Abstract
Healthcare projects that intend to decrease the economical and social costs of the real ageing population phenomenon, through the de-localisation of healthcare services delivery and management to the home, have been arising in the scientific community. The VirtualECare project is one of those, so called, Ambient Assisted Living environments, which we have taken a step forward with the introduction of proactive techniques for better adapting to its users, namely elderly or chronic patients, once it is able to learn with their interaction based in contexts. This learning, however, causes the system need to know with whom it is interacting, especially if we are dealing with multi-user environments. Basic detection techniques based in possible devices that users carries along with them (e.g. RFID tags, mobile phones, ...) arc not good enough, since they can lose/forgot/switch them. To obtain the expected results the technology used has to be more advanced and available in several platforms. One possible and already fairly developed technique is Facial Recognition, and it appears to be the most appropriate one to handle the problem. This document exposes the initial approach of the VirtualECare project to the Facial Recognition area.

Abstract
Memory is one of our most precious goods has it gives us the ability to store, retain and recall information thus giving a meaning to our past and help us to envision our future, dreams and expectations. However, ageing decreases the capacity of remembering and the capacity to store new memories, thus affecting our life quality. These presented problems configure a social and human dilemma. With the presented work we intend to address some of these problems, thru the use of the Personal Memory Assistant (PMA) concept in order to help its user to remember things and occurrences in a proactive manner. We will also address socialization and relaxation events that should be part of the user's life. With the use of a Multi-Agent System to implement the PMA, the objectives can be achieved in a ubiquitous and highly configurable manner. It is presented here the platform concept, scheme and the agent characteristics and their contribution to each and every agent.

Abstract

Abstract
The identification of patterns on large repositories of code can be of paramount importance to guide the design of new hardware accelerators, To acquire the suitability of a certain hardware accelerator and to generate application-specific architectures that maximize hardware reuse. This work intends to research and develop methods to both acquire the presence of a given pattern (map-suitability) and to identify common and highly similar patterns in code repositories (design-suggestions). The approach being proposed is based on a number of identification layers that refine the selections at each stage. We analyze two possible complementary options for a high-level layer. A first option is based on the representation of programs as a sequence of symbols and string matching and clustering algorithms are then used to expose similar patterns. A second option is based on tree matching techniques for identifying the presence of user's input patterns in the programs under inspection. We are evaluating our approach using the MiBench, Media-Bench, UTDSP, and SNU code repositories. The results show the potential of our approach to identify approximate patterns that can be implemented by merging highly similar structures. © 2010 IEEE.

Abstract
Typical computing systems based on general purpose processors (GPPs) are extended with coarse-grained reconfigurable arrays (CGRAs) to provide higher performance and/or energy savings. In order for applications to take advantage of these computing systems, efficient dynamic mapping techniques are required. Those dynamic mapping techniques will be responsible for automatically moving computations originally running in the GPP to the CGRA. The concept of dynamic compilation, widespread in the context of JIT compilation to GPPs, is receiving more attention by the reconfigurable computing community. This paper presents our approach to dynamically map computations to CGRAs coupled to a GPP. Specifically, we present the identification of large sequences of instructions, MegaBlocks, being executed in a GPP. These MegaBlocks are then mapped to the target CGRA. We evaluate the potential of the MegaBlocks over Basic Blocks and SuperBlocks to increase the IPC when targeting a CGRA and considering the execution of a number of representative benchmarks. © 2010 IEEE.

Abstract
Typical computing systems based on general purpose processors (GPPs) can be extended with coarse-grained reconfigurable arrays (CGRAs) to provide higher performance and/or energy savings. In order for applications to take advantage of these computing systems, possibly including CGRAs varying in size, efficient dynamic compilation/mapping techniques are required. Dynamic mapping will be responsible for automatically moving computations originally running in the GPP to the CGRA. This paper presents our approach to dynamically map computations to CGRAs coupled to a GPP. Specifically, we evaluate the potential of the MegaBlock to accelerate the execution of a number of representative benchmarks when targeting an architecture based on a GPP and a CGRA. In addition, we show the impact on performance when using constant folding and propagation optimizations. © 2010 IEEE.