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

Force Classification01:22

Force Classification

1.2K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.2K
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

6.4K
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...
6.4K
Improving Translational Accuracy02:07

Improving Translational Accuracy

10.5K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
10.5K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

327
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
327
Aggregates Classification01:29

Aggregates Classification

326
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
326
Reducing Line Loss01:18

Reducing Line Loss

154
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
154

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

Growth strategy of <i>Juniperus tibetica</i> ancient clusters under high-altitude and cold conditions in western Xizang, China.

Ying yong sheng tai xue bao = The journal of applied ecology·2026
Same author

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026
Same author

Dendritic nonlinearities mitigate communication costs.

Patterns (New York, N.Y.)·2026
Same author

Recent Advances in Neoadjuvant Treatment of Anaplastic Thyroid Carcinoma: A Narrative Review.

Current treatment options in oncology·2026
Same author

Extruded biodegradable Zn-5Cu alloys with integrated osteoimmunomodulatory, antibacterial, and anti-osteolytic properties for patellar fracture suture repair.

Acta biomaterialia·2026
Same author

Task-Dependent Cortico-Spinal Coupling in the Delta Band During Movement Execution and Inhibitory Control.

IEEE transactions on bio-medical engineering·2026

相关实验视频

Updated: Jul 5, 2025

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K

可训练的Spiking-YOLO用于低延迟和高性能物体检测.

Mengwen Yuan1, Chengjun Zhang1, Ziming Wang2

  • 1Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou 311100, China.

Neural networks : the official journal of the International Neural Network Society
|January 11, 2024
PubMed
概括

这项研究介绍了可训练的Spiking-YOLO (Tr-Spiking-YOLO),这是一个高效的尖端神经网络用于对象检测. 它在边缘设备上实现了高精度和速度,优于传统模型.

关键词:
动态视觉传感器是一个动态视觉传感器.边缘计算是一种边缘计算.对象检测检测对象检测对象检测尖的神经网络的神经网络.

更多相关视频

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

9.9K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

556

相关实验视频

Last Updated: Jul 5, 2025

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K
A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

9.9K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

556

科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 神经形态工程的神经形态工程

背景情况:

  • 尖端神经网络 (SNN) 提供高效,事件驱动的计算,适合边缘AI.
  • 在物体检测任务中为SNNs实现高精度和速度仍然存在挑战.

研究的目的:

  • 开发一个端到端可训练的Spiking-YOLO (Tr-Spiking-YOLO) 模型,用于低延迟,高性能物体检测.
  • 在基于框架和基于事件的数据集上评估模型的有效性.
  • 分析不同解码方法对检测性能的影响.

主要方法:

  • 提出了一个端到端可训练的Spiking-YOLO (Tr-Spiking-YOLO) 架构.
  • 对 PASCAL VOC (基于框架) 和 GEN1 汽车检测 (基于事件) 数据集的模型进行了评估.
  • 研究了各种解码策略对物体检测结果的影响.

主要成果:

  • 与ANN和基于转换的SNN模型相比,Tr-Spiking-YOLO在准确性,延迟和能耗方面表现出了竞争力或优异的性能.
  • 在边缘设备上实现实时检测 (14-39 FPS) 与理想的平均平均精度 (mAP).
  • 性能受到解码方法的选择的影响.

结论:

  • Tr-Spiking-YOLO有效地解决了SNNs在物体检测方面的挑战,使其能够高效准确地执行.
  • 该模型适用于资源有限的边缘平台上的实时对象检测.
  • 对解码方法的进一步研究可以优化基于SNN的对象检测.