Gaussian random number generators

Random-number Generators Physical Random-number Generators

R andom numbers have applications in many areas: simulation, game-playing, cryptography, statistical sampling, evaluation of multiple integrals, particletransport calculations, and computations in statistical physics, to name a few. Since each application involves slightly different criteria for judging the “worthiness” of the random numbers generated, a variety of generators have been develope...

Pseudo-Random Number Generators

The first option here is not easy. Some pieces of data of this kind exist, for example from the decay of radioactive elements, but generally generation is slow and you cannot get the quantity of random numbers you require for modern applications. The second option here is what I’ll talk about today. Algorithms of this type are called “PseudoRandom Number Generators” (PRNGs). Many bad algorithms...

Biometric random number generators

Up to now biometric methods have been used in cryptography for authentication purposes. In this paper we propose to use biological data for generating sequences of random bits. We point out that this new approach could be particularly useful to generate seeds for pseudo-random number generators and so-called ‘‘key sessions’’. Our method is very simple and is based on the observation that, for t...

Random Number Generators (algo)

Random Number Generators

Chris Dibble graduated Summa Cum Laude from Ball State University in 2013 with a degree in computer science and a minor in mathematics. He is currently employed as a Software Engineer at Interactive Intelligence in Indianapolis, Indiana. This paper is based on a project he completed in conjunction with Hannah Turner for an honors colloquium “Chaos Theory and Fractals” taught by Dr. Rich Stankew...