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相关概念视频

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

92
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....
92
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

83
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,...
83
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

207
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...
207
Lossy Lines and Overvoltages01:22

Lossy Lines and Overvoltages

89
Transmission-line series resistance and shunt conductance cause three primary effects: attenuation, distortion, and power losses.
Attenuation
When constant series resistance and shunt conductance are present, voltage and current equations are modified. The propagation constant indicates that voltage and current waves consist of both forward and backward traveling components. These waves attenuate as they propagate, with the attenuation factor related to the resistance and conductance. In a...
89
Linear time-invariant Systems01:23

Linear time-invariant Systems

262
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
262
Reducing Line Loss01:18

Reducing Line Loss

155
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
155

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相关实验视频

Updated: Jul 9, 2025

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

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在非线性通道的NN预扭曲中,性能与复杂性对比.

Hamza Imtiaz, Zibo Zheng, Rizan Homayoun Nejad

    Optics express
    |November 29, 2023
    PubMed
    概括

    我们引入了用于数字预扭曲 (DPD) 的神经网络 (NN),以改进高速光通信系统中的数字对模拟转换器 (DAC). 这种NN-DPD方法通过减轻DAC损害,显著提高信号质量.

    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:

    • 高带宽光通信系统依赖于数字对模拟转换器 (DAC).
    • DAC引入量子化和带限损害,降低信号质量.
    • 数字预扭曲 (DPD) 对于减轻这些损害至关重要.

    研究的目的:

    • 提出和实验验证一种基于神经网络 (NN) 的数字预扭曲 (DPD) 技术.
    • 为了减轻DAC在高速光学系统中引入的量化和带限损害.
    • 为了比较NN-DPD与传统DPD方法的性能.

    主要方法:

    • 使用64个Gbaud 8级脉冲振幅调制 (PAM-8) 信号进行实验验验证.
    • 使用直接和间接学习方法对NN-DPD进行培训.
    • 与Volterra,查找表 (LUT) 和线性DPD解决方案进行比较.

    主要成果:

    • 拟议的NN-DPD通过直接学习进行训练,其性能优于Volterra,LUT和线性DPD,分别为0.9dB,1.9dB和2.9dB.
    • 间接学习的反复NN提供了与Volterra相似的性能,具有相似的复杂性.
    • 直接学习的递归NN实现了超越Volterra能力的卓越性能.

    更多相关视频

    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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    An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
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    Last Updated: Jul 9, 2025

    Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
    08:51

    Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

    Published on: November 1, 2019

    5.7K
    Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
    11:18

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    Published on: March 2, 2015

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    An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

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    结论:

    • NN-DPD是一种有效的方法,用于缓解高速光通信中的DAC损伤.
    • 直接学习的递归NN-DPD提供了最先进的性能.
    • 间接学习的反复NN-DPD提供了一个具有竞争力的替代方案,具有平衡的复杂性和性能.