Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

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

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

First-principles study of Y<sub>2</sub>CCl<sub>2</sub> and Janus Y<sub>2</sub>CClX (X = F, Br, I) MXenes for photovoltaic applications.

Scientific reports·2026
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

相关实验视频

Updated: Sep 18, 2025

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

196

智能反射表面反射驱动物理层安全增强通过深度增强学习.

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
概括

本研究介绍了一种新的智能反射表面 (IRS) 战略,用于增强无线安全. 深度PLS是一种深度强化学习方法,优化beamforming以阻止窃听者并改善合法用户通信.

关键词:
背向散射通信是一种反向散射通信.深度决定性的政策梯度渐变.深度强化学习的学习.在深度PLS中.倾听落者的声音联合光束成形 联合光束成形恶意干扰器恶意干扰器机密度的比率 机密度的比率

更多相关视频

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

相关实验视频

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

科学领域:

  • 无线通信安全 无线通信安全
  • 智能反射表面 (IRS) 是一种智能反射表面.
  • 物理层安全性 (PLS)

背景情况:

  • 干扰攻击和窃听威胁危及无线通信安全.
  • 智能反射表面 (IRS) 为缓解这些威胁提供了一个有希望的解决方案.
  • 将IRS集成到反向散射通信系统中可以提高合法用户 (LUs) 的信号接收和保密率.

研究的目的:

  • 通过使用IRS引入一种新的无线通信安全策略.
  • 通过减轻干扰和窃听威胁,提高合法用户 (LUs) 的保密率.
  • 制定一个最佳的光束形成政策,以阻止在动态环境中的窃听者.

主要方法:

  • 战略部署IRS以重定向干扰信号并保护所需的通信信号.
  • 联合优化IRS反射系数和基站 (BS) 活性光束成形.
  • 开发一种名为Deep-PLS的深度强化学习 (DRL) 方法,以确定最佳的光束形成政策.

主要成果:

  • 拟议的深层PLS战略有效地减轻了干扰攻击和窃听威胁.
  • 动态调整IRS反射系数和BS主动光束成形显著提高了LU的保密率.
  • 该策略与传统的IRS方法和其他基准策略相比,表现优越.

结论:

  • 基于IRS的新策略,通过Deep-PLS进行优化,为无线通信安全提供了强大的解决方案.
  • 该方法通过智能地管理信号反射和光束成形,有效地提高了保密性能.
  • Deep-PLS提供了一种强大的工具,用于保护无线通信免受不断变化的威胁.