Antônio Cláudio Paschoarelli Veiga

Functional Languages in Signal Processing Applied to Prosthetic Limb Control

This article describes how one can use functional languages to develop a dedicated system for controlling a prosthetic arm. It shows the prototype artificial limb along with the development of the various algorithms and software used to process electromyographic (EMG) signals, to be used as inputs for the control mechanism. Great emphasis is also laid on the parametric modelling used to extract the necessary features from the EMG signals. An iterative least mean square (LMS) algorithm has been used to improve the efficiency of traditional LMS algorithms, which greatly enhanced the performance of the system. The use of the functional paradigm lead to a fast developing stage and a very compact set of programs that will run as fast as (sometimes even faster than) traditional C/C++ programs.

Genetic Algorithms Applied in Face Recognition

Face recognition methods are computationally very expensive and use too much memory and processing time. An example of a method that allocates many computer resources is the Principal Component Analysis (PCA). In order to reduce processing time, was developed in this paper a method using only genetic algorithms to perform face recognition and comparison in the PCA method obtains higher accurate rates and less processing time.

Recursive diameter prediction for calculating merchantable volume of eucalyptus clones using Multilayer Perceptron

A very common problem in forestry is the realization of the forest inventory. The forest inventory is very important because it allows the trading of medium- and long-term timber to be extracted. On completion , the inventory is necessary to measure different diameters and total height to calculate their volumes. However, due to the high number of trees and their heights, these measurements are an extremely time consuming and expensive. In this work, a new approach to predict recursively diameters of eucalyptus trees by means of Multilayer Perceptron artificial neural networks is presented. By taking only three diameter measures at the base of the tree, diameters are predicted recursively until they reach the value of 4 cm, with no previous knowledge of total tree height. The training was conducted with only 10% of the total trees planted site, and the remaining 90% of total trees were used for testing. The Smalian method was used with the predicted diameters to calculate merchantable tree volumes. To check the performance of the model, all experiments were compared with the least square polynomial approximator and the diameters and volumes estimates with both methods were compared with the actual values measured. The performance of the proposed model was satisfactory when predicted diameters and volumes are compared to actual ones.

Improving iris recognition through new target vectors in MLP artificial neural networks

This paper compares the performance of multilayer perceptron (MLP) networks trained with conventional bipolar target vectors (CBVs) and orthogonal bipolar new target vectors (OBVs) for biometric pattern recognition. The experimental analysis consisted of using biometric patterns from CASIA Iris Image Database developed by Chinese Academy of Sciences - Institute of Automation. The experiments were performed in order to obtain the best recognition rates, leading to the comparison of results from both conventional and new target vectors. The experimental results have shown that MLPs trained with OBVs can better recognize the patterns of iris images than MLPs trained with CBVs.

Recursive diameter prediction for calculating merchantable volume of Eucalyptus clones without previous knowledge of total tree height using artificial neural networks

In this work, diameters of Eucalyptus trees are predicted by means of Multilayer Perceptron and Radial Basis Function artificial neural networks. By taking only three diameter measures at the base of the tree, diameters are predicted recursively until they reach the value of minimum merchantable diameter, with no previous knowledge of total tree height. It was considered the diameter top of 4cm outside bark as minimum merchantable diameter. The training was conducted with only 10% of the trees from the total planted site. The Smalian method utilizes the predicted diameters to calculate merchantable tree volumes. The performance of the proposed model was satisfactory when predicted diameters and volumes are compared to actual ones.

Optimization of Calculation of Field Orientation Time and Binarization of Fingerprint Images

The field orientation and the binarization in an image are often used in fingerprint identification and authentication, and analysis of textures. The proposed algorithm reuses the additions and multiplications in the calculation of the field orientation using the switching property and uses the Digital Differential Analyzer (DDA) algorithm in the generation of convolution masks for the binarization of fingerprint images. The performance of the processing time and the result of the proposed binarization algorithm compared to the performance of algorithms that use convolutions masks were satisfactory compared to the other algorithms found in literature.

Iris Movements: The Best State to Dynamic Biometric Recognition Process

Substantially, biometric recognition systems that use the information on iris texture employ approximate models. They minimize the problems associated with iris dilation and contraction. This article demonstrates quantitatively that iris movements may lead to significant differences between the enrolment images from a database and the test image. In order to clarify this assumption two experiments were performed and the results indicate that the system performs better when the dilation rate is smaller. Unlike what was expected, images with greater dilation rate do not cause the worst system performance. Lastly, images with intermediate dilation rate brought the system to present the worst outcome.

Conteúdo didático multinível para personalização reativa em sistemas tutores inteligentes

This paper presents a model for organization of educational content in connectionists intelligent tutoring systems. The availability of educational content in a single format has emerged as a problem for many students. The inadequacy of a single format of content that ignores differences in individual profiles may have inefficient outcomes in the teaching-learning process. The proposed multilevel structure allows for different combinations of concepts for presentation to the same content. Assuming that the pattern of successful organization of the study by a student can be applied to other students with similar profiles, a system was structured to assist in the task of customization reactive content. The customization is provided by a neural network that links the students profile to a proximal learning pattern. This pattern is combined with expert rules to enable a probabilistic selection so that the system presents the reactivity in the different learning stages. The results of experiments indicate that the approach is effective in providing better use of the content in the personal study and its potential use in Distance Education.

Analysing the performance of the algorithms used to localize the iris region in eye images submitted to severely compressed images

This work aims to evaluate how does the performance of the iris localization algorithms is influenced by severe compression of eye images used in an automatic iris recognition system. The correct localization of the iris region is essential to guarantee the credibility of the biometric recognition. As it is the first processing stage of the system, it is the first stage that can have its efficiency depredated by the quality loss of the images to be processed. Fractal compression and JPEG2000 compression were applied to a public iris images database. Two traditional methods which localize the circular borders of the iris and a method for detection of eyelids and eyelashes were applied to the compressed images and their efficiencies were evaluated. The results obtained with the simulations, when the images were submitted to several compression levels, lead to the important conclusion that segmentation stage does not represent a barrier for the utilization of compressed images in an iris recognition system.