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使用自动微分的声学全息图优化

Acoustic hologram optimisation using automatic differentiation.

作者信息

Fushimi Tatsuki, Yamamoto Kenta, Ochiai Yoichi

机构信息

R&D Center for Digital Nature, University of Tsukuba, Tsukuba, 305-8550, Japan.

Faculty of Library, Information and Media Science, University of Tsukuba, Tsukuba, 305-8550, Japan.

出版信息

Sci Rep. 2021 Jun 16;11(1):12678. doi: 10.1038/s41598-021-91880-2.

Abstract

Acoustic holograms are the keystone of modern acoustics. They encode three-dimensional acoustic fields in two dimensions, and their quality determines the performance of acoustic systems. Optimisation methods that control only the phase of an acoustic wave are considered inferior to methods that control both the amplitude and phase of the wave. In this paper, we present Diff-PAT, an acoustic hologram optimisation platform with automatic differentiation. We show that in the most fundamental case of optimizing the output amplitude to match the target amplitude; our method with only phase modulation achieves better performance than conventional algorithm with both amplitude and phase modulation. The performance of Diff-PAT was evaluated by randomly generating 1000 sets of up to 32 control points for single-sided arrays and single-axis arrays. This optimisation platform for acoustic hologram can be used in a wide range of applications of PATs without introducing any changes to existing systems that control the PATs. In addition, we applied Diff-PAT to a phase plate and achieved an increase of > 8 dB in the peak noise-to-signal ratio of the acoustic hologram.

摘要

声全息图是现代声学的基石。它们将三维声场编码在二维中,其质量决定了声学系统的性能。仅控制声波相位的优化方法被认为不如同时控制波的幅度和相位的方法。在本文中,我们展示了Diff-PAT,一个具有自动微分功能的声全息图优化平台。我们表明,在将输出幅度优化以匹配目标幅度的最基本情况下,我们仅通过相位调制的方法比传统的幅度和相位调制算法具有更好的性能。通过为单面阵列和单轴阵列随机生成1000组多达32个控制点来评估Diff-PAT的性能。这个声全息图优化平台可用于光声断层成像(PATs)的广泛应用中,而无需对现有的控制PATs的系统进行任何更改。此外,我们将Diff-PAT应用于相位板,并使声全息图的峰值噪声与信号比提高了超过8dB。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc67/8209099/a0d87a3ffc04/41598_2021_91880_Fig1_HTML.jpg

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