Papers by Jefferson Souza
Acta Botanica Brasilica, 2010
Clays and Clay Minerals, 1975
Abstraet--A golden-colored, flaky mineral from Morro do Niquet, Minas Gerais, Brazil, gives an X-... more Abstraet--A golden-colored, flaky mineral from Morro do Niquet, Minas Gerais, Brazil, gives an X-ray diffraction pattern of a lib chlorite polytype, with basal spacing 14-21 + 0.02A and b = 9-23/~. Thermogravimetric analysis shows a progressive weight (water) loss up to 500~ followed by a rapid weight loss corresponding to dehydroxylation of the interlayer material and a slower weight loss due to dehydroxylation of the 2:1 layer. The structural formula derived from the chemical analysis on the basis of O10 (OH)s, or total cation valence of + 28, shows 5.3 total octahedral cations, i.e. probably 2'3 in the interlayers where normally 3 cations are found. This deficiency together with the appreciable loss of water below 500~ suggests a partially vermiculitized interlayer. A new method for deriving the interlayer composition gives RI.67 (OI'-1)4.08 (H20)0.59 , and a ratio (OH + H20)/R = 2.80, which approaches that of a dioctahedral interlayer and is consistent with a predominance of R 3 + ions. The mineral may resemble the golden, vermiculitized biotite described by Walker and others.
This paper proposes a clustering method SOMAK, which is composed by Self-Organizing Maps (SOM) fo... more This paper proposes a clustering method SOMAK, which is composed by Self-Organizing Maps (SOM) followed by the Ant K-means (AK) algorithm. SOM is an Artificial Neural Network (ANN), which has one of its characteristics, the nonlinear projection from a high dimensionality of the sensorial space. AK is based in the Ant Colony Optimization (ACO), which is a recently proposed meta-heuristic approach for solving hard combinatorial optimization problems. The AK algorithm modifies the K-means on locating the objects and these are then clustered according to the probabilities which in turn are updated by the pheromone. The SOMAK has a good performance when compared with some clustering techniques and reduces the computational time.
Intelligent Robotic Car for Autonomous Navigation: Platform and System Architecture
This paper presents the platform and system architecture of an intelligent vehicle, presenting th... more This paper presents the platform and system architecture of an intelligent vehicle, presenting the control system modules allowing the vehicle to navigate autonomously. Our research group has been developed works on autonomous navigation and driver assistance systems, using CaRINA I platform to experiments and validation. Our platform includes mechanical vehicle adaptations and the development of an embedded software architecture, and its practical implementation. This paper addresses in details the sensing and acting infrastructure. Several experimental tests have been carried out to evaluate both platform and proposed algorithms.
This paper presents a vehicle control system capable of learning to navigate autonomously. Our ap... more This paper presents a vehicle control system capable of learning to navigate autonomously. Our approach is based on image processing, road and navigable area identification, template matching classification for navigation control, and trajectory selection based on GPS way-points. The vehicle follows a trajectory defined by GPS points avoiding obstacles using a single monocular camera. The images obtained from the camera are classified into navigable and non-navigable regions of the environment using neural networks that control the steering and velocity of the vehicle. Several experimental tests have been carried out under different environmental conditions to evaluate the proposed techniques.
Template-based autonomous navigation and obstacle avoidance in urban environments
ACM Sigapp Applied Computing Review, 2011
Autonomous navigation is a fundamental task in mobile robotics. In the last years, several approa... more Autonomous navigation is a fundamental task in mobile robotics. In the last years, several approaches have been addressing the autonomous navigation in outdoor environments. Lately it has also been extended to robotic vehicles in urban environments. This paper presents a vehicle control system capable of learning behaviors based on examples from human driver and analyzing different levels of memory of
Autonomous navigation is a fundamental task in mobile robotics. In the last years, several approa... more Autonomous navigation is a fundamental task in mobile robotics. In the last years, several approaches have been addressing the autonomous navigation in outdoor environments. Lately it has also been extended to robotic vehicles in urban environments. This paper presents a vehicle control system capable of learning behaviors based on examples from human driver and analyzing different levels of memory of the templates, which are an important capability to autonomous vehicle drive. Our approach is based on image processing, template matching classification, finite state machine, and template memory. The proposed system allows training an image segmentation algorithm and a neural networks to work with levels of memory of the templates in order to identify navigable and non-navigable regions. As an output, it generates the steering control and speed for the Intelligent Robotic Car for Autonomous Navigation (CaRINA). Several experimental tests have been carried out under different environmental conditions to evaluate the proposed techniques.
Localization is information of fundamental importance to carry out various tasks in the mobile ro... more Localization is information of fundamental importance to carry out various tasks in the mobile robotic area. The exact degree of precision required in the localization depends on the nature of the task. The GPS provides global position estimation but is restricted to outdoor environments and has an inherent imprecision of a few meters. In indoor spaces, other sensors like lasers and cameras are commonly used for position estimation, but these require landmarks (or maps) in the environment and a fair amount of computation to process complex algorithms. These sensors also have a limited field of vision. Currently, Wireless Networks (WN) are widely available in indoor environments and can allow efficient global localization that requires relatively low computing resources. However, the inherent instability in the wireless signal prevents it from being used for very accurate position estimation. The growth in the number of Access Points (AP) increases the overlap signals areas and this could be a useful means of improving the precision of the localization. In this paper we evaluate the impact of the number of Access Points in mobile nodes localization using Artificial Neural Networks (ANN). We use three to eight APs as a source signal and show how the ANNs learn and generalize the data. Added to this, we evaluate the robustness of the ANNs and evaluate a heuristic to try to decrease the error in the localization. In order to validate our approach several ANNs topologies have been evaluated in experimental tests that were conducted with a mobile
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Papers by Jefferson Souza