P
Palaniappan Ramu
Researcher at Indian Institute of Technology Madras
Publications - 61
Citations - 1101
Palaniappan Ramu is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Computer science & Steganography. The author has an hindex of 13, co-authored 41 publications receiving 847 citations. Previous affiliations of Palaniappan Ramu include University of Florida.
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Journal ArticleDOI
Modelling with stakeholders - Next generation
Alexey Voinov,Nagesh Kolagani,Michael K. McCall,Pierre D. Glynn,Marit E. Kragt,Frank O. Ostermann,Suzanne A. Pierce,Palaniappan Ramu +7 more
TL;DR: Modelling with Stakeholders is updated and builds on Voinov and Bousquet, 2010, and structured mechanisms to examine and account for human biases and beliefs in participatory modelling are suggested.
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Discrete Wavelet Transform and Singular Value Decomposition Based ECG Steganography for Secured Patient Information Transmission
TL;DR: An approach that uses discrete wavelet transform to decompose signals and singular value decomposition (SVD) to embed the secret information into the decomposed ECG signal and the observations validate that HH is the ideal sub-band to hide data.
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Imperceptibility-Robustness tradeoff studies for ECG steganography using Continuous Ant Colony Optimization
TL;DR: The novelty of the proposed approach is to use CACO in ECG Steganography, to identify Multiple Scaling Factors (MSFs) that will provide a better tradeoff compared to uniform Single Scaling Factor (SSF) and the results validate that the tradeoff curve obtained through MSFs is better than the tradeoffs obtained for any SSF.
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A convex hull approach for the reliability-based design optimization of nonlinear transient dynamic problems
TL;DR: In this article, a convex hull approach is adopted to isolate the points corresponding to unwanted bifurcations in the design space, which is applied to a tube impacting a rigid wall representing a transient dynamic problem.
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ECG steganography using curvelet transform
TL;DR: An attempt has been made to use curvelet transforms which permit identifying the coefficients that store the crucial information about diagnosis in ECG steganography to validate that coefficients around zero are ideal for watermarking to minimize deterioration and there is no loss in the data retrieved.