Browsing by Author "Cuiñas, Iñigo"
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- Dual‐band single‐layer quarter ring frequency selective surface for Wi‐Fi applicationsPublication . Ferreira, David; Cuiñas, Iñigo; Caldeirinha, Rafael; Fernandes, Telmo R.This study proposes a frequency selective surface (FSS) design to be used in Wi‐Fi shielding applications as either a band reject or band pass dual‐band single‐layer filter. The proposed design consists of a combination of basic elements, that is, ring loops/slots, and is tuned at both 2.4 and 5.2 GHz Wi‐Fi frequency bands. It has a relatively stable frequency response in the aforementioned Wi‐Fi bands for incidence angles ranging from 0° to 45°. Both band reject and band pass designs are presented, along with their unit cell dimensions. Simulation and model validation through measurements demonstrate the performance of the proposed FSS design. Active variants are also proposed and briefly evaluated, in simulation environment, which should allow for applications where an on–off switching is desired at 2.4 and 5.2 GHz Wi‐Fi bands.
- Using artificial neural networks to scale and infer vegetation media phase functionsPublication . Gómez-Pérez, Paula; Caldeirinha, Rafael; Fernandes, Telmo, Telmo Rui Carvalhinho Cunha, Telmo R.; Cuiñas, IñigoAccurate vegetation models usually rely on experimental data obtained by means of measurement campaigns. Nowadays, RET and dRET models provide a realistic characterization of vegetation volumes, including not only in-excess attenuation, but also scattering, diffraction and depolarization. Nevertheless, both approaches imply the characterization of the forest media by means of a range of parameters, and thus, the construction of a simple parameter extraction method based on propagation measurements is required. Moreover, when dealing with experimental data, two common problems must be usually overcome: the scaling of the vegetation mass parameters into different dimensions, and the scarce number of frequencies available within the experimental data set. This paper proposes the use of Artificial Neural Networks as accurate and reliable tools able to scale vegetation parameters for varying physical dimensions and to predict them for new frequencies. This proposal provides a RMS error lower than 1 dB when compared to unbiased measured data, leading to an accurate parameter extracting method, while being simple enough for not to increase the computational cost of the model.