Abstract
This book presents research and development achievements regarding a design-flow approach where specifications for design decisions, monitorization, compilation, synthesis, mapping, and design patterns are first class entities that complement the application source code. The contents of this book reflects 3 years of structural improvements regarding a high-level design-flow and a domain-specific language - LAnguage for Reconfigurable Architectures (LARA) - designed to facilitate the mapping of applications to multi-core and heterogeneous embedded computing systems. © Springer Science+Business Media New York 2013. All rights are reserved.

Abstract
A good representation of a simple polygon, with a desired degree of approximation and complexity, is critical in many applications. This paper presents a method to achieve a complete Circle Covering Representation of a simple polygon, through a topological skeleton, the Medial Axis. The aim is to produce an efficient circle representation of irregular pieces, while considering the approximation error and the resulting complexity, i.e. the number of circles. This will help to address limitations of current approaches to some problems, such as Irregular Placement problems, which will, in turn, provide a positive economic and environmental impact where similar problems arise. © 2013 IFAC.

Abstract
Despite the rising number of emotional state detection methods motivated by the popularity increase in affective computing techniques in recent years, they are yet faced with subject and domain transferability issues. In this paper, we present an improved methodology for modelling individuals' emotional states in multimedia interactive environments. Our method relies on a two-layer classification process to classify Arousal and Valence based on four distinct physiological sensor inputs. The first classification layer uses several regression models to normalize each of the sensor inputs across participants and experimental conditions, while also correlating each input to either Arousal or Valence - effectively addressing the aforementioned transferability issues. The second classification layer then employs a residual sum of squares-based weighting scheme to merge the various regression outputs into one optimal Arousal/Valence classification in real-time, while maintaining a smooth prediction output. The presented method exhibited convincing accuracy ratings - 85% for Arousal and 78% for Valence -, which are only marginally worse than our previous non-real-time approach.

Abstract
Current affective response studies lack dedicated data analysis procedures and tools for automatically annotating and triangulating emotional reactions to game-related events. The development of such a tool would potentially allow for both a deeper and more objective analysis of the emotional impact of digital media stimuli on players, as well as towards the rapid implementation of this type of studies. In this paper we describe the development of such a tool that enables researchers to conduct objective a posteriori analyses, without disturbing the gameplay experience, while also automating the annotation and emotional response identification process. The tool was designed in a data-independent fashion and allows the identified responses to be exported for further analysis in third-party statistical software applications.

Abstract
Designing adaptive games for individual emotional experiences is a tricky task, especially when detecting a player's emotional state in real time requires physiological sensing hardware and signal processing software. There is currently a lack of software that can identify and learn how emotional states in games are triggered. To address this problem, we developed a system capable of understanding the fundamental relations between emotional responses and their eliciting events. We propose time-evolving Affective Reaction Models (ARM), which learn new affective reactions and manage conflicting ones. These models are then meant to provide information on how a set of predetermined game parameters (e.g., enemy and item spawning, music and lighting effects) should be adapted, to modulate the player's emotional state. In this paper, we propose and describe a framework for modulating player emotions and the main components involved in regulating players' affective experience. We expect our technique will allow game designers to focus on defining high-level rules for generating gameplay experiences instead of having to create and test different content for each player type.

Abstract
With the rising popularity of affective computing techniques, there have been several advances in the field of emotion recognition systems. However, despite the several advances in the field, these systems still face scenario adaptability and practical implementation issues. In light of these issues, we developed a nonspecific method for emotional state classification in interactive environments. The proposed method employs a two-layer classification process to detect Arousal and Valence (the emotion's hedonic component), based on four psychophysiological metrics: Skin Conductance, Heart Rate and Electromyography measured at the corrugator supercilii and zygomaticus major muscles. The first classification layer applies multiple regression models to correctly scale the aforementioned metrics across participants and experimental conditions, while also correlating them to the Arousal or Valence dimensions. The second layer then explores several machine learning techniques to merge the regression outputs into one final rating. The obtained results indicate we are able to classify Arousal and Valence independently from participant and experimental conditions with satisfactory accuracy (97% for Arousal and 91% for Valence).