Generating True Random Numbers Based on Multicore CPU Using Race Conditions and Chaotic Maps

2020 ◽  
Vol 45 (12) ◽  
pp. 10019-10032
Author(s):  
Je Sen Teh ◽  
Moatsum Alawida ◽  
Azman Samsudin
Keyword(s):  
Chaotic Maps ◽  
Random Numbers ◽  
Race Conditions ◽  
Multicore Cpu

scholarly journals WITHDRAWN: Adaptive chaotic maps and their application to pseudo-random numbers generation

2019 ◽  
pp. 100018
Author(s):  
Aleksandra V. Tutueva ◽  
Erivelton G. Nepomuceno ◽  
Artur I. Karimov ◽  
Valery S. Andreev ◽  
Denis N. Butusov
Keyword(s):  
Chaotic Maps ◽  
Random Numbers

Adaptive chaotic maps and their application to pseudo-random numbers generation

2020 ◽  
Vol 133 ◽  
pp. 109615 ◽  
Author(s):  
Aleksandra V. Tutueva ◽  
Erivelton G. Nepomuceno ◽  
Artur I. Karimov ◽  
Valery S. Andreev ◽  
Denis N. Butusov
Keyword(s):  
Chaotic Maps ◽  
Random Numbers

PSEUDO RANDOM NUMBER GENERATOR BASED ON SYNCHRONIZED CHAOTIC MAPS

2010 ◽  
Vol 21 (02) ◽  
pp. 275-290 ◽  
Author(s):  
AFSHIN AKHSHANI ◽  
SOHRAB BEHNIA ◽  
AMIR AKHAVAN ◽  
SIEW-CHOO LIM ◽  
ZAINURIAH HASSAN
Keyword(s):  
Random Number ◽  
Chaotic Maps ◽  
Chaotic Map ◽  
Random Number Generator ◽  
Random Numbers ◽  
Random Generator ◽  
Coupled Maps ◽  
The Individual ◽  
Test Suites ◽  
Pseudo Random Number Generator

In this paper, a hierarchy of coupled maps in synchronized state is introduced. Then some discussions about the individual properties of these chaotic maps are presented, from a dynamical systems viewpoint. Also, one of these chaotic map is used as a Nonlinear Pseudo Random Generator (NPRNG). This paper addresses the chaotic features of this map which are useful for generating nonlinear pseudo random numbers. Results of the analysis and extensive tests such as the NIST, DIEHARD and ENT test suites indicate that the NPRNG exhibits good statistical randomness properties.

scholarly journals Zooming into chaos as a pathway for the creation of a fast, light and reliable cryptosystem

2021 ◽  
Author(s):  
Jeaneth Machicao ◽  
Odemir M. Bruno ◽  
Murilo S. Baptista
Keyword(s):  
Probability Density ◽  
Uniform Distribution ◽  
Chaotic Maps ◽  
Computational Cost ◽  
Computational Effort ◽  
Random Numbers ◽  
Secret Keys ◽  
The Creation ◽  
Low Dimensional ◽  
Fast Light

AbstractMotivated by today’s huge volume of data that needs to be handled in secrecy, there is a wish to develop not only fast and light but also reliably secure cryptosystems. Chaos allows for the creation of pseudo-random numbers (PRNs) by low-dimensional transformations that need to be applied only a small number of times. These two properties may translate into a chaos-based cryptosystem that is both fast (short running time) and light (little computational effort). What we propose here is an approach to generate PRNs—and consequently digital secret keys—that can serve as a seed for an enhanced chaos-based cryptosystem. We use low-dimensional chaotic maps to quickly generate PRNs that have little correlation, and then, we quickly (“fast”) enhance secrecy by several orders (“reliability”) with very little computational cost (“light”) by simply looking at the less significant digits of the initial chaotic trajectory. This paper demonstrates this idea with rigor, by showing that a transformation applied a small number of times to chaotic trajectories significantly increases its entropy and Lyapunov exponents, as a consequence of the smoothing out of the probability density towards a uniform distribution.

