RESUMEN
This paper presents a Tamil language (TL) encoder that benefits advanced encryption technologies such as the advanced encryption standard (AES). It defines a product set of vowel and consonant sounds of the Tamil language and reveals its connection to Hardy-Ramanujan prime factors and Tamil letters as a one-to-one mapping. It also reveals that the Tamil letters, combined with the digits from 1 to 9, form a Galois field of 28 over an irreducible polynomial of degree 8. Additionally, it implements these two mathematical properties, models the TL encoder, and replaces the pre-round transformation of AES with the model to enhance cryptographic strengths. The cryptographic strengths are measured by the runs test scores of the bit sequences of the ciphers of AES and compared with that of English language. The modeling and simulation conclude that the TL encoder enhances the cryptographic strength of AES at every step of the encryption flow.
RESUMEN
Advances in multimodal imaging have revolutionized diagnostic and treatment monitoring in ophthalmic practice. In multimodal ophthalmic imaging, geometric deformations are inevitable and they contain inherent deformations arising from heterogeneity in the optical characteristics of imaging devices and patient related factors. The registration of ophthalmic images under such conditions is challenging. We propose a novel technique that overcomes these challenges, using Laplacian feature, Hessian affine feature space and phase correlation, to register blue autofluorescence, near-infrared reflectance and color fundus photographs of the ocular posterior pole with high accuracy. Our validation analysis - that used current feature detection and extraction techniques (speed-up robust features (SURF), a concept of wind approach (KAZE), and fast retina keypoint (FREAK)), and quantitative measures (Sørensen-Dice coefficient, Jaccard index, and Kullback-Leibler divergence scores) - showed that our approach has significant merit in registering multimodal images when compared with a mix-and-match SURF-KAZE-FREAK benchmark approach. Similarly, our evaluation analysis that used a state-of-the-art qualitative measure - the mean registration error (MRE) - showed that the proposed approach is significantly better than the mix-and-match SURF-KAZE-FREAK benchmark approach, as well as a cutting edge image registration technique - Linear Stack Alignment with SIFT (scale-invariant feature transform) - in registering multimodal ophthalmic images.