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相关概念视频

BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

293
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
293
Linear time-invariant Systems01:23

Linear time-invariant Systems

177
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
177
Classification of Systems-II01:31

Classification of Systems-II

119
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
119
Basic Continuous Time Signals01:22

Basic Continuous Time Signals

168
Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
168
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

176
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
176
Feedback control systems01:26

Feedback control systems

252
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
252

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相关实验视频

Updated: May 10, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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在使用CTMC的车辆物联网系统中,适应性通信模型用于QoS.

Adeel Iqbal1, Tahir Khurshaid2, Ali Nauman1

  • 1School of Computer Science and Engineering, Yeungnam University, Gyeongsan-si 38541, Republic of Korea.

Sensors (Basel, Switzerland)
|April 28, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了适应式多模式频谱接入 (AMSA) 方法,用于车辆网络. AMSA提高了频谱的使用和吞吐量,同时减少了智能运输系统的延迟.

关键词:
AMSASA AMSA 的意思是什么意思在C-ITS中,C-ITS可以使用C-ITS.在CTMC中,CTMC是CTMC.在 QoS 系统中,QoS 是 QoS.这就是V-IoT.通过量通过量.车辆网络的车辆网络.

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科学领域:

  • 智能运输系统 智能运输系统
  • 无线通信网络 无线通信网络
  • 物联网的物联网,就是物联网.

背景情况:

  • 车载物联网 (V-IoT) 系统对于智能运输至关重要.
  • 车辆和基础设施之间的有效通信是必不可少的.
  • 适应性通信模型可以改善车辆网络中的资源利用.

研究的目的:

  • 提出一种自适应式多模式频谱接入 (AMSA) 方法.
  • 在多类V-IoT网络中优化服务质量 (QoS).
  • 提高资源分配和沟通效率.

主要方法:

  • 开发了一种AMSA方法,可以动态切换频谱访问模式 (交织,底层,共存).
  • 评估AMSA性能与静态频谱访问方法相比.
  • 专注于优化 QoS 参数,如频谱使用,吞吐量和延迟.

主要成果:

  • 与静态方法相比,AMSA提高了56%的频谱使用率.
  • 通过AMSA方法,吞吐量提高了110%.
  • 低优先级交通的延迟减少了高达47.5%的时间.

结论:

  • 拟议的AMSA方法提供了强大的车辆通信.
  • 在各种网络条件下实现了最佳的资源配置.
  • 适应性频谱访问是推进V-IoT网络的关键.