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相关概念视频

Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

278
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...
278
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

110
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....
110
Sampling Theorem01:15

Sampling Theorem

382
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.
382
Upsampling01:22

Upsampling

262
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...
262
Determination of Expected Frequency01:08

Determination of Expected Frequency

2.2K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.2K
Basic Continuous Time Signals01:22

Basic Continuous Time Signals

239
Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
239

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相关实验视频

Updated: Jul 19, 2025

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
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基于ResNet的扩散频谱代码的周期估计

Han-Qing Gu1, Xia-Xia Liu1, Lu Xu1

  • 1School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China.

Sensors (Basel, Switzerland)
|August 12, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的CNN-ResNet方法,用于在复杂环境中准确估计直接序列扩散频谱 (DSSS) 信号参数. 该方法有效地识别了非合作信号的伪噪声 (PN) 代码周期.

关键词:
这就是ResNet ResNet.卷积神经网络 (CNN) 是一种神经网络.深度学习是一种深度学习.直接序列扩散频谱 (DSSS) 的使用扩散频谱代码期估计时间.

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科学领域:

  • 信号处理 信号处理
  • 机器学习 机器学习
  • 电磁战争 电磁战争

背景情况:

  • 在复杂的电磁环境中,准确的信号识别和参数估计对于非合作信号监控至关重要.
  • 直接序列扩散频谱 (DSSS) 信号分析的传统方法面临诸如峰值能量泄漏和虚假峰值干扰等挑战.

研究的目的:

  • 开发一种高效,准确的实时方法来检测DSSS信号,并在非合作场景中估计其参数.
  • 为了解决传统的时间延迟相关算法的局限性.

主要方法:

  • 基于残余网络 (ResNet) 的一维 (1D) 卷积神经网络 (CNN) 用于伪噪声 (PN) 代码周期估计.
  • 该方法将PN代码期估计作为扩散频谱代码长度估计的多分类问题.
  • DSSS信号的In-phase/Quadrature (I/Q) 数据直接输入到CNN-ResNet模型中进行自动特征学习.

主要成果:

  • 该CNN-ResNet模型有效估计了DSSS信号的PN代码长度,通过各种信号噪声比 (SNR) 从-20到10dB.
  • 使用二进制相位变换键 (BPSK) 调制信号验证性能,并使用方程相位变换键 (QPSK) 调制信号进行测试.
  • 分析指标包括损失函数,准确性,回忆率和混矩阵,展示了强大的概括能力.

结论:

  • 拟议的1D CNN-ResNet方法准确地估计了非合作的DSSS信号的PN代码周期.
  • 这种方法为传统算法提供了强大而有效的替代方案,特别是在具有挑战性的信号环境中.