高保真性和高强度的自由空间幽灵传输在复杂的介质中,具有连贯的光源,使用物理驱动的未经训练的神经网络
Optics express
|September 15, 2023
概括
这项研究引入了一种用于通过复杂环境进行强大的幽灵成像的新方法. 使用物理驱动的未经训练的神经网络 (UNN),在具有挑战性的条件下实现高保真度模拟信号传输.
科学领域:
- 量子光学就是量子光学.
- 信息科学 信息科学
- 机器学习 机器学习
背景情况:
- 具有连贯光的自由空间幽灵成像受到复杂媒体的阻碍,限制了忠实性和稳定性.
- 现有的方法在水或角落周围等环境中扎信号退化.
研究的目的:
- 通过复杂的介质开发一种新的高保真性和高强度幽灵传输方法.
- 利用未经训练的神经网络来生成光通信中的信息载体.
主要方法:
- 使用物理驱动的未经训练的神经网络 (UNN) 来编码模拟信号,生成只有振幅的随机模式.
- 调节一个连贯的光源与这些模式通过一个自由空间光学通道传输.
- 使用单像素探测器来捕捉接收器上的光强度.
主要成果:
- 已证明具有高可靠性和高稳定性的模拟信号 (幽灵) 传输.
- 通过复杂的环境成功传输信号,包括在拐角处和动态,的水中.
- 在光学实验中验证了物理驱动的UNN方法的有效性.
结论:
- 拟议的物理驱动的UNN方法能够通过具有挑战性的媒介进行强大的幽灵传输.
- 这种方法为高保真的自由空间光通信提供了一个有希望的新途径.
- 未经训练的神经网络可以有效地为复杂的光学任务生成信息载体.
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