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相关实验视频

Updated: Jul 24, 2025

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish
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适应性模糊逻辑深度学习等级器用于减轻水下可见光通信系统中的线性和非线性扭曲.

Radhakrishnan Rajalakshmi1, Sivakumar Pothiraj2, Miroslav Mahdal3

  • 1Department of Electronics and Communication Engineering, Ramco Institute of Technology, Rajapalayam 626117, India.

Sensors (Basel, Switzerland)
|July 8, 2023
PubMed
概括

本研究介绍了一种适应性模糊逻辑深度学习等分器,用于水下可见光通信 (UVLC). 新型均衡器显著减少错误和复杂性,使得高速,可靠的水下数据传输.

关键词:
适应性的模糊逻辑.深度学习是一种深度学习.深度学习等级器等级器均等化 均等化 均等化子搜索优化优化 搜索优化在水下可见光通信.

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科学领域:

  • 光学通信是指光学通信.
  • 信号处理 信号处理
  • 机器学习 机器学习

背景情况:

  • 水下可见光通信 (UVLC) 为水生环境提供了一种绿色替代方案,但面临信号衰减和流等挑战.
  • 现有的UVLC系统与线性和非线性损伤作斗争,限制了性能.

研究的目的:

  • 为64个方程振幅调制组件最小振幅相位移 (QAM-CAP) 调制的UVLC系统开发一个先进的等效器.
  • 为了减轻UVLC的线性和非线性损伤,提高数据传输的可靠性和速度.

主要方法:

  • 一个适应性的模糊逻辑深度学习等分器 (AFL-DLE),利用复杂值的神经网络和星座分区.
  • 使用增强的混沌子搜索优化算法 (ECSSOA) 来优化等效器的性能.

主要成果:

  • 实现了比特错误率的55%降低和扭曲率的45%降低.
  • 在计算复杂度下降了48%,计算成本下降了75%.
  • 保持了99%的高传输率.

结论:

  • 拟议的AFL-DLE有效地解决了UVLC系统中的损伤.
  • 这种方法为先进的水下通信系统提供了高速的在线数据处理.