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相关概念视频

Network Function of a Circuit01:25

Network Function of a Circuit

255
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
255
Maximum Power Transfer01:16

Maximum Power Transfer

224
Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
By substituting the entire circuit with...
224
¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

1.0K
The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
A broadband decoupling technique is used to simplify these complex, sometimes overlapping, signals. Broadband decoupling relies on a...
1.0K
The Maximum Power Transfer Theorem01:20

The Maximum Power Transfer Theorem

535
Consider a linear AC Thevenin equivalent circuit connected to a load impedance.
The load connected draws the current, and the circuit delivers the power to the load. The alternating current flowing through the load is determined using the rectangular form of voltages, currents, network impedance, and load impedance. The average power delivered to the load is obtained from the product of the square of current and load resistance.
535

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相关实验视频

Updated: Jun 1, 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

481

安全通道估计模型用于认知无线电网络物理层安全,使用双级共享密钥认证.

K Saravanan1, K B Gurumoorthy2, Allwin Devaraj Stalin3

  • 1Department of Mechatronics Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamilnadu, 641407, India.

Scientific reports
|January 19, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种使用深度学习的安全通道估计模型 (SCEM),用于在认知无线电网络中增强物理层安全性. 该模型提高了保密率,降低了对对手的检测概率.

关键词:
在 CSI 中,CSI 是 CSI 的代码.认知无线电网络是一种认知无线电网络.深度学习是一种深度学习.物理层安全是物理层的安全.分享身份验证认证

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相关实验视频

Last Updated: Jun 1, 2025

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Calibration of Vector Network Analyzer for Measurements in Radio Frequency Propagation Channels
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科学领域:

  • 无线通信无线通信
  • 网络安全 网络安全
  • 信号处理 信号处理

背景情况:

  • 物理层安全 (PLS) 对认知无线电网络 (CRN) 的保密性,可用性和完整性至关重要.
  • 在CRN中传感和光束成形的现有安全模型旨在减轻窃听者影响.
  • 需要有效的安全机制来应对动态CRN环境中的对手.

研究的目的:

  • 引入安全通道估计模型 (SCEM),以增强CRN中的PLS.
  • 利用通道状态信息 (CSI) 和深度学习 (DL) 来提高安全性和通道利用.
  • 确保主要和次要用户之间的安全通信和身份验证.

主要方法:

  • 开发了一个安全通道估计模型 (SCEM),集成CSI和DL.
  • 使用CSI来评估道利用率和容量.
  • 实现了基于DL的验证以实现安全通信的2级共享密钥身份验证.
  • 监控停机保密率,以管理分配失败.

主要成果:

  • 拟议的SCEM模型将保密率提高了10.77%.
  • 检测的概率降低了15.01%.
  • 干扰率在不同的传输功率下降了11.07%.

结论:

  • 通过最大限度地利用CSI和beamforming,SCEM模型有效地提高了CRN中的PLS.
  • 基于DL的身份验证确保了安全和高效的道容量利用.
  • 该模型提供了强大的安全对抗对手,改善整体网络性能.