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

Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

1.7K
In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
1.7K
One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

515
In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
515
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

1.4K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
1.4K
Functional Classification of Joints01:09

Functional Classification of Joints

4.2K
Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
4.2K
Structural Classification of Joints01:20

Structural Classification of Joints

3.5K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
3.5K
Curvilinear Motion: Rectangular Components01:23

Curvilinear Motion: Rectangular Components

483
Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
As the car advances, its position evolves over time. Quantifying the car's velocity involves computing the...
483

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

Correction to "Equation-of-Motion Block-Correlated Coupled Cluster Method for Excited Electronic States of Strongly Correlated Systems".

The journal of physical chemistry letters·2026
Same author

Physiological and transcriptomic analyses of Rosa persica in response to drought stress and functional validation of the transcription factor RpERF113-like.

BMC genomics·2026
Same author

Correction to "Peptide-Mimicking Poly(2-oxazoline)s Possessing Potent Antifungal Activity and BBB Penetrating Property to Treat Invasive Infections and Meningitis".

Journal of the American Chemical Society·2026
Same author

Enhancing Shape Sensing of Slender Medical Continuum Robot Using Carbon Nanotube Piezoresistive Fiber Bandage.

Cyborg and bionic systems (Washington, D.C.)·2026
Same author

The N-terminal region of pdm09/H1N1 PA synergizes with its cognate NP to enhance mammalian adaptation of avian-origin H9N2 canine influenza virus.

Veterinary microbiology·2026
Same author

A reverse genetics-based NS1-truncated live attenuated vaccine confers broad heterologous protection against swine influenza viruses.

Microbial pathogenesis·2026

相关实验视频

Updated: Jul 16, 2025

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

1.2K

DPCN++:用于多功能姿势注册的可差分相位相关联网络.

Zexi Chen, Yiyi Liao, Haozhe Du

    IEEE transactions on pattern analysis and machine intelligence
    |September 20, 2023
    PubMed
    概括

    我们介绍了DPCN++,这是一种使用光谱域分析进行无初始化姿势注册的新方法. 这种方法有效地处理同质和异质的测量,优于计算机视觉和机器人的现有方法.

    科学领域:

    • 计算机视觉 计算机视觉
    • 机器人技术 机器人技术 机器人技术
    • 三维重建的3D重建

    背景情况:

    • 姿势注册对于视觉和机器人的任务至关重要.
    • 现有的方法通常需要初始化或预定义的对应.
    • 处理异质测量是一个重大挑战.

    研究的目的:

    • 开发一个无初始化姿势注册方法,可达到7个自由度 (7DoF).
    • 为了应对记录同质和异质测量的挑战.
    • 改进现有的基于学习和经典姿势记录技术.

    主要方法:

    • 提议DPCN++,一个与特征提取网络相结合的可分化解决器.
    • 在光谱域中利用相位相关性进行无对应和无初始化注册.
    • 采用富里埃变换和球形辐射聚合用于翻译和尺度不变频谱表示.
    • 独立估计频谱中的旋转,尺度和转换.
    • 训练整个管道端到端.

    主要成果:

    • DPCN++在看不见的对象上表现出强大的概括性.
    • 该方法成功地对均质和异质输入进行了注册.

    更多相关视频

    Sample Drift Correction Following 4D Confocal Time-lapse Imaging
    10:04

    Sample Drift Correction Following 4D Confocal Time-lapse Imaging

    Published on: April 12, 2014

    16.5K
    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
    13:44

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

    Published on: August 30, 2013

    42.9K

    相关实验视频

    Last Updated: Jul 16, 2025

    Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
    07:13

    Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

    Published on: October 27, 2023

    1.2K
    Sample Drift Correction Following 4D Confocal Time-lapse Imaging
    10:04

    Sample Drift Correction Following 4D Confocal Time-lapse Imaging

    Published on: April 12, 2014

    16.5K
    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
    13:44

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

    Published on: August 30, 2013

    42.9K
  • 在各种任务和模式中表现优于经典和基于学习的基线.
  • 在部分观测和异质测量上实现卓越的性能.
  • 结论:

    • DPCN++提供了一个强大而高效的解决方案,用于无初始化姿势注册.
    • 谱域方法有效地解开了旋转,尺度和转换.
    • 该方法显示了各种应用的巨大潜力,包括2D图像,3D数据和医学成像.