scholarly journals The New Scramble for Faure Sequence Based on Irrational Numbers

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ali Mogharrabi O. ◽  
Behrooz Fathi V. ◽  
M. H. Behzadi ◽  
R. Farnoosh
Keyword(s):  
Uniform Distribution ◽  
Random Number ◽  
Inverse Function ◽  
Number Sequence ◽  
Efficient Tool ◽  
Irrational Numbers ◽  
New Form ◽  
Sequence Generator

This article intends to review quasirandom sequences, especially the Faure sequence to introduce a new version of scrambled of this sequence based on irrational numbers, as follows to prove the success of this version of the random number sequence generator and use it in future calculations. We introduce this scramble of the Faure sequence and show the performance of this sequence in employed numerical codes to obtain successful test integrals. Here, we define a scrambling matrix so that its elements are irrational numbers. In addition, a new form of radical inverse function has been defined, which by combining it with our new matrix, we will have a sequence that not only has a better close uniform distribution than the previous sequences but also is a more accurate and efficient tool in estimating test integrals.

scholarly journals Probability on Finite and Discrete Set and Uniform Distribution

2009 ◽  
Vol 17 (2) ◽  
pp. 173-178
Author(s):  
Hiroyuki Okazaki
Keyword(s):  
Computer Science ◽  
Uniform Distribution ◽  
Computer Simulations ◽  
Random Number ◽  
Pseudorandom Number ◽  
Pseudorandom Number Generator ◽  
Number Generator ◽  
Number Sequence ◽  
Number Sequences

Probability on Finite and Discrete Set and Uniform DistributionA pseudorandom number generator plays an important role in practice in computer science. For example: computer simulations, cryptology, and so on. A pseudorandom number generator is an algorithm to generate a sequence of numbers that is indistinguishable from the true random number sequence. In this article, we shall formalize the "Uniform Distribution" that is the idealized set of true random number sequences. The basic idea of our formalization is due to [15].

scholarly journals Problem of uniqueness in the renewal process generated by the uniform distribution

1992 ◽  
Vol 5 (4) ◽  
pp. 291-305 ◽  
Author(s):  
D. Ugrin-Šparac
Keyword(s):  
Integral Representation ◽  
Uniform Distribution ◽  
Gamma Function ◽  
Renewal Process ◽  
Random Number ◽  
Discrete Distribution ◽  
Inverse Image ◽  
Random Number Generators ◽  
Pseudo Random Number

The renewal process generated by the uniform distribution, when interpreted as a transformation of the uniform distribution into a discrete distribution, gives rise to the question of uniqueness of the inverse image. The paper deals with a particular problem from the described domain, that arose in the construction of a complex stochastic test intended to evaluate pseudo-random number generators. The connection of the treated problem with the question of a unique integral representation of Gamma-function is also mentioned.

VISUAL AND AUDITORY CUES SIGNIFICANTLY REDUCE HUMAN'S INTRINSIC BIASES WHEN TASKED TO GENERATE A RANDOM NUMBER SEQUENCE

2010 ◽  
Vol 21 (05) ◽  
pp. 567-581 ◽  
Author(s):  
IRENE CRISOLOGO ◽  
RENE BATAC ◽  
ANTHONY LONGJAS ◽  
ERIKA FILLE LEGARA ◽  
CHRISTOPHER MONTEROLA
Keyword(s):  
Visual Cues ◽  
Random Number ◽  
Pattern Analysis ◽  
Number Sequence ◽  
Random Series ◽  
Auditory Cues ◽  
Statistical Framework ◽  
Number Distribution ◽  
Discrete Sequences

Humans are deemed ineffective in generating a seemingly random number sequence primarily because of inherent biases and fatigue. Here, we establish statistically that human-generated number sequence in the presence of visual cues considerably reduce one's tendency to be fixated to a certain group of numbers allowing the number distribution to be statistically uniform. We also show that a stitching procedure utilizing auditory cues significantly minimizes human's intrinsic biases towards doublet and sequential ordering of numbers. The article provides extensive experimentation and comprehensive pattern analysis of the sequences formed when humans are tasked to generate a random series using numbers "0" to "9." In the process, we develop a statistical framework for analyzing the apparent randomness of finite discrete sequences via numerical measurements.

scholarly journals True-Random Number Generator Based on Image Histogram

2020 ◽  
Vol 3 (1) ◽  
pp. 301-307
Author(s):  
Serkan Dereli
Keyword(s):  
Virtual World ◽  
Random Distribution ◽  
Random Number ◽  
Random Number Generator ◽  
Physical World ◽  
Random Numbers ◽  
Number Sequence ◽  
Color Distribution ◽  
Gray Image ◽  
Image Histogram

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 distribution in that image. In this study, the color distribution resulting from the histogram has been converted into a random distribution between 0 and 1. As a result, it was observed that the resulting distribution of numbers overlapped with histogram. Since the distribution of the numbers depends on the ratio of the pixels and the ratio of the pixels on the image obtained, the result is a real random number sequence.

Random access to a random number sequence

1992 ◽  
Vol 98 (2) ◽  
pp. 349
Author(s):  
E.H. Canfield ◽  
J.A. Viecelli
Keyword(s):  
Random Number ◽  
Random Access ◽  
Number Sequence

Randomization in Online Experiments

2018 ◽  
Vol 238 (3-4) ◽  
pp. 223-241
Author(s):  
Konstantin Golyaev
Keyword(s):  
Uniform Distribution ◽  
Hash Function ◽  
Random Number ◽  
The Internet ◽  
Practical Reasons ◽  
Data Sets ◽  
Randomized Experiments ◽  
Industry Data ◽  
Random Number Generators ◽  
The Past

Abstract Most scientists consider randomized experiments to be the best method available to establish causality. On the Internet, during the past twenty-five years, randomized experiments have become common, often referred to as A/B testing. For practical reasons, much A/B testing does not use pseudo-random number generators to implement randomization. Instead, hash functions are used to transform the distribution of identifiers of experimental units into a uniform distribution. Using two large, industry data sets, I demonstrate that the success of hash-based quasi-randomization strategies depends greatly on the hash function used: MD5 yielded good results, while SHA512 yielded less impressive ones.

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