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相关概念视频

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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 C=O, C=N, and C=C occur between 1600–1850 cm−1.
The...

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Survey on Context-Aware Radio Frequency-Based Sensing.

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在FM无线电频段中用于人类姿势分类的被动射频测试台.

João Pereira1,2, Eugene Casmin1,2, Rodolfo Oliveira1,2

  • 1Departamento de Engenharia Electrotécnica e de Computadores, Faculdade de Ciências e Tecnologia (FCT), Universidade Nova de Lisboa, 2829-516 Caparica, Portugal.

Sensors (Basel, Switzerland)
|December 9, 2023
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概括

这项研究使用频率调制 (FM) 无线电信号用于室内被动人体姿势分类. 开发的测试台达到约90%的准确性,使实时姿势检测成为可能.

关键词:
无线电频率被动传感传感器环境意识 背景意识人类姿势分类人类姿势分类机器学习是机器学习.绩效评价 绩效评价 绩效评价 绩效评价 绩效评价

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科学领域:

  • 无线电频率 (RF) 工程
  • 人与计算机的交互
  • 信号处理 信号处理

背景情况:

  • 在室内对人体姿势进行分类是具有挑战性的.
  • 现有的方法可能需要专门的传感器或主动信号.
  • 利用像FM无线电这样的环境信号提供了被动感应的机会.

研究的目的:

  • 探索使用被动FM无线电信号对人类姿势进行分类的可行性.
  • 为人类姿势分类实验提供一种新型的被动射频试验台.
  • 开发和评估姿势检测和分类的方法.

主要方法:

  • 使用在FM无线电频段工作的被动射频测试台.
  • 实施一种涉及特征工程和传统分类技术的方法.
  • 在软件定义的无线电设备上部署系统,以便实时评估.

主要成果:

  • 证明了在室内环境中对人体姿势进行分类的能力.
  • 达到约90%的分类准确度.
  • 验证了被动射频测试台对实时姿势分析的有效性.

结论:

  • 拟议的被动射频测试台有效地使用FM无线电信号对人体姿势进行分类.
  • 该方法显示了开发创新的被动传感技术的巨大潜力.
  • 未来的研究可以在这个试验台上建立先进的人类活动识别的基础.