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相关概念视频

Sampling Theorem01:15

Sampling Theorem

385
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.
385
Upsampling01:22

Upsampling

265
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...
265
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

241
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...
241
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

279
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
279
Discrete Fourier Transform01:15

Discrete Fourier Transform

324
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
324
Aliasing01:18

Aliasing

163
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...
163

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相关实验视频

Updated: Jul 21, 2025

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
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基于优化的通用S转换和ResNet的频率跳跃信号检测.

Chun Li1, Ying Chen1, Hijin Zhao1

  • 1School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China.

Mathematical biosciences and engineering : MBE
|July 28, 2023
PubMed
概括

本研究介绍了一种优化的通用S转换和残余网络,用于强大的频率跳跃信号检测. 这种新的方法提高了检测准确度,并减少了与现有技术相比的计算复杂性.

科学领域:

  • 信号处理 信号处理
  • 机器学习 机器学习
  • 电信 电信服务 电信服务 电信服务

背景情况:

  • 传统的频率跳跃信号检测在时间频率分辨率和频谱泄漏方面存在局限性.
  • 对于频率跳跃信号检测的机器学习方法通常具有很高的复杂性.

研究的目的:

  • 提出一种新的频率跳跃信号检测方法,使用优化的通用S转换和残余网络.
  • 为了提高检测性能,同时降低计算复杂性.

主要方法:

  • 优化通用S转换 (OGST) 使用多种群遗传算法调整参数 $ \lambda $ 和 $ p $.
  • 时间频谱的噪声强度正常化.
  • 剩余网络架构用于从时间频谱中自动学习特征.

主要成果:

  • 多种群遗传算法比标准遗传算法表现出更高的优化效率,更快的融合,以及更稳定的结果.
  • 拟议的剩余网络和OGST方法实现了更好的检测性能.
  • 与混合卷积/循环神经网络算法相比,新技术的计算和存储复杂性较低.

结论:

  • 拟议的方法通过利用优化的时间频率表示和深度学习方法有效检测频率跳跃信号.
关键词:
卷积神经网络是一种卷积神经网络.频率跳跃信号检测检测频率跳跃信号检测一般化的S转换.遗传算法是一种遗传算法.

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  • 该技术为频率跳跃信号检测提供了更有效,更准确的替代方案,解决了传统和现有的机器学习方法的局限性.