Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Masking and Demasking Agents01:19

Masking and Demasking Agents

2.5K
EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
2.5K
Deconvolution01:20

Deconvolution

168
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
168
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

268
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
268
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

681
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
681

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

The ventricular assist device outflow cannula anastomosis to the descending aorta in a patient with porcelain aorta.

Acta cardiologica·2026
Same author

RLSFmode: A deep learning approach for predicting RNA-small molecule binding modes via molecular surface modeling.

International journal of biological macromolecules·2026
Same author

An asymmetric GaAs nanocylinder quasi-BIC metasurface for dual narrowband high-<i>Q</i> perfect absorption in the near-infrared region.

Physical chemistry chemical physics : PCCP·2026
Same author

Optical encryption-transmission via computational ghost imaging and fractional OAM multiplexing.

Optics letters·2025
Same author

Study on the mechanism of Sangbaipi Decoction in treating acute lung injury via the inflammasome NLRP3 signaling pathway.

Journal of ethnopharmacology·2025
Same author

Differential pseudo-random phase-modulated continuous-wave coherent LiDAR.

Optics express·2025
Same journal

Long-term stabilization of intensity-difference squeezing from four-wave mixing in rubidium vapor.

Optics express·2026
Same journal

Robust 3D topography measurement of large-range high-aspect-ratio structures based on dual-domain statistical filtering in SD-OCT.

Optics express·2026
Same journal

Broadband transmissive terahertz metasurface for simultaneous quad-mode OAM multiplexing.

Optics express·2026
Same journal

Leveraging two-dimensional materials for high-sensitivity optical sensors: quasi-bound states in the continuum within hybrid metasurfaces.

Optics express·2026
Same journal

Resolution investigation for dual-spherical-wave optical scanning holographic microscopy: methods and performance.

Optics express·2026
Same journal

Robustness of parallel subnetwork-filtered diffractive deep neural networks.

Optics express·2026
查看所有相关文章

相关实验视频

Updated: Jul 12, 2025

Shaping the Amplitude and Phase of Laser Beams by Using a Phase-only Spatial Light Modulator
08:39

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

Published on: January 28, 2019

9.8K

基于光斑的光学加密与复杂的广度编码和深度学习.

Lin Zhang, Shanshan Lin, Qingming Zhou

    Optics express
    |October 20, 2023
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了一种新的光学加密方法,使用斑点图案和深度学习用于复杂幅度图像. 该技术有效地加密和解密振幅和相位信息,显示有希望的结果.

    更多相关视频

    Lensless Fluorescent Microscopy on a Chip
    11:23

    Lensless Fluorescent Microscopy on a Chip

    Published on: August 17, 2011

    17.7K
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.8K

    相关实验视频

    Last Updated: Jul 12, 2025

    Shaping the Amplitude and Phase of Laser Beams by Using a Phase-only Spatial Light Modulator
    08:39

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

    Published on: January 28, 2019

    9.8K
    Lensless Fluorescent Microscopy on a Chip
    11:23

    Lensless Fluorescent Microscopy on a Chip

    Published on: August 17, 2011

    17.7K
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.8K

    科学领域:

    • 光学是什么?光学是什么?
    • 信息安全 信息安全
    • 人工智能的人工智能

    背景情况:

    • 光学加密提供了高安全性和速度.
    • 复杂的广度信息 (广度和相位) 需要先进的加密技术.
    • 深度学习模型可以学习用于数据处理的复杂映射.

    研究的目的:

    • 为复杂幅度图像开发基于斑点的光学加密方案.
    • 整合深度学习以实现高效的加密和解密.
    • 证明拟议方法的可行性和有效性.

    主要方法:

    • 复杂的广度纯文本调制使用超像素编码和数字微镜装置.
    • 在4f系统后,通过散射介质生成斑点图案.
    • 训练一个Y形卷积网络 (Y-Net),用于平文-密码文本映射.
    • 使用训练有素的Y-Net进行加密文本解密.

    主要成果:

    • 复杂幅度图像的成功加密和解密.
    • 从密码文本中提取高质量的振幅和相位信息.
    • 实验验证拟议的光斑加密和深度学习集成的实验验证.

    结论:

    • 拟议的基于光斑的光学加密方案有效处理复杂的广度信息.
    • 深度学习,特别是Y-Net模型,显著提高了解密速度和质量.
    • 这种综合方法显示了安全光学信息处理的巨大潜力.