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相关概念视频

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

203
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...
203
Deconvolution01:20

Deconvolution

162
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...
162
Convolution Properties I01:20

Convolution Properties I

153
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
153
Convolution Properties II01:17

Convolution Properties II

208
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
208
Visual System01:26

Visual System

585
Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
585
Reducing Line Loss01:18

Reducing Line Loss

154
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...
154

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相关实验视频

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CNN神经网络时间特征存储结构融合可见通道均等算法.

Xizheng Ke, Qingyang Zhang, Huanhuan Qin

    Applied optics
    |December 18, 2023
    PubMed
    概括

    这项研究引入了一种新的等级算法,用于使用卷积神经网络 (CNN) 和长短期记忆 (LSTM) 的可见光通信通道. 该方法有效地弥补了时间变化的通道特征,改善了信号恢复和传输性能.

    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:

    • 可见光通信 (VLC) 通道表现出不可预测的,时间变化的特征,降低了信号质量.
    • 精确的等分对于VLC系统中可靠的数据传输至关重要,特别是在移动条件下.

    研究的目的:

    • 为可见光通信通道开发一个先进的等效算法.
    • 为了提高信号恢复的准确性,并改善动态VLC环境中的比特错误率性能.

    主要方法:

    • 一种混合深度学习方法,将卷积神经网络 (CNN) 结合起来用于特征提取和长短期记忆 (LSTM) 用于时间序列分析.
    • 整合一个残余结构,以完善通道特征的学习,并提高重建的准确性.
    • 对移动VLC场景的接收器补偿策略的检查.

    主要成果:

    • 拟议的算法有效地减轻了可见光通道色的影响.
    • 观察到比特错误率 (BER) 性能显著改善.
    • 该方法准确地恢复了原始传输信号,并具有快速的收速度.
    • 与传统方法相比,在性能和计算复杂性之间实现了更好的平衡.

    结论:

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    • 开发的CNN-LSTM等分算法在补偿复杂和时间变化的VLC频道扭曲方面表现出高效率.
    • 这种方法为强大而高效的可见光通信系统提供了有前途的解决方案.
    • 该方法显示了在需要高可靠性的VLC应用程序中实际实施的巨大潜力.