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Journal articles on the topic 'Random numbers generator'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Chen, I.-Te. "Random Numbers Generated from Audio and Video Sources." Mathematical Problems in Engineering 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/285373.

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Random numbers are very useful in simulation, chaos theory, game theory, information theory, pattern recognition, probability theory, quantum mechanics, statistics, and statistical mechanics. The random numbers are especially helpful in cryptography. In this work, the proposed random number generators come from white noise of audio and video (A/V) sources which are extracted from high-resolution IPCAM, WEBCAM, and MPEG-1 video files. The proposed generator applied on video sources from IPCAM and WEBCAM with microphone would be the true random number generator and the pseudorandom number genera

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2

PEYTON JONES, SIMON. "27 Random Numbers." Journal of Functional Programming 13, no. 1 (2003): 235–40. http://dx.doi.org/10.1017/s0956796803002910.

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Harangus, Katalin, and András Kakucs. "Random Number Generator." Műszaki Tudományos Közlemények 18 (2023): 37–44. http://dx.doi.org/10.33894/mtk-2023.18.07.

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Illustration plays an important role during education: The Galton board is a suitable tool for illustrating random processes and explaining probability distributions. We have created this tool in a virtual version, which facilitates data collection for statistical processing of experimental data and also enables the study of non-symmetrical distributions. The random processes on the device are simulated, which requires a random number generator. Since there were some doubts about the software-generated pseudo-random numbers, we created a true random number generator based on the input noise of

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4

Park, Sungju, Kyungmin Kim, Keunjin Kim, and Choonsung Nam. "Dynamical Pseudo-Random Number Generator Using Reinforcement Learning." Applied Sciences 12, no. 7 (2022): 3377. http://dx.doi.org/10.3390/app12073377.

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Pseudo-random number generators (PRNGs) are based on the algorithm that generates a sequence of numbers arranged randomly. Recently, random numbers have been generated through a reinforcement learning mechanism. This method generates random numbers based on reinforcement learning characteristics that select the optimal behavior considering every possible status up to the point of episode closing to secure the randomness of such random numbers. The LSTM method is used for the long-term memory of previous patterns and selection of new patterns in consideration of such previous patterns. In addit

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Iavich, Maksim, Tamari Kuchukhidze, Giorgi Iashvili, and Sergiy Gnatyuk. "Hybrid quantum random number generator for cryptographic algorithms." RADIOELECTRONIC AND COMPUTER SYSTEMS, no. 4 (November 29, 2021): 103–18. http://dx.doi.org/10.32620/reks.2021.4.09.

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The subject matter of the article is pseudo-random number generators. Random numbers play the important role in cryptography. Using not secure pseudo-random number generators is a very common weakness. It is also a fundamental resource in science and engineering. There are algorithmically generated numbers that are similar to random distributions but are not random, called pseudo-random number generators. In many cases the tasks to be solved are based on the unpredictability of random numbers, which cannot be guaranteed in the case of pseudo-random number generators, true randomness is require

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Francis, Kennon. "True random numbers using the random number generator of the microcomputer." Trends in Pharmacological Sciences 7 (January 1986): 124–25. http://dx.doi.org/10.1016/0165-6147(86)90283-x.

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Roussille, Hugo, Lionel Djadaojee, and Frédéric Chevy. "A simple quantum generator of random numbers." Emergent Scientist 1 (2017): 7. http://dx.doi.org/10.1051/emsci/2017009.

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Cryptography techniques rely on chains of random numbers used to generate safe encryption keys. Since random number generator algorithms are in fact pseudo-random their behavior can be predicted if the generation method is known and as such they cannot be used for perfectly safe communications. In this article, we present a perfectly random generator based on quantum measurement processes. The main advantage of such a generator is that using quantum mechanics, its behavior cannot be predicted in any way. We verify the randomness of our generator and compare it to commonly used pseudo-random ge

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Okada, Kiyoshiro, Katsuhiro Endo, Kenji Yasuoka, and Shuichi Kurabayashi. "Learned pseudo-random number generator: WGAN-GP for generating statistically robust random numbers." PLOS ONE 18, no. 6 (2023): e0287025. http://dx.doi.org/10.1371/journal.pone.0287025.

