scholarly journals The Possibility of Uniform Pseudo-random Number Generation by a Group of Humans

2018 ◽  
Author(s):  
Samuel Toluwalope Ogunjo ◽  
Emmanuel Jesuyon Dansu ◽  
Oluwagbenga Olukanye-David ◽  
Ibiyinka Agboola Fuwape
Keyword(s):  
Test Score ◽  
Random Number ◽  
Statistical Tests ◽  
Random Number Generation ◽  
Random Numbers ◽  
Significant Relation ◽  
Random Number Generators ◽  
Number Generation ◽  
Pseudo Random Number ◽  
University Of Technology

The ability of humans to generate numbers that are really random has always been a subject of debate. This paper investigated the possibility for a group of humans to serve as random number generators. A total of 2344 students, who were not pre-informed to avoid bias, from different faculties within the Federal University of Technology Akure were asked to chose a random number between 1 and 10. Using various statistical tests, we sought answers to the possibility of predictors like participant’s test score, gender, age and school influencing their choice of random numbers. We discovered that the numbers generated are highly random and chaotic despite number 1 being the most selected number across all predictors that was considered. Our study found that gender, test score, age did not significantly influence the choice of number while faculty showed a significant relation α < 0.05.

PSEUDO RANDOM NUMBER GENERATION WITH THE AID OF ITERATED FUNCTION SYSTEMS ON ℝ2

2007 ◽  
Vol 18 (05) ◽  
pp. 861-882
Author(s):  
P. BOUBOULIS
Keyword(s):  
Random Number ◽  
Statistical Tests ◽  
Random Number Generation ◽  
Iterated Function Systems ◽  
Pseudorandom Number ◽  
Iteration Algorithm ◽  
Random Numbers ◽  
Number Generation ◽  
Pseudo Random Number ◽  
Iterated Function

Two new pseudorandom number generators, based on iterated function systems (IFS), are introduced. An IFS is created based on an arbitrary seed and a set is constructed using the deterministic iteration algorithm (DIA). From this set pseudo random numbers have been constructed. The generators have big periods and pass all major statistical tests, indicating that they can be used in any application requiring random numbers, such as cryptography.

Uniform Random Number Generation With Jumping Facilities

2018 ◽  
pp. 1297-1306
Author(s):  
E. Jack Chen
Keyword(s):  
Variance Reduction ◽  
Random Number ◽  
Simple Procedure ◽  
Random Number Generation ◽  
Random Numbers ◽  
Random Number Generators ◽  
Uniform Random Number ◽  
Number Generation ◽  
Multiple Recursive Generator ◽  
Generating Sequences

A facility for generating sequences of pseudorandom numbers is a fundamental part of computer simulation systems. Furthermore, multiple independent streams of random numbers are often required in simulation studies, for instance, to facilitate synchronization for variance-reduction purposes, and for making independent replications. A portable set of software utilities is described for uniform random-number generation. It provides for multiple generators (streams) running simultaneously, and each generator (stream) has its sequence of numbers partitioned into many long disjoint contiguous substreams. Simple procedure calls allow the user to make any generator “jump” ahead/back v steps (random numbers). Implementation issues are discussed. An efficient and portable code is also provided to implement the package. The basic underlying generator CMRG (combined Multiple Recursive Generator) combines two multiple recursive random number generators with a period length of approximately 2191 (˜ 3.1× 1057), good speed, and excellent theoretical properties.

scholarly journals Quantum Random Numbers Generated by a Cloud Superconducting Quantum Computer

2020 ◽  
pp. 17-37
Author(s):  
Kentaro Tamura ◽  
Yutaka Shikano
Keyword(s):  
Random Number ◽  
Quantum Computer ◽  
Statistical Tests ◽  
Random Number Generator ◽  
Random Number Generation ◽  
Black Box ◽  
Random Numbers ◽  
Number Generator ◽  
Random Number Generators ◽  
Simple Indicator

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. As a result, we observed that some qubits were more biased than others. Statistical tests for random number generators may provide a simple indicator of qubit condition and stability, enabling users to decide for themselves which qubits inside a cloud quantum computer to use.

