Abstract
The work described in this paper addresses the development of a serious game to promote the learning of the Portuguese Sign Language supported by an automatic bi-directional translator between Portuguese Sign Language and Portuguese written text. The translator from sign language to text is supported by two devices, namely the Microsoft Kinect and 5DT Sensor Gloves in order to gather data about the motion and shape of the hands. The hands configurations are classified using Support Vector Machines. The classification of the movement and orientation of the hands is achieved through the use of Dynamic Time Warping algorithm. The translation of Portuguese text to Portuguese Sign Language is supported by a 3D avatar which interprets the entered text and performs the corresponding animations. A serious game directed to assist in the difficult task of learning the Portuguese Sign Language is presented.

Abstract
This paper presents a platform for adaptive production management developed in the ARUM1 (Adaptive pRodUct Management, http://arumproject.eu/) project. The design of ARUM platform started with applying a traditional enterprise Service-Oriented Architecture (SOA) paradigm to solving an integration problem for the production ramp-up of highly customized products such as aircrafts, ships, etc. The production of such articles is exceptionally challenging for planning and control, especially in small lot sizes. Often requests for changes at any stage of the production, immature products and processes bring serious additional risks for the producers and customers. To counter such issues requires new strategies, the core elements of most of them include early detection of unexpected situations followed by rapid mitigation actions. Furthermore, human beings cannot cope any longer with processing a massive volume of data that comes with a high velocity from various sources that is a requirement for any modern production shop floor. The traditional IT solutions also fall short when trying to satisfy all those requirements and this motivates the need for ARUM platform to help in effective decision making. © Springer International Publishing Switzerland 2015.

Abstract
System Identification (SI) is a methodology for building mathematical models of dynamic systems from experimental data, i.e., using measurements of the system input/output (IO) signals to estimate the values of adjustable parameters in a given model structure. The process of SI requires some steps, such as measurement of the IO signals of the system in time or frequency domain, selection of a candidate model structure, choice and application of a method to estimate the value of the adjustable parameters in the candidate model structure, validation and evaluation of the estimated model to see if the model is right for the application needs, which should be done preferably with a different set of data, [PS] and [Lj1]. © 2015 Springer International Publishing.

Abstract
The development of new technologies, information systems, decision support systems and clinical parameters prediction algorithms using machine learning and data mining, opens a new outlook in many areas of health. In this context, the concept of Quality of Life (QOL) has relevance in health and the possibility of integrate this measure in developing systems Decision Support Clinic (SADC). Through individual expectation of physical well-being, psychological, mental, emotional and spiritual patient, clinical variables and quality of life assessment, we intend to make a study of data to establish correlations with clinical data and pharmaceutical data, socio-economic factors, among others, for obtaining knowledge in terms of behavioral patterns of chronically ill, reaching a number of reliable data and easily accessible, capable of enhancing the decision-making process on the part of specialist medical teams, seeking to improve treatments and consequently the quality of life related to health chronically ill. This paper studied and compared related studies that develop systems for decision support and prediction in the clinical area, with emphasis on studies in the area of quality of life. © 2015 AISTI.

Abstract
The field of Robotics has become one of the most rapidly growing fields in the research and technological world. The development of flexible robots represents the possibility of them becoming a highly efficient operator in the industrial environment. Intelligent robots present key characteristics that enable the streamlining of automated processes associated to industry. Adding the adaptive component to such robots facilitates the design of solutions for a wide range of problems. Pick and Place operations have attracted considerable interest from the research and industrial community as they present one of the most effective solutions to typical problems such as handling or transportation. Another aspect to consider when developing a robotic solution for pick and place approaches is the methodology for recognition of the objects to be handled. In this paper, it will be presented a methodology that can be applicable to different scenarios in order to both identify the objects of a given scene and reply to the need of handling those objects. Furthermore it will be presented one specific case study that used the proposed methodology, the Amazon Picking Challenge - a challenge aiming to develop solutions for the complete automation of a dispatching warehouse. Our proposed methodology was built using the Robotic Operative System (ROS) framework and is based in three tiers: recognition, movement / actuation and control. ROS allows the decomplexation of typical problems associated to robotics as this framework promotes the development of modular and simple software that together fulfill the state-of-art requests of the industry. Since ROS is becoming an important tool in robotics, using a methodology developed in ROS allows for the development of a standard approach to pick and place operations. Another advantage of our methodology is the ability to have a robot safely and efficiently inserted in an unknown environment. This is possible due to adaptive control tier. Proposed improvements to currently available methods will be also described in this project. Throughout the document, the importance of this project and the development of novel robots will be described taken into consideration the need for robots in the industrial setting.

Abstract
The prospect of distributed generation investment planning (DGIP) is especially relevant in insular networks because of a number of reasons such as energy security, emissions and renewable integration targets. In this context, this paper presents a DGIP model that considers various DG types, including renewables. The planning process involves an economic analysis considering the costs of emissions, reliability and other relevant cost components. In addition, a comprehensive sensitivity analysis is carried out in order to investigate the effect of variability and uncertainty of model parameters on DG investment decisions. The ultimate goal is to identify the parameters that significantly influence the decision-making process and to quantify their degree of influence. The results show that uncertainty has a meaningful impact on DG investment decisions. In fact, the degree of influence varies from one parameter to another. However, in general, ignoring or inadequately considering uncertainty and variability in model parameters has a quantifiable cost. The analyses made in this paper can be very useful to identify the most relevant model parameters that need special attention in planning practices. © 2015 IEEE.