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INDONESIA
Computer Engineering and Applications Journal (ComEngApp)
Published by Institute of Advanced Engineering and Science
ISSN : -     EISSN : -     DOI : -
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Articles 12 Documents
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Circularly Polarized Slotted Microstrip Patch Antenna with Finite Ground Plane Sanyog Rawat; K K Sharma
Computer Engineering and Applications Journal (ComEngApp) December 2012
Publisher : Computer Engineering and Applications Journal (ComEngApp)

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Abstract

In this paper a new geometry of circularly polarized patch antenna is proposed with improved bandwidth. The radiation performance of proposed patch antenna is investigated using IE3D simulation software and its performance is compared with that of conventional rectangular patch antenna. The simulated return loss, axial ratio and impedance with frequency for the proposed antenna are reported in this paper. It is shown that by selecting suitable ground-plane dimensions, air gap and location of the slots, the impedance bandwidth can be enhanced upto 10.15% as compared to conventional rectangular patch (4.24%) with an axial ratio bandwidth of 4.05%.
Combined Classifier for Face Recognition using Legendre Moments Sridhar Dasari; I.V. Murali Krishna
Computer Engineering and Applications Journal (ComEngApp) December 2012
Publisher : Computer Engineering and Applications Journal (ComEngApp)

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Abstract

In this paper, a new combined Face Recognition method based on Legendre moments with Linear Discriminant Analysis and Probabilistic Neural Network is proposed. The Legendre moments are orthogonal and scale invariants hence they are suitable for representing the features of the face images. The proposed face recognition method consists of three steps, i) Feature extraction using Legendre moments ii) Dimensionality reduction using Linear Discrminant Analysis (LDA) and iii) classification using Probabilistic Neural Network (PNN). Linear Discriminant Analysis searches the directions for maximum discrimination of classes in addition to dimensionality reduction. Combination of Legendre moments and Linear Discriminant Analysis is used for improving the capability of Linear Discriminant Analysis when few samples of images are available. Probabilistic Neural network gives fast and accurate classification of face images. Evaluation was performed on two face data bases. First database of 400 face images from Olivetty Research Laboratories (ORL) face database, and the second database of thirteen students are taken. The proposed method gives fast and better recognition rate when compared to other classifiers.

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