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相关概念视频

Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

250
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
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Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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Convolution Properties II01:17

Convolution Properties II

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The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
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Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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相关实验视频

Updated: Jun 29, 2025

Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo
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用于电阻断层扫描图像重建的TSS-ConvNet.

Ayman A Ameen1, Achim Sack2, Thorsten Pöschel2

  • 1Physics Department, Faculty of Science, Sohag University, Egypt.

Physiological measurement
|April 2, 2024
PubMed
概括

一个新的截断的空间光谱卷积神经网络 (TSS-ConvNet) 有效地解决了错误的反向问题. 这种数据驱动的方法使用模拟和实验数据准确检测管道中的气泡位置和大小.

关键词:
深度神经网络是一个神经网络.电阻断层扫描电阻断层扫描错误地提出了反向问题的问题.截断的空间光谱卷积神经网络

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科学领域:

  • 工程 工程师 工程师 工程师
  • 应用数学 应用数学 应用数学
  • 计算机科学 计算机科学

背景情况:

  • 错误设置的反向问题具有挑战性,特别是在时间差电阻断层扫描等应用中.
  • 现有的模型经常在有限的受体场上扎,仅依赖于局部欧几里德信息.

研究的目的:

  • 提出一种新的数据驱动方法来解决错误的反向问题.
  • 用时间差电阻断层扫描来解决检测管道内的气泡位置和大小的挑战.

主要方法:

  • 引入了一个截断的空间-光谱卷积神经网络 (TSS-ConvNet),具有相互连接的空间,光谱和截断的光谱路径.
  • 该架构包含一个瓶设计,以从噪音测量中恢复信号信息.
  • 在多样化的数据集上训练网络,随机配置以实现强大的概括.

主要成果:

  • 在模拟和实验数据上,TSS-ConvNet表现出卓越的准确性和高分辨率.
  • 该模型有效地克服了现有方法的受感场限制.
  • 在复杂的条件下实现精确检测气泡位置和大小.

结论:

  • 在解决错误的反向问题方面,TSS-ConvNet提供了显著的进步.
  • 这种数据驱动的方法显示了需要精确测量的现实应用的巨大潜力.
  • 该模型集成本地和全球信息的能力提高了其性能和适用性.