Abstract
This paper describes a control technique for control of voltage source converters (VSCs) in the multi-terminal high voltage direct current (HVDC) transmission systems. The proposed control technique is based on a multi-loop current and voltage control scheme for tracking the reference value of the DC link voltage in the rectifier side to achieve a proper performance during the irregular circumstances of wind farm operation. In addition, the proposed control scheme is able to transmit the maximum power to the consumption sectors. Moreover, the proposed control method on the grid side converter guarantees least amount of current harmonics injection into the AC power grid. The MATLAB simulation results are presented to confirm the effectiveness of the proposed control technique for variation in AC voltage amplitude and frequency of wind turbines output voltage.

Abstract
In northern Spain, the use of biomass to produce bioenergy has led to increased exploitation of both natural pedunculate oak (Quercus robur L.) stands and fast-growing plantations of natural or exotic species. In this study, we developed a model for estimating aboveground biomass, carbon and nutrient contents in different pedunculate oak components at individual-tree and at stand level. Six harvesting methods were simulated in an average stand, ranging from whole-tree to stem wood extraction (stem without bark) and including the conventional harvesting method used in the region (extraction of stem plus branches of diameter >7 cm). The biomass and macronutrients extracted were compared with those removed during harvesting of fast-growing tree species (Eucalyptus globulus Labill., Pinus radiata D. Don and Pinus pinaster Ait.) on the same temporal basis (mean annual values). Harvesting pedunculate oak stands generally extracted lower amounts of nutrients than harvesting fast-growing species, although the differences depended on the species, macronutrients and harvesting regime considered.

Abstract
Commercial-of-the-shelf based multi-core systems present timing anomalies that cannot be ignored by the real-time systems community due to their unpredictable behaviour. These timing anomalies, often caused by applications' uncontrolled accesses to shared resources such as the components in the memory hierarchy or in the 1/0 subsystem, introduce interference that may lead to deadline misses if the problem is neglected. The Acquisition Execution Restitution (AER) execution model was previously proposed to circumvent this problem and, therefore, mitigate inter-task interference. In this model, applications decouple communication (acquisition and restitution phases) from the actual execution in a way that at most one acquisition or restitution phase is in execution at any instant of time while the execution phase of different tasks can progress in parallel on multiple cores. Thus, keeping each task's derived worst-case execution time closer to the one measured in isolation. In this paper, we study the AER execution model and compare it against a global Earliest Deadline First (EDF) approach where interferences are considered. Our results show that a priority assignment heuristic which assigns the priorities based on the tasks' periods dominates all the other proposed heuristics and that due to interference it can also schedule task sets which are not schedulable by using the global EDF approach.

Abstract
The current vehicle stability control techniques relies on an accurate sensor information and a complete system definition, such information is not easily obtained and requires expensive sensor technology. In this work it is presented a fusion algorithm for estimating the vehicle handling dynamic states, using inertial measurements combined with Global Positioning System (GPS) information, based on the Extended Kalman Filter algorithm (EKF). The proposed method will be able to track the state of the variable vector that includes the yaw rate, lateral velocity and longitudinal velocity of the vehicle using the information of the available sensors combined with the non-linear model of the system. In order to validate the proposed sensor fusion algorithm a simulation with a high-fidelity CarSim model is carried out and its sensors are compared with Extended Kalman Filter state variables.

Abstract

Abstract
Hippocampal sclerosis (HS) is the most common cause of temporal lobe epilepsy (TLE) and can be identified in magnetic resonance imaging as hippocampal atrophy and subsequent volume loss. Detecting this kind of abnormalities through simple radiological assessment could be difficult, even for experienced radiologists. For that reason, hippocampal volumetry is generally used to support this kind of diagnosis. Manual volumetry is the traditional approach but it is time consuming and requires the physician to be familiar with neuroimaging software tools. In this paper, we propose an automated method, written as a script that uses FSL-FIRST, to perform hippocampal segmentation and compute an index to quantify hippocampi asymmetry (HAI). We compared the automated detection of HS (left or right) based on the HAI with the agreement of two experts in a group of 19 patients and 15 controls, achieving 84.2% sensitivity, 86.7% specificity and a Cohen's kappa coefficient of 0.704. The proposed method is integrated in the "Advanced Brain Imaging Lab" (ABrIL) cloud neurocomputing platform. The automated procedure is 77% (on average) faster to compute vs. the manual volumetry segmentation performed by an experienced physician.