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相关概念视频

Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

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A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
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Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
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Determination of Expected Frequency01:08

Determination of Expected Frequency

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Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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相关实验视频

Updated: Jan 17, 2026

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
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通过引入ELM网络,对RIS辅助OTFS系统进行增强的通道估计.

Mintao Zhang1, Zhiying Liu1, Li Wang1

  • 1School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China.

Sensors (Basel, Switzerland)
|September 19, 2025
PubMed
概括

本研究介绍了极端学习机器 (ELM) 用于可重新配置的智能表面 (RIS) 辅助直角时间频率空间 (OTFS) 系统中的通道估计 (CE). 该方法提高了符号检测性能,即使通信参数变化.

关键词:
频道估计 频道估计极端学习的机器学习.正交的时间频率空间空间.可重新配置的智能表面.符号检测 符号检测 符号检测

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相关实验视频

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科学领域:

  • 无线通信无线通信
  • 信号处理 信号处理
  • 机器学习 机器学习

背景情况:

  • 可重新配置的智能表面 (RIS) 在高流动性场景中增强了正交时频空间 (OTFS) 系统.
  • 将RIS集成到OTFS系统中显著增加了通道估计 (CE) 的复杂性.
  • 机器学习 (ML) 提供了降低CE复杂性的潜力,但在RIS辅助OTFS中基于ML的CE尚未得到充分探索.

研究的目的:

  • 为了解决使用ML的RIS辅助OTFS系统中通道估计 (CE) 的复杂性.
  • 调查极端学习机器 (ELM) 对提高这些系统中CE准确性的有效性.
  • 通过结合基于值的初始特征提取方法来提高ELM的学习能力.

主要方法:

  • 为RIS辅助OTFS系统提出了一个基于极端学习机器 (ELM) 的通道估计 (CE) 方法.
  • 纳入了基于值的初始特征提取方法,以克服ELM的参数限制.
  • 使用传递信息的算法来进行数据符号检测 (SD).

主要成果:

  • 拟议的ELM方法在RIS辅助OTFS系统中显著提高了通道估计 (CE) 的准确性.
  • 与现有方法相比,模拟结果显示了增强的符号检测 (SD) 性能.
  • 该方法显示了对调制顺序,最大速度和子表面数量变化的稳定性.

结论:

  • 基于ELM的方法与初始特征提取是有效的道估计 (CE) 在RIS辅助OTFS系统.
  • 这种方法提高了符号检测 (SD) 的性能,并在高流动性通信中提供了稳定性.
  • 该研究弥合了基于ML的CE中的差距,用于RIS辅助的OTFS,为智能应用铺平了道路.