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相关概念视频

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

84
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....
84
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

59
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
59
Ultrasound II: Endoscopic Ultrasound and FibroScan01:25

Ultrasound II: Endoscopic Ultrasound and FibroScan

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Endoscopic Ultrasound (EUS) and FibroScan are valuable diagnostic tools in gastroenterology and hepatology, each with specific applications and techniques.
Endoscopic Ultrasound (EUS):
63

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相关实验视频

Updated: May 24, 2025

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使用相一致性估计灵活的超声波阵列形状.

Marcus Ingram, Jan D'hooge

    IEEE transactions on bio-medical engineering
    |March 3, 2025
    PubMed
    概括

    一个新的算法准确地估计了灵活的超声波阵列传感器的形状,使用反向分散的信号. 这种方法精确地确定了阵列形状,这对于先进的医学成像应用至关重要.

    科学领域:

    • 医疗成像医学成像
    • 超声波技术 超声波技术 超声波技术
    • 声学工程 声学工程

    背景情况:

    • 灵活的超声波阵列传感器在医学成像中具有优势,但其精确的形状很难确定.
    • 精确了解传感器形状对于光束成型和图像质量至关重要.

    研究的目的:

    • 开发和验证一个算法来估计柔性超声波阵列传感器的形状.
    • 量化拟议的形状估计方法的精度和计算效率.

    主要方法:

    • 一种利用接收通道之间的相位变化的算法来计算数组形状的聚焦标准.
    • 在形状估计工作流程中使用来自128元,2.5MHz灵活阵列的模拟和实验数据实现.
    • 对已知的阵列形状进行光束响应的校准,以准确估计.

    主要成果:

    • 该算法准确地预测了数组形状,根平均平方误差为0.18 λ (模拟) 和0.4 λ (实验).
    • 使用单个传输事件的数据实现了形状估计.
    • 形状估计的计算时间大约为300 ms.

    结论:

    • 开发的算法提供了一个快速而准确的方法来估计灵活的超声波阵列传感器形状.

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  • 这种技术对于优化利用灵活阵列的超声波成像系统至关重要.
  • 该方法在模拟和现实世界的实验数据中证明了实际效用.