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相关概念视频

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

85
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....
85
¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

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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...
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IR Spectrum01:19

IR Spectrum

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When infrared (IR) radiation passes through a molecule, the bonds stretch or bend by absorbing the radiation. This absorption creates the molecule's absorption spectrum, which is the plot of its percentage transmittance versus wavenumber.
Transmittance is defined as the ratio of the radiant power passing through a sample to that from the radiation's source. Multiplying the transmittance by 100 gives the percent transmittance (%T), which varies between 100% (no absorption) and 0%...
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Cognitive Learning01:21

Cognitive Learning

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
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IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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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: Jun 3, 2025

Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks
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基于深度学习的频谱传感用于认知无线电应用.

Sara E Abdelbaset1, Hossam M Kasem2,3, Ashraf A Khalaf4

  • 1Electronics and Electrical Communications Engineering Department, Higher Institute of Engineering and Technology, New Damietta 34517, Egypt.

Sensors (Basel, Switzerland)
|January 8, 2025
PubMed
概括

认知无线电使用卷积神经网络 (CNN) 进行先进的频谱传感,改善未使用频段的识别. 这种基于CNN的方法比传统技术提供了更高的准确性和适应性,即使在噪音下也是如此.

关键词:
认知无线电是一种认知无线电.卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.频谱传感传感器是什么?

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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:

  • 频谱传感对于认知无线电来检测和利用空缺频段至关重要.
  • 传统的频谱传感方法通常依赖于局部信号特征提取.
  • 像CNN和RNN这样的深度学习模型显示了提高频谱传感精度的潜力.

研究的目的:

  • 引入一种基于CNN的新型频谱传感方法.
  • 提高识别未使用频段的精度和有效性.
  • 为了证明CNN模型对不同信号类型和噪声条件的适应性.

主要方法:

  • 频谱传感被视为一个分类问题.
  • 一个CNN模型使用各种信号类型和噪音的数据集进行训练.
  • 性能是根据传统的方法进行评估的,例如最大-最小自值比和频域.

主要成果:

  • 拟议的基于CNN的频谱传感方法显著提高了精度和有效性.
  • 与传统技术相比,该模型表现出卓越的性能和适应性.
  • 即使在添加式白色高斯噪声 (AWGN) 条件下,也可以实现非常高的精度.

结论:

  • 在认知无线电中,CNN为先进的频谱传感提供了强大而适应性的解决方案.
  • 开发的方法在准确性和稳定性方面超过了传统方法.
  • 这项研究为更有效地利用无线电频谱铺平了道路.