Aplicação do método LTT às estruturas retangulares e triangulares em multicamadas e empilhadas em substratos PBG para comunicações móveis

Recently the planar antennas have been studied due to their characteristics as well as the advantages that they offers when compared with another types of antennas. In the mobile communications area, the need for this kind of antennas have became each time bigger due to the intense increase of the mobile communications that needs of antennas which operate in multifrequency and wide bandwidth. The microstrip antennas presents narrow bandwidth due the loss in the dielectric generated by radiation. Another limitation is the radiation pattern degradation due the generation of surface waves in the substrate. In this work some used techniques to minimize the disadvantages (previously mentioned) of the use of microstrip antennas are presented, those are: substrates with PBG material - Photonic Bandgap, multilayer antennas and with stacked patches. The developed analysis in this work used the TTL - Transverse Transmission Line method in the domain of Fourier transform, that uses a component of propagation in the y direction (transverse to the direction real of propagation z), treating the general equations of electric and magnetic field as functions of y and y . This work has as objective the application of the TTL method to microstrip structures with single and multilayers of rectangular and triangular patches, to obtaining the resonance frequency and radiation pattern of each structure. This method is applied for the treatment of the fields in stacked structures. The Homogenization theory will be applied to obtaining the effective permittivity for s and p polarizations of the substrate composed of PBG material. Numerical results for the triangular and rectangular antennas with single layer, multilayers resonators with triangular and rectangular patches are presented (in photonic and isotropic substrates). Conclusions and suggestions for continuity of this work are presented

Algoritmos genéticos aplicados a um comitê de LS-SVM em problemas de classificação

The pattern classification is one of the machine learning subareas that has the most outstanding. Among the various approaches to solve pattern classification problems, the Support Vector Machines (SVM) receive great emphasis, due to its ease of use and good generalization performance. The Least Squares formulation of SVM (LS-SVM) finds the solution by solving a set of linear equations instead of quadratic programming implemented in SVM. The LS-SVMs provide some free parameters that have to be correctly chosen to achieve satisfactory results in a given task. Despite the LS-SVMs having high performance, lots of tools have been developed to improve them, mainly the development of new classifying methods and the employment of ensembles, in other words, a combination of several classifiers. In this work, our proposal is to use an ensemble and a Genetic Algorithm (GA), search algorithm based on the evolution of species, to enhance the LSSVM classification. In the construction of this ensemble, we use a random selection of attributes of the original problem, which it splits the original problem into smaller ones where each classifier will act. So, we apply a genetic algorithm to find effective values of the LS-SVM parameters and also to find a weight vector, measuring the importance of each machine in the final classification. Finally, the final classification is obtained by a linear combination of the decision values of the LS-SVMs with the weight vector. We used several classification problems, taken as benchmarks to evaluate the performance of the algorithm and compared the results with other classifiers