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相关概念视频

Sound as Pressure Waves01:17

Sound as Pressure Waves

2.4K
Sound waves, which are longitudinal waves, can be modeled as the displacement amplitude varying as a function of the spatial and temporal coordinates. As a column of the medium is displaced, its successive columns are also displaced. As the successive displacements differ relatively, a pressure difference with the surrounding pressure is created. The gauge pressure varies across the medium.
The pressure fluctuation depends on the difference in displacements between the successive points in the...
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Perception of Sound Waves01:01

Perception of Sound Waves

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The human ear is not equally sensitive to all frequencies in the audible range. It may perceive sound waves with the same pressure but different frequencies as having different loudness. Moreover, the perception of sound waves depends on the health of an individual's ears, which decays with age. The health of one's ears may also be affected by regular exposure to loud noises.
The pitch of a sound depends on the frequency and the pressure amplitude of the source. Two sounds of the same...
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Sound Waves: Interference00:53

Sound Waves: Interference

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Sound waves can be modeled either as longitudinal waves, wherein the molecules of the medium oscillate around an equilibrium position, or as pressure waves. When two identical waves from the same source superimpose on each other, the combination of two crests or two troughs results in amplitude reinforcement known as constructive interference. If two identical waves, that are initially in phase, become out of phase because of different path lengths, the combination of crests with troughs...
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相关实验视频

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Recording Ultra-Realistic Full-Color Analog Holograms for Use in a Moving Hologram Display
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基于物理的生成对抗网络,用于实时声学全息.

Qingyi Lu1, Chengxi Zhong1, Hu Su2

  • 1School of Information Science and Technology, Shanghaitech University, Shanghai 201210, China.

Ultrasonics
|February 2, 2025
PubMed
概括

本研究介绍了用于仅相位全息 (POH) 的深度学习算法,以实现高可靠性,实时声场重建. 具有Y-Net结构的新型软-GAN显著提高了全息图生成准确度.

关键词:
声学全息图声学全息图深度学习 (Deep Learning) 是一种深度学习.只有相位的全息图.波浪传播 波浪传播

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科学领域:

  • 声学 声学 在声学方面
  • 全息影像的使用方法.
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 声学全息 (AH) 将复杂的声场编码为2D全息图.
  • 只有相位全息 (POH) 提供优越的信息量和存储效率.
  • 现有的POH算法缺乏高保真性和实时性能.

研究的目的:

  • 开发一种深度学习算法,用于准确和实时的POH计算.
  • 通过基于物理的方法来解决当前POH方法的局限性.

主要方法:

  • 一个深度学习算法,将角光谱方法 (ASM) 与称为软GAN的生成对抗网络 (GAN) 结合起来.
  • 实施一个Y-Net结构,用于频率和空间域的双解码器来处理高频特征.

主要成果:

  • 实现了24.05dB的最先进的峰值信号噪声比率 (PSNR).
  • 证明了准确和实时的声学全息图重建.
  • 拟议的方法显示了实际应用的巨大潜力.

结论:

  • 基于物理的深度学习方法有效地学习POH生成的反向映射.
  • 软GAN和Y-Net结构克服了POH的忠诚度和速度的限制.
  • 这种方法为先进的动态声学操纵铺平了道路.