Relevant bibliographies by topics / Random numbers generator

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Random numbers generator'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Random numbers generator"

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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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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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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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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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Dissertations / Theses on the topic "Random numbers generator"

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Crunk, Anthony Wayne. "A portable C random number generator." Thesis, Virginia Tech, 1985. http://hdl.handle.net/10919/45720.

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Proliferation of computers with varying word sizes has led to increases in software use where random number generation is required. Several techniques have been developed. Criteria of randomness, portability, period, reproducibility, variety, speed, and storage are used to evaluate developed generation methods. The Tausworthe method is the only method to meet the portability requirement, and is chosen to be implemented. A C language implementation is proposed as a possible implementation and test results are presented to confirm the acceptability of the proposed code.<br>Master of Science
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Hörmann, Wolfgang, and Gerhard Derflinger. "A portable uniform random number generator well suited for the rejection method." Institut für Statistik und Mathematik, Abt. f. Angewandte Statistik u. Datenverarbeitung, WU Vienna University of Economics and Business, 1992. http://epub.wu.ac.at/1288/1/document.pdf.

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Up to now all known efficient portable implementations of linear congruential random number generators with modulus 2^(31)-1 are working only with multipliers which are small compared with the modulus. We show that for non-uniform distributions, the rejection method may generate random numbers of bad quality if combined with a linear congruential generator with small multiplier. Therefore a method is described that works for any multiplier smaller than 2^(30). It uses the decomposition of multiplier and seed in high order and low order bits to compute the upper and the lower half of the produc
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Gautham, Smitha. "An Efficient Implementation of an Exponential Random Number Generator in a Field Programmable Gate Array (FPGA)." VCU Scholars Compass, 2010. http://scholarscompass.vcu.edu/etd/2173.

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Many physical, biological, ecological and behavioral events occur at times and rates that are exponentially distributed. Modeling these systems requires simulators that can accurately generate a large quantity of exponentially distributed random numbers, which is a computationally intensive task. To improve the performance of these simulators, one approach is to move portions of the computationally inefficient simulation tasks from software to custom hardware implemented in Field Programmable Gate Arrays (FPGAs). In this work, we study efficient FPGA implementations of exponentially dist
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Jíra, Roman. "Generování náhodných čísel pomocí magnetických nanostruktur." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2015. http://www.nusl.cz/ntk/nusl-232088.

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Random number generation can be based on physical events with probabilistic character, or on algorithms that use complex or one-way functions, alternatively on both of these approaches. A magnetic vortex is a basic state of magnetization that forms in magnetic micro- and nanostructures of an appropriate shape, dimensions and material. Quantities of the magnetic vortex form randomly if ambient conditions are chosen eligibly. A concept of a true random number generator using a random switching of states of the magnetic vortex is presented in this thesis. This concept is realized and random numbe
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Michálek, Tomáš. "Efektivní generátor náhodných čísel v nízko-výkonových zařízení." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2017. http://www.nusl.cz/ntk/nusl-317140.

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This thesis solves the problem of generating random numbers on low-power devices. Author describes possible ways of generating and implements selected generators of (pseudo)random numbers on MSP430F5438A. 4 generators were added by the enhancement of one of them and a new generator was created, using the phenomenon of temperature change in the surroundings. For each generator, test sequences were generated and these sequences were tested by the Dieharder, STS-NIST, and Visual Test. The output of the thesis is the functional implementation of the generators, their testing by statistical methods
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Matoušek, Jiří. "Network Traffic Simulation and Generation." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-412835.

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Development of computer networks able to operate at the speed of 10 Gb/s imposes new requirements on newly developed network devices and also on a process of their testing. Such devices are tested by replaying synthetic or previously captured network traffic on an input link of the tested device. We must be able to perform both tasks also at full wire speed. Current testing devices are either not able to operate at the speed of 10 Gb/s or they are too expensive. Therefore, the aim of this thesis is to design and implement a hardware accelerated application able to generate and replay network t
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Karanam, Shashi Prashanth. "Tiny true random number generator." Fairfax, VA : George Mason University, 2009. http://hdl.handle.net/1920/4587.

