留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码
期刊信息更多+
  • 主办单位：
    中国光学工程学会清华大学上海理工大学
  • 名誉主编： 庄松林 院士
  • 国际主编： 顾敏 院士
  • 主       编：
    孙洪波 教授仇旻 教授
  • 创       刊：2020年3月
  • ISSN：2662-1991
作者投稿 专家审稿 编辑办公
新闻资讯更多+
最新上线
Artificial intelligence-driven distributed acoustic sensing technology and engineering application
Liyang Shao, Jingming Zhang, Xingwei Chen, Deyu Xu, Huaxin Gu, Qi Mu, Feihong Yu, Shuaiqi Liu, Xiaobing Shi, Jiayao Sun, Zixing Huang, Xiongji Yang, Haifeng Zhang, Yunbin Ma, Han Lu, Chuanqing Liu, Changyuan Yu
 doi: 10.1186/s43074-025-00160-z
Abstract(0) PDF(0)
Abstract:
Distributed acoustic sensing (DAS) technology is a fiber-optic based distributed sensing technology. It achieves real-time monitoring of acoustic signals by detecting weak disturbances along the fiber. It has advantages such as long measurement distance, high spatial resolution and large dynamic range. Artificial intelligence (AI) has great application potential in DAS technology, including data augmentation, preprocessing and classification and recognition of acoustic events. By introducing AI algorithms, DAS system can process massive data more automatically and intelligently. Through data analysis and prediction, AI-enabled DAS technology has wide applications in fields such as transportation, energy and security due to its accuracy of monitoring data and reliability of intelligent decision-making. In the future, the continuous advancement of AI technology will bring greater breakthroughs and innovations for the engineering application of DAS technology, play a more important role in various fields, and promote the innovation and development of the industry.
Harnessing nonlinear optoelectronic oscillator for speeding up reinforcement learning
Ziwei Xu, Huan Tian, Zhen Zeng, Lingjie Zhang, Yaowen Zhang, Heping Li, Zhiyao Zhang, Yong Liu
 doi: 10.1186/s43074-025-00163-w
Abstract(7) PDF(0)
Abstract:
Reinforcement learning is an indispensable branch of artificial intelligence (AI), referring to the technology and methods of maximizing the rewards from an uncertain environment. As Moore’s law is coming to an end, the operation speed and the energy consumption of the advanced integrated circuits are gradually unable to meet the ever-increasing requirements of reinforcement learning. In recent years, photonic accelerator evolves as a powerful candidate to solve this issue. Here, a brand-new photonic accelerator based on a nonlinear optoelectronic oscillator (NOEO) is proposed and demonstrated to solve the multi-armed bandit (MAB) problem and simulate the Tic Tac Toe (TTT) game, both of which are the most famous reinforcement learning problems. Through adjusting the balance between the gain and the nonlinearity in the NOEO cavity, four parallel orthogonal chaotic sequences are generated with a 6-dB bandwidth up to 18.18 GHz and a permutation entropy (PE) as high as 0.9983. With assistance of tug-of-war and time differential methods, a 512-armed bandit problem and an intelligent TTT game are successfully accelerated, respectively. This work presents an innovative photonic accelerator for solving reinforcement learning problems more efficiently. Apart from reinforcement learning, the proposed scheme can find applications in other fields of AI, such as reservoir computing and neural networks. Reinforcement learning is an indispensable branch of artificial intelligence (AI), referring to the technology and methods of maximizing the rewards from an uncertain environment. As Moore’s law is coming to an end, the operation speed and the energy consumption of the advanced integrated circuits are gradually unable to meet the ever-increasing requirements of reinforcement learning. In recent years, photonic accelerator evolves as a powerful candidate to solve this issue. Here, a brand-new photonic accelerator based on a nonlinear optoelectronic oscillator (NOEO) is proposed and demonstrated to solve the multi-armed bandit (MAB) problem and simulate the Tic Tac Toe (TTT) game, both of which are the most famous reinforcement learning problems. Through adjusting the balance between the gain and the nonlinearity in the NOEO cavity, four parallel orthogonal chaotic sequences are generated with a 6-dB bandwidth up to 18.18 GHz and a permutation entropy (PE) as high as 0.9983. With assistance of tug-of-war and time differential methods, a 512-armed bandit problem and an intelligent TTT game are successfully accelerated, respectively. This work presents an innovative photonic accelerator for solving reinforcement learning problems more efficiently. Apart from reinforcement learning, the proposed scheme can find applications in other fields of AI, such as reservoir computing and neural networks.
