Abstract
There is a trend towards the use of Virtual Reality (VR) environments and its evolution has promoted new interaction approaches so there is a need for studying a number of factors that can have impact on its usability. This paper studies the impact of the body position on the usability of VR environments. For the effect, a case study was undertaken based on a bicycle ride that considers two body positions: riding the bicycle seated with the feet on the pedals and hands in the handlebar; and standing with the feet on the ground and the hands on the handlebar. On both cases they had control over the bicycle (steer and brakes). These two body positions were considered as they will allow studying in detail the impact of the different body positions: the first condition mimics the real body position of the depicted scenario while the second condition tests an alternate body position. Results regarding the system's effectiveness have shown an 100% success rate as all participants concluded the task successfully and there were no dropouts. The efficiency results have revealed that the more the participants used the VE the less the number of errors they made and that the completion time differences between the tested conditions were insignificant (> 0.5 seconds). As for satisfaction, participants reported a preference towards the standing position. Furthermore, results reveal that body position has impact on the users' performance but it does not necessarily affect their satisfaction over the virtual experience. © 2016 ACM.

Abstract
The self-sustainability of micro grids is an important challenge in the smart grids field due to the need to balance the fast growing energy consumption, reduce greenhouse gas emissions and increase energy independence by using renewable resources. The use of decentralized paradigms, and particularly multi-agent systems and holonic control, enable to face this challenge by implementing intelligent mechanisms that allow an efficient management of the power flow. In this paper, a holonic based model is introduced, considering load scheduling and forecast mechanisms to improve the micro grids self-sustainability, and consequently reduce the energy cost and the energy dependency from the main utility. The designed holonic based model and strategies were developed by using the agent technology, and particularly the JADE framework, showing important improvements in the self-sustainability of micro grids working in different operating modes.

Abstract
Biopsies for diagnosis can sometimes be replaced by non-invasive techniques such as CT and MRI. Surgeons require accurate and efficient methods that allow proper segmentation of the organs in order to ensure the most reliable intervention planning. Automated liver segmentation is a difficult and open problem where CT has been more widely explored than MRI. MRI liver segmentation represents a challenge due to the presence of characteristic artifacts, such as partial volumes, noise and low contrast. In this paper, we present a novel method for multichannel MRI automatic liver segmentation. The proposed method consists of the minimization of a 3D active surface by means of the dual approach to the variational formulation of the underlying problem. This active surface evolves over a probability map that is based on a new compact descriptor comprising spatial and multisequence information which is further modeled by means of a liver statistical model. This proposed 3D active surface approach naturally integrates volumetric regularization in the statistical model. The advantages of the compact visual descriptor together with the proposed approach result in a fast and accurate 3D segmentation method. The method was tested on 18 healthy liver studies and results were compared to a gold standard made by expert radiologists. Comparisons with other state-of-the-art approaches are provided by means of nine well established quality metrics. The obtained results improve these methodologies, achieving a Dice Similarity Coefficient of 98.59. © 2016 Elsevier Ireland Ltd.

Abstract
Sonar imaging is currently the exemplary choice used in underwater imaging. However, since sound signals are absorbed by water, an image acquired by a sonar will have gradient illumination; thus, underwater maps will be difficult to process. In this work, we investigated this phenomenon with the objective to propose methods to normalize the images with regard to illumination. We propose to use MIxed exponential Regression Analysis (MIRA) estimated from each image that requires normalization. Two sidescan sonars have been used to capture the seabed in Lake Vattern in Sweden in two opposite directions west-east and east-west; hence, the task is extremely difficult due to differences in the acoustic shadows. Using the structural similarity index, we performed similarity analyses between corresponding regions extracted from the sonar images. Results showed that MIRA has superior normalization performance. This work has been carried out as part of the SWARMs project (http://www.swarms.eu/).

Abstract
The measurement and analysis of the arterial pulse waveform (APW) are the means for cardiovascular risk assessment. Optical sensors represent an attractive instrumental solution to APW assessment due to their truly non-contact nature that makes the measurement of the skin surface displacement possible, especially at the carotid artery site. In this work, an automatic method to extract and classify the acquired data of APW signals and noise segments was proposed. Two classifiers were implemented: k-nearest neighbours and support vector machine (SVM), and a comparative study was made, considering widely used performance metrics. This work represents a wide study in feature creation for APW. A pool of 37 features was extracted and split in different subsets: amplitude features, time domain statistics, wavelet features, cross-correlation features and frequency domain statistics. The support vector machine recursive feature elimination was implemented for feature selection in order to identify the most relevant feature. The best result (0.952 accuracy) in discrimination between signals and noise was obtained for the SVM classifier with an optimal feature subset .

Abstract