Desenvolvimento teórico e experimental de FSS com elementos fractais de Gosper em estruturas de multicamadas

The fractal self-similarity property is studied to develop frequency selective surfaces (FSS) with several rejection bands. Particularly, Gosper fractal curves are used to define the shapes of the FSS elements. Due to the difficulty of making the FSS element details, the analysis is developed for elements with up to three fractal levels. The simulation was carried out using Ansoft Designer software. For results validation, several FSS prototypes with fractal elements were fabricated. In the fabrication process, fractals elements were designed using computer aided design (CAD) tools. The prototypes were measured using a network analyzer (N3250A model, Agilent Technologies). Matlab software was used to generate compare measured and simulated results. The use of fractal elements in the FSS structures showed that the use of high fractal levels can reduce the size of the elements, at the same time as decreases the bandwidth. We also investigated the effect produced by cascading FSS structures. The considered cascaded structures are composed of two FSSs separated by a dielectric layer, which distance is varied to determine the effect produced on the bandwidth of the coupled geometry. Particularly, two FSS structures were coupled through dielectric layers of air and fiberglass. For comparison of results, we designed, fabricated and measured several prototypes of FSS on isolated and coupled structures. Agreement was observed between simulated and measured results. It was also observed that the use of cascaded FSS structures increases the FSSs bandwidths and, in particular cases, the number of resonant frequencies, in the considered frequency range. In future works, we will investigate the effects of using different types of fractal elements, in isolated, multilayer and coupled FSS structures for applications on planar filters, high-gain microstrip antennas and microwave absorbers

An application of fractal image-set coding in facial recognition

Faces are complex patterns that often differ in only subtle ways. Face recognition algorithms have difficulty in coping with differences in lighting, cameras, pose, expression, etc. We propose a novel approach for facial recognition based on a new feature extraction method called fractal image-set encoding. This feature extraction method is a specialized fractal image coding technique that makes fractal codes more suitable for object and face recognition. A fractal code of a gray-scale image can be divided in two parts – geometrical parameters and luminance parameters. We show that fractal codes for an image are not unique and that we can change the set of fractal parameters without significant change in the quality of the reconstructed image. Fractal image-set coding keeps geometrical parameters the same for all images in the database. Differences between images are captured in the non-geometrical or luminance parameters – which are faster to compute. Results on a subset of the XM2VTS database are presented.