Jove
Visualize
Contact Us

Related Concept Videos

Observational Learning01:12

Observational Learning

321
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
321
Reinforcement01:23

Reinforcement

353
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
353
Masking and Demasking Agents01:19

Masking and Demasking Agents

2.7K
EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
2.7K
Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview01:13

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview

581
Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
The ATR process begins by directing a beam...
581

You might also read

Related Articles

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

Sort by
Same author

High Adoption, Higher Expectations: A Cross-Sectional Survey of Radiologist Engagement with Artificial Intelligence in the United Arab Emirates.

Journal of imaging informatics in medicine·2026
Same author

Thyroid disease detection using enhanced extreme learning machine based on drop-connect method.

Scientific reports·2026
Same author

Integrated experimental design and machine learning framework for predicting UV influenced mechanical properties in polyurethane nanodiamond nanocomposites.

Scientific reports·2026
Same author

Sunlight-driven fast photo-degradation of Eriochrome Black T dye using highly efficient La-doped Ag<sub>3</sub>PO<sub>4</sub> decorated with ZnS QDs.

RSC advances·2026
Same author

Machine learning and response surface methodology for optimization and prediction of tribological performance of PLA/rice husk biochar composites.

Scientific reports·2026
Same author

Facile synthesis and synergistic cytotoxic effect of Ag/Co-ZnO nanoparticles in epithelial breast cancer cells.

Scientific reports·2026
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 Experiment Video

Updated: Sep 18, 2025

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

196

Intelligent reflecting surface backscatter-enabled physical layer security enhancement via deep reinforcement

Manzoor Ahmed1,2, Touseef Hussain3, Muhammad Shahwar4

  • 1Artificial Intelligence Industrial Technology, Research Institute, Hubei Engineering University, Xiaogan City, China.

Peerj. Computer Science
|June 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel intelligent reflecting surface (IRS) strategy for enhanced wireless security. Deep-PLS, a deep reinforcement learning approach, optimizes beamforming to thwart eavesdroppers and improve legitimate user communication.

Keywords:
Backscatter communicationDeep deterministic policy gradientDeep reinforcement learningDeep-PLSEavesdropperJoint-beamformingMalicious jammerSecrecy rate

More Related Videos

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.5K
Observation and Analysis of Blinking Surface-enhanced Raman Scattering
05:52

Observation and Analysis of Blinking Surface-enhanced Raman Scattering

Published on: January 11, 2018

7.5K

Related Experiment Videos

Last Updated: Sep 18, 2025

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

196
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.5K
Observation and Analysis of Blinking Surface-enhanced Raman Scattering
05:52

Observation and Analysis of Blinking Surface-enhanced Raman Scattering

Published on: January 11, 2018

7.5K

Area of Science:

  • Wireless Communication Security
  • Intelligent Reflecting Surfaces (IRS)
  • Physical Layer Security (PLS)

Background:

  • Jamming attacks and eavesdropper threats compromise wireless communication security.
  • Intelligent Reflecting Surfaces (IRS) offer a promising solution for mitigating these threats.
  • Integrating IRS into backscatter communication systems can enhance signal reception and secrecy rates for legitimate users (LUs).

Purpose of the Study:

  • To introduce a novel strategy for wireless communication security using IRS.
  • To enhance the secrecy rate of legitimate users (LUs) by mitigating jamming and eavesdropper threats.
  • To develop an optimal beamforming policy for thwarting eavesdroppers in dynamic environments.

Main Methods:

  • Strategic deployment of IRS to redirect jamming signals and protect desired communication signals.
  • Joint optimization of IRS reflection coefficients and base station (BS) active beamforming.
  • Development of a deep reinforcement learning (DRL) approach named Deep-PLS to determine optimal beamforming policies.

Main Results:

  • The proposed Deep-PLS strategy effectively mitigates jamming attacks and eavesdropper threats.
  • Dynamic adjustment of IRS reflection coefficients and BS active beamforming significantly enhances the secrecy rate of LUs.
  • The strategy demonstrates superior performance compared to traditional IRS methods and other benchmark strategies.

Conclusions:

  • The novel IRS-based strategy, optimized via Deep-PLS, provides a robust solution for wireless communication security.
  • The approach effectively improves secrecy performance by intelligently managing signal reflection and beamforming.
  • Deep-PLS offers a powerful tool for securing wireless communications against evolving threats.