Relevant bibliographies by topics / Random number generator

Contents

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

Academic literature on the topic 'Random number generator'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 March 2023

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Journal articles on the topic "Random number generator"

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Manli Xu, Manli Xu, Jingzheng Huang Jingzheng Huang, Wenye Liang Wenye Liang, Chunmei Zhang Chunmei Zhang, Shuang Wang Shuang Wang, Zhenqiang Yin Zhenqiang Yin, Wei Chen Wei Chen, and Zhengfu Han Zhengfu Han. "Adjustable unbalanced quantum random-number generator." Chinese Optics Letters 13, no. 2 (2015): 021405–21409. http://dx.doi.org/10.3788/col201513.021405.

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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 required. In such situations, we use real random number generators whose source of randomness is unpredictable random events. Quantum Random Number Generators (QRNGs) generate real random numbers based on the inherent randomness of quantum measurements. The goal is to develop a mathematical model of the generator, which generates fast random numbers at a lower cost. At the same time, a high level of randomness is essential. Through quantum mechanics, we can obtain true numbers using the unpredictable behavior of a photon, which is the basis of many modern cryptographic protocols. It is essential to trust cryptographic random number generators to generate only true random numbers. This is why certification methods are needed which will check both the operation of the device and the quality of the random bits generated. The goal of the research is also to develop the model of a hybrid semi self-testing certification method for quantum random number generators (QRNG). The tasks to be solved are to create the mathematical model of a random number generator, which generates the fast random numbers at a lower cost. To create the mathematical model of a hybrid semi self-testing certification method for quantum random number generators. To integrate a hybrid semi self-testing certification method to the hybrid random number generator. the methods used are mathematical optimization and simulation. The following results were obtained: we present the improved hybrid quantum random number generator, which is based on QRNG, which uses the time of arrival of photons. The model of a hybrid semi self-testing certification method for quantum random number generators (QRNG) is offered in the paper. This method combines different types of certification approaches and is rather secure and efficient. Finally, the hybrid certification method is integrated into the model of the new quantum random number generator. Conclusions. The scientific novelty of the results obtained is as follows: 1. The hybrid quantum random number generator is offered, which is based on QRNG, which uses the time of the arrival of photons. It uses the simple version of the detectors with few requirements. The hybrid QRNG produces more than one random bit per the detection of each photon. It is rather efficient and has a high level of randomness. 2. The hybrid semi self-testing certification method for quantum random number generators (QRNG) is offered. The Self-testing, as well as device-independent quantum random number generation methods, are analyzed. The advantages and disadvantages of both methods are identified. Based on the result the hybrid method is offered. 3. The hybrid semi self-testing certification method for quantum random number generators is integrated into the offered model of the quantum random number generator. The paper analyzes its security and efficiency. The paper offers to use the new random number generator in the crypto-schemes.
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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 (March 26, 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 addition, feature vectors extracted from the LSTM are accumulated, and their images are generated to overcome the limitation of LSTM long-term memory. From these generated images, features are extracted using CNN. This dynamical pseudo-random number generator secures the randomness of random numbers.
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Thomas, Antu Annam, and Varghese Paul. "Nested Random Number Generator." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 5 (May 30, 2017): 767–73. http://dx.doi.org/10.23956/ijarcsse/sv7i5/0327.

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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 generator when applied on video sources from MPEG-1 video file. In addition, when applying NIST SP 800-22 Rev.1a 15 statistics tests on the random numbers generated from the proposed generator, around 98% random numbers can pass 15 statistical tests. Furthermore, the audio and video sources can be found easily; hence, the proposed generator is a qualified, convenient, and efficient random number generator.
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LUI, OI-YAN, CHING-HUNG YUEN, and KWOK-WO WONG. "A PSEUDO-RANDOM NUMBER GENERATOR EMPLOYING MULTIPLE RÉNYI MAPS." International Journal of Modern Physics C 24, no. 11 (October 14, 2013): 1350079. http://dx.doi.org/10.1142/s0129183113500794.