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Pseudo-random number generators (PRNGs) are software algorithms generating a sequence of numbers approximating the properties of random numbers. They are critical components in many information systems that require unpredictable and nonarbitrary behaviors, such as parameter configuration in machine learning, gaming, cryptography, and simulation. A PRNG is commonly validated through a statistical test suite, such as NIST SP 800-22rev1a (NIST test suite), to evaluate its robustness and the randomness of the numbers. In this paper, we propose a Wasserstein distance-based generative adversarial ne

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Lopez Leyva, Josue Aaron, and Arturo Arvizu-Mondragón. "Simultaneous dual true random numbers generator." DYNA 83, no. 195 (2016): 93–98. http://dx.doi.org/10.15446/dyna.v83n195.46652.

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This paper details the design and implementation of a simultaneous dual true random numbers generator using only one laser and a digital signal processing system with a DE0 Nano FPGA. We implemented the random generator in such a way that a vacuum optical field will exist in our system. Taking advantage of the inherently random nature of the field, simultaneously quadrature components are measured in order to generate a truly random voltage signal. Also, we used a dynamical system of statistical analysis to eliminate any residual component of direct current on output voltage signal due to an (

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10

Zurbenko, I. G. "On weakly correlated random numbers generator." Journal of Statistical Computation and Simulation 47, no. 1-2 (1993): 79–88. http://dx.doi.org/10.1080/00949659308811512.

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11

Yakut, Selman, Taner Tuncer, and Ahmet Bedri Özer. "A New Secure and Efficient Approach for TRNG and Its Post-Processing Algorithms." Journal of Circuits, Systems and Computers 29, no. 15 (2020): 2050244. http://dx.doi.org/10.1142/s0218126620502448.

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Random numbers are important parameters for the security of cryptographic applications. In this study, a secure and efficient generator is proposed to generate random numbers. The first part of the generator is a true random number generator that consists of chaotic systems implemented on FPGA. The second part of the generator is a post-processing algorithm used to overcome the problems that emerge from the generator or environmental factors. As the post-processing algorithm, Keccak, the latest standard of hash algorithm, was rearranged and used. Random numbers with the proposed approach meet

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Pikuza, M. O. "Statistical Characteristics Improvement of a Hardware Random Number Generator by a Software Method." Doklady BGUIR 20, no. 7 (2022): 43–47. http://dx.doi.org/10.35596/1729-7648-2022-20-7-43-47.

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As a source of random numbers, hardware random number generators are often used, the operation of which is based on randomly changing parameters of various physical processes. The statistical characteristics of such generators do not always allow their use in the field of information security. To improve the statistical characteristics, various software tools for processing the output data of the generator are used. The purpose of this work is to study the possibility to improve the statistical characteristics of a hardware random number generator by software. The investigated hardware random

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Yakut, Selman, Taner Tuncer, and Ahmet Bedri Ozer. "Secure and Efficient Hybrid Random Number Generator Based on Sponge Constructions for Cryptographic Applications." Elektronika ir Elektrotechnika 25, no. 4 (2019): 40–46. http://dx.doi.org/10.5755/j01.eie.25.4.23969.

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Random numbers constitute the most important part of many applications and have a vital importance in the security of these applications, especially in cryptography. Therefore, there is a need for secure random numbers to provide their security. This study is concerned with the development of a secure and efficient random number generator that is primarily intended for cryptographic applications. The generator consists of two subsystems. The first is algorithmic structure, Keccak, which is the latest standard for hash functions. The structure provides to generate secure random numbers. The sec

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Metin, Zülfiye Beyza, and Fatih Özkaynak. "PCG-Generated Randomness: A NIST Analysis of 100-Million Bits." Turkish Journal of Science and Technology 20, no. 1 (2024): 55–61. https://doi.org/10.55525/tjst.1528213.

