This new circuit (Fig. 8) shows simulated losses of only 15%, which is 30% less than that of Fig. 4.
CONCLUSIONThis study has shown that the coupling between two parallel microstrip lines is enhanced by the use of a slot split-ring resonators (SSRRs) defected ground plane. Since the gap between the two lines is 0.6 mm, this device can be easily designed in microstrip technology, thus relaxing the fabrication-technology constraints at high frequencies. The first results obtained from the simulation and measurements have shown the possibility of separating the coupling frequencies on two different channels; hence, the design of novel-type compact couplers, diplexers, or transponders. Such a device can also be very useful in an autonomous system. The parasitic signal recovered on port 2 can be used, after being rectified to DC power, to bias the active components of a circuit which is itself fed by the main signal recovered on port 3 through backward coupling.Such a device can be made to operate at any frequency, which is set by designing the SSRR with the appropriate dimensions in order to have the desired resonance frequency.Concerning the losses, which are mainly due to radiation in the defected ground plane structures, a structure consisting of two dielectric boards and a common ground plane has been presented in order to overcome this problem. It has been noted that the losses were reduced by 30%. 6. HFSS, High-frequency structure simulator ver. 9.2, Finite-element package, Ansoft Corporation. 7. G.L. Matthaei, L. Young, and E.M.T. Jones, Microwave filters, impedance-matching networks and coupling structures, McGraw-Hill, New York, 1964. 8. ADS, Advanced design system, ver. 2004A, Agilent Technologies.
ABSTRACT: A new combination of particle-swarm optimization (PSO) and the least-squares support vector machine (LS-SVM) technique forFDTD time-series forecasting is presented. In this paper, the PSO is extended to optimize the hyperparameter used in the LS-SVM algorithm. The numerical simulations demonstrate that the PSO method can efficiently obtain the optimal value of the hyperparameter used in the LS-SVM algorithm. And the PSO_LS-SVM method can improve the computational efficiency of the FDTD algorithm, as compared to the direct FDTD method.
