Abstract
The development of applications for high-performance embedded systems is a long and error-prone process because in addition to the required functionality, developers must consider various and often conflicting nonfunctional requirements such as performance and/or energy efficiency. The complexity of this process is further exacerbated by the multitude of target architectures and mapping tools. This article describes LARA, an aspect-oriented programming language that allows programmers to convey domain-specific knowledge and nonfunctional requirements to a toolchain composed of source-to-source transformers, compiler optimizers, and mapping/synthesis tools. LARA is sufficiently flexible to target different tools and host languages while also allowing the specification of compilation strategies to enable efficient generation of software code and hardware cores (using hardware description languages) for hybrid target architectures - a unique feature to the best of our knowledge not found in any other aspect-oriented programming language. A key feature of LARA is its ability to deal with different models of join points, actions, and attributes. In this article, we describe the LARA approach and evaluate its impact on code instrumentation and analysis and on selecting critical code sections to be migrated to hardware accelerators for two embedded applications from industry. Copyright (c) 2014 John Wiley & Sons, Ltd.

Abstract
Mobile phones are powerful tools to connect people. The streams of Call Detail Records (CDR’s) generating from these devices provide a powerful abstraction of social interactions between individuals, representing social structures. Call graphs can be deduced from these CDRs, where nodes represent subscribers and edges represent the phone calls made. These graphs may easily reach millions of nodes and billions of edges. Besides being large-scale and generated in real-time, the underlying social networks are inherently complex and, thus, difficult to analyze. Conventional data analysis performed by telecom operators is slow, done by request and implies heavy costs in data warehouses. In face of these challenges, real-time streaming analysis becomes an ever increasing need to mobile operators, since it enables them to quickly detect important network events and optimize business operations. Sampling, together with visualization techniques, are required for online exploratory data analysis and event detection in such networks. In this chapter, we report the burgeoning body of research in network sampling, visualization of streaming social networks, stream analysis and the solutions proposed so far. © 2016, Springer International Publishing Switzerland.

Abstract

Abstract
This paper presents a methodology based on genetic Algorithms (GA) to solve the problem of optimal participation in multiple electricity markets. With the emergence of new requirements for electrical power markets, it has become fundamental to develop tools to aid in decision making, understanding the functioning of markets and forecast iterations that occur between the different entities in the market. Artificial intelligence plays a crucial role in the development of these tools. Using artificial intelligence techniques, it is possible to simulate the different existing players in the market, to enable these players to be adaptive to any situation, and to model any type of trading. Artificial intelligence based metaheuristic optimization tools allow solving problems in a short time, and with very close results to those that deterministic techniques are able to achieve, at the cost of a high execution time. The achieved results, using a simulation scenario based on real data from the Iberian electricity market, show that the proposed method is able to reach better results than previous implementations of a Particle Swarm Optimization (PSO) and a Simulated Annealing (SA) methods, while achieving very similar objective function results to those of a deterministic approach, in a much faster execution time. © 2016 IEEE.

Abstract
Low voltage actuators based on poly(vinylidene fluoride) (PVDF) with 10, 25 and 40% 1-hexyl-3-methylimidazolium chloride ([C(6)mim][Cl]) and 1-hexyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([C(6)mim][NTf2]) are prepared by solvent casting in order to evaluate the effect of anion size in the bending properties. Independently of the ionic liquid type and content, its presence leads to the crystallization of PVDF in the beta-phase. The addition of ionic liquid into the polymer matrix decreases significantly its degree of crystallinity and the elastic modulus. It is also confirmed the good miscibility between PVDF and IL, determined by the interaction of the CF2 groups from the PVDF chains with the imidazolium ring in the ionic liquid (IL). The AC conductivity of the composites depends both on the amount of ionic liquid content and anion size. The bending movement of the IL/PVDF composites is correlated to their degree of crystallinity, mechanical properties and ionic conductivity value and the best value of bending response (0.53%) being found for IL/PVDF composite with 40 wt of [C(6)min][Cl] at an applied voltage of 10 V square signal.

Abstract
In this paper we introduce a bilinear repetitive process and present an iterative subspace algorithm for its identification. The advantage of the proposed approach is that it overcomes the "curse of dimensionality", a hurdle commonly encountered with classical bilinear subspace identification algorithms. Simulation results show that the algorithm converges quickly and provides new alternatives for modeling/identifying nonlinear repetitive processes.