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Thesis (M.S.)--George Mason University, 2009.<br>Vita: p. 91. Thesis director: Jens-Peter Kaps. Submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Engineering. Title from PDF t.p. (viewed Oct. 12, 2009). Includes bibliographical references (p. 88-90). Also issued in print.
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Narayanan, Ramaswamy Karthik. "ROLLBACK-ABLE RANDOM NUMBER GENERATORS FOR THE SYNCHRONOUS PARALLEL ENVIRONMENT FOR EMULATION AND DISCRETE-EVENT SIMULATION (SPE." Master's thesis, University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4352.

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Random Numbers form the heart and soul of a discrete-event simulation system. There are few situations where the actions of the entities in the process being simulated can be completely predicted in advance. The real world processes are more probabilistic than deterministic. Hence, such chances are represented in the system by using various statistical models, like random number generators. These random number generators can be used to represent a various number of factors, such as length of the queue. However, simulations have grown in size and are sometimes required to run on multiple machin
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AMINO, ROBERT, and JONI BAITAR. "Probabilistic Pseudo-random Number Generators." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-157351.

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Random numbers are essential in many computer applications and games. The goal of this report is to examine two of the most commonly used random number generators and try to determine some of their strengths and weaknesses. These generators are the Linear Congruential Generator(LCG) and the Mersenne Twister(MT). The main objective will be to determine which one of these is the most optimal for low intensive usage and everyday work. Although some of the test results were in conclusive,there were some indications that MT is the better Pseudorandom Number Generator (PRNG) and therefore the prefer
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Xu, Xiaoke. "Benchmarking the power of empirical tests for random number generators." Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/B41508464.

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Books on the topic "Random numbers generator"

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M, Kelsey John, and Information Technology Laboratory (National Institute of Standards and Technology). Computer Security Division, eds. Recommendation for random number generation using deterministic random bit generators (revised). U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, Computer Security Division, Information Technology Laboratory, 2007.

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de la Fraga, Luis Gerardo, José David Rodríguez-Muñoz, and Esteban Tlelo-Cuautle. Random Number Generators. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-82865-2.

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Alonso, Laurent. Random generation of trees: Random generators in computer science. Kluwer Academic, 1995.

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Arthur, Jeffrey L. Notes on using the random problem generators GENGUB and RANDN̲ET. Dept. of Statistics, Oregon State University, 1993.

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Lewis, Peter A. W. Graphical analysis of some pseudo-random number generators. Naval Postgraduate School, 1986.

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Bukin, A. D. Correlations of pseudo-random numbers of multiplicative sequence. Institute of Nuclear Physics, 1989.

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Kollmitzer, Christian, Stefan Schauer, Stefan Rass, and Benjamin Rainer, eds. Quantum Random Number Generation. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-72596-3.

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Percus, O. E. Random number generators for ultracomputers. Courant Institute of Mathematical Sciences, New York University, 1987.

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István, Deák. Random number generators and simulation. Akadémiai Kiadó, 1990.

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Peter, Hellekalek, and Larcher Gerhard, eds. Random and quasi-random point sets. Springer, 1998.

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Book chapters on the topic "Random numbers generator"

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Lugrin, Thomas. "Random Number Generator." In Trends in Data Protection and Encryption Technologies. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33386-6_7.

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AbstractMost modern encryption and authentication methods rely on generating random numbers for key generation, initial vectors, or nonces. A Random Number Generator is cryptographically secure if the sequences of numbers that it generates are unpredictable. They are typically grouped into two categories: Pseudo-Random Number Generators and True Random Number Generators. Small-size, low-cost true Random Number Generators have already been integrated into off-the-shelf devices such as smartphones, computers, and hardware security modules. In addition, applications involving particularly sensiti
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Tamura, Kentaro, and Yutaka Shikano. "Quantum Random Numbers Generated by a Cloud Superconducting Quantum Computer." In International Symposium on Mathematics, Quantum Theory, and Cryptography. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5191-8_6.

