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

Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been developed.

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

Deep-Learning-Based Baseline Evaluation of Public WiFi CSI Datasets for Contactless RF-Based Human Activity Recognition.

Sensors (Basel, Switzerland)·2026
Same author

Integration of DSRC, mmWave, and THz Bands in a 6G CR-SDVN.

Sensors (Basel, Switzerland)·2025
Same author

Enhancing Heterogeneous Communication for Foggy Highways Using Vehicular Platoons and SDN.

Sensors (Basel, Switzerland)·2025
Same author

Auscultation-Based Pulmonary Disease Detection through Parallel Transformation and Deep Learning.

Bioengineering (Basel, Switzerland)·2024
Same author

Robust Epileptic Seizure Detection Using Long Short-Term Memory and Feature Fusion of Compressed Time-Frequency EEG Images.

Sensors (Basel, Switzerland)·2023
Same author

Mixed-Input Deep Learning Approach to Sleep/Wake State Classification by Using EEG Signals.

Diagnostics (Basel, Switzerland)·2023

相关实验视频

Updated: Jul 9, 2026

A Lightweight, Headphones-based System for Manipulating Auditory Feedback in Songbirds
10:13

A Lightweight, Headphones-based System for Manipulating Auditory Feedback in Songbirds

Published on: November 26, 2012

14.3K

重塑生物声学事件检测:利用通过传导推理和数据增强的少数射击学习 (FSL).

Nouman Ijaz1, Farhad Banoori2,3, Insoo Koo1

  • 1Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea.

Bioengineering (Basel, Switzerland)
|July 27, 2024
PubMed
概括

这项研究引入了一种用于生物声学事件检测的新型少数射击学习 (FSL) 方法. 该方法提高了鉴定动物声音的准确性,使用有限的数据,优于现有方法.

关键词:
生物声学事件检测事件检测数据增强数据增强短时间学习 (FSL)传导性推理推理的推理.

更多相关视频

Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

25.1K
Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.5K

相关实验视频

Last Updated: Jul 9, 2026

A Lightweight, Headphones-based System for Manipulating Auditory Feedback in Songbirds
10:13

A Lightweight, Headphones-based System for Manipulating Auditory Feedback in Songbirds

Published on: November 26, 2012

14.3K
Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

25.1K
Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.5K

科学领域:

  • 生物声学是一种生物声学.
  • 机器学习 机器学习
  • 信号处理 信号处理

背景情况:

  • 生物声学事件检测是具有挑战性的,因为缺乏标记数据用于训练监督学习模型.
  • 现有的方法在有限的记录和少数标记的例子中扎,阻碍了准确的动物声音分类.

研究的目的:

  • 开发和评估用于生物声学事件检测的几次射击学习 (FSL) 方法,以克服数据限制.
  • 改进自然息地动物声音的识别和分类,使用最小的标记数据.

主要方法:

  • 一个简单的学习 (FSL) 方法,结合了传导推断和数据增强 (Mel谱图上的 SpecAugment).
  • 传导推理反复地改进类原型和特征提取器以捕获关键模式.
  • 数据增强技术用于增加培训数据的数量和多样性.

主要成果:

  • 与最先进的方法相比,DCASE-2022数据集的F-score显著改善了27%,DCASE-2021数据集的F-score显著改善了10%.
  • 拟议的方法在各种动物物种,记录条件和持续时间中显示出强大的性能.
  • FSL方法的所有组件都为显著的性能增长做出了贡献.

结论:

  • 开发的FSL方法有效地解决了生物声学中有限的标记数据的挑战.
  • 这种方法提供了一个有希望的解决方案,用于准确和适应性动物声音检测在现实世界的场景.
  • 该方法能够在不同的声环境和物种中进行概括,这突显了其实用性的实用性.