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The generation of random numbers is crucial for various applications, including cryptography, simulation, sampling, and statistical analysis. Cryptography utilizes random numbers to secure communication through the generation of encryption keys, thereby safeguarding sensitive information from unauthorized access. This study aims to evaluate the randomness and suitability of the Permuted Congruential Generator (PCG) algorithm for cryptography applications, through testing its generated random numbers using the National Institute of Standards and Technology (NIST) statistical tests. A novel meth

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Ostapets, Denys, Volodymyr Dziuba, and Pavlo Ivin. "Hardware random numbers generator based on microcontroller." MATEC Web of Conferences 390 (2024): 04002. http://dx.doi.org/10.1051/matecconf/202439004002.

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The work considers the principles of development and organization of the random number generation system using a hardware generator of true random numbers. An analysis of noise sources for a hardware generator of true random numbers has been carried out. The architecture of the random and pseudo-random number generation system, the structure of the hardware part, and the interaction protocol of the system elements have been developed. Hardware and software of the system are implemented. An analysis of numbers sequences randomness degree obtained in different modes was carried out using a NIST

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Andrecut, M. "Logistic Map as a Random Number Generator." International Journal of Modern Physics B 12, no. 09 (1998): 921–30. http://dx.doi.org/10.1142/s021797929800051x.

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For the largest value of the control parameter, the logistic map is able to generate an infinite chaotic sequence of numbers. Here we describe a simple method for obtaining a random number generator based on this property of the logistic map. Comparing to usual congruential random generators, which are periodic, the logistic random number generator is infinite, aperiodic and not correlated. An aperiodic random number generator is a valuable tool for computer simulation methods.

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Xiao, Xiao, Li Xuan Ye, and Jun Pu. "A Cost-Effective Approach of Hardware Random Number Generator." Applied Mechanics and Materials 602-605 (August 2014): 2803–6. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.2803.

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This paper shows the research on hardware random number generator (HRNG). As truly random numbers are strongly required in encryption and computer simulation areas, developing a simple and inexpensive HRNG has significant value. The whole system is divided into the noise generating module and the processing module. After the numbers are generated, a randomness test has been carried out which indicates that the random numbers generated are truly random. It is concluded that the final product of this HRNG meets the requirements of the objectives.

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Budiman, Arif, Efori Bulolo, and Imam Saputra. "Middle Square Method Analysis of Number Pseudorandom Process." IJICS (International Journal of Informatics and Computer Science) 4, no. 2 (2020): 35. http://dx.doi.org/10.30865/ijics.v4i2.1386.

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Random numbers can be generated from a calculation of mathematical formulas. Such random numbers are often referred to as pseudo random numbers, random numbers are used for various algorithms, especially cryptographic algorithms such as AES, RSA, IDEA, GOST that require the use of Middle-Square Method random numbers which is very useful for adding research references to algorithms concerning random number generator and better understand how random numbers are generated using the Middle-Square Method algorithm. Both data collection and report making as for the objectives achieved in the form of

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Averin, A. S., N. D. Zyulyarkina, and E. M. Izhberdeeva. "RANDOM NUMBER GENERATOR BASED ON HUMAN-COMPUTER INTERACTION." Journal of the Ural Federal District. Information security, no. 2 (2020): 17–23. http://dx.doi.org/10.14529/secur200203.

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The paper is devoted to the development and analysis of a software random number gen-erator based on human-computer interaction for software and cryptographic applications. De-scribed is a method of generating random numbers based on human-computer interaction, using time and position of a cursor on a computer or smartphone display. Based on this meth-od, a random number generator has been developed and programmatically implemented. A preliminary analysis of the algorithm was performed, during which 11,000 numbers were gen-erated. The test software for this method has been created, preliminary

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Deon, A. F., V. A. Onuchin, and Yu A. Menyaev. "Twister Generator of Stochastic Planes." Herald of the Bauman Moscow State Technical University. Series Instrument Engineering, no. 3 (126) (June 2019): 27–45. http://dx.doi.org/10.18698/0236-3933-2019-3-27-45.

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Various pseudorandom number generation algorithms may be used to create a discrete stochastic plane. If a Cartesian completeness property is required of the plane, it must be uniform. The point is, employing the concept of uncontrolled random number generation may yield low-quality results, since original sequences may omit random numbers or not be sufficiently uniform. We present a novel approach for generating stochastic Cartesian planes according to the model of complete twister sequences featuring uniform random numbers without omissions or repetitions. Simulation results confirm that the

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Deon, Aleksei F., and Yulian A. Menyaev. "Poisson Twister Generator by Cumulative Frequency Technology." Algorithms 12, no. 6 (2019): 114. http://dx.doi.org/10.3390/a12060114.

