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相关概念视频

Deconvolution01:20

Deconvolution

159
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...
159
Wave Parameters01:10

Wave Parameters

7.7K
The simplest mechanical waves are associated with simple harmonic motion and repeat themselves for several cycles. These simple harmonic waves can be modeled using a combination of sine and cosine functions. Consider a simplified surface water wave that moves across the water's surface. Unlike complex ocean waves, in surface water waves, water moves vertically, oscillating up and down, whereas the disturbance of the wave moves horizontally through the medium. If a seagull is floating on the...
7.7K
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
Discrete Fourier Transform01:15

Discrete Fourier Transform

272
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
272
Convolution Properties II01:17

Convolution Properties II

198
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...
198
Pulse amplitude and quality01:17

Pulse amplitude and quality

1.7K
Pulse amplitude is a crucial indicator of cardiac health because it provides valuable insights into the strength of left ventricular contractions and the overall uniformity of blood circulation within the vasculature. The strength of the pulse is directly related to the force with which the heart contracts and the volume of blood being pumped.
A weak or absent pulse may indicate reduced cardiac output or poor left ventricular contraction, which can be signs of cardiovascular dysfunction or...
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相关实验视频

Updated: Jun 29, 2025

Data Acquisition and Analysis In Brainstem Evoked Response Audiometry In Mice
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Data Acquisition and Analysis In Brainstem Evoked Response Audiometry In Mice

Published on: May 10, 2019

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对于PAM VLC系统的离散波形变换辅助卷积神经网络等分器.

Xingyu Lu, Yi Li, Xiang Chen

    Optics express
    |April 4, 2024
    PubMed
    概括

    一个新的离散波纹变换辅助卷积神经网络 (DWTCNN) 均衡器有效地补偿可见光通信 (VLC) 系统损坏. 这种方法显著减少了非线性损伤,改善了系统性能和比特错误率 (BER).

    科学领域:

    • 光学通信是指光学通信.
    • 信号处理 信号处理
    • 人工智能的人工智能

    背景情况:

    • 可见光通信 (VLC) 系统面临线性和非线性信号损坏的挑战.
    • 现有的均等化方法难以全面解决各种类型的损害.
    • 需要先进的信号处理和深度学习才能实现强大的VLC性能.

    研究的目的:

    • 为可见光通信 (VLC) 系统提出一种新的等分器,可以解决线性和非线性损伤.
    • 结合离散波波变换 (DWT) 和卷积神经网络 (CNN) 的优势,以增强信号补偿.
    • 为了提高VLC中的比特错误率 (BER) 和整体系统性能.

    主要方法:

    • 开发了一个离散波形变换辅助卷积神经网络 (DWTCNN) 均衡器.
    • 波形变换将信号分解为系数序列,然后进行自适应软值以删除冗余信息.
    • 重建的系数实现了完全的信号补偿,减轻了非线性损害.

    主要成果:

    • 在VLC系统中,DWTCNN等分器显著降低了非线性损伤.
    • 实现了比特错误率 (BER) 低于7%的硬决策前置错误校正 (HD-FEC) 极限3.8 × 10-3.
    • 性能优于长期短期记忆 (LSTM) 和实体提取神经网络 (EXNN) 均等器,Q系数分别提高了0.76dB和0.53dB,DC偏差操作范围增加了4.76%和23.5%.

    更多相关视频

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    Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

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    Last Updated: Jun 29, 2025

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    Data Acquisition and Analysis In Brainstem Evoked Response Audiometry In Mice

    Published on: May 10, 2019

    11.7K
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    06:04

    Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

    Published on: January 17, 2025

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    Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
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    Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

    Published on: August 16, 2024

    419

    结论:

    • 拟议的DWTCNN等分器为VLC系统中补偿信号损坏提供了卓越的解决方案.
    • 这种混合方法有效地减轻了非线性损害,提高了系统的可靠性和性能.
    • 在VLC应用程序中,DWTCNN在现有的深度学习等级技术上显示了显著的优势.