Monopole directional antenna bioinspired in elliptical leaf with golden ratio for WLAN and 4G applications.

In this work, it is proposed the development a new monopole directional antenna, bioinspired in elliptical leaf, with cut by golden ratio, for 4G band application, by the use of the technique of the cut of the radiating element for the increasing of the antenna perimeter, being the first work to use this technique in a bioinspired antenna, promotes resonance frequency turned, and reconfiguring of the antenna parameters as bandwidth, radiation pattern and gain, with the use of the reflector near to the group plane, without the insertion of active devices as the pin diode or change in radiating element. The shape antenna is generated by Gielis formula, built in FR4 substrate, with cuts calculated by golden ratio. To compare the results of the bioinspired monopole on the elliptical sheet, a square-shaped monopole antenna was designed, simulated and measured, the structures were designed in the MATLAB software version 2015(b) and the simulations were performed in the Ansys software version 2016. In the results compared between the square monopole and the bioinspired antenna in the elliptical sheet, it can be seen that the measured bioinspired antenna, compared to the square monopole, presented a bandwidth reduction of 77.27%, a more compact structure, with a reduction of 98%, covering the wireless local area network, and long-time evolution 4G at 2.5 GHz. The proposed technique uses a reflector on the ground plane, to change the parameters of the monopole planar antenna, of omnidirectional radiation pattern to a directional, maintaining the characteristics of the broadband, half-power beamwidth great than 100°, with high current density, and similar gain of a directional antenna. From the results, it has been observed that the elliptical leaf monopole antenna shows broadband characteristics, with a half-power beamwidth of 128°, wideband, the bandwidth of 500 MHz, a gain of 6.28 dBi, a current density of 13.01 A/m2, and circular polarization.

Diagnostic Classification of Cases of Canine Leishmaniasis Using Machine Learning.

Proposal techniques that reduce financial costs in the diagnosis and treatment of animal diseases are welcome. This work uses some machine learning techniques to classify whether or not cases of canine visceral leishmaniasis are present by physical examinations. For validation of the method, four machine learning models were chosen: K-nearest neighbor, Naïve Bayes, support vector machine and logistic regression models. The tests were performed on three hundred and forty dogs, using eighteen characteristics of the animal and the ELISA (enzyme-linked immunosorbent assay) serological test as validation. Logistic regression achieved the best metrics: Accuracy of 75%, sensitivity of 84%, specificity of 67%, a positive likelihood ratio of 2.53 and a negative likelihood ratio of 0.23, showing a positive relationship in the evaluation between the true positives and rejecting the cases of false negatives.