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相关概念视频

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

85
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....
85
Upsampling01:22

Upsampling

195
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...
195
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

60
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,...
60
Cascaded Op Amps01:16

Cascaded Op Amps

581
Operational amplifiers (op-amps) are versatile electronic components that can be interconnected in a cascade - one after another in a linear sequence. This cascading is possible due to their infinite input resistance and zero output resistance, allowing them to maintain their input-output relationships even when connected in series.
In a cascaded system, each op-amp is referred to as a stage. The output of one stage drives the input of the subsequent stage. As the input signal passes through...
581
Design Example: Capacitance Multiplier Circuit01:20

Design Example: Capacitance Multiplier Circuit

686
In integrated circuit technology, a capacitance multiplier is often utilized to produce a larger capacitance value when a small physical capacitance falls short. This is achieved by a circuit that multiplies capacitance values by a factor of up to 1000, such that a 10-pF capacitor can replicate the performance of a 100-nF capacitor.
The circuit illustrated in Figure 1 below incorporates two op-amps, with the first operating as a voltage follower and the second acting as an inverting amplifier.
686
Linear time-invariant Systems01:23

Linear time-invariant Systems

209
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...
209

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低复杂度的神经网络等级器用于在数字子载波多重系统中减轻非线性.

Sasipim Srivallapanondh, Pedro Freire, Giuseppe Parisi

    Optics express
    |January 29, 2025
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    概括

    神经网络 (NN) 模型,特别是biLSTM和1D-CNN,显著降低了数字子载波复杂化 (DSCM) 光学系统中非线性补偿的计算复杂性. 重量聚类进一步增强了这种减少,为光学非线性均等器提供了一个有前途的解决方案.

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    科学领域:

    • 光学通信是指光学通信.
    • 机器学习在通信中的应用
    • 信号处理 信号处理

    背景情况:

    • 数字子载波复杂化 (DSCM) 系统由于信号非线性而面临性能下降.
    • 传统的补偿方法,如染色分散补偿 (CDC) 和数字反向传播 (DBP),在复杂性和有效性方面存在局限性.
    • 神经网络 (NN) 模型为先进的非线性补偿提供了潜力.

    研究的目的:

    • 对DSCM光学系统中非线性补偿的NN模型的复杂性降低进行比较研究.
    • 评估基于NN的等效器与传统方法和先前的NN方法的性能.
    • 调查重量聚类对NN计算复杂性的影响.

    主要方法:

    • 使用双向长期短期记忆 (biLSTM) 和1D卷积NN (1D-CNN) 层的NN模型的实施.
    • 应用重量聚类技术来降低NN模型的计算复杂性.
    • 使用Q因子和计算复杂度指标进行比较性性能分析,与CDC,DBP以及基于扰动分析的NN.N.进行比较.

    主要成果:

    • 拟议的基于NN的等分器实现了具有竞争力的Q因子改进.
    • 权重聚类显著降低了NN计算复杂度,高达91.1%与扰乱分析NN相比和31.5%与DBP相比 (1步/跨度).
    • 基于NN的方法证明了高性能非线性补偿,计算需求要低得多.

    结论:

    • 基于NN的非线性补偿,特别是重量聚类,在DSCM光学系统中非常有效.
    • 这些优化的NN模型为传统的补偿技术提供了一个有希望的,计算效率高的替代方案.
    • 该研究强调了NN方法对未来高性能光学非线性均等器的潜力.