Itamaracá: A Novel Simple Way to Generate Pseudo-random Numbers

2021 ◽  
Author(s):  
Daniel Henrique Pereira
Keyword(s):  
Random Number ◽  
Statistical Tests ◽  
Random Numbers ◽  
Microsoft Excel ◽  
Chi Square ◽  
Random Number Generators ◽  
Pseudo Random Number ◽  
Run Test

In this paper was presented Itamaracá, a novel simple way to generate pseudo random numbers. In general vision we can say that Itamaracá tends to pass in some statistical tests like frequency, chi square, autocorrelation, run sequence and run test. As an effect to comparison also was taking into account the results of the function R and Between by Microsoft Excel and true random numbers by Random Org analyzed its distinctive characteristics as well as with the proposal model. In this sense, the goal of this study is contributing to growing the existing Pseudo Random Number Generators (PRNGs) portfolio.

Itamaracá: A Novel Simple Way to Generate Pseudo-random Numbers

2021 ◽  
Author(s):  
Daniel Henrique Pereira
Keyword(s):  
Random Number ◽  
Statistical Tests ◽  
Random Numbers ◽  
Microsoft Excel ◽  
Chi Square ◽  
Random Number Generators ◽  
Pseudo Random Number ◽  
Run Test

In this paper was presented Itamaracá, a novel simple way to generate pseudo random numbers. In general vision we can say that Itamaracá tends to pass in some statistical tests like frequency, chi square, autocorrelation, run sequence and run test. As an effect to comparison also was taking into account the results of the function R and Between by Microsoft Excel and true random numbers by Random Org analyzed its distinctive characteristics as well as with the proposal model. In this sense, the goal of this study is contributing to growing the existing Pseudo Random Number Generators (PRNGs) portfolio.

On the Generation of Random Numbers for Symmetric Cryptography Utilizing Astronomical Data

2011 ◽  
Vol 367 ◽  
pp. 185-190
Author(s):  
P.M. Rubesh Anand ◽  
Vidhyacharan Bhaskar ◽  
Gaurav Bajpai ◽  
Godwin Norense Osarumwense Asemota
Keyword(s):  
Random Number ◽  
Statistical Tests ◽  
Random Number Generation ◽  
The Other ◽  
Rank Test ◽  
Random Numbers ◽  
Symmetric Cryptography ◽  
Astronomical Data ◽  
Number Generation ◽  
Astronomical Object

In this paper, a novel method for obtaining the random numbers utilizing astronomical data is proposed. The method uses two different algorithms for generation of random numbers sequence. Astronomical data collected from the scientific study of the universe, especially of the relative motions, relative positions of astronomical objects are utilized in our algorithms. The first algorithm uses a particular astronomical object in a fixed position for the random number generation. The random sequence is obtained from the relative positions of other astronomical objects with reference to the selected object. The second algorithm selects any diverse astronomical object as a reference in a varying mode for computation of the relative positions of different objects with that reference to generate the random number stream. Both algorithms use mathematical equations for computing the next jump or hop to the other astronomical object. The generated random numbers obtained from the two algorithms are tested with a standard statistical test suite including, frequency test, run test, random binary matrix rank test, complexity test, universal test and entropy test. The results obtained from the statistical tests of the two algorithms are compared with the other publicly available random number generation techniques, like, linear congruential and modular exponentiation. The preliminary results show that the algorithms perform well. The random numbers generated by our method has sufficient period and unpredictability that makes them suitable for consideration as encryption keys in symmetric cryptography.

