Abstract
Optimization problems in electric power systems under high levels of uncertainty have been solved using stochastic programming methods for years. This is especially the case for medium-term problems and systems with a large share of hydro storages. The increased uncertainty in power system operation coming from volatile renewables has made the stochastic techniques interesting in shorter time frames as well. In the stochastic models the uncertainty is typically included by a discretized set of scenarios. This increases the computational burden significantly so a common approach is to preprocess and reduce the number of scenarios. Scenario reduction methods have been shown to function relatively well in expected value stochastic optimization. However, such setting of stochastic optimization is often criticized as being risk-prone so other risk-averse models exist. The evolutionary computation algorithms' flexibility permits the implementation of these risk models with relative simplicity. In this paper, a population-based evolutionary computation algorithm is applied to unit commitment problem under uncertainty and the paper illustrates several approaches to including risk policies in an evolutionary algorithm fitness function and illustrates its flexibility along with the link between scenario reduction similarity metric and risk policy.

Abstract
This paper presents a new stochastic multi-layer agent-based model to study the behavior of electricity market participants. The wholesale market players including renewable power producers are modeled in the first layer of the proposed multi-agent environment. The players optimize bidding/offering strategies to participate in the electricity markets. In the second layer, responsive customers including plug-in electric vehicle (PEV) owners and consumers who participate in demand response (DR) programs are 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. Due to the uncertainties of resources and customers' behavior, the model is developed using a stochastic framework. A case study containing wind power producers (WPPs), PEV aggregators and retailers providing DR is considered to demonstrate the usefulness and proficiency of the proposed multi-layer agent-based model.

Abstract
This paper presents a novel nonparametric methodology to evaluate convergence in an industry, considering a multi-input multi-output setting for the assessment of total factor productivity. In particular, we develop two new indexes to evaluate sigma-convergence and beta-convergence that can be computed using nonparametric techniques such as Data Envelopment Analysis. The methodology developed is particularly useful to enhance productivity assessments based on the Malmquist index. The methodology is applied to a real world context, consisting of a sample of Portuguese construction companies that operated in the sector between 2008 and 2010. The empirical results show that Portuguese companies tended to converge, both in the sense of a and beta, in all construction activity segments in the aftermath of the financial crisis.

Abstract
This chapter describes outdoor transmission tests and field measurements in TV white spaces (TVWS) carried out in Europe. TVWS Measurements in Germany showed that the extended Hata model is appropriate to describe the path loss over distances up to a few kilometers. During the TVWS trial in Slovenia, we combine infrastructure sensing with geo-location database access to protect not only DVB-T, but also wireless microphone (WM) signals from TVWS devices interference. © 2015 John Wiley & Sons, Inc.

Abstract
In this paper we introduce a recursive subspace system identification algorithm for MIMO linear parameter varying systems driven by general inputs and a white noise time varying parameter vector. The new algorithm is based on a convergent sequence of linear deterministic-stochastic state-space approximations, thus considered a Picard based method. Such methods have proven to be convergent for the bilinear state-space system identification problem. The key to the proposed algorithm is the fact that the bilinear term between the time varying parameter vector and the state vector behaves like a white noise process. Using a linear Kalman filter model, the bilinear term can be efficiently estimated and then used to construct an augmented input vector at each iteration. Since the previous state is known at each iteration, the system becomes linear, which can be identified with a linear-deterministic subspace algorithm such as MOESP, N4SID, or CVA. Furthermore, the model parameters obtained with the new algorithm converge to those of a linear parameter varying model. Finally, the dimensions of the data matrices are comparable to those of a linear subspace algorithm, thus avoiding the curse of dimensionality. © 2007 EUCA.

Abstract
Hand gesture recognition is a natural way of human computer interaction and an area of very active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research applied to Human-Computer Interaction (HCI) is to create systems, which can identify specific human gestures and use them to convey information or controlling devices. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. This paper presents a solution, generic enough, with the help of machine learning algorithms, allowing its application in a wide range of human-computer interfaces, for real-time gesture recognition. Experiments carried out showed that the systemwas able to achieve an accuracy of 99.4%in terms of hand posture recognition and an average accuracy of 93.72%in terms of dynamic gesture recognition. To validate the proposed framework, two applications were implemented. The first one is a real-time system able to help a robotic soccer referee judge a game in real time. The prototype combines a vision-based hand gesture recognition system with a formal language definition, the Referee CommLang, into what is called the Referee Command Language Interface System (ReCLIS). The second one is a real-time system able to interpret the Portuguese Sign Language. Sign languages are not standard and universal and the grammars differ from country to country. Although the implemented prototype was only trained to recognize the vowels, it is easily extended to recognize the rest of the alphabet, being a solid foundation for the development of any vision-based sign language recognition user interface system. © Springer International Publishing Switzerland 2015