A dual-band rectangular microstrip antenna using a novel photonic bandgap ground plane of unequal orthogonal periods

2001 ◽  
Vol 30 (4) ◽  
pp. 280-283 ◽  
Author(s):  
Yen-Liang Kuo ◽  
Tzung-Wern Chiou ◽  
Kin-Lu Wong
Author(s):  
Kanhaiya Sharma ◽  
Ganga Prasad Pandey

This paper presents how machine learning techniques may be applied in the process of designing a compact dual-band H-shaped rectangular microstrip antenna (RMSA) operating in 0.75–2.20 GHz and 3.0–3.44 GHz frequency ranges. In the design process, the same dimensions of upper and lower notches are incorporated, with the centered position right in the middle. Notch length and width are verified for investigating the antenna. An artificial neural network (ANN) model is developed from the simulated dataset, and is used for shape prediction. The same dataset is used to create a mathematical model as well. The predicted outcome is compared and it is determined that the model relying on ANN offers better results


2013 ◽  
Vol 2 (2) ◽  
pp. 22
Author(s):  
S. Benkouda ◽  
T. Fortaki ◽  
M. Amir ◽  
A. Benghalia

This paper presents a rigorous full-wave analysis of a high Tc superconducting rectangular microstrip antenna with a rectangular aperture in the ground plane. To include the effect of the superconductivity of the microstrip patch in the full-wave analysis, a complex surface impedance is considered. The proposed approach is validated by comparing the computed results with previously published data. Results showing the effect of the aperture on the resonance of the superconducting microstrip antenna are given.


Author(s):  
Ramaska Prima Agusta ◽  
Heroe Wijanto ◽  
Budi Syihabuddin ◽  
Agus D. Prasetyo

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