RBF) neural network were trained to estimate the diameter of machined holes. The multisensory approach includes an acoustic emission sensor, accelerometer, dynamometer and an electric power sensor. The optimum configuration for each artificial intelligence system was determined based on algorithms designed to examine the influence of each system's signals and specific parameters on the final result of the estimate. The results indicated the MLP ANN was more robust in withstanding data variation. The ANFIS system and RBF network showed markedly varying results in response to variations in the obtained data during training, suggesting these systems should always be trained with the dataset presented in the same order. A satisfactory response between the multisensory approach and MLP network was observed. The vertical component of force, along the z axis, was the only parameter able to present valid results for all the artificial intelligence systems analysed.

Humanoid robots are an extremely complex interdisciplinary research field. Particularly, the development of high size humanoid robots usually requires joint efforts and skills from groups that are in many different research centers around the world. However, there are serious constraints in this kind of collaborative development. Some efforts have been made in order to propose new software frameworks that can allow distributed development with also some degree of hardware abstraction, allowing software reuse in successive projects. However, computation represents only one of the dimensions in robotics tasks, and the need for reuse and exchange of full robot modules between groups are growing. Large advances could be reached if physical parts of a robot could be reused in a different robot constructed with other technologies by other researcher or group. This paper proposes a new robot framework, from now on called TORP (The Open Robot Project), that aims to provide a standard architecture in all dimensions (electrical, mechanical and computational) for this collaborative development. This methodology also represents an open project that is fully shared. In this paper, the first robot constructed following the TORP specification set is presented as well as the advances proposed for its improvement.

This paper presents research and development results of a prototype portable system designed for monitoring and recording HPP (Hydroelectric Power Plant) workplace environment data (noise, vibration, pressure, temperature, gas concentration and light level), related to working health conditions observing the requirements from current national and international standards. This has been achieved by using a series of dedicated sensors connected via wireless network to a portable intelligent computing platform in order to diagnosis the risks which workers are subjected to and also for improving the quality of life at work. The main contributions of this work are highlighted by technological innovation in state of the art of hardware and software components used (sensors, microcontrollers, computer, operating system, etc.), simultaneous multivariate acquisition and recording of environment workplace data, a novel methodology able to measure, record, analyze and diagnose risks, automatic generation of technical reports and risk maps, and creation of an innovative database for planning and following preventive and corrective maintenance actions over workplace conditions. The final product may be used by any company of electricity generation, especially HPP. Furthermore, this system will be of great value to the whole electric power industry, particularly power generation, as it will allow an integrated real-time and continuous monitoring of occupational risks, helping workrelated injury and illness preventive actions as a permanent preventive occupational safety and health program, thus resulting in an increasing of quality and reliability aimed for excellence. The research and development prototype was successfully designed, produced and tested through P&D funding from a HPP company at Sao Paulo.

Este trabalho apresenta um sistema multiprocessador voltado ao processamento de imagens, utilizando processadores digitais de sinais TMS320C30 da Texas Instruments, incluindo os elementos de hardware e software funcionamento. Alguns aspectos de apresentados através de resultados necessários para o análise de desempenho obtidos por simulação sistema executando a transformada de Fourier bidimensional.

A alocação de tarefas de aplicações distribuidas em arquiteturas multiprocessadoras deve escolher os processadores mais adequados para execução das tarefas, visando a minimização do tempo de execução e de comunicação entre outros, bem como o balanceamento de carga no sistema. O processo de alocação depende basicamente da estrutura da aplicação e da configuração da arquitetura envolvidas. Desta forma, o projetista de aplicações distribuidas ou de arquiteturas paralelas possui uma tarefa complexa nas mãos se desejar obter o máximo desempenho na execução da aplicação. Este trabalho apresenta o sistema FAT, uma Ferramenta para Alocação de Tarefas baseada em uma heuristica de alocação, para o auxilio no projeto de aplicações distribuidas ou no projeto de arquiteturas multiprocessadoras. São apresentados o escopo de aplicação, a heuristica de alocação e a interface com o usuário.