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The increasing risk along with the drastic development of multimedia data transmission has raised a big concern on data security. A good pseudo-random number generator is an essential tool in cryptography. In this paper, we propose a novel pseudo-random number generator based on the controlled combination of the outputs of several digitized chaotic Rényi maps. The generated pseudo-random sequences have passed both the NIST 800-22 Revision 1a and the DIEHARD tests. Moreover, simulation results show that the proposed pseudo-random number generator requires less operation time than existing generators and is highly sensitive to the seed.
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Dodge, Yadolah. "A Natural Random Number Generator." International Statistical Review / Revue Internationale de Statistique 64, no. 3 (December 1996): 329. http://dx.doi.org/10.2307/1403789.

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Stefanov, André, Nicolas Gisin, Olivier Guinnard, Laurent Guinnard, and Hugo Zbinden. "Optical quantum random number generator." Journal of Modern Optics 47, no. 4 (March 2000): 595–98. http://dx.doi.org/10.1080/09500340008233380.

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Li, Pu, Jian-Zhong Zhang, and Yun-Cai Wang. "All-optical Random Number Generator." IEICE Proceeding Series 1 (March 17, 2014): 130–33. http://dx.doi.org/10.15248/proc.1.130.

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Komo, John J., and William J. Park. "Decimal pseudo-random number generator." SIMULATION 57, no. 4 (October 1991): 228–30. http://dx.doi.org/10.1177/003754979105700405.

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Dissertations / Theses on the topic "Random number generator"

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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.
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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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.
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Franco, Juan. "Rapid Prototyping and Design of a Fast Random Number Generator." Thesis, University of North Texas, 2011. https://digital.library.unt.edu/ark:/67531/metadc115036/.

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Information in the form of online multimedia, bank accounts, or password usage for diverse applications needs some form of security. the core feature of many security systems is the generation of true random or pseudorandom numbers. Hence reliable generators of such numbers are indispensable. the fundamental hurdle is that digital computers cannot generate truly random numbers because the states and transitions of digital systems are well understood and predictable. Nothing in a digital computer happens truly randomly. Digital computers are sequential machines that perform a current state and move to the next state in a deterministic fashion. to generate any secure hash or encrypted word a random number is needed. But since computers are not random, random sequences are commonly used. Random sequences are algorithms that generate a pattern of values that appear to be random but after some time start repeating. This thesis implements a digital random number generator using MATLAB, FGPA prototyping, and custom silicon design. This random number generator is able to use a truly random CMOS source to generate the random number. Statistical benchmarks are used to test the results and to show that the design works. Thus the proposed random number generator will be useful for online encryption and security.
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Franco, Juan. "Rapid Prototyping and Design of a Fast Random Number Generator." Thesis, University of North Texas, 2012. https://digital.library.unt.edu/ark:/67531/metadc115040/.

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Information in the form of online multimedia, bank accounts, or password usage for diverse applications needs some form of security. the core feature of many security systems is the generation of true random or pseudorandom numbers. Hence reliable generators of such numbers are indispensable. the fundamental hurdle is that digital computers cannot generate truly random numbers because the states and transitions of digital systems are well understood and predictable. Nothing in a digital computer happens truly randomly. Digital computers are sequential machines that perform a current state and move to the next state in a deterministic fashion. to generate any secure hash or encrypted word a random number is needed. But since computers are not random, random sequences are commonly used. Random sequences are algorithms that generate a pattern of values that appear to be random but after some time start repeating. This thesis implements a digital random number generator using MATLAB, FGPA prototyping, and custom silicon design. This random number generator is able to use a truly random CMOS source to generate the random number. Statistical benchmarks are used to test the results and to show that the design works. Thus the proposed random number generator will be useful for online encryption and security.
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Mitchum, Sam. "Digital Implementation of a True Random Number Generator." VCU Scholars Compass, 2010. http://scholarscompass.vcu.edu/etd/2327.