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The widely known generators of Poisson random variables are associated with different modifications of the algorithm based on the convergence in probability of a sequence of uniform random variables to the created stochastic number. However, in some situations, this approach yields different discrete Poisson probability distributions and skipping in the generated numbers. This article offers a new approach for creating Poisson random variables based on the complete twister generator of uniform random variables, using cumulative frequency technology. The simulation results confirm that probabil

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22

Prasannanjali, C. "Ring Oscillator Based True Random Number Generator." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (2024): 276–83. http://dx.doi.org/10.22214/ijraset.2024.58320.

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Abstract: A true random number generator (TRNG), also known as a hardware random number generator (HRNG), does not use a computer algorithm. Instead, it uses an external unpredictable physical variable such as stochastic models to generate random numbers. Here it gathers data from random electronic signals. Then, the data is converted into digital form and any patterns registered are removed to make it random. This data is used to create random numbers. It is mainly used in Cryptographic Security, authentication, secure communications, e-commerce transactions, Digital Signatures etc. In Existi

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Vicario, Carmelo Mario. "Perceiving Numbers Affects the Internal Random Movements Generator." Scientific World Journal 2012 (2012): 1–6. http://dx.doi.org/10.1100/2012/347068.

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According to the evidence of direct relationships among space, numbers, and finger representations, a random movement generation (RMG) task was employed in order to investigate whether numerical exposure can influence the finger selection of healthy humans. To this purpose a group of participants were asked to generate random finger movements during the exposure to several numerical cues. Although participants were explicitly asked to move finger as random as possible, results showed that left-hand fingers were moved more frequently than right-hand fingers when low numerical cues (from 1 to 3)

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The Vinh, Tran. "Congruential generator of complex pseudo-random of numbers." Annales Universitatis Scientiarum Budapestinensis de Rolando Eötvös Nominatae. Sectio computatorica, no. 44 (2015): 211–19. https://doi.org/10.71352/ac.44.211.

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25

Dereli, Serkan. "True-Random Number Generator Based on Image Histogram." Academic Perspective Procedia 3, no. 1 (2020): 301–7. http://dx.doi.org/10.33793/acperpro.03.01.60.

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It is the non-repetitive distribution that makes the random numbers important in artificial intelligence techniques, cryptology, transferring a real environment to the virtual world and many more applications. Since the source of true random numbers consists of data from the physical world, the same number chain is never produced. In this study, images taken from the outside world were used as the source of randomness. The resulting image was first converted into an 8-bit gray image, and then the histogram of this gray image was revealed. As is known, an image histogram shows the color distrib

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Łoza, Szymon, Łukasz Matuszewski, and Mieczysław Jessa. "A Random Number Generator Using Ring Oscillators and SHA-256 as Post-Processing." International Journal of Electronics and Telecommunications 61, no. 2 (2015): 199–204. http://dx.doi.org/10.1515/eletel-2015-0026.

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Abstract Today, cryptographic security depends primarily on having strong keys and keeping them secret. The keys should be produced by a reliable and robust to external manipulations generators of random numbers. To hamper different attacks, the generators should be implemented in the same chip as a cryptographic system using random numbers. It forces a designer to create a random number generator purely digitally. Unfortunately, the obtained sequences are biased and do not pass many statistical tests. Therefore an output of the random number generator has to be subjected to a transformation c

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Deon, Aleksei, and Yulian Menyaev. "Twister Generator of Arbitrary Uniform Sequences." JUCS - Journal of Universal Computer Science 23, no. (4) (2017): 353–84. https://doi.org/10.3217/jucs-023-04-0353.