Study of Adaptive Chaos Embedded Particle Swarm Optimization Algorithm

2012 ◽  
Vol 605-607 ◽  
pp. 2217-2221
Author(s):  
Rong Hua ◽  
Dan Jiang Chen ◽  
Yin Zhong Ye
Keyword(s):  
Particle Swarm Optimization ◽  
Chaotic Maps ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Random Numbers ◽  
Weight Factor ◽  
Swarm Optimization ◽  
Local Optima ◽  
Inertia Weight ◽  
Searching Ability

Chaos particle swarm optimization (CPSO) can not guarantee the population multiplicity and the optimized ergodicity, because its algorithm parameters are still random numbers in form. This paper proposes a new adaptive chaos embedded particle swarm optimization (ACEPSO) algorithm that uses chaotic maps to substitute random numbers of the classical PSO algorithm so as to make use of the properties of stochastic and ergodicity in chaotic search and introduces an adaptive inertia weight factor for each particle to adjust its inertia weight factor adaptively in response to its fitness, which can overcome the drawbacks of CPSO algorithm that is easily trapped in local optima. The experiments with complex and Multi-dimensional functions demonstrate that ACEPSO outperforms the original CPSO in the global searching ability and convergence rate.

scholarly journals Generation of pseudo-random numbers with the use of inverse chaotic transformation

2018 ◽  
Vol 16 (1) ◽  
pp. 16-22
Author(s):  
Marcin Lawnik
Keyword(s):  
Cumulative Distribution Function ◽  
Chaotic Maps ◽  
Continuous Distribution ◽  
Random Variable ◽  
Cumulative Distribution ◽  
Standard Normal Distribution ◽  
Random Numbers ◽  
Simulation And Modeling ◽  
Standard Normal ◽  
The Given

AbstractIn (Lawnik M., Generation of numbers with the distribution close to uniform with the use of chaotic maps, In: Obaidat M.S., Kacprzyk J., Ören T. (Ed.), International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH) (28-30 August 2014, Vienna, Austria), SCITEPRESS, 2014) Lawnik discussed a method of generating pseudo-random numbers from uniform distribution with the use of adequate chaotic transformation. The method enables the “flattening” of continuous distributions to uniform one. In this paper a inverse process to the above-mentioned method is presented, and, in consequence, a new manner of generating pseudo-random numbers from a given continuous distribution. The method utilizes the frequency of the occurrence of successive branches of chaotic transformation in the process of “flattening”. To generate the values from the given distribution one discrete and one continuous value of a random variable are required. The presented method does not directly involve the knowledge of the density function or the cumulative distribution function, which is, undoubtedly, a great advantage in comparison with other well-known methods. The described method was analysed on the example of the standard normal distribution.

scholarly journals Chaotic optimization algorithm based on the modified probability density function of Lozi map

2021 ◽  
Vol 39 (6) ◽  
pp. 9-22
Author(s):  
Rabah Bououden ◽  
Mohamed Salah Abdelouahab
Keyword(s):  
Global Optimization ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Optimization Algorithm ◽  
Chaotic Maps ◽  
New Technique ◽  
Random Numbers ◽  
Chaos Optimization ◽  
Design Variables

Chaos optimization algorithms (COAs) usually utilize different chaotic maps(logistic, tent, Hénon, Lozi,...) to generate the pseudo-random numbers mapped as the design variables for global optimization. In this paper we are going to propose new technique to improve the chaotic optimization algorithm by using some transformations to modify the density of the map instead of changing it.

Generation of Random Numbers by Means of Optical Manipulator

2011 ◽  
Vol 43 (8) ◽  
pp. 76-80
Author(s):  
Rostislav M. Mikhersky ◽  
Oleg I. Popov
Keyword(s):  
Random Numbers
Sign in / Sign up

Export Citation Format

Share Document