Corrugated flange technique for beam shaping of sectoral electromagnetic horn antennas

The need for improved feed systems for large reflector antennas employed in Radio Astronomy and Satellite tracking spurred the interest in horn antenna research in the 1960's. The major requirements were to reduce spill over, cross-polarisation losses,and to enhance the aperture efficiency to the order of about 75-8O%L The search for such a feed culminated in the corrugated horn. The corrugat1e 1 horn triggered widespread interest and enthusiasm, and a large amount of work(32’34’49’5O’52’53’58’65’75’79)has already been done on this type of antennas. The properties of corrugated surfaces has been investigated in detail. It was strongly felt that the flange technique and the use of corrugated surfaces could be merged together to obtain the advantages of both. This is the idea behind the present work. Corrugations are made on the surface of flange elements. The effect of various corrugation parameters are studied. By varying the flange parameters, a good amount of data is collected and analysed to ascertain the effects of corrugated flanges. The measurements are repeated at various frequencies, in the X— and S-bands. The following parameters of the system were studied: (a) beam shaping (b) gain (c) variation of V.S.U.R. (d) possibility of obtaining circularly polarised radiation from the flanged horn. A theoretical explanation to the effects of corrugated flanges is attempted on the basis of the line-source theory. Even though this theory utilises a simplified model for the calculation of radiation patterns, fairly good agreement between the computed pattern and experimental results are observed.

Face Recognition using Probabilistic Neural Networks

In this paper we address the problem of face detection and recognition of grey scale frontal view images. We propose a face recognition system based on probabilistic neural networks (PNN) architecture. The system is implemented using voronoi/ delaunay tessellations and template matching. Images are segmented successfully into homogeneous regions by virtue of voronoi diagram properties. Face verification is achieved using matching scores computed by correlating edge gradients of reference images. The advantage of classification using PNN models is its short training time. The correlation based template matching guarantees good classification results

Face Recognition using Probabilistic Neural Networks

n this paper we address the problem of face detection and recognition of grey scale frontal view images. We propose a face recognition system based on probabilistic neural networks (PNN) architecture. The system is implemented using voronoi/ delaunay tessellations and template matching. Images are segmented successfully into homogeneous regions by virtue of voronoi diagram properties. Face verification is achieved using matching scores computed by correlating edge gradients of reference images. The advantage of classification using PNN models is its short training time. The correlation based template matching guarantees good classification results.