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Twisting generators for pseudorandom numbers may use a congruential array to simulate stochastic sequences. Typically, the computer program controls the quantity of elements in array to limit the random access memory. This technique may have limitations in situations where the stochastic sequences have an insufficient size for some application tasks, ranging from theoretical mathematics and technic constructions to biological and medical studies. This paper proposes a novel approach to generate complete stochastic sequences which don't need a congruential twisting array. The results of simulat

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Shcherbina, Yurii, Nadiia Kazakova, Oleksii Fraze-Frazenko, and Oleh Domaskin. "METHODS OF CHOOSING A RANDOM NUMBER GENERATOR FOR MODELING STOCHASTIC PROCESSES." Ukrainian Scientific Journal of Information Security 30, no. 1 (2024): 124–29. http://dx.doi.org/10.18372/2225-5036.30.18613.

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Modern computer modeling is an important stage in the design of control systems for the distribution of information flows in computer networks and in modern control systems for complex technological processes. The core of any computer model is a source of randomness, which should generate a uniformly distributed stream of random integers or real numbers. In addition to the uniformity of distribution, such a source must meet the requirements of economic use of computing system resources. An analysis of simple arithmetic generators is given and, based on it, it is shown that generators such as t

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Lee, Kyungroul, and Manhee Lee. "True Random Number Generator (TRNG) Utilizing FM Radio Signals for Mobile and Embedded Devices in Multi-Access Edge Computing." Sensors 19, no. 19 (2019): 4130. http://dx.doi.org/10.3390/s19194130.

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As transmissions of data between mobile and embedded devices in multi-access edge computing (MEC) increase, data must be protected, ensuring confidentiality and integrity. These issues are usually solved with cryptographic algorithms systems, which utilize a random number generator to create seeds and keys randomly. Their role in cryptography is so important that they need to be generated securely. In this paper, a true random number generator (TRNG) utilizing FM radio signals as a source is proposed. The proposed method can generate random numbers with high entropy, increased by at least 118%

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Kim, Moon-Seok, Il-Woong Tcho, and Yang-Kyu Choi. "Strategy to enhance entropy of random numbers in a wind-driven triboelectric random number generator." Nano Energy 89 (November 2021): 106359. http://dx.doi.org/10.1016/j.nanoen.2021.106359.

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Wang, Tianle, Mohammad Atif, Zhihua Dong, Charles Leggett, and Meifeng Lin. "A new portable random number generator wrapper library." EPJ Web of Conferences 295 (2024): 11001. http://dx.doi.org/10.1051/epjconf/202429511001.

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Random number generator is an important component of many scientific projects. Many projects are written using programming models (like OpenMP and SYCL) to target different architectures. However, some programming models do not provide a random number generator. In this work, we introduce our random number generator wrapper. It is a header-only library that supports three distributions of random numbers: uniform, normal, and poisson. On the GPU backend, it wraps the cuRAND and rocRAND library, and supports various random number engines. It also wraps random123, a counterbased random number gen

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Huang, Min, Ziyang Chen, Yichen Zhang, and Hong Guo. "A Gaussian-Distributed Quantum Random Number Generator Using Vacuum Shot Noise." Entropy 22, no. 6 (2020): 618. http://dx.doi.org/10.3390/e22060618.

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Among all the methods of extracting randomness, quantum random number generators are promising for their genuine randomness. However, existing quantum random number generator schemes aim at generating sequences with a uniform distribution, which may not meet the requirements of specific applications such as a continuous-variable quantum key distribution system. In this paper, we demonstrate a practical quantum random number generation scheme directly generating Gaussian distributed random sequences based on measuring vacuum shot noise. Particularly, the impact of the sampling device in the pra

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Maksymovych, Volodymyr, Mariia Shabatura, Oleh Harasymchuk, Ruslan Shevchuk, Pawel Sawicki, and Tomasz Zajac. "Combined Pseudo-Random Sequence Generator for Cybersecurity." Sensors 22, no. 24 (2022): 9700. http://dx.doi.org/10.3390/s22249700.

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Random and pseudo-random number and bit sequence generators with a uniform distribution law are the most widespread and in demand in the market of pseudo-random generators. Depending on the specific field of application, the requirements for their implementation and the quality of the generator’s output sequence change. In this article, we have optimized the structures of the classical additive Fibonacci generator and the modified additive Fibonacci generator when they work together. The ranges of initial settings of structural elements (seed) of these generators have been determined, which gu

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Risdianto, Dian Arief, and Bambang Nurcahyo Prastowo. "Pengembangan True Random Number Generator berbasis Citra menggunakan Algoritme Kaotis." IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) 10, no. 1 (2020): 87. http://dx.doi.org/10.22146/ijeis.36517.

