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The finite element-boundary integral (FE-BI) method is applied for the analysis of scattering and radiation by cavity-backed patch antenna and arrays. In this investigation, the FE-BI formulations have been implemented using brick element volumes and it allows for a particular use of the efficient FFT-based iterative solver. The inner–outer flexible GMRES algorithm is applied to solve the equation with a higher convergence speed when compared with the standard GMRES algorithm. [ABSTRACT FROM AUTHOR]

In this paper, ferrite waveguide devices are analysed by use of the edge-based vector finite element method combined with the short-open calibration (SOC) technique. The formulation with both permittivity and permeability tensors for tetrahedral vector elements is analytically given in terms of the electric field. The SOC technique is directly accommodated in the FEM algorithm and used to truncate the computational domain. The SOC technique removes or separates unwanted parasitics obtained by the approximation of the impressed voltage source and the inconsistency between different simulations. Several types of ferrite waveguide device are analysed and the results are compared with those of the available publications. A good agreement is observed between them. [ABSTRACT FROM AUTHOR]

An off-nadir canopy reflectance model, the Liang and Strahler algorithm for the Coupled Atmosphere and Canopy (CAC) model, was used to simulate multi-angle reflectances based on various combinations of canopy biophysical parameters. Biophysical parameters such as leaf angle distribution (LAD) and leaf area index (LAI) were input to the CAC model along with reflectances of leaf and soil and aerosol optical depth. The CAC model, however, can only be inverted through numerical iterations and it is extremely difficult to use for retrieval of those biophysical parameters with ordinary inversion methods. In order to retrieve those biophysical parameters, we employed an error backpropagation feed-forward neural network program. We constructed a number of neural network models based on the simulated results from the CAC model. In this paper, we report the results obtained from retrieving any individual parameter from multi-angle reflectances and results obtained from simultaneously retrieving some combinations of two parameters and five parameters. We tested the use of a different number of multi-angle reflectances as input to the neural networks. This number varied in the range 1-64. The test results showed that a relative error between 1 and 5% or better was achievable for retrieving one parameter at a time or two parameters simultaneously. The relative errors for two of the five simultaneously retrieved parameters were less than 17%. The amount of computation required by simultaneous retrieval of five parameters was prohibitively high for a regular workstation. [ABSTRACT FROM AUTHOR]