Abstract
The problem of electromagnetic wave propagation in time varying media is very old, but in recent years it has been revisited at a more fundamental level leading to the introduction of several new concepts, such as Time Refraction. These concepts explore the symmetries between space and time and can be transposed to different fields by establishing powerful analogies between effects in Electrodynamics, Optics and problems in Quantum Cosmology and in what is sometimes called Analogue Gravity. We examine the alteration of the ordinary (spatial) Fresnel laws of refraction at the interface between two media when the optical properties of one of the media varies in time.

Abstract

Abstract
The present work shows some results obtained by the Time-Domain Electromagnetic Method (TDEM) to help characterize the geology and Penacova- Régua-Verin fault structure in the Vila Pouca de Aguiar sector. The Fault is an important tardi-variscan structure with NNE-SSW direction, more than 500 km long, extending from the North of Spain until near Nazaré. The application of the TDEM method aimed to acquire data towards obtaining profiles of subsurface electrical resistivity in the zone affected by the fault and adjacent terrain. Electrical resistivity profiles, resulting from the combination of individual soundings, were performed in the SW quadrant of 6D-Vila Pouca de Aguiar of Carta Geológica de Portugal sheet, at a scale of 1/50 000, along Penacova-Régua-Verin Fault and perpendicularly, with WNW-ESE orientation, in the western sector of the fault. Some confirmation and preliminary models were accomplished using the results obtained by other authors, allowing to verify the validity of the obtained inverted data. The method responded in accordance with the conceptual model but allowed the additional distinction between alteration zones and apparently identical lithologies. Overall, three main blocks were identified, the most superficial shows high resistivities and low thickness. The second block has lower resistivity values due to the influence of the water level at this depth. In the third block, more oscillatory characteristics were identified throughout the various surveys. Although, in general, its high thickness and high values of electrical resistivity were highlighted. However, one of the obstacles in the implementation of the method was the presence of wind farms in Northern Portugal, bridges, metal fences, as well as power lines and high voltage pylons that produce electromagnetic noise and thus interfere with the readings obtained.

Abstract
In this paper it will be presented a proposal of a supervisory approach to be applied to the global localization algorithms in mobile robots. One of the objectives of this work is the increase of the robustness in the estimation of the robot's pose, favoring the anticipated detection of the loss of spatial reference and avoiding faults like tracking derail. The proposed supervisory system is also intended to increase accuracy in localization and is based on two of the most commonly used global feature based localization algorithms for pose tracking in robotics: Augmented Monte Carlo Localization (AMCL) and Perfect Match (PM). The experimental platform was a robotic wheelchair and the navigation used the sensory data from encoders and laser rangers. The software was developed using the ROS framework. The results showed the validity of the proposal, since the supervisor was able to coordinate the action of the AMCL and PM algorithms, benefiting the robot's localization system with the advantages of each one of the methods.

Abstract

Abstract
Epilepsy being one of the most prevalent neurological disorders, affecting approximately 50 million people worldwide, and with almost 30-40% of patients experiencing partial epilepsy being nonresponsive to medication, epilepsy surgery is widely accepted as an effective therapeutic option. Presurgical evaluation has advanced significantly using noninvasive techniques based on video monitoring, neuroimaging, and electrophysiological and neuropsychological tests; however, certain clinical settings call for invasive intracranial recordings such as stereoelectroencephalography (SEEG), aiming to accurately map the eloquent brain networks involved during a seizure. Most of the current presurgical evaluation procedures focus on semiautomatic techniques, where surgery diagnosis relies immensely on neurologists' experience and their time-consuming subjective interpretation of semiology or the manifestations of epilepsy and their correlation with the brain's electrical activity. Because surgery misdiagnosis reaches a rate of 30%, and more than one-third of all epilepsies are poorly understood, there is an evident keen interest in improving diagnostic precision using computer-based methodologies that in the past few years have shown near-human performance. Among them, deep learning has excelled in many biological and medical applications, but has advanced insufficiently in epilepsy evaluation and automated understanding of neural bases of semiology. In this paper, we systematically review the automatic applications in epilepsy for human motion analysis, brain electrical activity, and the anatomoelectroclinical correlation to attribute anatomical localization of the epileptogenic network to distinctive epilepsy patterns. Notably, recent advances in deep learning techniques will be investigated in the contexts of epilepsy to address the challenges exhibited by traditional machine learning techniques. Finally, we discuss and propose future research on epilepsy surgery assessment that can jointly learn across visually observed semiologic patterns and recorded brain electrical activity.