with two different base classifiers, an SVM and a Classification and Regression Tree (CART).

Abstract
In recent years Serious Games have evolved substantially, solving problems in diverse areas. In particular, in Cognitive Rehabilitation, Serious Games assume a relevant role. Traditional cognitive therapies are often considered repetitive and discouraging for patients and Serious Games can be used to create more dynamic rehabilitation processes, holding patients' attention throughout the process and motivating them during their road to recovery. This paper reviews Serious Games and user interfaces in rehabilitation area and details a Serious Games platform for Cognitive Rehabilitation that includes a set of features such as: natural and multimodal user interfaces and social features (competition, collaboration, and handicapping) which can contribute to augment the motivation of patients during the rehabilitation process. The web platform was tested with healthy subjects. Results of this preliminary evaluation show the motivation and the interest of the participants by playing the games.