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Random numbers are important for gaming, simulation and cryptography. Random numbers have been generated using analog circuitry. Two problems exist with using analog circuits in a digital design: (1) analog components require an analog circuit designer to insure proper structure and functionality and (2) analog components are not easily transmigrated into a different fabrication technology. This paper proposes a class of random number generators that are constructed using only digital components and typical digital design methodology. The proposed classification is called divergent path since the path of generated numbers through the range of possible values diverges at every sampling. One integrated circuit was fabricated and several models were synthesized into a FPGA. Test results are given.
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Yadav, Avantika. "Design and Analysis of Digital True Random Number Generator." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/3229.

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Random number generator is a key component for strengthening and securing the confidentiality of electronic communications. Random number generators can be divided as either pseudo random number generators or true random number generators. A pseudo random number generator produces a stream of numbers that appears to be random but actually follow predefined sequence. A true random number generator produces a stream of unpredictable numbers that have no defined pattern. There has been growing interest to design true random number generator in past few years. Several Field Programmable Gate Array (FPGA) and Application Specific Integrated Circuit (ASIC) based approaches have been used to generate random data that requires analog circuit. RNGs having analog circuits demand for more power and area. These factors weaken hardware analog circuit-based RNG systems relative to hardware completely digital-based RNGs systems. This thesis is focused on the design of completely digital true random number generator ASIC.
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Pilcher, Martha Geraldine. "Development and validation of random cut test problem generator." Diss., Georgia Institute of Technology, 1985. http://hdl.handle.net/1853/24560.

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Mattioli, Federico. "Testing a Random Number Generator: formal properties and automotive application." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18187/.

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L'elaborato analizza un metodo di validazione dei generatori di numeri casuali (RNG), utilizzati per garantire la sicurezza dei moderni sistemi automotive. Il primo capitolo fornisce una panoramica della struttura di comunicazione dei moderni autoveicoli attraverso l'utilizzo di centraline (ECU): vengono riportati i principali punti di accesso ad un automobile, assieme a possibili tipologie di hacking; viene poi descritto l'utilizzo dei numeri casuali in crittografia, con particolare riferimento a quella utilizzata nei veicoli. Il secondo capitolo riporta le basi di probabilità necessarie all'approccio dei test statistici utilizzati per la validazione e riporta i principali approcci teorici al problema della casualità. Nei due capitoli centrali, viene proposta una descrizione dei metodi probabilistici ed entropici per l'analisi di dati reali utilizzati nei test. Vengono poi descritti e studiati i 15 test statistici proposti dal National Institute of Standards and Technology (NIST). Dopo i primi test, basati su proprietà molto semplici delle sequenze casuali, vengono proposti test più sofisticati, basati sull'uso della trasformata di Fourier (per testare eventuali comportamenti periodici), dell'entropia (strettamente connessi con la comprimibilità della sequenza), o sui random path. Due ulteriori test, permettono di valutare il buon funzionamento del generatore, e non solo delle singole sequenze generate. Infine, il quinto capitolo è dedicato all'implementazione dei test al fine di testare il TRNG delle centraline.
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Gärtner, Joel. "Analysis of Entropy Usage in Random Number Generators." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214567.

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Cryptographically secure random number generators usually require an outside seed to be initialized. Other solutions instead use a continuous entropy stream to ensure that the internal state of the generator always remains unpredictable. This thesis analyses four such generators with entropy inputs. Furthermore, different ways to estimate entropy is presented and a new method useful for the generator analysis is developed. The developed entropy estimator performs well in tests and is used to analyse entropy gathered from the different generators. Furthermore, all the analysed generators exhibit some seemingly unintentional behaviour, but most should still be safe for use.
Kryptografiskt säkra slumptalsgeneratorer behöver ofta initialiseras med ett oförutsägbart frö. En annan lösning är att istället konstant ge slumptalsgeneratorer entropi. Detta gör det möjligt att garantera att det interna tillståndet i generatorn hålls oförutsägbart. I den här rapporten analyseras fyra sådana generatorer som matas med entropi. Dessutom presenteras olika sätt att skatta entropi och en ny skattningsmetod utvecklas för att användas till analysen av generatorerna. Den framtagna metoden för entropiskattning lyckas bra i tester och används för att analysera entropin i de olika generatorerna. Alla analyserade generatorer uppvisar beteenden som inte verkar optimala för generatorns funktionalitet. De flesta av de analyserade generatorerna verkar dock oftast säkra att använda.
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Saiprasert, Chalermpol. "Design exploration of an FPGA-based multivariate Gaussian random number generator." Thesis, Imperial College London, 2010. http://hdl.handle.net/10044/1/6212.

