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

Convolution Properties II01:17

Convolution Properties II

203
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
203
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

262
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
262
Convolution Properties I01:20

Convolution Properties I

152
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
152
Deconvolution01:20

Deconvolution

160
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
160
Neural Circuits01:25

Neural Circuits

1.2K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.2K
Upsampling01:22

Upsampling

238
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...
238

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

Effectiveness of stereotactic body radiotherapy for hepatocellular carcinoma with portal vein and/or inferior vena cava tumor thrombosis.

PloS one·2013
Same author

Phylogenomic analyses of nuclear genes reveal the evolutionary relationships within the BEP clade and the evidence of positive selection in Poaceae.

PloS one·2013
Same author

A general and robust strategy for the synthesis of nearly monodisperse colloidal nanocrystals.

Nature nanotechnology·2013
Same author

AG10 inhibits amyloidogenesis and cellular toxicity of the familial amyloid cardiomyopathy-associated V122I transthyretin.

Proceedings of the National Academy of Sciences of the United States of America·2013
Same author

Identification and functional characteristics of chlorpyrifos-degrading and plant growth promoting bacterium Acinetobacter calcoaceticus.

Journal of basic microbiology·2013
Same author

[Study on the chemical constituents of Buddleja davidii].

Zhong yao cai = Zhongyaocai = Journal of Chinese medicinal materials·2013

相关实验视频

Updated: Jul 5, 2025

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

对于卷积神经网络而言,一种改进的聚合方法.

Lei Zhao1, Zhonglin Zhang2

  • 1School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China.

Scientific reports
|January 18, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的T-Max-Avg聚合层,用于卷积神经网络 (CNN). 这种自适应层提高了与标准聚合方法相比,在各种数据集上提取特征和分类准确度.

更多相关视频

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

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

405
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K

相关实验视频

Last Updated: Jul 5, 2025

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

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

405
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 在卷积神经网络 (CNN) 中,聚合层对于降低维度和计算效率至关重要.
  • 标准的聚合方法,如最大聚合和平均聚合,具有局限性,可能不适合所有数据集.
  • 需要可定制的聚合层,以适应性学习和提取特定应用程序的相关功能.

研究的目的:

  • 设计和实施一种新的可定制的聚合层,用于在CNN中增强功能提取.
  • 引入T-Max-Avg聚合层与适应性特征选择的值参数 (T).
  • 通过学习最佳的聚合策略来提高分类性能.

主要方法:

  • 为CNNs提出了一个新的T-Max-Avg聚合层.
  • T-Max-Avg层使用值参数 (T) 来选择K个最高交互的像素.
  • 这允许控制输出特征是否基于最大值或加权平均值.
  • 该层在培训期间学习最佳的聚合策略.

主要成果:

  • T-Max-Avg聚合层在三个不同的数据集中表现良好.
  • 与标准平均聚合,最大聚合和Avg-TopK方法相比,获得了更高的准确性.
  • 当与LeNet-5模型集成时,在CIFAR-10,CIFAR-100和MNIST数据集上表现优于现有的方法.

结论:

  • 拟议的T-Max-Avg聚合层有效地提高了CNN的特征提取能力.
  • 聚合策略的自适应学习导致了更好的歧视性信息捕获.
  • 这种定制的聚合方法在基准数据集上提供了比传统方法更好的分类性能.