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.

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.

A General-Purpose Uniform Random Number Package

2021 ◽  
pp. 164-176
Author(s):  
E. Jack Chen
Keyword(s):  
Computer Simulation ◽  
Random Number ◽  
General Purpose ◽  
Random Numbers ◽  
Pseudorandom Generator ◽  
Random Number Generators ◽  
Uniform Random Number ◽  
Modeling And Analysis ◽  
Generating Sequences ◽  
Simulation Systems

As computer capacities and simulation technologies advance, simulation has become the method of choice for modeling and analysis. The fundamental advantage of simulation is that it can tolerate far less restrictive modeling assumptions, leading to an underlying model that is more reflective of reality and thus more valid, leading to better decisions. Simulation studies are typically preceded by transforming in a more or less complicated way of a sequence of numbers between 0 and 1 produced by a pseudorandom generator into an observation of the measure of interest. Random numbers are a fundamental resource in science and technology. A facility for generating sequences of pseudorandom numbers is a fundamental part of computer simulation systems. Furthermore, random number generators also play an important role in cryptography and in the blockchain ecosystem. All samples of the sequence are generated independently of each other, and the value of the next sample in the sequence cannot be predicted, regardless of how many samples have already been produced.

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.

scholarly journals True Random Number Generation from Bioelectrical and Physical Signals

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Seda Arslan Tuncer ◽  
Turgay Kaya
Keyword(s):  
Blood Volume ◽  
Random Number ◽  
Logistic Map ◽  
Random Number Generation ◽  
Statistical Properties ◽  
Random Numbers ◽  
Number Generation ◽  
Skin Response ◽  
Volume Pulse ◽  
Blood Volume Pulse

It is possible to generate personally identifiable random numbers to be used in some particular applications, such as authentication and key generation. This study presents the true random number generation from bioelectrical signals like EEG, EMG, and EOG and physical signals, such as blood volume pulse, GSR (Galvanic Skin Response), and respiration. The signals used in the random number generation were taken from BNCIHORIZON2020 databases. Random number generation was performed from fifteen different signals (four from EEG, EMG, and EOG and one from respiration, GSR, and blood volume pulse datasets). For this purpose, each signal was first normalized and then sampled. The sampling was achieved by using a nonperiodic and chaotic logistic map. Then, XOR postprocessing was applied to improve the statistical properties of the sampled numbers. NIST SP 800-22 was used to observe the statistical properties of the numbers obtained, the scale index was used to determine the degree of nonperiodicity, and the autocorrelation tests were used to monitor the 0-1 variation of numbers. The numbers produced from bioelectrical and physical signals were successful in all tests. As a result, it has been shown that it is possible to generate personally identifiable real random numbers from both bioelectrical and physical signals.

A Low-Cost Highly Reliable Spintronic True Random Number Generator Circuit for Secure Cryptography

SPIN ◽  
2019 ◽  
Vol 10 (01) ◽  
pp. 2050003 ◽  
Author(s):  
Iman Alibeigi ◽  
Abdolah Amirany ◽  
Ramin Rajaei ◽  
Mahmoud Tabandeh ◽  
Saeed Bagheri Shouraki
Keyword(s):  
High Speed ◽  
High Performance ◽  
Random Number ◽  
Low Cost ◽  
Random Number Generator ◽  
Magnetic Tunnel Junction ◽  
Random Number Generation ◽  
Cmos Technology ◽  
Random Numbers ◽  
Number Generation

Generation of random numbers is one of the most important steps in cryptographic algorithms. High endurance, high performance and low energy consumption are the attractive features offered by the Magnetic Tunnel Junction (MTJ) devices. Therefore, they have been considered as one of the promising candidates for next-generation digital integrated circuits. In this paper, a new circuit design for true random number generation using MTJs is proposed. Our proposed circuit offers a high speed, low power and a truly random number generation. In our design, we employed two MTJs that are configured in special states. Generated random bit at the output of the proposed circuit is returned to the write circuit to be written in the relevant cell for the next random generation. In a random bitstream, all bits must have the same chance of being “0”or “1”. We have proposed a new XOR-based method in this paper to resolve this issue in multiple random generators that produce truly random numbers with a different number of ones and zeros in the output stream. The simulation results using a 45[Formula: see text]nm CMOS technology with a special model of MTJ validated the advantages offered by the proposed circuit.

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