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相关概念视频

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

156
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...
156
Downsampling01:20

Downsampling

118
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
118
Upsampling01:22

Upsampling

180
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...
180
Deconvolution01:20

Deconvolution

127
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
127
Aliasing01:18

Aliasing

106
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...
106
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

195
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...
195

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    使用知识蒸的新型深度神经网络等级器提高了光通信系统的半监督性能. 这种方法在25公里内实现50Gb/s的数据速率,降低成本而不会牺牲准确性.

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    科学领域:

    • 光学通信是指光学通信的应用.
    • 信号处理 信号处理
    • 机器学习 机器学习

    背景情况:

    • 强度调制和直接检测 (IMDD) 系统需要先进的等分技术来实现高速数据传输.
    • 传统的盲式均方法可能无法在复杂的光学通道中实现最佳性能.
    • 半监督学习提供了一条减少对标记数据的依赖的途径,但需要有效的培训策略.

    研究的目的:

    • 为IMDD系统提供高性能半监督等级的基于知识蒸的多模块算法级联深度神经网络 (KD-CMMA-DNN).
    • 利用预先训练的教师模型来增强CMMA模型的训练,使用专门的蒸损失函数.
    • 评估KD-CMMA-DNN等分器在实用的O频脉冲振幅调制4级 (PAM-4) IMDD系统中的有效性.

    主要方法:

    • 开发一个KD-CMMA-DNN方案,结合一个教师-学生模型架构.
    • 实现定制的蒸损失函数,将知识从教师转移到学生模型.
    • 在O频段PAM-4 IMDD系统中进行实验验证,该系统可以在25公里的标准单模光纤上传输50Gb/s的数据.

    主要成果:

    • 该KD-CMMA-DNN等分器实现了性能优于典型的盲目CMMA等分器.
    • 实验结果表明,50Gb/s的PAM-4传输在25公里内达到7%的硬决策前置错误校正值.
    • 拟议的半监督方案消除了对标记数据的需求,与监督深度神经网络 (DNN) 均等器相比,从而降低了系统成本.

    结论:

    • 该KD-CMMA-DNN方案提供了一个高性能,高性价比的解决方案,用于IMDD系统的均等化.
    • 知识蒸是一种有效的技术,用于提高半监督学习在光通信等级.
    • 拟议的等分器可以实现可靠的高速光学数据传输,并减少训练数据需求.