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相关概念视频

Signal and System01:26

Signal and System

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A signal x(t) is a set of data or a time function representing a variable of interest. Signals typically convey information about a phenomenon, such as atmospheric temperature, humidity, human voice, television images, a dog's bark, or birdsongs. More generally, a signal can be a function of more than one independent variable. For instance, images depend on horizontal and vertical positions and can be regarded as two-dimensional signals. However, this text will focus on one-dimensional...
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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
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Direct Method
This invasive approach involves cannulating a peripheral artery. During each cardiac contraction, pressure generates mechanical motion within the catheter, transmitted through rigid, fluid-filled tubing to a transducer. This transducer converts mechanical motion into electrical signals displayed as waveforms on a monitor. An automatic flushing system prevents blood backflow. Due to the potential risk of unexpected arterial blood loss, this method is primarily used in intensive...
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The Doppler effect has several practical, real-world applications. For instance, meteorologists use Doppler radars to interpret weather events based on the Doppler effect. Typically, a transmitter emits radio waves at a specific frequency toward the sky from a weather station. The radio waves bounce off the clouds and precipitation and travel back to the weather station. The radio frequency of the waves reflected back to the station appears to decrease if the clouds or precipitation are moving...
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Wideband Optical Detector of Ultrasound for Medical Imaging Applications
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基于深度学习的信号处理和检测,用于多个医疗设备的OFDM系统.

Haoyu Wang1, Yaoping Yu1, Xiaoguang Liu1

  • 1Ningbo Rehabilitation Hospital, Ningbo, 315000, Zhejiang, China.

Scientific reports
|November 29, 2024
PubMed
概括

深度学习算法显著改善了多个医疗器械OFDM系统中的信号检测,优于传统方法. 这些先进的技术提供了强大的性能,可以抵御干扰和通道变化.

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科学领域:

  • 无线通信系统无线通信系统
  • 生物医学工程 生物医学工程
  • 人工智能的人工智能

背景情况:

  • 使用正角频率分割多重复合 (OFDM) 的多个医疗设备可能会受到合法用户的干扰.
  • 有效的信号处理和检测对于在这些环境中可靠的通信至关重要.

研究的目的:

  • 评估三种深度学习 (DL) 算法的性能,用于在未编码的多种医疗器械OFDM系统中检测信号.
  • 将DL方法与传统的线性最小平均平方误差 (LMMSE) 检测器进行比较.
  • 调查信号与干扰比率,信号与噪声比率,干扰数和调制类型对比特错误率 (BER) 的影响.

主要方法:

  • 实现和评估完全连接的深度神经网络,卷积神经网络和长期短期记忆神经网络.
  • 将DL算法的比特错误率 (BER) 与线性最小平均平方错误 (LMMSE) 检测器进行比较.
  • 在不同的信号与干扰比率,信号与噪声比率,干扰数量和调制方案下分析BER性能.

主要成果:

  • 深度学习方法在多个医疗器械干扰场景中表现出比LMMSE检测器更好的性能.
  • DL算法在具有高可变性的无线通道中显示出稳定性.
  • 该研究证实了DL方法在这些系统中的强大抗干扰能力.

结论:

  • 深度学习算法是有效的信号处理和检测在多个医疗设备的OFDM系统.
  • DL方法在减轻干扰和提高通信可靠性方面提供了显著的优势.
  • 这些发现凸显了DL在提高医疗物联网通信系统性能方面的潜力.