Filipe Wall Mutz

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We present a biologically inspired approach to traffic sign detection based on Virtual Generalizing Random Access Memory Weightless Neural Networks (VG-RAM WNN). VG-RAM WNN are effective machine learning tools that offer simple implementation and fast training and test. Our VG-RAM WNN architecture models the saccadic eye movement system and the(More)
(IARA). IARA is a fully autonomous car that uses a path planner to compute a path from its current position to the desired destination. Using this path, the current position, a goal in the path and a map, IARA's MPMP is able to compute smooth trajectories from its current position to the goal in less than 50 ms. MPMP computes the poses of these trajectories(More)
In this paper, we propose a simulator for robotic cars based on two time-delay neural networks. These networks are intended to simulate the mechanisms that govern how a set of effort commands changes the car's velocity and the direction it is moving. The first neural network receives as input a temporal sequence of current and previous throttle and brake(More)
We propose a light-weight yet accurate localization system for autonomous cars that operate in large-scale and complex urban environments. It provides appropriate localization accuracy and processing time at high frequency suitable for fast control actions, besides low power consumption desirable for limited energy availability in commercial cars. The(More)
We propose a Virtual Generalizing Random Access Memory (VG-RAM) Weightless Neural Network (WNN) Computer (V'Ger Computer for short). VG-RAM WNNs are very effective pattern recognition tools, offering fast training (one shot training) and competitive recognition performance, if compared with other current techniques. The V'Ger Computer architecture was(More)
Virtual Generalizing Random Access Memory Weightless Neural Networks (VG-RAM WNN) is an effective machine learning technique that offers simple implementation and fast training and test. We examined the performance of VG-RAM WNN on binocular dense stereo matching using the Middlebury Stereo Datasets. Our experimental results showed that, even without(More)
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