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相关概念视频

Aliasing01:18

Aliasing

119
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
119
Upsampling01:22

Upsampling

204
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
204
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

173
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...
173
Sampling Theorem01:15

Sampling Theorem

302
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
302

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Updated: Jun 6, 2025

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基于低采样的多频电阻断层扫描系统与快速数字解调算法相结合.

Jinzhen Liu1,2, Yapeng Zhou1,2, Hui Xiong1,2

  • 1The School of Control Science and Engineering, TianGong University, TianJin 300387, China.

The Review of scientific instruments
|November 22, 2024
PubMed
概括
此摘要是机器生成的。

一个新的高精度多频电阻断层扫描 (MFEIT) 系统使用低采样和快速数字解调算法来进行增强的生物医学成像. 这种MFEIT系统实现了高精度和信号噪声比,以改进数据采集.

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科学领域:

  • 生物医学工程 生物医学工程
  • 医疗成像医学成像
  • 电气工程 电气工程

背景情况:

  • 多频电阻断层扫描 (MFEIT) 在生物医学成像中提供了显著的潜力.
  • 准确获取多频电阻数据对于高性能MFEIT系统至关重要.

研究的目的:

  • 开发一个高精度的MFEIT系统,以改进多频电阻信息的获取.
  • 为了提高多频激发信号解调的准确性和速度.

主要方法:

  • 采用半平行采集的16个电极MFEIT系统的实施.
  • 应用一种新型的多频数字解调算法,结合下采样和快速数字解调技术.

主要成果:

  • 拟议的低采样方法在5-500 kHz频率范围内实现了低于0.7%的解调误差.
  • MFEIT系统的最大信号噪声比为62.92dB.
  • 平均性能指标包括0.953的模糊半径和9.3%的位置错误百分比.

结论:

  • 开发的MFEIT系统表现出强大的性能和高的信号噪声比.
  • 低采样和快速数字解调的整合大大提高了MFEIT数据采集的准确性和速度.