Arranjos Log-Periódicos Compactos em Microfita com Elementos Fractais de Koch

This work aims to present how the application of fractal geometry to the elements of a log-periodic array can become a good alternative when one wants to reduce the size of the array. Two types of log-periodic arrays were proposed: one with fed by microstrip line and other fed by electromagnetic coupling. To the elements of these arrays were applied fractal Koch contours, at two levels. In order to validate the results obtained some prototypes were built, which were measured on a vector network analyzer and simulated in a software, for comparison. The results presented reductions of 60% in the total area of the arrays, for both types. By analyzing the graphs of return loss, it was observed that the application of fractal contours made different resonant frequencies appear in the arrays. Furthermore, a good agreement was observed between simulated and measured results. The array with feeding by electromagnetic coupling presented, after application of fractal contours, radiation pattern with more smooth forms than the array with feeding by microstrip line

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

Nucleação de vortices e paredes de domínio em nanoestruturas magnéticas

We report a theoretical investigation of the magnetic phases and hysteresis of exchange biased ferromagnetic (F) nanoelements for three di erent systems: exchange biased nanoparticles, exchange biased narrow ferromagnetic stripes and exchange biased thin ferromagnetic lms. In all cases the focus is on the new e ects produced by suitable patterns of the exchange energy coupling the ferromagnetic nanoelement with a large anisotropy antiferromagnetic (AF) substrate. We investigate the hysteresis of iron and permalloy nanoparticles with a square basis, with lateral dimensions between 45 nm and 120 nm and thickness between 12 nm and 21 nm. Interface bias is aimed at producing large domains in thin lms. Our results show that, contrary to intuition, the interface exchange coupling may generate vortex states along the hysteresis loop. Also, the threshold value of the interface eld strength for vortex nucleation is smaller for iron nanoelements. We investigate the nucleation and depinning of an array of domain walls pinned at interface defects of a vicinal stripe/AF bilayer. The interface exchange eld displays a periodic pattern corresponding to the topology of the AF vicinal substrate. The vicinal AF substrate consists of a sequence of terraces, each with spins from one AF subalattice, alternating one another. As a result the interface eld of neighboring terraces point in opposite direction, leading to the nucleation of a sequence of domain walls in the ferromagnetic stripe. We investigated iron an permalloy micrometric stripes, with width ranging from 100 nm and 300 nm and thickness of 5 nm. We focused in domain wall sequences with same chirality and alternate chirality. We have found that for 100nm terraces the same chiraility sequence is more stable, requiring a larger value of the external eld for depinning. The third system consists of an iron lm with a thickness of 5 nm, exchange coupled to an AF substrate with a periodic distribution of islands where the AF spins have the opposite direction of the spins in the background. This corresponds to a two-sublattice noncompensated AF plane (such as the surface of a (100) FeF2 lm), with monolayer-height islands containing spins of one sublattice on a surface containing spins of the opposite sublattice. The interface eld acting in the ferromagnetic spins over the islands points in the opposite direction of that in the spins over the background. This a model system for the investigation of interface roughness e ects. We have studied the coercicivity an exchange bias hysteresis shift as a function of the distance between the islands and the degree of interface roughness. We have found a relevant reduction of coercivity for nearly compensated interfaces. Also the e ective hysteresis shift is not proportional to the liquid moment of the AF plane. We also developed an analytical model which reproduces qualitatively the results of numerical simulations