ENCORE: Enhanced Galileo code receiver for surveying applications

In this paper we describe the development of a low-cost high-accuracy Galileo Code receiver, user application software and positioning algorithms for land management applications, which have been implemented using a dedicated FPGA board and dual frequency Galileo E5/L1 Radio Frequency Front-End. The current situation of rural property surveying in Brazil is described and the use of code measurements from the new Galileo signals E5 AltBOC combined with E1 MBOC for use in land management applications is explored. We explain how such approach is expected to allow delivering an absolute positioning solution which could bridge the gap between receivers of high cost/complexity/accuracy based on carrier phase and receivers of lower cost/accuracy based on pseudorange observables. The system is presented together with a detailed description of main components: the Code Receiver and the Application Software. The work presented is part of an ongoing European-Brazilian consortium effort to explore the use of new Galileo for land management applications in Brazil and sponsored by the GNSS Supervisory Authority (GSA).

Computer techniques towards the automatic characterization of graphite particles in metallographic images of industrial materials

The automatic characterization of particles in metallographic images has been paramount, mainly because of the importance of quantifying such microstructures in order to assess the mechanical properties of materials common used in industry. This automated characterization may avoid problems related with fatigue and possible measurement errors. In this paper, computer techniques are used and assessed towards the accomplishment of this crucial industrial goal in an efficient and robust manner. Hence, the use of the most actively pursued machine learning classification techniques. In particularity, Support Vector Machine, Bayesian and Optimum-Path Forest based classifiers, and also the Otsu's method, which is commonly used in computer imaging to binarize automatically simply images and used here to demonstrated the need for more complex methods, are evaluated in the characterization of graphite particles in metallographic images. The statistical based analysis performed confirmed that these computer techniques are efficient solutions to accomplish the aimed characterization. Additionally, the Optimum-Path Forest based classifier demonstrated an overall superior performance, both in terms of accuracy and speed. © 2012 Elsevier Ltd. All rights reserved.

Simulation in NS-2 of DNP3 protocol encapsulated over TCP/IP in smart grid applications

In dealing with computer networks, these allow the flow of information through the resources of various equipment's. This work describes the implementation through the encapsulation of Protocol DNP3, usually employed in Smart Grid communication, in a simulator of discrete events. The NS-2 is a simulator in open source of network events, that facilitate the development of communication networks scenarios considering the protocols involved, in wireless or wired technologies. The objective of this work is to develop the DNP3 protocol encapsulation over a TCP/IP in the in the discrete event Simulator NS-2, allowing an analysis of behavior of a middle or large network sized in Smart Grid applications. © 2013 IEEE.

Social-spider optimization-based artificial neural networks training and its applications for Parkinson's disease identification

Evolutionary algorithms have been widely used for Artificial Neural Networks (ANN) training, being the idea to update the neurons' weights using social dynamics of living organisms in order to decrease the classification error. In this paper, we have introduced Social-Spider Optimization to improve the training phase of ANN with Multilayer perceptrons, and we validated the proposed approach in the context of Parkinson's Disease recognition. The experimental section has been carried out against with five other well-known meta-heuristics techniques, and it has shown SSO can be a suitable approach for ANN-MLP training step.

Supervised variational relevance learning, an analytic geometric feature selection with applications to omic datasets

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)