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相关概念视频

Aliasing01:18

Aliasing

100
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...
100
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

145
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...
145
Sampling Theorem01:15

Sampling Theorem

244
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.
244
Bandpass Sampling01:17

Bandpass Sampling

141
In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
141
Upsampling01:22

Upsampling

159
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...
159
Fast Fourier Transform01:10

Fast Fourier Transform

198
The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
198

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Proton Transfer and Protein Conformation Dynamics in Photosensitive Proteins by Time-resolved Step-scan Fourier-transform Infrared Spectroscopy
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对宽带压缩频谱传感的快速时间频率重建算法的研究

Rangang Zhu1,2, Ce Li1, Yanhua Wu1

  • 1College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China.

Sensors (Basel, Switzerland)
|April 28, 2025
PubMed
概括

本研究介绍了用于认知无线电 (CR) 宽带频谱传感 (WBSS) 的快速时间频率重建 (FTFR) 算法. 在复杂的环境中,FTFR算法提高了频谱传感效率,即使在信号噪声比 (SNR) 低的情况下也是如此.

关键词:
压缩的功率频谱估计.压缩感应传感器 压缩感应采用多子样本采样方法时间频率重建宽带频谱传感传感器

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科学领域:

  • 电气工程 电气工程
  • 信号处理 信号处理
  • 无线通信无线通信

背景情况:

  • 认知无线电 (CR) 对频谱资源管理至关重要.
  • 转换域通信系统 (TDCS) 是关键的CR技术.
  • 在TDCS中的传统宽带频谱传感 (WBSS) 在复杂的电磁环境中面临局限性.

研究的目的:

  • 为了解决WBSS中时间频率重建的挑战.
  • 提出一个新的快速时间频率重建 (FTFR) 算法.
  • 提高CR中频谱传感的适应性和效率.

主要方法:

  • 使用多Coset采样结构进行亚Nyquist采样.
  • 使用低复杂度的操作重建跨窗口的信号自相对应.
  • 整合相邻时间窗口的光谱以捕捉动态信号变化.

主要成果:

  • 与现有方法相比,拟议的FTFR算法显著降低了计算复杂性.
  • 实现了信号时频特征的有效重建.
  • 主要的时间和频率分布得到恢复,即使在低信号噪声比 (SNR) 条件下也是如此.

结论:

  • FTFR算法为CR中的WBSS提供了一个计算效率高的解决方案.
  • 它增强了感知和适应动态频谱使用的能力.
  • 该算法显示,它有望在具有挑战性的环境中提高CR系统的性能.