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Abstract A cloud quantum computer is similar to a random number generator in that its physical mechanism is inaccessible to its users. In this respect, a cloud quantum computer is a black box. In both devices, its users decide the device condition from the output. A framework to achieve this exists in the field of random number generation in the form of statistical tests for random number generators. In the present study, we generated random numbers on a 20-qubit cloud quantum computer and evaluated the condition and stability of its qubits using statistical tests for random number generators.
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Anderson, Peter G. "A Fibonacci-Based Pseudo-Random Number Generator." In Applications of Fibonacci Numbers. Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3586-3_1.

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Mohammed, Saja J. "Developing a Hybrid Pseudo-Random Numbers Generator." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62881-8_23.

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Guyeux, Christophe, Qianxue Wang, and Jacques M. Bahi. "A Pseudo Random Numbers Generator Based on Chaotic Iterations: Application to Watermarking." In Web Information Systems and Mining. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16515-3_26.

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Tezuka, Shu. "Linear Congruential Generators." In Uniform Random Numbers. Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-2317-8_3.

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Tezuka, Shu. "Beyond Linear Congruential Generators." In Uniform Random Numbers. Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-2317-8_4.

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de la Fraga, Luis Gerardo, José David Rodríguez-Muñoz, and Esteban Tlelo-Cuautle. "Verilog Descriptions of Digital Blocks to Synthesize Chaotic Maps and Systems." In Random Number Generators. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-82865-2_3.

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de la Fraga, Luis Gerardo, José David Rodríguez-Muñoz, and Esteban Tlelo-Cuautle. "PRNGs Based on Chaotic Maps and 3D, 4D, and 5D Chaotic Systems." In Random Number Generators. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-82865-2_5.

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de la Fraga, Luis Gerardo, José David Rodríguez-Muñoz, and Esteban Tlelo-Cuautle. "PRNG Based on Fractional-Order Chaotic Systems." In Random Number Generators. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-82865-2_6.

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Conference papers on the topic "Random numbers generator"

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Bruynsteen, Cedric, Axl Bomhals, Sarah Bastiaens, Ali Al Hadi Amhaz, Siebe Arnout, and Xin Yin. "High-Speed On-Chip Real-Time QRNG." In Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.th3i.6.

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Quantum random number generators harness the inherent unpredictability of quantum entropy sources to produce truly random numbers. This talk explores how integration enables high-speed, real-time generation of these numbers for use in secure communication.
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Milinkovic, Luka, Marija Antic, and Zoran Cica. "Pseudo-random number generator based on irrational numbers." In TELSIKS 2011 - 2011 10th International Conference on Telecommunication in Modern Satellite, Cable and Broadcasting Services. IEEE, 2011. http://dx.doi.org/10.1109/telsks.2011.6143212.

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Merah, Lahcene, Ali-Pacha Adda, and Hadj-said Naima. "Enhanced Chaos-based Pseudo Random Numbers Generator." In 2018 International Conference on Applied Smart Systems (ICASS). IEEE, 2018. http://dx.doi.org/10.1109/icass.2018.8652079.

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Kozlov, Sergey, Aleksey Korkin, and Sergey Kornilov. "RANDOM NUMBER GENERATOR TO SIMULATE CALL FLOW." In CAD/EDA/SIMULATION IN MODERN ELECTRONICS 2021. Bryansk State Technical University, 2021. http://dx.doi.org/10.30987/conferencearticle_61c997ef000fe4.90061792.

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The paper presents an approach to the development of generators providing the generation of sequences of random numbers for generating the flow of applications and determining the duration of connections.
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Martino, Anthony J., and G. Michael Morris. "An Optical Random Number Generator for Monte Carlo Calculations." In Optical Computing. Optica Publishing Group, 1985. http://dx.doi.org/10.1364/optcomp.1985.wa5.

