Electromagnetic modeling of conformal wideband and multi-band patch antennas by bridging a solid-object modeler with MoM software

The advantages of antennas that can resemble the shape of the body to which they are attached are obvious. However, electromagnetic modeling of such unusually shaped antennas can be difficult. In this paper, the commercially available software SolidWorks(TM) is used for accurately drawing complex shapes in conjunction with the electromagnetic software FEKO(TM) to model the EM behavior of conformal antennas. The application of SolidWorks and custom-written software allows all the required information that forms the analyzed structure to be automatically inserted into FEKO, and gives the user complete control over the antenna being modeled. This approach is illustrated by a number of simulation examples of single, wideband, multi-band planar and curved patch antennas.

Practical dual-helical antenna array for diversity/MIMO receiving antennas on mobile handsets

A practical, small-size, dual-helical antenna array mounted on a mobile handset model is designed for use as diversity/MIMO receiving antennas. The array is rigorously studied with respect to its diversity performance and the achievable channel capacity. It is found that a very low correlation coefficient, a high diversity gain, an equal-mean branch SNR, and a relatively matched input impedance can be achieved at the same time. It is shown that, at a remarkably small antenna separation (similar to 0.05 lambda), the signal correlation can be reduced to nearly zero, an almost ideal independent operation of the diversity antennas. The increase in MIMO channel capacity is 100% over a single antenna system. Both measured and simulation results are presented.

A new decoupling method for quadrature coils in magnetic resonance imaging

A powerful decoupling method is introduced to obtain decoupled signal voltages from quadrature coils in magnetic resonance imaging (MRI). The new method uses the knowledge of the position of the signal source in MRI, the active slice, to define a new mutual impedance which accurately quantifies the coupling voltages and enables them to be removed almost completely. Results show that by using the new decoupling method, the percentage errors in the decoupled voltages are of the order of 10(-7)% and isolations between two coils are more than 170 dB.

Fully spatial wide-band beamforming using a rectangular array of planar monopoles

This paper describes a spatial beamformer which by using a rectangular array antenna steers a beam in azimuth over a wide frequency band without frequency filters or tap-delay networks. The weighting coefficients are real numbers which can be realized by attenuators or amplifiers. A prototype including a 4 x 4 array of square planar monopoles and a feeding network composed of attenuators, power divider/combiners and a rat-race hybrid is developed to test the validity of this wide-band beamforming concept. The experimental results prove the validity of this wide-band spatial beamformer for small size arrays.

Adaptive back-propagation in on-line learning of multilayer networks

An adaptive back-propagation algorithm is studied and compared with gradient descent (standard back-propagation) for on-line learning in two-layer neural networks with an arbitrary number of hidden units. Within a statistical mechanics framework, both numerical studies and a rigorous analysis show that the adaptive back-propagation method results in faster training by breaking the symmetry between hidden units more efficiently and by providing faster convergence to optimal generalization than gradient descent.

On-line learning with adaptive back-propagation in two-layer networks

An adaptive back-propagation algorithm parameterized by an inverse temperature 1/T is studied and compared with gradient descent (standard back-propagation) for on-line learning in two-layer neural networks with an arbitrary number of hidden units. Within a statistical mechanics framework, we analyse these learning algorithms in both the symmetric and the convergence phase for finite learning rates in the case of uncorrelated teachers of similar but arbitrary length T. These analyses show that adaptive back-propagation results generally in faster training by breaking the symmetry between hidden units more efficiently and by providing faster convergence to optimal generalization than gradient descent.