Abstract
Demographical and social changes have an enormous effect on health care, emergency and welfare services. Indeed, as the average age continues to rise, it is set the mood to an exponential growth in assistance and care, resulting in higher service costs, a decrease in quality of service, or even both. On the other hand, as part of the evolution of traditional Virtual Reality Environments (or Intelligent Mixed Reality), a striving expression for Ambient Intelligence (AmI) it is possible to outline the role of AmI in healthcare, by focusing on its technological, logical (relational) and common sense nature. Our goal is to have in place an electronically-based monitoring system. This would reduce response time to adverse events, improve analytics and reporting, and will provide caregivers with the information they need to positively impact the care of individual patients.

Abstract
With the growing numbers of the elderly population, the society is face to face with a set of new problems, namely the lack of resources to assist their living in a noble mode. Nevertheless, with the use of new computational technologies and novel methodologies for problem solving, some solutions to these problems are emerging (e.g., remote sensing/assistance/supervision). Therefore, it is our goal to show that under such scenarios, it is possible to bring into play different interconnected virtual organizations, through which will be provided to the population, in general, and the elderly, in particular, a number of services (e.g., healthcare, entertainment, learning), without derealization or messing up with their routine. © 2007 by International Federation for Information Processing.

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The increasing use of battery-powered embedded systems has motivated the development of power consumption models in order to help designers to build low-power systems. Due to the configurability features of FPGAs, the adoption of systems containing one or more soft-core processors on a single chip is becoming more and more attractive. This paper presents an adaptation of the instruction-level power estimation model to soft-core processors implemented in FPGAs. This model allowed to estimate the power dissipated in eleven test applications with a maximum error of 4.78%. The Ongoing work includes efforts towards a software power estimation model for multi-core systems embedded in a single FPGA device.

Abstract
The problem of simultaneous localization and mapping has been studied by the mobile robotics scientific community over the last two decades. Most solutions for this problem are based on probabilistic theory in order to represent the uncertainty in robot perception and action. One of the most efficient probabilistic methods is the Extended Kalman Filter (EKF). However, the EKF demands a considerable amount of computing power and is usually processed by high-end laptops coupled to the robots. In this work, we present an implementation of the EKF targeting an embedded system based on an FPGA device. In order to improve performance, our approach combines a softcore processor with customized hardware. We present experiments with four different FPGA implementations, being the first purely based on software, the second using custom instruction logic directly connected to the processor's ALU, the third using hardware accelerators connected to the processor's data bus, and finally the fourth combining those two hardware/software solutions. For the experiments conducted, the results obtained with a small addition of hardware resources permitted to increase from 2x to 4x the performance of the global system.