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相关概念视频

Aliasing01:18

Aliasing

144
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...
144
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

94
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....
94
Bandpass Sampling01:17

Bandpass Sampling

188
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....
188
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
Sampling Theorem01:15

Sampling Theorem

353
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.
353
Properties of Fourier Transform II01:24

Properties of Fourier Transform II

225
The Fourier Transform (FT) is an essential mathematical tool in signal processing, transforming a time-domain signal into its frequency-domain representation. This transformation elucidates the relationship between time and frequency domains through several properties, each revealing unique aspects of signal behavior.
The Frequency Shifting property of Fourier Transforms highlights that a shift in the frequency domain corresponds to a phase shift in the time domain. Mathematically, if x(t) has...
225

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相关实验视频

Updated: Jul 12, 2025

Generation and Coherent Control of Pulsed Quantum Frequency Combs
06:42

Generation and Coherent Control of Pulsed Quantum Frequency Combs

Published on: June 8, 2018

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用连续频谱调制进行非线性频率分割复杂化的频率偏移估计.

Yonghua He, Jianping Li, Jianqing He

    Optics express
    |October 20, 2023
    PubMed
    概括
    此摘要是机器生成的。

    一种新的频率偏移估计方法提高了使用连续频谱非线性频率分割复杂化 (CS-NFDM) 的光纤通信的准确性. 这种方法可以显著降低开销,同时保持高性能,使其适用于先进的系统.

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    相关实验视频

    Last Updated: Jul 12, 2025

    Generation and Coherent Control of Pulsed Quantum Frequency Combs
    06:42

    Generation and Coherent Control of Pulsed Quantum Frequency Combs

    Published on: June 8, 2018

    9.0K
    Quasi-light Storage for Optical Data Packets
    07:45

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    Published on: February 6, 2014

    10.9K
    Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
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    科学领域:

    • 光学通信是指光学通信.
    • 信号处理 信号处理

    背景情况:

    • 载波频率偏移 (CFO) 估计在光纤系统中至关重要.
    • 现有的方法对于基于非线性里叶变换 (NFT) 的系统是不够的,特别是连续频谱非线性频率分割复杂化 (CS-NFDM).
    • 在CS-NFDM中高过量采样率降低了传统频率偏移估计 (FOE) 的准确性.

    研究的目的:

    • 为CS-NFDM系统开发一个改进的频率偏移估计 (FOE) 方法.
    • 在高超样本场景中解决基于FT的传统FOE方法的局限性.
    • 为了提高CS-NFDM系统的性能和效率.

    主要方法:

    • 一种经过修改的FOE方法,将快速里埃转换 (FFT) 与训练序列 (TS) 和自相关性结合起来.
    • 理论分析和模拟以验证拟议的方法.
    • 与传统的 FFT-FOE 和 Schmidl & Cox 方法进行比较.

    主要成果:

    • 拟议的方法在CS-NFDM系统中达到约0.1 MHz的最低FO估计误差.
    • 证明了各种调制格式的适用性.
    • 与现有方法相比,实现了显著的开销降低 (至少87.5%和50%).

    结论:

    • 修改后的FOE方法在CS-NFDM系统中是有效和准确的,即使采用过量采样率很高.
    • 在空头效率方面提供了实质性的改进.
    • 提供了一个可行的解决方案,用于在先进的光通信系统中进行可靠的频率偏移估计.