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Confocal Fluorescence Microscopy

Confocal Fluorescence Microscopy

Confocal microscopy is an advanced microscopic technique. The prime advantage of the confocal microscope over other microscopy techniques is its ability to block the out-of-focus light from the illuminated samples using pinholes. It is widely used with fluorescence optics to obtain high-resolution, sharp contrast images. Unlike optical microscopes, confocal microscopes use a focused beam of light laser to scan the entire sample surface at different z-planes. These microscopes are, therefore,...

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相关实验视频

Updated: May 10, 2026

Quasi-light Storage for Optical Data Packets

Quasi-light Storage for Optical Data Packets

Published on: February 6, 2014

使用基于OAM的结构光和智能自适应信号处理的强大的高容量自由空间光通信.

Muhammad Ahmad^1,2,3, Babar Hayat⁴, Ming Fang^1,2,3

¹The Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei, 230601, China.

Scientific reports

|February 26, 2026

概括

本研究介绍了一种混合的自由空间光学 (FSO) 通信系统,使用结构光和AI来克服大气流 (AT). 新的框架显著降低了比特错误率,并改善了强大的FSO链路的信号稳定性.

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相关实验视频

Last Updated: May 10, 2026

Quasi-light Storage for Optical Data Packets

Quasi-light Storage for Optical Data Packets

Published on: February 6, 2014

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Published on: March 20, 2017

Shaping the Amplitude and Phase of Laser Beams by Using a Phase-only Spatial Light Modulator

Shaping the Amplitude and Phase of Laser Beams by Using a Phase-only Spatial Light Modulator

Published on: January 28, 2019

科学领域:

光学通信是指光学通信.
信号处理信号处理
大气物理学大气物理学

背景情况:

自由空间光学 (FSO) 通信提供高速,安全的数据传输,但受到大气流 (AT) 的限制.
现有的方法在实时适应性补偿AT诱导的信号退化方面扎.
挑战包括光束扭曲,强度色,以及长距离FSO链路中的多式联络干扰.

研究的目的:

开发一种新的混合FSO框架,以提高对大气动荡的弹性.
整合结构光束,自适应光学和智能信号处理以实现实时补偿.
在恶劣大气条件下提高FSO通信系统的性能和可扩展性.

主要方法:

提出了一个混合FSO框架,包括贝塞尔,空气和轨道角动量 (OAM) 束.
实现了自适应光学 (AO) 和智能信号处理,包括动态神经模糊推理系统 (DNFIS) 进行等分.
利用深度卷积神经网络与时间域相关序列生成 (DCNN-TCSGm) 进行实时流预测和补偿.
在中红外光谱中使用光学元表面模拟OAM多重复合与波长分区多重复合 (WDM).

主要成果:

与传统系统相比,比特错误率 (BER) 降低了 55%.
在信号电压稳定性方面表现出22%的改善.
与传统的模式分割多重复合 (MDM) -FSO和决策反等效器 (DFE) 系统相比,获得了高达10dB的功率增益.
在具有挑战性的大气条件下验证了强度和可扩展性.

结论:

拟议的混合FSO框架有效地减轻大气流的影响.
整合结构光,AO和人工智能驱动的信号处理显著提高了FSO通信性能.
该框架为未来的高性能FSO网络提供了可扩展和强大的解决方案.