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

Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

4.8K
Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
4.8K
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

726
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.
726
Light Acquisition02:16

Light Acquisition

8.5K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.5K

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

Design and Optimization of Dolmen-like Nanoantenna on Silicon Dioxide for Sensing Applications.

Sensors (Basel, Switzerland)·2026
Same author

Mid-Infrared Gas Sensing Based on Electromagnetically Induced Transparency in Coupled Plasmonic Resonators.

Sensors (Basel, Switzerland)·2023
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Jul 20, 2025

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

对光学传感器应用的深度学习:一篇评论

Nagi H Al-Ashwal1,2, Khaled A M Al Soufy1,2, Mohga E Hamza1

  • 1Department of Physics, The American University in Cairo, New Cairo 11835, Egypt.

Sensors (Basel, Switzerland)
|July 29, 2023
PubMed
概括
此摘要是机器生成的。

深度学习 (DL) 提高了光学传感器的准确性,并减少了噪声. 集成DL解决了诸如大数据集和高成本等挑战,为先进的智能传感应用铺平了道路.

关键词:
自动编码器 自动编码器卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.深度神经网络是一个神经网络.这些光学传感器是光学传感器.

更多相关视频

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

802

相关实验视频

Last Updated: Jul 20, 2025

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
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

802

科学领域:

  • 光学传感技术的技术.
  • 人工智能的人工智能是人工智能.
  • 数据处理数据处理.

背景情况:

  • 光学传感器对于工艺监控,质量控制和安全等各个领域的智能传感至关重要.
  • 传统的光学传感器面临诸多挑战,包括大量的数据,处理速度慢,成本高.
  • 深度学习 (DL) 为这些局限性提供了潜在的解决方案.

研究的目的:

  • 审查最近的研究,将深度学习算法与光学传感器应用集成在一起.
  • 突出光学传感中DL有希望的方向.
  • 为DL增强光学传感器提出未来研究途径.

主要方法:

  • 在光学传感中对DL的最新研究的文献综述.
  • 对DL算法与光学传感器数据集成的分析.
  • 识别当前的挑战和未来的机会.

主要成果:

  • 深度学习显著提高了精度,并减少了光学传感器数据中的噪声.
  • 集成DL可以缓解与大数据集和处理速度相关的挑战.
  • 在包括污染监测和国防在内的各个领域,DL的应用正在扩大.

结论:

  • 深度学习是提高光学传感器能力的关键技术.
  • 对DL算法的进一步研究可以为智能传感解锁新的潜力.
  • 通过DL集成解决数据和成本挑战对于未来的光学传感器开发至关重要.