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

Upsampling01:22

Upsampling

188
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
188
Random Sampling Method01:09

Random Sampling Method

10.9K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
10.9K
Cluster Sampling Method01:20

Cluster Sampling Method

11.6K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.6K
Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

4.6K
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...
4.6K
Sampling Theorem01:15

Sampling Theorem

277
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
277

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

Co-exposure to environmental lead and hypertension exacerbates anxiety and depression via mtDNA-mediated cGAS phase separation.

Environmental pollution (Barking, Essex : 1987)·2026
Same author

Drying methods for <i>Rheum tanguticum</i>: a comprehensive study of quality traits and metabolite dynamics.

Frontiers in pharmacology·2026
Same author

Nanopore Polyphenol Fingerprinting with Anthocyanin-Catechin Signatures for Tea Quality Grading and Cultivar Discrimination.

Nano letters·2026
Same author

Artificial grassland establishment alters soil metabolomes by reshaping multi-domain microbial networks.

Journal of environmental management·2026
Same author

Optimized composite cryoprotectants enhance survival and membrane integrity of Bifidobacterium breve BX-18 during lyophilization.

Journal of dairy science·2026
Same author

Mapping the Invisible Landscape of Pesticides and Adjuvants in Peri-Urban Agricultural Waterways of the Megacity Shanghai.

Environmental science & technology·2026
Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
查看所有相关文章

相关实验视频

Updated: May 24, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

475

向强大的点云识别与样本自适应自动增强.

Jianan Li, Jie Wang, Junjie Chen

    IEEE transactions on pattern analysis and machine intelligence
    |March 3, 2025
    PubMed
    概括
    此摘要是机器生成的。

    通过使用样本适应性转换,AdaptPoint++提高了3D点云感知强度. 这种自动增强框架通过学习内在结构来改善损坏的数据分类.

    更多相关视频

    Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
    07:46

    Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

    Published on: August 9, 2024

    610
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    348

    相关实验视频

    Last Updated: May 24, 2025

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    475
    Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
    07:46

    Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

    Published on: August 9, 2024

    610
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    348

    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 3D数据处理 3D数据处理

    背景情况:

    • 3D感知需要对数据腐败的稳定性.
    • 传统的增强方法无法适应样本结构,导致不均的增强.
    • 现有的方法缺乏足够的现实世界受损的3D点云数据.

    研究的目的:

    • 开发一个样本适应的自动增强框架,以实现强大的3D感知.
    • 通过考虑内在的样本结构来改进处理受损的3D点云数据.
    • 引入新的数据集,用于培训和评估现实世界受损数据模型.

    主要方法:

    • 拟议的AdaptPoint++自动增强框架与模拟器和区分器.
    • 模拟器使用位置感知特征提取,变形控制器和面具控制器进行自适应腐败模拟.
    • 引入结构重建辅助学习和感知指导反.
    • 创建了两个新的数据集:ScanObjectNNN-C和MVPNET-C.

    主要成果:

    • 在多个腐败基准上,AdaptPoint++实现了最先进的性能.
    • 该框架有效地生成了根据内在数据结构量身定制的受损样本.
    • 结构重建辅助学习增强了对腐败的分类器稳定性.

    结论:

    • 样本适应增强优于传统方法的3D腐败强度.
    • AdaptPoint++提供了一种有效的解决方案,用于在受损环境中增强3D感知.
    • 这些新型数据集促进了对受损3D数据处理的进一步研究和开发.