Random Walk of Foraging

2020 ◽  
Vol 23 (3) ◽  
pp. 254-261
Author(s):  
I. Grabec ◽  
A.E. Sok
Keyword(s):  
Random Walk ◽  
Random Number ◽  
Random Number Generator ◽  
Number Generator ◽  
Lévy Flight ◽  
Levy Flight ◽  
Gaussian Random Walk ◽  
Stochastic Properties

The model of vibration driven random walk is adapted to description of foraging performed by simple organisms. Stochastic properties of foraging are described by the Gaussian random number generator, while the attraction of food is represented by a deterministic signal that directs walkers from surroundings to the food. This attraction causes transition from the Gaussian random walk to the Levy flight.

ON THE PERIODS OF THE ${\tt ranshi}$ RANDOM NUMBER GENERATOR

1996 ◽  
Vol 07 (06) ◽  
pp. 909-922 ◽  
Author(s):  
F. GUTBROD
Keyword(s):  
Phase Space ◽  
Random Number ◽  
Random Number Generator ◽  
Random Motion ◽  
Number Generator ◽  
Average Period ◽  
Perfect Agreement ◽  
Deterministic Dynamics ◽  
Stochastic Properties ◽  
Pseudo Random Number Generator

The stochastic properties of the pseudo-random number generator [Formula: see text] are discussed, with emphasis on the average period. Within a factor 2 this turns out to be the root of the maximally possible period. The actual set of periods depends on minor details of the algorithm, and the system settles down in one of only a few different cycles. These features are in perfect agreement with absolute random motion in phase space, to the extent allowed by deterministic dynamics.

scholarly journals The RANLUX Generator: Resonances in a Random Walk Test

1998 ◽  
Vol 09 (04) ◽  
pp. 607-624 ◽  
Author(s):  
Lev N. Shchur ◽  
Paolo Butera
Keyword(s):  
Random Walk ◽  
Dynamical Systems ◽  
Random Number ◽  
Jacobi Matrix ◽  
Random Number Generator ◽  
Number Generator ◽  
Walk Test

Using a recently proposed directed random walk test, we systematically investigate the popular random number generator RANLUX developed by Lüscher and implemented by James. We confirm the good quality of this generator with the recommended luxury level. At a smaller luxury level (for instance equal to 1) resonances are observed in the random walk test. We also find that the lagged Fibonacci and Subtract-with-Carry recipes exhibit similar failures in the random walk test. A revised analysis of the corresponding dynamical systems leads to the observation of resonances in the eigenvalues of Jacobi matrix.

Modeling of Lizard Skin Pattern by Cellular Automaton

2020 ◽  
Vol 23 (1) ◽  
pp. 1-16
Author(s):  
Igor Grabec ◽  
Ada Elizabeta Sok
Keyword(s):  
Probability Distribution ◽  
Cellular Automaton ◽  
Conditional Probability ◽  
Random Number ◽  
Random Number Generator ◽  
Similarity Function ◽  
Number Generator ◽  
Parametric Regression ◽  
Input Field ◽  
Stochastic Properties

Properties of lizard skin pattern (LSP) comprised of light and dark scales are characterized statistically and compared with the corresponding properties of a random binary field (RBF). The similarity function of these fields exhibits an outstanding peak that indicates their stochastic character. Stochastic properties are still more generally indicated by the probability distribution of scales in hexagonal cells comprised of a center and ring. It shows that similar scales are grouped together in LSP, but not in RBF. This difference is characterized by the conditional probability that reveals why LSP appears more striped than RBF. For generation of fields resembling LSP the cellular automaton (CA) is adapted to LSP by the non-parametric regression. Its deterministic performance is demonstrated by the operation on RBF. By adding a random number generator to this model the deterministic CA is generalized to a probabilistic one. Its actions cause more expressive changing of the input field as the actions of the deterministic CA.

