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H

H. Krishna

Researcher at Syracuse University

Publications -  12
Citations -  329

H. Krishna is an academic researcher from Syracuse University. The author has contributed to research in topics: Error detection and correction & Coding theory. The author has an hindex of 6, co-authored 11 publications receiving 323 citations.

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A coding theory approach to error control in redundant residue number systems. I. Theory and single error correction

TL;DR: A coding theory approach to error control in redundant residue number systems (RRNSs) is presented and an efficient numerical procedure is derived for a single error correction.
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A coding theory approach to error control in redundant residue number systems. II. Multiple error detection and correction

TL;DR: The coding theory approach to error control in redundant residue number systems (RRNSs) is extended by deriving computationally efficient algorithms for correcting multiple errors, single-burst-error, and detecting multiple errors that reduce the computational complexity of the previously known algorithms.
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On theory and fast algorithms for error correction in residue number system product codes

TL;DR: A coding theory approach to error control in residue number system product codes is developed, and computationally efficient algorithms are derived for correcting single errors, double errors, and multiple errors, while simultaneously detecting multiple errors and additive overflow.
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Rings, fields, the Chinese remainder theorem and an extension-Part I: theory

TL;DR: The Chinese Remainder Theorem is extended to the case of a ring of polynomials with coefficients defined over a finite ring of integers, which is expected to serve as a keystone in the future design of number-theoretic algorithms for performing some of the most computationally intensive tasks.
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Rings, fields, the Chinese remainder theorem and an extension-Part II: applications to digital signal processing

TL;DR: The application of the theory developed in Part I of the research work is studied to deriving computationally efficient algorithms for performing tasks having multilinear form, especially the cyclic and acyclic convolution as they are two of the most frequently occurring computationally intensive tasks in digital signal processing.