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IntroductionWe aimed to assess longitudinal changes in television (TV) food advertising during 2013 compared to 2007, measuring children's exposure to healthy and unhealthy advertisements, after the new European and Spanish Public Health laws published in 2011.Material and methodsTwo thematic channels for children (TC), and 2 generalist channels (GC) for all ages were recorded, between April and May 2013, on 2 week and 2 weekend days. Food advertisements were classified as core (CFA) (nutrient dense, low energy), non-core (NCFA) (unbalanced energy profile or high in energy), or others (OFA) (supermarkets and special food).ResultsOne thousand two hundred sixty-three food advertisements were recorded (TC: 579/GC: 684) in 2013. NCFA were the most shown (54.9%) in the regular full day TV programming (p < 0.001). In 2013, children watching GC had a higher relative risk of being exposed to fast food advertisements than when watching TC (RR = 2.133, 95% CI: 1.398–3.255); CFA were broadcast most frequently in 2013 (GC: 23.7%; and TC: 47.2%) vs. 2007 (TC: 22.9%) (p < 0.001). The proportion of broadcasting between NCFA/CFA and OFA food advertisements in children's peak time slots was higher on TC (203/162) during 2013 than on GC (189/140), and significantly higher than that shown on TC in 2007 (180/36, p < 0.001).ConclusionsBroadcasting of unhealthy TV food advertising on TC is lower today than six years ago; but, children's exposure to TV advertising of unhealthy food is worrying in Spain, and there is more exposure to unhealthy than healthy food by TV. Watching GC in 2013 had higher risk of being exposed to fast food advertisements than watching TC.

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This paper presents an embedded hardware/software architecture specially designed to be applied on mini/micro Unmanned Aerial Vehicles (UAV). A UAV is a low-cost non-piloted airplane designed to operate in D-cube (Dangerous-Dirty-Dull) situations [8]. Many types of UAVs exist today; however with the advent of UAV's civil applications, the class of mini/micro UAVs is emerging as a valid option in a commercial scenario. This type of UAV shares limitations with most computer embedded systems: limited space, limited power resources, increasing computation requirements, complexity of the applications, time to market requirements, etc. UAVs are automatically piloted by an embedded system named "Flight Control System." Many of those systems are commercially available today, however no commercial system exists nowadays that provides support to the actual mission that the UAV should perform.This introduces a hardware/software architecture specially designed to operate as a flexible payload and mission controller in a mini/micro UAV. Given that the missions UAVs can carry on justify their existence; we believe that specific payload and mission controllers for UAVs should be developed. Our architectonic proposal for them orbits around four key elements: a LAN-based distributed and scalable hardware architecture, a service/subscription based software architecture and an abstraction communication layer.

This work presents a novel approach for automatic epilepsy seizure detection based on EEG analysis that exploits the underlying non-linear nature of EEG data. In this paper, two main contributions are presented and validated: the use of non-linear classifiers through the so-called kernel trick and the proposal of a Bag-of-Words model for extracting a non-linear feature representation of the input data in an unsupervised manner. The performance of the resulting system is validated with public datasets, previously processed to remove artifacts or external disturbances, but also with private datasets recorded under realistic and non-ideal operating conditions. The use of public datasets caters for comparison purposes whereas the private one shows the performance of the system under realistic circumstances of noise, artifacts, and signals of different amplitudes. Moreover, the proposed solution has been compared to state-of-the-art works not only for pre-processed and public datasets but also with the private datasets. The mean F1-measure shows a 10% improvement over the second-best ranked method including cross-dataset experiments. The obtained results prove the