J. Parron

HUMAN BODY EFFECTS ON IMPLANTABLE ANTENNAS FOR ISM BANDS APPLICATIONS: MODELS COMPARISON AND PROPAGATION LOSSES STUDY

In this paper propagation losses of body implanted antennas are studied at the ISM bands of 433MHz, 915MHz, 2450MHz and 5800MHz. Two body models are used, one based on a single equivalent layer and the other based on a three layer structure, showing the advantages and limitations of each one. Firstly, the antenna pair gain at difierent implanted antenna depths is analyzed. Next, we show the efiects of the thickness of the difierent body tissue layers. Finally, we discuss the consequences of using a coating layer to isolate the antenna from the harsh environment of the human body.

Multiscale Compressed Block Decomposition for Fast Direct Solution of Method of Moments Linear System

The multiscale compressed block decomposition algorithm (MS-CBD) is presented for highly accelerated direct (non iterative) solution of electromagnetic scattering and radiation problems with the method of moments (MoM). The algorithm is demonstrated to exhibit N2 computational complexity and storage requirements scaling with N1.5, for electrically large objects. Several numerical examples illustrate the efficiency of the method, in particular for problems with multiple excitation vectors. The largest problem presented in this paper is the monostatic RCS of the NASA almond at 50 GHz, for one thousand incidence angles, discretized using 442,089 RWG basis functions. Being entirely algebraic, MS-CBD is independent of the Greens function of the problem.

On the testing of the magnetic field integral equation with RWG basis functions in method of moments

For electromagnetic analysis using method of moments (MoM), three-dimensional (3-D) arbitrary conducting surfaces are often discretized in Rao, Wilton and Glisson basis functions. The MoM Galerkin discretization of the magnetic field integral equation (MFIE) includes a factor /spl Omega//sub 0/ equal to the solid angle external to the surface at the testing points, which is 2/spl pi/ everywhere on the surface of the object, except at the edges or tips that constitute a set of zero measure. However, the standard formulation of the MFIE with /spl Omega//sub 0/=2/spl pi/ leads to inaccurate results for electrically small sharp-edged objects. This paper presents a correction to the /spl Omega//sub 0/ factor that, using Galerkin testing in the MFIE, gives accuracy comparable to the electric field integral equation (EFIE), which behaves very well for small sharp-edged objects and can be taken as a reference.

Fast Iterative Solution of Integral Equations With Method of Moments and Matrix Decomposition Algorithm – Singular Value Decomposition

The multilevel matrix decomposition algorithm (MLMDA) was originally developed by Michielsen and Boag for 2D TMz scattering problems and later implemented in 3D by Rius et al. The 3D MLMDA was particularly efficient and accurate for piece-wise planar objects such as printed antennas. However, for arbitrary 3D problems it was not as efficient as the multilevel fast multipole algorithm (MLFMA) and the matrix compression error was too large for practical applications. This paper will introduce some improvements in 3D MLMDA, like new placement of equivalent functions and SVD postcompression. The first is crucial to have a matrix compression error that converges to zero as the compressed matrix size increases. As a result, the new MDA-SVD algorithm is comparable with the MLFMA and the adaptive cross approximation (ACA) in terms of computation time and memory requirements. Remarkably, in high-accuracy computations the MDA-SVD approach obtains a matrix compression error one order of magnitude smaller than ACA or MLFMA in less computation time. Like the ACA, the MDA-SVD algorithm can be implemented on top of an existing MoM code with most commonly used Greens functions, but the MDA-SVD is much more efficient in the analysis of planar or piece-wise planar objects, like printed antennas.

Fast Direct Solution of Method of Moments Linear System

A fast direct (non iterative) solution method for the method of moments (MoM) in electromagnetics is proposed. The method uses the well known matrix decomposition method (MDA) and singular value decomposition (SVD) to achieve block-wise compression of the MoM impedance matrix, followed by a block-wise LU factorization that preserves the initial compression. A number of examples are presented involving problems ranging from ten to seventy thousand unknowns.

Multilevel matrix decomposition algorithm for analysis of electrically large electromagnetic problems in 3-D

Reference LEMA-ARTICLE-1999-010doi:10.1002/(SICI)1098-2760(19990805)22:3 3.0.CO;2-2 Record created on 2006-11-30, modified on 2016-08-08

Application of the multilevel matrix decomposition algorithm to the frequency analysis of large microstrip antenna arrays

The application of integral equation methods based on the method of moments discretization to solve large antenna arrays is difficult due to the fact that the computational requirements increase rapidly with the number of unknowns. This is critical when a frequency analysis of the antenna is required. We propose the multilevel matrix decomposition algorithm (MLMDA) to carry out this purpose efficiently. As the MLMDA method is particularly well-suited for the analysis of planar structures with any Greens function, it is a very efficient approach for the frequency analysis of microstrip antenna arrays.

Method of moments enhancement technique for the analysis of Sierpinski pre-fractal antennas

The numerical analysis of highly iterated Sierpinski microstrip patch antennas by the method of moments (MoM) involves many tiny subdomain basis functions, resulting in a very large number of unknowns. The Sierpinski pre-fractal can be defined by an iterated function system (IFS). As a consequence, the geometry has a multilevel structure with many equal subdomains. This property, together with a multilevel matrix decomposition algorithm (MLMDA) implementation in which the MLMDA blocks are equal to the IFS generating shape, is used to reduce the computational cost of the frequency analysis of a Sierpinski based structure.

Compressed Block-Decomposition Algorithm for Fast Capacitance Extraction

A novel algorithm, the compressed block decomposition, is presented for highly accelerated direct (noniterative) method-of-moment capacitance extraction. The algorithm is based on a blockwise subdivision of the method-of-moment potential coefficient matrix. Matrix subblocks corresponding to distant subregions of the problem geometry are not calculated directly but approximated in a compressed form. Subsequently, the matrix is decomposed using an algorithm that preserves the compression. The efficiency of the method is demonstrated on a common benchmark problem - a 6 times 6 bus crossing. The numerical cost of the algorithm is shown, both theoretically and numerically, to scale with N log3 N and the storage space with N log2 N.

Improving the Performance of the Multilevel Matrix Decomposition Algorithm for 2.5-D Structures. Application to metamaterials

This paper presents an optimization of the multilevel matrix decomposition algorithm (MLMDA) that allows the efficient analysis of 2.5-D structures. This tool could be useful in the analysis of planar multilayer metamaterials (MM). Comparing its performance against the multilevel fast multiple algorithm (MLFMA) it was shown that MLMDA 2.5-D requires less computation time and provides better precision. Future work on the algorithm will be directed to take advantage of the periodicity present in MM