Gilberto Arantes Carrijo

Classification of masses in mammographic image using wavelet domain features and polynomial classifier

Breast cancer is the most common cancer among women. In CAD systems, several studies have investigated the use of wavelet transform as a multiresolution analysis tool for texture analysis and could be interpreted as inputs to a classifier. In classification, polynomial classifier has been used due to the advantages of providing only one model for optimal separation of classes and to consider this as the solution of the problem. In this paper, a system is proposed for texture analysis and classification of lesions in mammographic images. Multiresolution analysis features were extracted from the region of interest of a given image. These features were computed based on three different wavelet functions, Daubechies 8, Symlet 8 and bi-orthogonal 3.7. For classification, we used the polynomial classification algorithm to define the mammogram images as normal or abnormal. We also made a comparison with other artificial intelligence algorithms (Decision Tree, SVM, K-NN). A Receiver Operating Characteristics (ROC) curve is used to evaluate the performance of the proposed system. Our system is evaluated using 360 digitized mammograms from DDSM database and the result shows that the algorithm has an area under the ROC curve Az of 0.98+/-0.03. The performance of the polynomial classifier has proved to be better in comparison to other classification algorithms.

Intrusion Detection System with Wavelet and Neural Artifical Network Approach for Networks Computers

As the Internet has become an enormous interconnected network, the information security today is very important to guarantee confidentiality, integrity and availability of computing resources. Advanced Intrusions Detections Systems IDS should be capable of identifying malicious actions that may compromise these guarantees, as quickly as possible. In this paper, we present a proposal for an IDS based on the wavelet and artificial neural network that is applied to the well know Knowledge Discovery and Data Mining KDD. The experiment showed high detection rate, suggesting that the approach is very promising.

Single image super-resolution using sparse representations with structure constraints

This paper describes a new single-image super-resolution algorithm based on sparse representations with image structure constraints. A structure tensor based regularization is introduced in the sparse approximation in order to improve the sharpness of edges. The new formulation allows reducing the ringing artefacts which can be observed around edges reconstructed by existing methods. The proposed method, named Sharper Edges based Adaptive Sparse Domain Selection (SE-ASDS), achieves much better results than many state-of-the-art algorithms, showing significant improvements in terms of PSNR (average of 29.63, previously 29.19), SSIM (average of 0.8559, previously 0.8471) and visual quality perception.

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.

A three-dimensional microcellular propagation street model using vectorial analysis in UHF band

In this work we present a vectorial analysis of the three dimensional (3D) street waveguide model in UHF band. For such model the street is modeled as a 3D multislit waveguide and an ideal dipole is assumed to be the transmitting aerial. Horizontal and vertical polarizations are investigated in terms of direct incidence, ground reflection, multiple reflections on the lateral borders of the waveguide and lateral-ground (wall-road) reflections. A comparison is made between different transmitting aerial heights. After that, slits are inserted and comparisons and analysis are made. Comparisons provided with measured data show that a 3D multislit analysis together with a vectorial and gain analysis can be a very useful tool toward increasing theoretical analysis in spite of empirical analysis in the field of prediction.

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.

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.

Solutions for Iris Segmentation

The growing concern with security and access control to places and sensitive information has contributed for the increased utilization of biometric systems. Biometry is the name given to the techniques used to recognize people automatically through physical and behavioural characteristics of the human body such as those found on the face, fingerprint, hand geometry, iris, signature or voice. From all the biometric options, iris recognition deserves special attention as the iris contains a huge and unique richness of characteristics, which do not change over time and enables the construction of extremely reliable and accurate systems. The iris recognition process is relatively complex and involves several stages of processing as illustrated in Figure 1. The first stage corresponds to the localization of the region of the iris on the image of the eye, which also involves the extraction of the regions corrupted by the superior and inferior eyelids and eyelashes.

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.

Performance Analysis of M-QAM/OFDM Systems for PLC under Gaussiano and Impulsive Noise

This paper presents an evaluation of data transmission through M-QAM (Quadrature Amplitude Modulation) and OFDM (Orthogonal Frequency Division Multiplexing) systems under the influence of Gaussian and impulsive noise on channel PLC (Power Line Communication). The goal is the comparative analysis of symbol error probability in cases: only M-QAM modulation and M-QAM/OFDM systems in the presence of Gaussian and impulsive noise. The results obtained show that the value of the symbol error probability increases when data transmission is performed only with M-QAM modulation and decreases when data transmission is performed by the M-QAM system/OFDM.