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相关概念视频

Echo01:06

Echo

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The human ear cannot distinguish between two sources of sound if they happen to reach within a specific time interval, typically 0.1 seconds apart. More than this, and they are perceived as separate sources.
Imagine the sound is reflected back to the ears. Assuming that the source is very close to the human, the difference between hearing the two sounds—the emitted sound and the reflected sound—may be more than the minimum time for perceiving distinct sounds. If this is the case,...
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Dissolution, the process by which drug particles dissolve in a solvent, is explained by the diffusion layer model, a theoretical framework that simulates the absorption of oral drugs and allows us to analyze experimental data.
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Diffusion01:12

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Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
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A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
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相关实验视频

Updated: Jan 11, 2026

Author Spotlight: A Stable Phantom Material for Optical and Acoustic Imaging
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基于扩散模型的水下声通信接收器)

Yufei Liu1, Yunjiang Zhao2, Peter Gerstoft3

  • 1Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China.

The Journal of the Acoustical Society of America
|November 14, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种新的水下声通信系统,使用无声扩散概率模型 (DDPM) 来进行信号重建和卷积神经网络 (CNN) 来进行调节,从而提高动态环境中的性能.

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科学领域:

  • 水下声学 (UWA) 通信
  • 信号处理 信号处理
  • 机器学习 机器学习

背景情况:

  • 由于信号扭曲,水下声道具有挑战性.
  • 像深度转移学习 (DTL) 这样的现有系统需要为新环境进行再培训.
  • 强大的和适应性的UWA通信对于各种应用至关重要.

研究的目的:

  • 提出一个新的频率跳跃二进制频率转换密钥 (FH-BFSK) UWA通信系统.
  • 为了利用无声扩散概率模型 (DDPM) 来进行信号重建和卷积神经网络 (CNN) 来进行调节.
  • 在动态UWA环境中实现高性能而不需要额外的适应.

主要方法:

  • 提出了一个FH-BFSK UWA通信系统架构.
  • 使用DDPM来重建来自UWA通道的扭曲的Mel光谱图.
  • 一个CNN被用来调节重建的光谱图.

主要成果:

  • 与传统方法相比,拟议的系统表现出优越的性能.
  • 该系统的性能与基于DTL的系统相提并论.
  • 模拟和实验验证了DDPM-CNN方法的有效性.

结论:

  • 基于DDPM的方法可以在动态的UWA环境中直接部署,而不需要再培训.
  • 这种方法为复杂和可变的UWA传播通道提供了更高的灵活性和适用性.
  • 拟议的系统代表了强大的UWA通信的重大进步.