Fuzzy Based Pseudo Random Number Generator used for Wireless Networks

2013 ◽  
Vol 16 (2) ◽  
pp. 210-216 ◽  
Author(s):  
Sattar B. Sadkhan ◽  
◽  
Sawsan K. Thamer ◽  
Najwan A. Hassan ◽  
◽  
...  
Keyword(s):  
Wireless Networks ◽  
Random Number ◽  
Random Number Generator ◽  
Number Generator ◽  
Pseudo Random Number ◽  
Pseudo Random Number Generator

0.4 mW, 0.27 pJ/bit true random number generator using jitter, metastability and current starved topology

2020 ◽  
Vol 14 (7) ◽  
pp. 1001-1011
Author(s):  
Dhirendra Kumar ◽  
Rahul Anand ◽  
Sajai Vir Singh ◽  
Prasanna Kumar Misra ◽  
Ashok Srivastava ◽  
...  
Keyword(s):  
Random Number ◽  
Random Number Generator ◽  
Number Generator ◽  
True Random Number Generator

scholarly journals TRNGs from Pre-Formed ReRAM Arrays

Cryptography ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 8
Author(s):  
Bertrand Cambou ◽  
Donald Telesca ◽  
Sareh Assiri ◽  
Michael Garrett ◽  
Saloni Jain ◽  
...  
Keyword(s):  
Random Number ◽  
Random Number Generator ◽  
Random Access ◽  
Random Number Generators ◽  
Cryptographic Algorithms ◽  
Cryptographic Keys ◽  
Low Levels ◽  
Stochastic Properties ◽  
Pseudo Random Number Generator ◽  
Number Of Cells

Schemes generating cryptographic keys from arrays of pre-formed Resistive Random Access (ReRAM) cells, called memristors, can also be used for the design of fast true random number generators (TRNG’s) of exceptional quality, while consuming low levels of electric power. Natural randomness is formed in the large stochastic cell-to-cell variations in resistance values at low injected currents in the pre-formed range. The proposed TRNG scheme can be designed with three interconnected blocks: (i) a pseudo-random number generator that acts as an extended output function to generate a stream of addresses pointing randomly at the array of ReRAM cells; (ii) a method to read the resistance values of these cells with a low injected current, and to convert the values into a stream of random bits; and, if needed, (iii) a method to further enhance the randomness of this stream such as mathematical, Boolean, and cryptographic algorithms. The natural stochastic properties of the ReRAM cells in the pre-forming range, at low currents, have been analyzed and demonstrated by measuring a statistically significant number of cells. Various implementations of the TRNGs with ReRAM arrays are presented in this paper.

scholarly journals A High‐Speed True Random Number Generator Based on a Cu x Te 1− x Diffusive Memristor

2021 ◽  
pp. 2100062
Author(s):  
Kyung Seok Woo ◽  
Jaehyun Kim ◽  
Janguk Han ◽  
Jin Myung Choi ◽  
Woohyun Kim ◽  
...  
Keyword(s):  
High Speed ◽  
Random Number ◽  
Random Number Generator ◽  
Number Generator ◽  
True Random Number Generator

scholarly journals A Hardware Pseudo-Random Number Generator Using Stochastic Computing and Logistic Map

Micromachines ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 31
Author(s):  
Junxiu Liu ◽  
Zhewei Liang ◽  
Yuling Luo ◽  
Lvchen Cao ◽  
Shunsheng Zhang ◽  
...  
Keyword(s):  
Random Number ◽  
Chaotic Map ◽  
Logistic Map ◽  
Random Number Generator ◽  
Alternative Methods ◽  
Random Numbers ◽  
Number Generator ◽  
Stochastic Computing ◽  
Pseudo Random Number ◽  
Pseudo Random Number Generator

Recent research showed that the chaotic maps are considered as alternative methods for generating pseudo-random numbers, and various approaches have been proposed for the corresponding hardware implementations. In this work, an efficient hardware pseudo-random number generator (PRNG) is proposed, where the one-dimensional logistic map is optimised by using the perturbation operation which effectively reduces the degradation of digital chaos. By employing stochastic computing, a hardware PRNG is designed with relatively low hardware utilisation. The proposed hardware PRNG is implemented by using a Field Programmable Gate Array device. Results show that the chaotic map achieves good security performance by using the perturbation operations and the generated pseudo-random numbers pass the TestU01 test and the NIST SP 800-22 test. Most importantly, it also saves 89% of hardware resources compared to conventional approaches.

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