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The security of most cryptographic systems depends on key generation using a nondeterministic RNG. PRNG generates a random numbers with repeatable patterns over a period of time and can be predicted if the initial conditions and algorithms are known. TRNG extracts entropy from physical sources to generate random numbers. However, most of these systems have relatively high cost, complexity, and difficulty levels. If the camera is directed to a random scene, the resulting random number can be assumed to be random. However, the weakness of a digital camera as a source of random numbers lies in th

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LÜ, HUAPING, SHIHONG WANG, and GANG HU. "PSEUDO-RANDOM NUMBER GENERATOR BASED ON COUPLED MAP LATTICES." International Journal of Modern Physics B 18, no. 17n19 (2004): 2409–14. http://dx.doi.org/10.1142/s0217979204025440.

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A one-way coupled chaotic map lattice is used for generating pseudo-random numbers. It is shown that with suitable cooperative applications of both chaotic and conventional approaches, the output of the spatiotemporally chaotic system can easily meet the practical requirements of random numbers, i.e., excellent random statistical properties, long periodicity of computer realizations, and fast speed of random number generations. This pseudo-random number generator system can be used as ideal synchronous and self-synchronizing stream cipher systems for secure communications.

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Al-Daraiseh, Ahmad, Yousef Sanjalawe, Salam Al-E’mari, Salam Fraihat, Mohammad Bany Taha, and Muhammed Al-Muhammed. "Cryptographic Grade Chaotic Random Number Generator Based on Tent-Map." Journal of Sensor and Actuator Networks 12, no. 5 (2023): 73. http://dx.doi.org/10.3390/jsan12050073.

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In recent years, there has been an increasing interest in employing chaotic-based random number generators for cryptographic purposes. However, many of these generators produce sequences that lack the necessary strength for cryptographic systems, such as Tent-Map. However, these generators still suffer from common issues when generating random numbers, including issues related to speed, randomness, lack of statistical properties, and lack of uniformity. Therefore, this paper introduces an efficient pseudo-random number generator, called State-Based Tent-Map (SBTM), based on a modified Tent-Map

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Sabir, Firas Ali, and Sadiq Habeeb Abdulhussain. "IMAGE BASED MULTI-LENGTH RANDOM KEY GENERATOR." Journal of Engineering 17, no. 03 (2011): 486–98. http://dx.doi.org/10.31026/j.eng.2011.03.11.

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Random Number Generators (RNGs) are an important building block for algorithms and protocols incryptography. They are dominant in the construction of encryption keys and other cryptographic algorithm parameters. In practice, statistical testing is employed to gather evidence that a generator indeed produces numbers that appear to be random. In this paper a new algorithm is proposed to generate variable length random binary sequence. The random sequence is generated by selecting different point from hashed digital images; the selecting process is organized in such a way to ensure randomness and

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Tanackov, Sinani, Stanković, et al. "Natural Test for Random Numbers Generator Based on Exponential Distribution." Mathematics 7, no. 10 (2019): 920. http://dx.doi.org/10.3390/math7100920.

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We will prove that when uniformly distributed random numbers are sorted by value, their successive differences are a exponentially distributed random variable Ex(λ). For a set of n random numbers, the parameters of mathematical expectation and standard deviation is λ =n−1. The theorem was verified on four series of 200 sets of 101 random numbers each. The first series was obtained on the basis of decimals of the constant e=2.718281…, the second on the decimals of the constant π =3.141592…, the third on a Pseudo Random Number generated from Excel function RAND, and the fourth series of True Ran

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Yang, Binbin, Daniel Arumí, Salvador Manich, et al. "RRAM Random Number Generator Based on Train of Pulses." Electronics 10, no. 15 (2021): 1831. http://dx.doi.org/10.3390/electronics10151831.

