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相关概念视频

Neural Circuits01:25

Neural Circuits

1.1K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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Deconvolution01:20

Deconvolution

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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
141
Convolution Properties II01:17

Convolution Properties II

177
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
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Neuronal Communication01:28

Neuronal Communication

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Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
812
Convolution Properties I01:20

Convolution Properties I

142
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
142
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

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In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
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相关实验视频

Updated: Jun 15, 2025

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
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在5G中使用卷积神经网络进行V2V通信的移交.

Sarah M Alhammad1, Doaa Sami Khafaga1, Mahmoud M Elsayed2

  • 1Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.

Heliyon
|August 22, 2024
PubMed
概括

本研究使用深度学习 (DL) 与5G网络进行车辆检测和障碍物识别,达到97%的准确性. 一种新的移交预测方法可以提高异质网络的性能.

关键词:
连接的自动驾驶汽车 (CAV)卷积神经网络 (CNN) 是一种神经网络.深度学习 (DL) 是指深度学习.收到的信号强度指示器 (RSSI)

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科学领域:

  • 电信工程 电信工程 电信工程
  • 人工智能的人工智能
  • 计算机视觉 计算机视觉

背景情况:

  • 车辆通信对于交通流动和安全至关重要.
  • 5G技术为先进的运输系统提供了高数据速率和服务质量.
  • 深度学习 (DL) 在处理大型数据集以识别特征方面表现出色.

研究的目的:

  • 在5G环境中使用DL检测车辆和识别障碍物.
  • 为异质网络开发一种新的水平移交预测方法.
  • 提高车辆通信效率和安全.

主要方法:

  • 使用VGG19深度学习模型通过转移学习来检测车辆和障碍物.
  • 基于频道特征提出了一个新的水平交付预测算法.
  • 在模拟的5G网络环境中实施和评估算法.

主要成果:

  • 在识别车辆方面取得了97%的成功率.
  • 成功预测了下一个水平交付站.
  • 证明了DL算法在5G车辆通信中的有效性.

结论:

  • 提出的基于DL的车辆检测和交付预测方法在5G环境中是有效的.
  • VGG19模型和新的交付算法显示出高精度和可靠性.
  • 这项研究有助于更安全,更有效的智能运输系统.