Real-time acoustic holography with physics-reinforced contrastive learning for acoustic field reconstruction

doi:10.1063/5.0174978

	Real-time acoustic holography with physics-reinforced contrastive learning for acoustic field reconstruction
	Zhong, Chengxi1 ; Lu, Qingyi1 ; Li, Teng1 ; Su, Hu 2; Liu, Song1,3
	2024-01-07
发表期刊	JOURNAL OF APPLIED PHYSICS
ISSN	0021-8979
EISSN	1089-7550
卷号	135 期号:1
发表状态	已发表
DOI	10.1063/5.0174978
摘要	Acoustic holography (AH) provides a promising technique for arbitrary acoustic field reconstruction, supporting many applications like robotic micro-nano manipulation, neuromodulation, volumetric imaging, and virtual reality. In AH, three-dimensional (3D) acoustic fields quantified with complex-valued acoustic pressures are reconstructed by virtue of two-dimensional (2D) acoustic holograms. Phase-only hologram (POH) is recently regarded as an energy-efficient way for AH, which is typically implemented by a dynamically programmable phased array of transducers (PATs). As a result, spatiotemporal precise acoustic field reconstruction is enabled by precise, dynamic, and individual actuation of PAT. Thus, 2D POH is required per arbitrary acoustic fields, which can be viewed as a physical inverse problem. However, solving the aforementioned physical inverse problem in numerical manners poses challenges due to its non-linear, high-dimensional, and complex coupling natures. The existing iterative algorithms like the iterative angular spectrum approach (IASA) and iterative backpropagation (IB) still suffer from speed-accuracy trade-offs. Hence, this paper explores a novel physics-iterative-reinforced deep learning method, in which frequency-argument contrastive learning is proposed facilitated by the inherent physical nature of AH, and the energy conservation law is under consideration. The experimental results demonstrate the effectiveness of the proposed method for acoustic field reconstruction, highlighting its significant potential in the domain of acoustics, and pushing forward the combination of physics into deep learning. © 2024 Author(s).
关键词	Deep learning Economic and social effects Energy efficiency Holograms Inverse problems Iterative methods Learning systems Spectrum analysis Virtual reality Acoustic field reconstruction Acoustic pressures Complex-valued Micro-nano manipulation Neuromodulation Phase-only Phased-arrays Real- time Two-dimensional Volumetric Imaging
URL	查看原文
收录类别	EI ; SCI
语种	英语
WOS研究方向	Physics
WOS类目	Physics, Applied
WOS记录号	WOS:001206623100006
出版者	American Institute of Physics Inc.
EI入藏号	20240215358129
EI主题词	Acoustic fields
EI分类号	461.4 Ergonomics and Human Factors Engineering ; 525.2 Energy Conservation ; 723 Computer Software, Data Handling and Applications ; 743 Holography ; 751 Acoustics, Noise. Sound ; 921.6 Numerical Methods ; 971 Social Sciences
原始文献类型	Journal article (JA)
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/348622
专题	信息科学与技术学院信息科学与技术学院_硕士生信息科学与技术学院_博士生信息科学与技术学院_PI研究组_刘松组
通讯作者	Su, Hu; Liu, Song
作者单位	1.School of Information Science and Technology, Shanghaitech University, Shanghai; 201210, China 2.Institute of Automation, Chinese Academy of Science, Beijing; 100190, China 3.Shanghai Engineering Research Center of Intelligent Vision and Imaging, Shanghai; 201210, China
第一作者单位	信息科学与技术学院
通讯作者单位	信息科学与技术学院
第一作者的第一单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Zhong, Chengxi,Lu, Qingyi,Li, Teng,et al. Real-time acoustic holography with physics-reinforced contrastive learning for acoustic field reconstruction[J]. JOURNAL OF APPLIED PHYSICS,2024,135(1).
APA	Zhong, Chengxi,Lu, Qingyi,Li, Teng,Su, Hu,&Liu, Song.(2024).Real-time acoustic holography with physics-reinforced contrastive learning for acoustic field reconstruction.JOURNAL OF APPLIED PHYSICS,135(1).
MLA	Zhong, Chengxi,et al."Real-time acoustic holography with physics-reinforced contrastive learning for acoustic field reconstruction".JOURNAL OF APPLIED PHYSICS 135.1(2024).