Hierarchical bases for non-hierarchic 3Dtriangular meshes

We describe a novel basis of hierarchical, multiscale functions that are linear combinations of standard Rao-Wilton- Glisson (RWG) functions. When the basis is used for discretizing the electric field integral equation (EFIE) for PEC objects it gives rise to a linear system immune from low-frequency breakdown, and well conditioned for dense meshes. The proposed scheme can be applied to any mesh with triangular facets, and therefore it can be used as if it were an algebraic preconditioner. The properties of the new system are confirmed by numerical results that show fast convergence rates of iterative solvers, significantly better than those for the loop-tree basis. As a byproduct of the basis generation, a generalization of the RWG functions to nonsimplex cells is introduced

Grid Infrastructure for Domain Decomposition Methods in Computational ElectroMagnetics

The accurate and efficient solution of Maxwell's equation is the problem addressed by the scientific discipline called Computational ElectroMagnetics (CEM). Many macroscopic phenomena in a great number of fields are governed by this set of differential equations: electronic, geophysics, medical and biomedical technologies, virtual EM prototyping, besides the traditional antenna and propagation applications. Therefore, many efforts are focussed on the development of new and more efficient approach to solve Maxwell's equation. The interest in CEM applications is growing on. Several problems, hard to figure out few years ago, can now be easily addressed thanks to the reliability and flexibility of new technologies, together with the increased computational power. This technology evolution opens the possibility to address large and complex tasks. Many of these applications aim to simulate the electromagnetic behavior, for example in terms of input impedance and radiation pattern in antenna problems, or Radar Cross Section for scattering applications. Instead, problems, which solution requires high accuracy, need to implement full wave analysis techniques, e.g., virtual prototyping context, where the objective is to obtain reliable simulations in order to minimize measurement number, and as consequence their cost. Besides, other tasks require the analysis of complete structures (that include an high number of details) by directly simulating a CAD Model. This approach allows to relieve researcher of the burden of removing useless details, while maintaining the original complexity and taking into account all details. Unfortunately, this reduction implies: (a) high computational effort, due to the increased number of degrees of freedom, and (b) worsening of spectral properties of the linear system during complex analysis. The above considerations underline the needs to identify appropriate information technologies that ease solution achievement and fasten required elaborations. The authors analysis and expertise infer that Grid Computing techniques can be very useful to these purposes. Grids appear mainly in high performance computing environments. In this context, hundreds of off-the-shelf nodes are linked together and work in parallel to solve problems, that, previously, could be addressed sequentially or by using supercomputers. Grid Computing is a technique developed to elaborate enormous amounts of data and enables large-scale resource sharing to solve problem by exploiting distributed scenarios. The main advantage of Grid is due to parallel computing, indeed if a problem can be split in smaller tasks, that can be executed independently, its solution calculation fasten up considerably. To exploit this advantage, it is necessary to identify a technique able to split original electromagnetic task into a set of smaller subproblems. The Domain Decomposition (DD) technique, based on the block generation algorithm introduced in Matekovits et al. (2007) and Francavilla et al. (2011), perfectly addresses our requirements (see Section 3.4 for details). In this chapter, a Grid Computing infrastructure is presented. This architecture allows parallel block execution by distributing tasks to nodes that belong to the Grid. The set of nodes is composed by physical machines and virtualized ones. This feature enables great flexibility and increase available computational power. Furthermore, the presence of virtual nodes allows a full and efficient Grid usage, indeed the presented architecture can be used by different users that run different applications

Phase management for extended scan range antenna arrays based on Rotman lens

This paper presents an implementation of a technique aimed to double the scanning range of a 24 GHz array antenna system based on Rotman lens beamforming. The new concept of the enhanced beam forming network consists of a combination of Rotman lens and 1-bit phase shifters, positioned in a peculiar way on the array side of the lens, and together with a particular beam arrangement allows to overcome the scan limitations which is typical of the standalone Rotman lens solution. Simulations will demonstrate that a Rotman lens, designed to steer the beam up to ±30°, when arranged in combination with properly designed Ratrace based phase shifters, allows to increase the scan range up to ±60°

Simulation-based Machine Learning Training for Brain Anomalies Localization at Microwaves

Machine learning enters the world of medical application and, in this paper, it joins microwave imaging technique for brain stroke classification. One of the main challenges in this application is the need of a large amount of data for the machine learning algorithm training that can be performed via measurements or simulations. In this work, we propose to make the algorithm training via simulations based on a linear integral operator that reduces by three orders of magnitude the data generation time with respect to standard full-wave simulations. This method is used here to train the multilayer perceptron algorithm. The data-set is organized in nine classes, related to the presence, the type and the position of the stroke within the brain. We verified that the algorithm metrics (accuracy, recall and precision) reach values close to 1 for each class

Realistic Numerical Modelling for 3-D brain stroke monitoring

This paper aims to provide a realistic 3-D modelling framework of a real-world microwave imaging system for brain monitoring that mimics pre-assessment experimental clinical scenarios and lab setups. The model considers an anthropomorphic adult head with multiple tissues, a hemorrhagic brain stroke and a detailed prototype of a modular microwave antennas helmet. The set of antennas detect changes in the permittivity of biological tissues and then imaging reconstruction algorithms generates a 3-D representation of stroke using EM fields and scattering data generated by a full-wave numerical simulation. As results, it is presented a reconstruction of onset stroke in the white matter area of the brain using a TSVD algorithm and the born approximation for imaging

Polarization reconfigurable patch antenna for compact and low cost UHF RFID reader

This paper presents a patch antenna designed for Ultra High Frequency (UHF) Radio Frequency IDentification (RFID) reader including a reconfigurable feeding for achieving polarization agility. The switchable polarization improves the polarization efficiency in comparison with standard circular polarized antenna solutions. CMOS switches are used in the reconfigurable feeding network for enabling higher power transmission and uncomplicated control with respect to solutions involving varactors and PIN diodes. Moreover, the designed patch antenna and ground planes have reduced size, for best integration of the reader in the required application. The combination of antenna and reconfigurable feeding network has been tested through simulations, showing good performance over the EU RFID frequency band (865-868 MHz). Due to its flexible and inexpensive structure, the proposed reconfigurable feeding system is a promising alternative to standard circular polarized reader antenna approaches

Spectral Filtering of the Spatial Green\u2019s Function

N/

Discretization Error Analysis in the Contrast Source Inversion Algorithm

This paper describes the use of the contrast source inversion method combined with the finite element method for the numerical solution of 3-D microwave inversion problems. In particular, this work is focused on the discretization of the involved physical vector quantities, analyzing the impact of the chosen discretization on the solution process with the goal of optimizing the implemented algorithm in terms of accuracy, memory requirements and computational cost