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In this paper, the modulation of the conductance levels of resistive random access memory (RRAM) devices is used for the generation of random numbers by applying a train of RESET pulses. The influence of the pulse amplitude and width on the device resistance is also analyzed. For each pulse characteristic, the number of pulses required to drive the device to a particular resistance threshold is variable, and it is exploited to extract random numbers. Based on this behavior, a random number generator (RNG) circuit is proposed. To assess the performance of the circuit, the National Institute of

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Aldossari, Haifa, and Michael Mascagni. "Scrambling additive lagged-Fibonacci generators." Monte Carlo Methods and Applications 28, no. 3 (2022): 199–210. http://dx.doi.org/10.1515/mcma-2022-2115.

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Abstract Random numbers are used in a variety of applications including simulation, sampling, and cryptography. Fortunately, there exist many well-established methods of random number generation. An example of a well-known pseudorandom number generator is the lagged-Fibonacci generator (LFG). Marsaglia showed that the lagged-Fibonacci generator using addition failed some of his DIEHARD statistical tests, while it passed all when longer lags were used. This paper presents a scrambler that takes bits from a pseudorandom number generator and outputs (hopefully) improved pseudorandom numbers. The

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Du, Wanlin, Ling Wang, Yuanzhe Zhu, and Hong Lv. "Machine Learning-Based Randomness Analysis For a Auantum Random Number Generator." Journal of Physics: Conference Series 2829, no. 1 (2024): 012023. http://dx.doi.org/10.1088/1742-6596/2829/1/012023.

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Abstract Random numbers are critical to information security. Quantum random numbers are theoretically truly random and unpredictable. However, the measurement process of a quantum random number generator (QRNG) can be affected by environmental disturbances that compromise the integrity of the generated random numbers. A machine learning model is proposed to assess the stochasticity of the continuous variable QRNG under the influence of vacuum noise. The model is designed to detect the correlation between the randomness of the QRNG being corrupted under the influence of classical noise (electr

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Zulfikar, Away Yuwaldi, and Shahnaz Noor Rafiqa. "FPGA-based Design System for a Two-Segment Fibonacci LFSR Random Number Generator." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 4 (2017): 1882–91. https://doi.org/10.11591/ijece.v7i4.pp1882-1891.

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For a long time, random numbers have been used in many fields of application. Much work has been conducted to generate truly random numbers and is still in progress. A popular method for generating random numbers is a linear-feedback shift register (LFSR). Even though a lot of work has been done using this method to search for truly random numbers, it is an area that continues to attract interest. Therefore, this paper proposes a circuit for generating random numbers. The proposed circuit is designed to produce different sequences of numbers. Two segments of Fibonacci LFSR are used to form a g

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Deon, A. F., D. D. Dmitriev, and Yu A. Menyaev. "Twister Generator of Poisson Random Numbers with the Use of Cumulative Frequency Technology." Herald of the Bauman Moscow State Technical University. Series Instrument Engineering, no. 1 (130) (February 2020): 101–23. http://dx.doi.org/10.18698/0236-3933-2020-1-101-123.

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The widely known generators of Poisson random variables are associated with different modifications of the algorithm based on the convergence in probability of a sequence of uniform random variables to the created stochastic number. However, in some situations, this approach yields different discrete Poisson probability distributions and skipping in the generated numbers. This paper offers a new approach for creating Poisson random variables based on the complete twister generator of uniform random variables, using cumulative frequency technology. The simulation results confirm that probabilis

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Fischer, Viktor, Florent Bernard, and Nathalie Bochard. "Modern random number generator design – Case study on a secured PLL-based TRNG." it - Information Technology 61, no. 1 (2019): 3–13. http://dx.doi.org/10.1515/itit-2018-0025.

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Abstract Random number generators (RNGs) are basic cryptographic primitives. They are used to generate cryptographic keys, initialization vectors, challenges and nonces in cryptographic protocols, and random masks in countermeasures against side channel attacks. RNGs designed for cryptography must generate unpredictable random numbers. According to recent security standards, the unpredictability of generated random numbers must be thoroughly evaluated. In this paper, we provide a concrete example – a phase-locked loop based RNG protected by novel dedicated embedded tests, on which we show how

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Pikuza, M. O., and S. Yu Mikhnevich. "Testing a hardware random number generator using NIST statistical test suite." Doklady BGUIR 19, no. 4 (2021): 37–42. http://dx.doi.org/10.35596/1729-7648-2021-19-4-37-42.