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Monte Carlo simulation is one of the most widely used techniques for computationally intensive simulations in a variety of applications including mathematical analysis and modeling and statistical physics. A multivariate Gaussian random number generator (MVGRNG) is one of the main building blocks of such a system. Field Programmable Gate Arrays (FPGAs) are gaining increased popularity as an alternative means to the traditional general purpose processors targeting the acceleration of the computationally expensive random number generator block due to their fine grain parallelism and reconfigurability properties and lower power consumption. As well as the ability to achieve hardware designs with high throughput it is also desirable to produce designs with the flexibility to control the resource usage in order to meet given resource constraints. This work proposes a novel approach for mapping a MVGRNG onto an FPGA by optimizing the computational path in terms of hardware resource usage subject to an acceptable error in the approximation of the distribution of interest. An analysis on the impact of the error due to truncation/rounding operation along the computational path is performed and an analytical expression of the error inserted into the system is presented. Extra dimensionality is added to the feature of the proposed algorithm by introducing a novel methodology to map many multivariate Gaussian random number generators onto a single FPGA. The effective resource sharing techniques introduced in this thesis allows further reduction in hardware resource usage. The use of MVGNRG can be found in a wide range of application, especially in financial applications which involve many correlated assets. In this work it is demonstrated that the choice of the objective function employed for the hardware optimization of the MVRNG core has a considerable impact on the final performance of the application of interest. Two of the most important financial applications, Value-at-Risk estimation and option pricing are considered in this work.
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Books on the topic "Random number generator"

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Percus, O. E. A note on random number generator of Chung et al. New York: Courant Institute of Mathematical Sciences, New York University, 1986.

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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). Gaithersburg, MD]: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, Computer Security Division, Information Technology Laboratory, 2007.

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

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

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

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Pseudorandomness and cryptographic applications. Princeton, NJ: Princeton University Press, 1996.

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

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

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Uniform random numbers: Theory and practice. Boston, Mass: Kluwer Academic Publishers, 1995.

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

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Book chapters on the topic "Random number generator"

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Quirk, Thomas J., and Simone Cummings. "Random Number Generator." In Excel 2016 for Social Work Statistics, 21–34. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66221-3_2.

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Quirk, Thomas J. "Random Number Generator." In Excel 2013 for Social Sciences Statistics, 23–37. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19177-5_2.

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Quirk, Thomas J. "Random Number Generator." In Excel 2016 for Engineering Statistics, 21–34. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39182-3_2.

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Quirk, Thomas J. "Random Number Generator." In Excel 2013 for Educational and Psychological Statistics, 21–34. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26712-8_2.

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Quirk, Thomas J., Meghan Quirk, and Howard F. Horton. "Random Number Generator." In Excel 2013 for Biological and Life Sciences Statistics, 21–35. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12517-6_2.

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Quirk, Thomas J., and Eric Rhiney. "Random Number Generator." In Excel 2016 for Marketing Statistics, 21–35. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43376-9_2.

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Quirk, Thomas J. "Random Number Generator." In Excel 2016 in Applied Statistics for High School Students, 23–36. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89993-0_2.

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Quirk, Thomas J. "Random Number Generator." In Excel 2013 for Engineering Statistics, 21–34. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23555-4_2.

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Quirk, Thomas J., and Julie Palmer-Schuyler. "Random Number Generator." In Excel 2010 for Human Resource Management Statistics, 21–35. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10650-2_2.

