Análise e otimização de superfícies seletivas de Frequência utilizando redes neurais artificiais e algoritmos de otimização natural

The bidimensional periodic structures called frequency selective surfaces have been well investigated because of their filtering properties. Similar to the filters that work at the traditional radiofrequency band, such structures can behave as band-stop or pass-band filters, depending on the elements of the array (patch or aperture, respectively) and can be used for a variety of applications, such as: radomes, dichroic reflectors, waveguide filters, artificial magnetic conductors, microwave absorbers etc. To provide high-performance filtering properties at microwave bands, electromagnetic engineers have investigated various types of periodic structures: reconfigurable frequency selective screens, multilayered selective filters, as well as periodic arrays printed on anisotropic dielectric substrates and composed by fractal elements. In general, there is no closed form solution directly from a given desired frequency response to a corresponding device; thus, the analysis of its scattering characteristics requires the application of rigorous full-wave techniques. Besides that, due to the computational complexity of using a full-wave simulator to evaluate the frequency selective surface scattering variables, many electromagnetic engineers still use trial-and-error process until to achieve a given design criterion. As this procedure is very laborious and human dependent, optimization techniques are required to design practical periodic structures with desired filter specifications. Some authors have been employed neural networks and natural optimization algorithms, such as the genetic algorithms and the particle swarm optimization for the frequency selective surface design and optimization. This work has as objective the accomplishment of a rigorous study about the electromagnetic behavior of the periodic structures, enabling the design of efficient devices applied to microwave band. For this, artificial neural networks are used together with natural optimization techniques, allowing the accurate and efficient investigation of various types of frequency selective surfaces, in a simple and fast manner, becoming a powerful tool for the design and optimization of such structures

Contribuição para o estudo do embarque de uma rede neural artificial em field programmable gate array (FPGA)

This study shows the implementation and the embedding of an Artificial Neural Network (ANN) in hardware, or in a programmable device, as a field programmable gate array (FPGA). This work allowed the exploration of different implementations, described in VHDL, of multilayer perceptrons ANN. Due to the parallelism inherent to ANNs, there are disadvantages in software implementations due to the sequential nature of the Von Neumann architectures. As an alternative to this problem, there is a hardware implementation that allows to exploit all the parallelism implicit in this model. Currently, there is an increase in use of FPGAs as a platform to implement neural networks in hardware, exploiting the high processing power, low cost, ease of programming and ability to reconfigure the circuit, allowing the network to adapt to different applications. Given this context, the aim is to develop arrays of neural networks in hardware, a flexible architecture, in which it is possible to add or remove neurons, and mainly, modify the network topology, in order to enable a modular network of fixed-point arithmetic in a FPGA. Five synthesis of VHDL descriptions were produced: two for the neuron with one or two entrances, and three different architectures of ANN. The descriptions of the used architectures became very modular, easily allowing the increase or decrease of the number of neurons. As a result, some complete neural networks were implemented in FPGA, in fixed-point arithmetic, with a high-capacity parallel processing

Caracterização de superfícies seletivas de frequência de periódicas e não-periódicas

The past years have seen a great interest in the use of frequency selective surfaces (FSS), as spatial filters, in many microwave applications. Among these, we highlight applications in telecommunication systems (such as satellite communications and radar), high gain antennas (combined with planar antennas) and (home and industrial) microwave ovens. The FSS is usually composed of two-dimensional periodic arrays, with equally spaced elements, which may be metallic patches (printed on dielectric substrates) or aperture (holes in thin metal surfaces). Using periodic arrays, the FSS have been able to meet the demands of the telecommunications industry. However, new demands are finding technological limitations. In this context, adverse filtering requirements have forced designers to use FSS optimization methods to find specific formats of FSS elements. Another alternative that has been used to increase the selectivity of the FSS is the cascaded FSS, a simple technique that has as main drawback the increased dimensions of the structure, as well as its weight. This work proposes the development of a new class of selective surfaces frequency (FSS) composed of quasi-periodic (or non-periodic) arrangements. The proposed FSS have no array periodicity, in relation with the spatial position of their elements. The frequency responses of these structures were simulated using commercial softwares that implement full-wave methods. For the purpose of validation of this study, FSS prototypes were built and measured, being possible to observe a good agreement between simulated and measured results. The main conclusions of this work are presented, as well as suggestions for future works.