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Random number generators are required for the operation of cryptographic information protection systems. For а correct application of the generator in the field of information security, it is necessary that its output sequence to be indistinguishable from a uniformly distributed random sequence. To verify this, it is necessary to test the generator output sequence using various statistical test suites such as Dihard and NIST. The purpose of this work is to test a prototype hardware random number generator. The generator is built on the basis of the ND103L noise diode and has a random digital s

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Burton, F. Warren, and Rex L. Page. "Distributed random number generation." Journal of Functional Programming 2, no. 2 (1992): 203–12. http://dx.doi.org/10.1017/s0956796800000320.

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AbstractIn a functional program, a simple random number generator may generate a lazy list of random numbers. This is fine when the random numbers are consumed sequentially at a single point in the program. However, things are more complicated in a program where random numbers are used at many locations, such as in a large simulation. The programmer should not need to worry about providing separate generators with a unique seed at each point where random numbers are used. At the same time, the programmer should not need to coordinate the use of a single stream of random numbers in many parts o

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Bhattacharjee, Kamalika, Dipanjyoti Paul, and Sukanta Das. "Pseudo-random number generation using a 3-state cellular automaton." International Journal of Modern Physics C 28, no. 06 (2017): 1750078. http://dx.doi.org/10.1142/s0129183117500784.

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This paper investigates the potentiality of pseudo-random number generation of a 3-neighborhood 3-state cellular automaton (CA) under periodic boundary condition. Theoretical and empirical tests are performed on the numbers, generated by the CA, to observe the quality of it as pseudo-random number generator (PRNG). We analyze the strength and weakness of the proposed PRNG and conclude that the selected CA is a good random number generator.

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Zhao, Xianyue, Li-Wei Chen, Kefeng Li, Heidemarie Schmidt, Ilia Polian, and Nan Du. "Memristive True Random Number Generator for Security Applications." Sensors 24, no. 15 (2024): 5001. http://dx.doi.org/10.3390/s24155001.

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This study explores memristor-based true random number generators (TRNGs) through their evolution and optimization, stemming from the concept of memristors first introduced by Leon Chua in 1971 and realized in 2008. We will consider memristor TRNGs coming from various entropy sources for producing high-quality random numbers. However, we must take into account both their strengths and weaknesses. The comparison with CMOS-based TRNGs will serve as an illustration that memristor TRNGs stand out due to their simpler circuits and lower power consumption— thus leading us into a case study involving

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Nagy, Shady Ahmed, Mohamed A. El-Beltagy, and Mohamed Wafa. "Multilevel Monte Carlo by using the Halton sequence." Monte Carlo Methods and Applications 26, no. 3 (2020): 193–203. http://dx.doi.org/10.1515/mcma-2020-2065.

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AbstractMonte Carlo (MC) simulation depends on pseudo-random numbers. The generation of these numbers is examined in connection with the Brownian motion. We present the low discrepancy sequence known as Halton sequence that generates different stochastic samples in an equally distributed form. This will increase the convergence and accuracy using the generated different samples in the Multilevel Monte Carlo method (MLMC). We compare algorithms by using a pseudo-random generator and a random generator depending on a Halton sequence. The computational cost for different stochastic differential e

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Chaurasia, Rajashree. "Evaluation of Random Number Generator Functions Using Statistical Analysis." Asian Journal of Computer Science and Technology 8, no. 2 (2019): 1–5. http://dx.doi.org/10.51983/ajcst-2019.8.2.2150.

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Most programming languages have in-built functions for the sole purpose of generating pseudo-random numbers. This manuscript is aimed at analyzing the appropriateness of some of these in-built functions for some basic goodness-of-fit statistical tests for random number generators. The document is divided into four sections. The first section gives a broad introduction about randomness and the methods of generation of pseudo-random numbers. Section two discusses the statistical tests that were employed for testing the built-in library functions for random number generation. This section is foll

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