Eduardo do Valle Simões

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This paper describes a fully embedded distributed evolutionary system that is able to achieve collision-free navigation in a few hundreds of trials. It reports the first experimental proof of the embedded evolution concept applied to the evolution of morphology and an unstructured control circuit of a population of six real robots in real time. Evolution(More)
This work addresses the real time control of the Khepera mobile robot [1] navigation in a maze with reflector walls. Boolean Neural Networks such as RAM [2] and GSN [3] models are applied to drive the vehicle, following a light source, while avoiding obstacles. Both neural networks are implemented with simple logic and arithmetic functions (NOT, AND, OR,(More)
This paper presents a vision system to be embedded in a mobile robot, both of them implemented using recon-figurable computing technology. The vision system captures gestures by means of a digital color camera, and then performs some pre-processing steps in order to use the image as input to a RAM-based neural network. The set of recognized gestures can be(More)
This work describes a framework for a GSN (Goal Seeking Neuron) Boolean neural network fast prototyping into a user-programmable gate array. This system provides a VHDL language description of the trained network, allowing the direct implementation of the circuit on an academic FPGA (Field-Programmable Gate Array). A GSN software tool was designed to train(More)
This article describes the implementation of a strategy that selects, destroys, and replaces some individuals of a population of six real autonomous mobile robots. This strategy was called Predation. We introduce Predation as a methodology for improving the performance of an embedded evolutionary system developed for the automatic design of robotic(More)
Although embedded systems have been around for quite a long time, just in recent years they have attracted major industry and academic interest. There is a perception that a computing paradigm shift is taking place, and so the need to provide computer science students with the required expertise in the field. In this paper we describe our experience of(More)
a distorted electrical signal is modeled as an optimization problem. The advantages of GAs in this approach include the use of coding for a number of solutions which facilitates computer implementation, as well as the search for an appropriate solution from a population of possible solutions. The GA is programmed in a FPGA (Field-Programmable Gate Array)(More)
The utilization of Field-Programmable Gate Arrays (FPGA) to implement Artificial Neural Networks (ANN) becomes very attractive since it allows fast hardware design and modification at low costs. This work presents a comparison between two implementation strategies of ANN hardware design: the VLSI-Full Custom approach and FPGA. For that reason, three(More)