Performance of a 2-layer radial hole effective media lens
The outer polyethylene layer of a 8λ0 radius 2-layer polyethylene/cross-linked polystyrene lens was implemented as a radial hole effective media that allows the entire lens to be milled from a single billet of cross-linked polystyrene, simplifying construction by avoiding layer fitting problems. Full wave simulations in a commercially available simulator showed that increasing the number of radial holes to 3000 gave peak directivity within 0.3dB of the original cross-linked polystyrene/polyethylene lens. Both Particle Swarm Optimization and icosahedron based methods were trialed for distributing the holes evenly over the sphere. The latter method produced symmetrical meshes allowing symmetry planes to be used to minimize computational burden, but was bound to the numbers of holes produced by successive triangular face subdivision iterations.
Scalar feeds for 8 wavelength diameter homogeneous lenses
Improving the connectedness of airliners in flight to allow the realisation of passengers' expectations for uninterrupted cell phone usage and internet access requires overcoming the bottleneck between the airliners' internal networks and the wider world in a cost effective manner while still providing sufficient bandwidth. The 43.5 – 47GHz band is attractive for airliner to ground links due to lack of utilisation and the small physical size of high gain antennas. Consequently, NICT plans to conduct a demonstration of such a system in late 2009.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.