Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

298
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
298
Random Sampling Method01:09

Random Sampling Method

11.8K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
11.8K
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

704
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
704
Random Variables01:09

Random Variables

13.2K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
13.2K
Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

11.4K
In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...
11.4K
Randomized Experiments01:13

Randomized Experiments

7.1K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
7.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Model of an Open-Source MicroPython Library for GSM NB-IoT.

Sensors (Basel, Switzerland)·2025
Same author

Enhancing Steganography Detection with AI: Fine-Tuning a Deep Residual Network for Spread Spectrum Image Steganography.

Sensors (Basel, Switzerland)·2024
Same author

Permutation-Based Block Code for Short Packet Communication Systems.

Sensors (Basel, Switzerland)·2022
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Aug 16, 2025

Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA
12:36

Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA

Published on: May 9, 2011

10.3K

Combined Pseudo-Random Sequence Generator for Cybersecurity.

Volodymyr Maksymovych1, Mariia Shabatura1, Oleh Harasymchuk2

  • 1Department of Information Technology Security, Lviv Polytechnic National University, 79013 Lviv, Ukraine.

Sensors (Basel, Switzerland)
|December 23, 2022
PubMed
Summary
This summary is machine-generated.

Optimized combined Fibonacci generators produce high-quality pseudo-random sequences. These enhanced generators offer reliable performance for cybersecurity applications.

Keywords:
authenticationencryption of informationpseudo-random numberpseudo-random sequence generators

More Related Videos

High-throughput Parallel Sequencing to Measure Fitness of Leptospira interrogans Transposon Insertion Mutants During Golden Syrian Hamster Infection
11:50

High-throughput Parallel Sequencing to Measure Fitness of Leptospira interrogans Transposon Insertion Mutants During Golden Syrian Hamster Infection

Published on: December 18, 2017

9.0K
BEST: Barcode Enabled Sequencing of Tetrads
12:59

BEST: Barcode Enabled Sequencing of Tetrads

Published on: May 1, 2014

10.2K

Related Experiment Videos

Last Updated: Aug 16, 2025

Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA
12:36

Genomic MRI - a Public Resource for Studying Sequence Patterns within Genomic DNA

Published on: May 9, 2011

10.3K
High-throughput Parallel Sequencing to Measure Fitness of Leptospira interrogans Transposon Insertion Mutants During Golden Syrian Hamster Infection
11:50

High-throughput Parallel Sequencing to Measure Fitness of Leptospira interrogans Transposon Insertion Mutants During Golden Syrian Hamster Infection

Published on: December 18, 2017

9.0K
BEST: Barcode Enabled Sequencing of Tetrads
12:59

BEST: Barcode Enabled Sequencing of Tetrads

Published on: May 1, 2014

10.2K

Area of Science:

  • Computer Science
  • Information Theory
  • Cryptography

Background:

  • Uniformly distributed random and pseudo-random number generators are essential across various applications.
  • The quality requirements for pseudo-random sequences vary based on the specific application.
  • Cybersecurity heavily relies on robust random number generation for encryption and security protocols.

Purpose of the Study:

  • To optimize the combined structure of classical and modified additive Fibonacci generators.
  • To determine optimal seed ranges for acceptable statistical characteristics in the output pseudo-random sequence.
  • To expand the application scope of these generators, particularly in cybersecurity.

Main Methods:

  • Optimization of classical additive Fibonacci generator and modified additive Fibonacci generator structures.
  • Analysis of statistical characteristics of the modified additive Fibonacci generator, focusing on logic circuit output signals.
  • Determination of conditions (odd module values) for acceptable statistical properties of the modified and combined generators.

Main Results:

  • Identified optimal initial settings (seed ranges) for the combined generators, ensuring acceptable statistical properties.
  • Demonstrated that acceptable statistical characteristics for the modified and combined generators are achieved with odd module values.
  • The combined generator's output signal exhibits acceptable characteristics across a broad spectrum of initial settings.

Conclusions:

  • The optimized combined additive Fibonacci generator provides a reliable source of high-quality pseudo-random sequences.
  • The findings significantly expand the potential applications of these generators, especially in critical areas like cybersecurity.
  • The study underscores the importance of advanced random number generation techniques for modern encryption and security.