Uniform Random Number Generation With Jumping Facilities

2019 ◽  
pp. 337-349
Author(s):  
E. Jack Chen
Keyword(s):  
Variance Reduction ◽  
Random Number ◽  
Simple Procedure ◽  
Random Number Generation ◽  
Random Numbers ◽  
Random Number Generators ◽  
Uniform Random Number ◽  
Number Generation ◽  
Multiple Recursive Generator ◽  
Generating Sequences

A facility for generating sequences of pseudorandom numbers is a fundamental part of computer simulation systems. Furthermore, multiple independent streams of random numbers are often required in simulation studies, for instance, to facilitate synchronization for variance-reduction purposes, and for making independent replications. A portable set of software utilities is described for uniform random-number generation. It provides for multiple generators (streams) running simultaneously, and each generator (stream) has its sequence of numbers partitioned into many long disjoint contiguous substreams. Simple procedure calls allow the user to make any generator “jump” ahead/back v steps (random numbers). Implementation issues are discussed. An efficient and portable code is also provided to implement the package. The basic underlying generator CMRG (combined multiple recursive generator) combines two multiple recursive random number generators with a period length of approximately 2191 (≈ 3.1× 1057), good speed, and excellent theoretical properties.

scholarly journals Memristor based ring oscillators true random number generator with different window functions for applications in cryptography

2019 ◽  
Vol 14 (1) ◽  
pp. 201 ◽  
Author(s):  
Noor Alia Nor Hashim ◽  
Julius Teo Han Loong ◽  
Azrul Ghazali ◽  
Fazrena Azlee Hamid
Keyword(s):  
Random Number ◽  
Statistical Tests ◽  
Random Number Generator ◽  
Random Number Generation ◽  
Random Numbers ◽  
Ring Oscillators ◽  
Number Generator ◽  
True Random Number Generator ◽  
Number Generation ◽  
Window Functions

<span>Cryptographic applications require numbers that are random and pseudorandom. Keys must be produced in a random manner in order to be used in common cryptosystems. Random or pseudorandom inputs at different terminals are also required in a lot of cryptographic protocols. For example, producing digital signatures using supporting quantities or in verification procedures that requires generating challenges. Random number generation is an important part of cryptography because there are flaws in random number generation that can be taken advantage by attackers that compromised encryption systems that are algorithmically secure. True random number generators (TRNGs) are the best in producing random numbers. This paper presents a True Random Number Generator that uses memristor based ring oscillators in the design. The designs are implemented in 0.18 µm complementary metal oxide semiconductor (CMOS) technology using LT SPICE IV. Different window functions for the memristor model was applied to the TRNG and compared. Statistical tests results of the output random numbers produced showed that the proposed TRNG design can produce random output regardless of the window function.</span>

scholarly journals An overview of hybrid random number generators

2020 ◽  
Vol XXIII (1) ◽  
pp. 248-252
Author(s):  
Veronica Cornaciu
Keyword(s):  
Detailed Analysis ◽  
Random Number ◽  
Statistical Tests ◽  
Random Numbers ◽  
Random Number Generators ◽  
Pseudo Random Number ◽  
First Category ◽  
Mouse Movement ◽  
The One ◽  
Special Category

The generation of random numbers is a important topic in cryptography. Random number generators are bradly divided into two categories: random number generators(RNGs) and pseudo-random number generators(PRNGs). If the PRNGs werw intensively studied in the specialized literature, many such generators being built and analyzed, the topic of RNGs did not capture the researchers atention so much. Candidates in this first category generate nondeterministic sequences and are often based on physical reactions, such as radioactive degradation or mouse movement. A special category of generators is the one that combines the two categories, namely, the category of hybrid generators (HRGs). The purpose of this paper is to study in detail the category of hybrid generators and to provide a detailed analysis of the results of statistical tests, security , portability and how to improve some of the generators of this category.

scholarly journals Evaluation of Random Number Generator Functions Using Statistical Analysis

2019 ◽  
Vol 8 (2) ◽  
pp. 1-5
Author(s):  
Rajashree Chaurasia
Keyword(s):  
Programming Languages ◽  
Goodness Of Fit ◽  
Random Number ◽  
Statistical Tests ◽  
Random Number Generator ◽  
Random Number Generation ◽  
Random Numbers ◽  
Number Generator ◽  
Random Number Generators ◽  
The Third

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 followed by an analysis of the data collected for the various statistics in the third section, and lastly, the fourth section presents the results of the data analysis.

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