Real Random Numbers: Discovery, Generation & Encryption (Paperback or Softback)

What You Will Discover Inside:

Unlike dense, overly academic texts, this book takes a refreshingly practical route. It is short, simple, and completely free of tedious theorem-proving, relying instead on actionable algorithms and methodologies across five distinct frameworks:

• The TRIVAR Approximation: Reviving the author's pioneering 1983-84 research on Approximation Algorithms, backed by authoritative insights from Schneier (2010) and Agnew (1988).
• The By-Product of Error: Utilizing the filtering processes of Jazwinski, Ludeman, and Das & Das (2021) to extract pure RRNs from the magnitude of differences between observed data and fitted mathematical functions.
• Ancient Mathematics Meet Cryptography: Harnessing Bhaskaracharya's orthogonal trios alongside Diophantine Equations.
• Hilbert Space Multipliers: Employing a pair of conjugate complex roots as multipliers of random numbers at both the source and the sink.
• The Chaos of Nature: Demonstrating how RRNs can be derived from direct measurements of our vast world-featuring real-world data from the dimensions of money plant and betel leaves, to the unpredictable flood intensities of river flows.

Who Is This Book For?

Whether you are a cryptographer looking to challenge the status quo, a data scientist navigating uncertainty, or a strategic decision-maker looking for a competitive edge, this text provides a paradigm shift in how we view randomness, data filtering, and encryption. The journey to obtaining RRNs has been successful-but its ultimate impact lies in your hands. Read the methodologies, test the algorithms, and join the conversation.

Professor N. C. Das is former Professor-cum-Chief Scientist at the Department of Statistics and Computer Applications, Birsa Agricultural University, Ranchi, India. He has over seven decades of teaching- and research-experience in the field of Statistical Inference, Design of Experiments, Operations Research and Computer Science. Data-based modelling for prediction has been integral part of all above. These are now essential components of Data Science.

Harshit Ranjan is a final-year B.Tech Bioengineering student at Vellore Institute of Technology, Bhopal. With a strong foundation in computational tools and their applications in Bioengineering, he has made contributions to this book, focusing on the sections related to MATLAB graphics.His expertise lies in leveraging MATLAB for data Analysis, Simulation, and Visualization. His passion for blending technology with biological sciences drives his work, aiming to innovate in the field of Bioengineering and Computational Technology.He is the youngest grandson of Professor N.C. Das.