Abstract
In a dynamically changing social environment, humans have to face the challenge of prioritizing stimuli that compete for attention. In the context of social communication, the voice is the most important sound category. However, the existing studies do not directly address whether and how the salience of an unexpected vocal change in an auditory sequence influences the orientation of attention. In this study, frequent tones were interspersed with task-relevant infrequent tones and task-irrelevant infrequent vocal sounds (neutral, happy and angry vocalizations). Eighteen healthy college students were asked to count infrequent tones. A combined event-related potential (ERP) and EEG time-frequency approach was used, with the focus on the P3 component and on the early auditory evoked gamma band response, respectively. A spatial-temporal principal component analysis was used to disentangle potentially overlapping ERP components. Although no condition differences were observed in the 210-310 ms window, larger positive responses were observed for emotional than neutral vocalizations in the 310-410 ms window. Furthermore, the phase synchronization of the early auditory evoked gamma oscillation was enhanced for happy vocalizations. These findings support the idea that the brain prioritizes the processing of emotional stimuli, by devoting more attentional resources to salient social signals even when they are not task-relevant.

Abstract
In this paper we introduce a bilinear repetitive process and present an iterative subspace algorithm for its identification. The advantage of the proposed approach is that it overcomes the "curse of dimensionality", a hurdle commonly encountered with classical bilinear subspace identification algorithms. Simulation results show that the algorithm converges quickly and provides new alternatives for modeling/identifying nonlinear repetitive processes.

Abstract
Epilepsy is a common neurological disorder which affects 0.5-1% of the world population. Its diagnosis relies both on Electroencephalogram (EEG) findings and characteristic seizure -induced body movements - called seizure semiology. Thus, synchronous EEG and (2D) video recording systems (known as Video-EEG) are the most accurate tools for epilepsy diagnosis. Despite the establishment of several quantitative methods for EEG analysis, seizure semiology is still analyzed by visual inspection, based on epileptologists' subjective interpretation of the movements of interest (MOIs) that occur during recorded seizures. In this contribution, we present NeuroKinect, a low-cost, easy to setup and operate solution for a novel 3Dvideo-EEG system. It is based on a RGB-D sensor (Microsoft Kinect camera) and performs 24/7 monitoring of an Epilepsy Monitoring Unit (EMU) bed. It does not require the attachment of any reflectors or sensors to the patient's body and has a very low maintenance load. To evaluate its performance and usability, we mounted a state-of-the-art 6-camera motion-capture system and our low-cost solution over the same EMU bed. A comparative study of seizure-simulated MOIs showed an average correlation of the resulting 3D motion trajectories of 84.2%. Then, we used our system on the routine of an EMU and collected 9 different seizures where we could perform 3D kinematic analysis of 42 MOIs arising from the temporal (TLE) (n = 19) and extratemporal (ETE) brain regions (n = 23). The obtained results showed that movement displacement and movement extent discriminated both seizure MOI groups with statistically significant levels (mean = 0.15 m vs. 0.44 m, p<0.001; mean = 0.068 m(3) vs. 0.14 m(3), p< 0.05, respectively). Furthermore, TLE MOIs were significantly shorter than ETE (mean = 23 seconds vs 35 seconds, p< 0.01) and presented higher jerking levels (mean = 345 ms(-3) vs 172 ms(-3), p< 0.05). Our newly implemented 3D approach is faster by 87.5% in extracting body motion trajectories when compared to a 2D frame by frame tracking procedure. We conclude that this new approach provides a more comfortable (both for patients and clinical professionals), simpler, faster and lower-cost procedure than previous approaches, therefore providing a reliable tool to quantitatively analyze MOI patterns of epileptic seizures in the routine of EMUs around the world. We hope this study encourages other EMUs to adopt similar approaches so that more quantitative information is used to improve epilepsy diagnosis.

Abstract
In this paper the nonparametric identification of state-space linear parameter-varying models with dynamic mapping between the scheduling signal and the model matrices is considered. Indeed, we are particularly interested on the problem of estimating a model using data generated from an LPV system with static dependence, which is however represented on a different state-basis from the one considered by the estimator.

Abstract
In this paper an identification method for statespace LPV models is presented. The method is based on a particular parameterization that can be written in linear regression form and enables model estimation to be handled using Least-Squares Support Vector Machine (LS-SVM). The regression form has a set of design variables that act as filter poles to the underlying basis functions. In order to preserve the meaning of the Kernel functions (crucial in the LS-SVM context), these are filtered by a 2D-system with the predictor dynamics. A data-driven, direct optimization based approach for tuning this filter is proposed. The method is assessed using a simulated example and the results obtained are twofold. First, in spite of the difficult nonlinearities involved, the nonparametric algorithm was able to learn the underlying dependencies on the scheduling signal. Second, a significant improvement in the performance of the proposed method is registered, if compared with the one achieved by placing the predictor poles at the origin of the complex plane, which is equivalent to considering an estimator based on an LPV auto-regressive structure.

Abstract
In this work, the feasibility of applying a Sliding Mode Predictive Controller (SMPC) to improve greenhouse inside air temperature control is addressed in terms of energy consumption, disturbance handling and set point tracking accuracy. Major research issues addressed concern the SMPC robustness study in greenhouse control, as well as to evaluate if the levels of performance and energy consumptions are acceptable when compared with the traditional generalized predictive controller. Besides the external disturbances related to weather conditions throughout the considered period, such as solar radiation and temperature variations, internal effects of irrigation system and external air flow entering the greenhouse must be taken into account. Simulations based on real data, carried out for a period of 4months, suggest that the strategy herein described not only appropriately rejects these disturbances, but also keeps the manipulated variables (heating and cooling) within feasible practical limits, with low levels of energy consumption, motivating its refinement for real application. SMPC results are presented and compared with the ones obtained with the generalized predictive controller. Both controllers are subject to actuator constraints and employ the Quadratic Programming for optimization. Copyright (c) 2015 John Wiley & Sons, Ltd.