Abstract

Abstract
Electroencephalographic (EEG) signals present a myriad of challenges to analysis, beginning with the detection of artifacts. Prior approaches to noise detection have utilized multiple techniques, including visual methods, independent component analysis and wavelets. However, no single method is broadly accepted, inviting alternative ways to address this problem. Here, we introduce a novel approach based on a statistical physics method, multiscale entropy (MSE) analysis, which quantifies the complexity of a signal. We postulate that noise corrupted EEG signals have lower information content, and, therefore, reduced complexity compared with their noise free counterparts. We test the new method on an open-access database of EEG signals with and without added artifacts due to electrode motion. © 2015 IEEE.

Abstract
Apresentamos um conceito de jogo inovador e o seu protótipo inicial, integrando tipos distintos de interação e de visualização. Dois jogadores interagem como timoneiro de uma nau portuguesa e gigante Adamastor. Um joga em realidade virtual controlada por gestos finos, outro usa aumentação contextual com gestos amplos e fi-nos. Pretende o conceito e o protótipo servirem como exemplificadores das potencialidades das novas formas de interação e de como as concretizar.

Abstract

Abstract
This paper presents a new stochastic multi-layer agent-based decision making model to study the behavior of market participants in the future smart grid. In the agent-based model proposed, wholesale market players are modeled in the first layer. The players include renewable/sustainable power producers, optimizing the bidding/offering strategies to participate in the electricity markets. In the second layer, responsive customers include electric vehicle owners and consumers who participate in demand response programs, being modeled as independent agents. The objective of the responsive customers is to increase their benefit while retaining welfare. The interaction between market players in day-ahead and real-time markets is modeled using an incomplete information game theory algorithm. According to the high uncertainty of resources and customers' behavior, the model is developed using a stochastic framework. A case study containing wind power producers, aggregators and retailers providing demand response is considered to confirm the usefulness of the proposed multi-layer model.

Abstract
In some Wireless Sensor Network applications the sensor nodes share the same sensing activity, which means that for a considerable number of applications, not all nodes are required to perform sensing tasks during the network lifetime. Sleep-scheduling approaches can be applied in this scenario, enabling that some nodes turn off their radios, saving energy and bandwidth, as long as there are enough nodes to ensure the required Quality of Service (QoS) of the network. This paper presents a new adaptive approach for QoS and energy management in IEEE 802.15.4 networks, entitled Skip Game. This approach targets a trade-off between increasing the network lifetime and maintaining the QoS of the network, aiming a greater number of nodes to participate in the monitoring application. In order to evaluate the proposed approach, we performed some experiments using the OMNeT++ simulator tool under the MiXiM framework. The results show that the Skip Game outperforms both the traditional Gur Game and Gureen Game approaches in terms of QoS provision and network lifetime.