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

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
Vector Functions and Motion: Problem Solving01:30

Vector Functions and Motion: Problem Solving

Accurate position tracking is fundamental to the safe and effective operation of unmanned aerial vehicles (UAVs), particularly during precision maneuvers near complex structures. In this scenario, a drone is programmed to perform a high-precision inspection of a vertical structure, starting at position ((x, y, z) = (3, 0, 0)), with an initial velocity oriented in the positive z-direction. The trajectory of the drone is governed by a time-dependent acceleration function a(t), which is predefined...

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

AI-driven 5G IoT e-nose for whiskey classification.

Applied intelligence (Dordrecht, Netherlands)·2025
Same author

5G AI-IoT System for Bird Species Monitoring and Song Classification.

Sensors (Basel, Switzerland)·2024
Same author

Design and Implementation of an AI-Enabled Sensor for the Prediction of the Behaviour of Software Applications in Industrial Scenarios.

Sensors (Basel, Switzerland)·2024
Same author

Infrastructure-Wide and Intent-Based Networking Dataset for 5G-and-beyond AI-Driven Autonomous Networks.

Sensors (Basel, Switzerland)·2024
Same author

AI-IoT Low-Cost Pollution-Monitoring Sensor Network to Assist Citizens with Respiratory Problems.

Sensors (Basel, Switzerland)·2023
Same author

Machine-Learning-Based Carbon Dioxide Concentration Prediction for Hybrid Vehicles.

Sensors (Basel, Switzerland)·2023

相关实验视频

Updated: Jul 1, 2026

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.7K

基于无人机的面部验证算法的基于动态距离的值.

Julio Diez-Tomillo1, Jose Maria Alcaraz-Calero1, Qi Wang1

  • 1School of Computing, Engineering and Physical Sciences (CEPS), University of the West of Scotland (UWS), Paisley PA1 2BE, UK.

Sensors (Basel, Switzerland)
|December 23, 2023
PubMed
概括

本研究介绍了一种适应性面部验证系统,用于无人机 (UAV). 新方法在各种公共安全场景中提高了15%的准确性.

科学领域:

  • 计算机视觉 计算机视觉
  • 生物识别信息 生物识别信息
  • 人工智能的人工智能

背景情况:

  • 面部验证对于安全至关重要,但与不同的图像条件 (如距离,角度和照明) 相斗争.
  • 不同环境中的分辨率变化大大降低了验证准确性.

研究的目的:

  • 为基于无人机 (UAV) 的公共安全开发适应性面部验证解决方案.
  • 为了应对现实世界的场景中不同距离,角度和照明条件所带来的挑战.

主要方法:

  • 开发了一个创新的自适应验证值算法.
  • 一个优化的操作管道被设计用于处理无人机和受试者之间的不同距离.
  • 该解决方案在无人机平台上进行实证测试.

主要成果:

  • 拟议的自适应面部验证解决方案显示了更好的准确性.
  • 经验性比较显示,与最先进的方法相比,准确度增加了15%.
  • 该系统有效地适应不同距离和环境条件.

结论:

  • 适应性面部验证系统为基于无人机的公共安全应用提供了强大的解决方案.
关键词:
欧几里德距离是什么意思距离与等号 cos 的距离.面部验证 面部验证 面部验证西安网络的西安网络.门值是指一个值.

更多相关视频

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.4K
Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders
05:49

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Published on: November 1, 2024

808

相关实验视频

Last Updated: Jul 1, 2026

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.7K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.4K
Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders
05:49

Author Spotlight: Deciphering Electrical Networks Behind Complex Brain Activities and Disorders

Published on: November 1, 2024

808
  • 开发的算法和管道在具有挑战性的条件下提高了面部验证的准确性.
  • 这种方法在不同环境中显著提高了身份认证的可靠性.