18,276 research outputs found
Pseudo Random Coins Show More Heads Than Tails
Tossing a coin is the most elementary Monte Carlo experiment. In a computer
the coin is replaced by a pseudo random number generator. It can be shown
analytically and by exact enumerations that popular random number generators
are not capable of imitating a fair coin: pseudo random coins show more heads
than tails. This bias explains the empirically observed failure of some random
number generators in random walk experiments. It can be traced down to the
special role of the value zero in the algebra of finite fields.Comment: 10 pages, 12 figure
Pseudo-random number generators for Monte Carlo simulations on Graphics Processing Units
Basic uniform pseudo-random number generators are implemented on ATI Graphics
Processing Units (GPU). The performance results of the realized generators
(multiplicative linear congruential (GGL), XOR-shift (XOR128), RANECU, RANMAR,
RANLUX and Mersenne Twister (MT19937)) on CPU and GPU are discussed. The
obtained speed-up factor is hundreds of times in comparison with CPU. RANLUX
generator is found to be the most appropriate for using on GPU in Monte Carlo
simulations. The brief review of the pseudo-random number generators used in
modern software packages for Monte Carlo simulations in high-energy physics is
present.Comment: 31 pages, 9 figures, 3 table
A novel pseudo-random number generator based on discrete chaotic iterations
Security of information transmitted through the Internet, against passive or
active attacks is an international concern. The use of a chaos-based
pseudo-random bit sequence to make it unrecognizable by an intruder, is a field
of research in full expansion. This mask of useful information by modulation or
encryption is a fundamental part of the TLS Internet exchange protocol. In this
paper, a new method using discrete chaotic iterations to generate pseudo-random
numbers is presented. This pseudo-random number generator has successfully
passed the NIST statistical test suite (NIST SP800-22). Security analysis shows
its good characteristics. The application for secure image transmission through
the Internet is proposed at the end of the paper.Comment: The First International Conference on Evolving Internet:Internet 2009
pp.71--76 http://dx.doi.org/10.1109/INTERNET.2009.1
Improvement and analysis of a pseudo random bit generator by means of cellular automata
In this paper, we implement a revised pseudo random bit generator based on a
rule-90 cellular automaton. For this purpose, we introduce a sequence matrix
H_N with the aim of calculating the pseudo random sequences of N bits employing
the algorithm related to the automaton backward evolution. In addition, a
multifractal structure of the matrix H_N is revealed and quantified according
to the multifractal formalism. The latter analysis could help to disentangle
what kind of automaton rule is used in the randomization process and therefore
it could be useful in cryptanalysis. Moreover, the conditions are found under
which this pseudo random generator passes all the statistical tests provided by
the National Institute of Standards and Technology (NIST)Comment: 20 pages, 12 figure
Properties making a chaotic system a good Pseudo Random Number Generator
We discuss two properties making a deterministic algorithm suitable to
generate a pseudo random sequence of numbers: high value of Kolmogorov-Sinai
entropy and high-dimensionality. We propose the multi dimensional Anosov
symplectic (cat) map as a Pseudo Random Number Generator. We show what chaotic
features of this map are useful for generating Pseudo Random Numbers and
investigate numerically which of them survive in the discrete version of the
map. Testing and comparisons with other generators are performed.Comment: 10 pages, 3 figures, new version, title changed and minor correction
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