Glaucoma is a domineering and irretrievable neurodegenerative eye disease produced by the optical nerve head owed to extended intra-ocular stress inside the eye. Recognition of glaucoma is an essential job for ophthalmologists. In this paper, we propose a methodology to classify fundus images into normal and glaucoma categories. The proposed approach makes use of image denoising of digital fundus images by utilizing a non-Gaussian bivariate probability distribution function to model the statistics of wavelet coefficients of glaucoma images. The traditional image features were extracted followed by the popular feature selection algorithm. The selected features are then fed to the least square support vector machine classifier employing various kernel functions. The comparison result shows that the proposed approach offers maximum classification accuracy of nearly 91.22% over the existing best approaches.

A high speed N × N bit multiplier architecture that supports signed and unsigned multiplication operations is proposed in this paper. This architecture incorporates the modified two's complement circuits and also N × N bit unsigned multiplier circuit. This unsigned multiplier circuit is based on decomposing the multiplier circuit into smaller-precision independent multipliers using Vedic Mathematics. These individual multipliers generate the partial products in parallel for high speed operation, which are combined by using high speed adders and parallel adder to generate the product output. The proposed architecture has regular-shape for the partial product tree that makes easy to implement. Finally, this multiplier architecture is implemented in UMC 65 nm technology for N = 8, 16 and 32 bits. The synthesis results shows that the proposed multiplier architecture improves in terms of speed and also reduces power-delay product (PDP), compared to the architectures in the literature.

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