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Many of the tasks that are performed on a digital computer require a source of random numbers that are independent and fit a specified probability density. The usual random number generator provides numbers that are uniformly distributed over the interval (0,1). A transformation is then employed to obtain deviates that follow the specified distribution. A complicated transformation can reduce the generation rate by as much as an order of magnitude or more.
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SÁNCHEZ, SERGIO, REGINO CRIADO, and CARLOS VEGA. "A GENERATOR OF PSEUDO-RANDOM NUMBERS SEQUENCES WITH MAXIMUM PERIOD." In Proceedings of the International Conference (ICCMSE 2003). WORLD SCIENTIFIC, 2003. http://dx.doi.org/10.1142/9789812704658_0125.

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Martino, Anthony J., and G. Michael Morris. "Optical generation of random numbers: theory and experiments." In OSA Annual Meeting. Optica Publishing Group, 1985. http://dx.doi.org/10.1364/oam.1985.tue2.

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Simulation of physical processes and Monte Carlo solutions to numerical problems on digital computers require sources of random numbers that follow specified density functions. In our experiments, the spatial coordinates of detected photoevents are used as a fast source of true random numbers to solve numerical problems by the Monte Carlo method. The probability density function for the location of a photoevent on the detector surface is proportional to the irradiance. We have constructed an optical random number generator using a microchannel plate detector with a resistive anode. It can prod
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Quinn, Liam, Gang Xu, Zongda Li, et al. "Leveraging self-symmetrized symmetry breaking in Kerr resonators for robust random number generation." In Nonlinear Photonics. Optica Publishing Group, 2022. http://dx.doi.org/10.1364/np.2022.nptu4f.4.

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We present an all-optical random number generator employing polarization symmetry-breaking in a nonlinear Kerr cavity. Statistically confirmed random numbers are generated at MHz rates without post-processing, paving the way for a novel coherent Ising machine.
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Quinn, Liam, Gang Xu, Zongda Li, et al. "Random number generation using spontaneous symmetry breaking in a Kerr resonator." In CLEO: Applications and Technology. Optica Publishing Group, 2022. http://dx.doi.org/10.1364/cleo_at.2022.jtu3a.39.

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We present an all-optical random number generator based on polarization symmetry breaking in a passive, nonlinear Kerr cavity. The system generates numbers with statistically confirmed randomness at MHz rates with no post-processing.
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Ablaev, Farid, Sergey Andrianov, and Aleksey Soloviev. "Quantum Electronic Generator of Random Numbers for Information Security in Automatic Control Systems." In 2019 International Russian Automation Conference. IEEE, 2019. http://dx.doi.org/10.1109/rusautocon.2019.8867682.

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Reports on the topic "Random numbers generator"

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Bailey, David H. A Pseudo-Random Number Generator Based on Normal Numbers. Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/860344.

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Barker, E. B., and J. M. Kelsey. Recommendation for random number generation using deterministic random bit generators. National Institute of Standards and Technology, 2012. http://dx.doi.org/10.6028/nist.sp.800-90a.

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Barker, Elaine B., and John M. Kelsey. Recommendation for Random Number Generation Using Deterministic Random Bit Generators. National Institute of Standards and Technology, 2015. http://dx.doi.org/10.6028/nist.sp.800-90ar1.

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Barker, E. B., and J. M. Kelsey. Recommendation for random number generation using deterministic random bit generators (revised). National Institute of Standards and Technology, 2007. http://dx.doi.org/10.6028/nist.sp.800-90.

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Barker, E. B., and J. M. Kelsey. Recommendation for random number generation using deterministic random bit generators (revised). National Institute of Standards and Technology, 2007. http://dx.doi.org/10.6028/nist.sp.800-90r.

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Mcdonald, Kathleen Herrera. Quantum Random Number Generator. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1557201.

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Newell, Raymond Thorson. Record Breaking Random Number Generator. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1170694.

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Everhart - Erickson, Michael. Quantum Random Number Generator (QRNG). Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1764183.

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Stern, Ariana. Quantum Random Number Generator (QRNG). Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1829616.

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Josey, Colin. Reassessing the MCNP Random Number Generator. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/1998091.

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