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相关概念视频

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

89
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....
89
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

81
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
81
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

682
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
682
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

195
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...
195
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

518
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
518
Power Factor Correction01:20

Power Factor Correction

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The power transmission to a factory involves the transfer of apparent power, a combination of active and reactive power. The power factor measures how effectively electrical power is converted into useful work output. The ratio of the real power (KW) that does the work to the apparent power (KVA) supplied to the circuit.
176

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用DACNN辅助的非线性等分器来进行概率化塑造连贯光通信系统.

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    一个新的分布对齐卷积神经网络 (DACNN) 均衡器提高了对概率造型 (PS) 光纤系统的性能. 这种方法减少了训练的复杂性,并提高了接收器对于高速光通信的灵敏度.

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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
    • 机器学习 机器学习

    背景情况:

    • 概率造型 (PS) 对于光纤系统接近香农极限至关重要.
    • 非线性均等器与PS系统固有的不均分布作斗争.
    • 由于分布不匹配,现有的方法在有效平衡PS信号方面面临挑战.

    研究的目的:

    • 提出一种新的非线性等分器,用于概率化塑造光通信系统.
    • 为了解决传统的非线性等分器的无效性,具有非均的PS信号分布.
    • 为了减少训练的复杂性,同时提高均衡器的性能.

    主要方法:

    • 一个分布对齐卷积神经网络 (DACNN) 辅助的非线性均衡器被开发出来.
    • DACNN使用概率造型的先前分布对等器进行校准.
    • 这种方法使均衡器的训练与信号的不均分布保持一致.

    主要成果:

    • DACNN等分器证明了在375公里的120 Gb/sPS 64QAM信号的非线性等分.
    • 与Volterra等分器相比,它提高了2.6dB的接收器灵敏度.
    • 它比标准卷积神经网络 (CNN) 均等器提高了1.1dB,并显示了更快的收.

    结论:

    • 拟议的DACNN等分器有效地减轻了光纤系统概率造型中的非线性扭曲.
    • 与现有方法相比,DACNN提供了更高的性能和更少的培训复杂性.
    • 这种技术在推进高容量光通信系统方面具有重大潜力.