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

Random Sampling Method01:09

Random Sampling Method

12.3K
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...
12.3K
Random and Systematic Errors01:20

Random and Systematic Errors

12.5K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
12.5K
Random Variables01:09

Random Variables

13.4K
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...
13.4K
Randomized Experiments01:13

Randomized Experiments

7.2K
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...
7.2K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

1.1K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.1K
Random Error01:04

Random Error

1.6K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
1.6K

You might also read

Related Articles

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

Sort by
Same author

CAT-Site: Predicting Protein Binding Sites Using a Convolutional Neural Network.

Pharmaceutics·2023
Same author

Disease Progression of Hypertrophic Cardiomyopathy: Modeling Using Machine Learning.

JMIR medical informatics·2022
See all related articles

Related Experiment Video

Updated: Sep 10, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

657

Machine Learning-Assisted Secure Random Communication System.

Areeb Ahmed1, Zoran Bosnić1

  • 1University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, 1000 Ljubljana, Slovenia.

Entropy (Basel, Switzerland)
|August 28, 2025
PubMed
Summary
This summary is machine-generated.

We introduce a novel machine learning-assisted random communication system (ML-RCS) for enhanced physical layer security (PLS). This system uses a decision tree receiver and alpha-stable noise for secure data transmission with a high data rate.

Keywords:
covert communicationdecision treemachine learningrandom communication systemα-stable distributions

More Related Videos

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
06:32

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

1.4K
Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

4.6K

Related Experiment Videos

Last Updated: Sep 10, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

657
Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
06:32

Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring

Published on: July 14, 2023

1.4K
Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

4.6K

Area of Science:

  • Communication Systems Engineering
  • Machine Learning Applications
  • Information Security

Background:

  • Machine learning (ML) significantly advances physical layer security (PLS) in communication systems.
  • Optimizing performance and security in modern communication networks remains a key challenge.

Purpose of the Study:

  • To propose the first machine learning-assisted random communication system (ML-RCS).
  • To enhance the security and data rate of communication systems using ML and unconventional noise carriers.

Main Methods:

  • Developed a pretrained decision tree (DT) receiver for extracting binary information from random noise signals.
  • Employed skewed alpha-stable (α-stable) noise as a secure random carrier for encoding binary bits.
  • Utilized a predetermined key (pulse length) and the DT model for secure decoding by the legitimate receiver.

Main Results:

  • Achieved a bit error rate (BER) of 10-3, confirming successful secure communication.
  • Demonstrated an increased data rate compared to existing random communication systems.
  • Showcased eavesdropper failure to decode information (50.2% false negative rate) without the key and dataset.

Conclusions:

  • The ML-RCS effectively establishes secure communication with improved data rates.
  • The system's security is validated by its resistance to eavesdropping attempts.
  • Unconventional ML-RCSs show promise for developing secure next-generation communication devices with integrated PLS.