UPC’s institutional transformation towards sustainability

Peer ReviewedPostprint (published version

BRAIN COMPUTER INTERFACE - Application of an Adaptive Bi-stage Classifier based on RBF-HMM

Brain Computer Interface is an emerging technology that allows new output paths to communicate the users intentions without the use of normal output paths, such as muscles or nerves. In order to obtain their objective, BCI devices make use of classifiers which translate inputs from the users brain signals into commands for external devices. This paper describes an adaptive bi-stage classifier. The first stage is based on Radial Basis Function neural networks, which provides sequences of pre-assignations to the second stage, that it is based on three different Hidden Markov Models, each one trained with pre-assignation sequences from the cognitive activities between classifying. The segment of EEG signal is assigned to the HMMwith the highest probability of generating the pre-assignation sequence. The algorithm is tested with real samples of electroencephalografic signal, from five healthy volunteers using the cross-validation method. The results allow to conclude that it is possible to implement this algorithm in an on-line BCI device. The results also shown the huge dependency of the percentage of the correct classification from the user and the setup parameters of the classifier

Sharp weighted estimates for classical operators

We give a new proof of the sharp one weight $L^p$ inequality for any operator $T$ that can be approximated by Haar shift operators such as the Hilbert transform, any Riesz transform, the Beurling-Ahlfors operator. Our proof avoids the Bellman function technique and two weight norm inequalities. We use instead a recent result due to A. Lerner to estimate the oscillation of dyadic operators. Our method is flexible enough to prove the corresponding sharp one-weight norm inequalities for some operators of harmonic analysis: the maximal singular integrals associated to $T$, Dyadic square functions and paraproducts, and the vector-valued maximal operator of C. Fefferman-Stein. Also we can derive a very sharp two-weight bump type condition for $T$.Comment: We improve different parts of the first version, in particular we show the sharpness of our theorem for the vector-valued maximal functio

Internet of Things and Their Coming Perspectives: A Real Options Approach

Internet of things is developing at a dizzying rate, and companies are forced to implement it in order to maintain their operational efficiency. The high flexibility inherent to these technologies makes it necessary to apply an appropriate measure, which properly assesses risks and rewards. Real options methodology is available as a tool which fits the conditions, both economic and strategic, under which investment in internet of things technologies is developed. The contribution of this paper is twofold. On the one hand, it offers an adequate tool to assess the strategic value of investment in internet of things technologies. On the other hand, it tries to raise awareness among managers of internet of things technologies because of their potential to contribute to economic and social progress. The results of the research described in this paper highlight the importance of taking action as quickly as possible if companies want to obtain the best possible performance. In order to enhance the understanding of internet of things technologies investment, this paper provides a methodology to assess the implementation of internet of things technologies by using the real options approach; in particular, the option to expand has been proposed for use in the decision-making process