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Quirk, Thomas J., Meghan H. Quirk, and Howard F. Horton. "Random Number Generator." In Excel 2016 for Environmental Sciences Statistics, 21–34. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40057-0_2.

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Conference papers on the topic "Random number generator"

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Mogos, Gabriela. "Quantum random number generator vs. random number generator." In 2016 International Conference on Communications (COMM). IEEE, 2016. http://dx.doi.org/10.1109/iccomm.2016.7528306.

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Soubusta, Jan, Ondrej Haderka, and Martin Hendrych. "Quantum random number generator." In 12th Czech-Slovak-Polish Optical Conference on Wave and Quantum Aspects of Contemporary Optics, edited by Jan Perina, Sr., Miroslav Hrabovsky, and Jaromir Krepelka. SPIE, 2001. http://dx.doi.org/10.1117/12.417868.

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Shikano, Yutaka. "Unpredictable random number generator." In APPLICATION OF MATHEMATICS IN TECHNICAL AND NATURAL SCIENCES: 12th International On-line Conference for Promoting the Application of Mathematics in Technical and Natural Sciences - AMiTaNS’20. AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0029701.

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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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Aguilar Angulo, Julio A., Edith Kussener, Herve Barthelemy, and Benjamin Duval. "Discrete chaos - based Random Number Generator." In 2014 IEEE Faible Tension Faible Consommation (FTFC). IEEE, 2014. http://dx.doi.org/10.1109/ftfc.2014.6828610.

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Tisa, Simone, and Franco Zappa. "One-chip quantum random number generator." In SPIE OPTO: Integrated Optoelectronic Devices, edited by Yasuhiko Arakawa, Masahide Sasaki, and Hideyuki Sotobayashi. SPIE, 2009. http://dx.doi.org/10.1117/12.807957.

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Wang, Yuhua, HongYong Wang, Aihong Guan, and Huanguo Zhang. "Evolutionary Design of Random Number Generator." In 2009 International Joint Conference on Artificial Intelligence (JCAI). IEEE, 2009. http://dx.doi.org/10.1109/jcai.2009.46.

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Cox, George, Charles Dike, and D. J. Johnston. "Intel's digital random number generator (DRNG)." In 2011 IEEE Hot Chips 23 Symposium (HCS). IEEE, 2011. http://dx.doi.org/10.1109/hotchips.2011.7477490.

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Evtyushkin, Dmitry, and Dmitry Ponomarev. "Covert Channels through Random Number Generator." In CCS'16: 2016 ACM SIGSAC Conference on Computer and Communications Security. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2976749.2978374.

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Marton, K., A. Suciu, and D. Petricean. "A parallel unpredictable random number generator." In 2011 RoEduNet International Conference 10th Edition: Networking in Education and Research. IEEE, 2011. http://dx.doi.org/10.1109/roedunet.2011.5993701.

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Reports on the topic "Random number generator"

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Mcdonald, Kathleen Herrera. Quantum Random Number Generator. Office of Scientific and Technical Information (OSTI), August 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), February 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), January 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), November 2021. http://dx.doi.org/10.2172/1829616.

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

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Colwell, C. J., R. A. Dramstad, and M. E. Lopez. A Quickly Tested Pascal Random Number Generator for Microcomputers. Fort Belvoir, VA: Defense Technical Information Center, May 1985. http://dx.doi.org/10.21236/ada156059.

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Dolotii, Marharyta H., and Pavlo V. Merzlykin. Using the random number generator with a hardware entropy source for symmetric cryptography problems. [б. в.], December 2018. http://dx.doi.org/10.31812/123456789/2883.

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Abstract:
The aim of the research is to test the possibility of using the developed random number generator [1], which utilizes the sound card noise as an entropy source, in the symmetric cryptography algorithms.
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Barker, E. B., and J. M. Kelsey. Recommendation for random number generation using deterministic random bit generators. Gaithersburg, MD: 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, June 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). Gaithersburg, MD: National Institute of Standards and Technology, 2007. http://dx.doi.org/10.6028/nist.sp.800-90.

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