Wide-viewing-angle color holographic 3D display system with high brightness encoding
Yi-Wei Zheng, Fan Chu, Fan-Chuan Lin, Yi-Xiao Hu, Yi-Long Li, Yi Zheng, Di Wang, Qiong-Hua Wang
 doi: 10.1186/s43074-025-00162-x
Abstract(12) PDF(1)
Abstract:
Holographic 3D display technology, widely considered the ultimate solution for real 3D display, has broad applications in fields including advertisement, industrial manufacturing and military. However, it is difficult to simultaneously realize color holographic 3D display with wide viewing angle and high brightness required for an immersive visual experience. Here, a novel holographic 3D display system based on a customized achromatic liquid crystal grating and a phase-only spatial light modulator is proposed. Thanks to the secondary diffraction performed by the achromatic liquid crystal grating, nine secondary diffraction images of red, green and blue channels overlap in space in time sequence. Additionally, a high brightness hologram encoding method is developed, which introduces a frequency loss function with dynamic weights to ensure that differences of all frequency components in the frequency domain can be learned. The proposed method dramatically enhances light energy utilization by a factor of five, resulting in significantly brighter reconstructed images while substantially attenuating background noise in non-target regions. This groundbreaking system, achieving a remarkable ~ 65° wide viewing angle with good image quality and high brightness, represents a significant advancement in holographic technology, offering a comprehensive solution for wide-viewing-angle, high-brightness, color 3D displays with potential applications across diverse technological domains.
Orthogonal GHz harmonic dual-comb generation in monolithic fiber cavity for acquisition speed multiplication
Guorui Wang, Zixuan Ding, Fei Xu
 doi: 10.1186/s43074-025-00161-y
Abstract(10) PDF(0)
Abstract:
Asynchronous dual-comb generated in single laser cavity offer potent tools for simplified coherent measurements, owing to the common mode rejection which spares the sophisticated locking systems. However, the limited dimensional inhomogeneity in monolithic cavity induces relatively small repetition-rate-difference, hindering high-speed measurements. Here, a monolithic linear fiber laser with integrated multifunctional device employing polarization multiplexing is proposed and demonstrated for dual-comb acquisition speed enhancement. By tuning the inherent polarization-dependent degrees of freedom within the device, optical intensity distribution between orthogonal polarizations can be finely manipulated, thus boosting controllable asynchronous harmonic mode-locking. The two sets of harmonic mode-locked pulses enable the multiplication of the equivalent repetition-rate-difference and produce more temporal interferograms via least common multiple principles. With a fundamental-repetition-rate of 383 MHz, harmonic-repetition-rate up to 2.3 GHz and acquisition speed over 244 kHz are obtained in experiments, faster by 2 orders of magnitudes than previous single-fiber-cavity dual-combs. Equivalent repetition-rate-difference up to 400 kHz is also achieved with shorter laser cavity. This orthogonally polarized GHz harmonic dual-comb laser offers insights for a novel dual-comb generation paradigm and provides a single-fiber-integrated solution for acquisition boosting in wide measurement applications.
查看更多
引用排行
Terahertz spectroscopy in biomedical field: a review on signal-to-noise ratio improvement
Yan Peng, Chenjun Shi, Yiming Zhu, Min Gu, Songlin Zhuang
2020, 1(1).    doi: 10.1186/s43074-020-00011-z
PDF(37)
Recent advances in multi-dimensional metasurfaces holographic technologies
Ruizhe Zhao, Lingling Huang, Yongtian Wang
2020, 1(1).    doi: 10.1186/s43074-020-00020-y
PDF(58)
Information Metamaterials: bridging the physical world and digital world
Qian Ma, Tie Jun Cui
2020, 1(1).    doi: 10.1186/s43074-020-00006-w
PDF(61)
Vacuum-ultraviolet photodetectors
Lemin Jia, Wei Zheng, Feng Huang
2020, 1(1).    doi: 10.1186/s43074-020-00022-w
PDF(35)
查看更多
推荐论文更多+
下载排行更多+

PhotoniX

联系我们

  • 地址： 北京丰台区海鹰路1号院6楼
  • 邮政编码： 100070
  • 电话： 010-63728336，022-58168516

友情链接

Copyright © PhotoniX    京ICP备15010856号-13

本系统由 北京仁和汇智信息技术有限公司 开发