Jove
Visualize
联系我们

相关概念视频

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

89
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
89
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

81
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
81
Reducing Line Loss01:18

Reducing Line Loss

150
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...
150
Downsampling01:20

Downsampling

149
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
149
Sampling Methods: Overview01:06

Sampling Methods: Overview

302
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
302
Upsampling01:22

Upsampling

225
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...
225

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

Synergy Effect of Plasmonic Field Enhancement and Light Confinement in Mesoporous Titania-Coated Aluminum Nanovoid Photoelectrode.

The journal of physical chemistry letters·2023
Same journal

Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.

Journal of imaging·2026
Same journal

Coronary Artery Anomalies and Anatomical Variants: Cross-Sectional Diagnostic Imaging and Clinical Background.

Journal of imaging·2026
Same journal

YoLeTooth: A Unified Framework for Joint Tooth Segmentation and Periapical Lesion Detection in Panoramic Radiographs.

Journal of imaging·2026
Same journal

Radiomics-Guided Multi-Sequence Learning for Pathological Complete Response Prediction from Breast MRI with Missing Auxiliary Sequences.

Journal of imaging·2026
Same journal

Cutaneous Thermography in Arthropathies: Quantitative Imaging, Machine Learning, and Clinical Translation.

Journal of imaging·2026
Same journal

Two-Stage Dynamic Synergistic Segmentation Method for Myocardial Pathology.

Journal of imaging·2026
查看所有相关文章
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

相关实验视频

Updated: Jun 23, 2025

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
07:05

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Published on: February 15, 2022

2.6K

一种基于神经网络的水印方法,接近JPEG量子化.

Shingo Yamauchi1, Masaki Kawamura1

  • 1Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Yamaguchi 753-8512, Japan.

Journal of imaging
|June 26, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的神经网络水印方法,使用量子化激活函数来准确模拟JPEG压缩. 这种方法提高了对JPEG压缩攻击的稳定性,在图像质量和比特错误率方面表现优于传统方法.

关键词:
在 JPEG 压缩中使用 JPEG 压缩.激活功能的激活功能神经网络的神经网络的神经网络水印法是一种水印方法.

更多相关视频

Using Computer Vision Libraries to Streamline Nuclei Quantification
06:25

Using Computer Vision Libraries to Streamline Nuclei Quantification

Published on: June 6, 2025

125
Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ
08:44

Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ

Published on: June 5, 2018

67.5K

相关实验视频

Last Updated: Jun 23, 2025

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
07:05

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Published on: February 15, 2022

2.6K
Using Computer Vision Libraries to Streamline Nuclei Quantification
06:25

Using Computer Vision Libraries to Streamline Nuclei Quantification

Published on: June 6, 2025

125
Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ
08:44

Quantifying Microglia Morphology from Photomicrographs of Immunohistochemistry Prepared Tissue Using ImageJ

Published on: June 5, 2018

67.5K

科学领域:

  • 计算机视觉 计算机视觉
  • 数字图像处理 数字图像处理
  • 机器学习 机器学习

背景情况:

  • 基于神经网络的水印对于数字内容的保护至关重要.
  • 传统的方法往往模拟JPEG压缩不准确地使用噪声添加.
  • 现有的方法缺乏对实际的JPEG压缩工件的强大性能.

研究的目的:

  • 开发一种神经网络水印方法,提高了对JPEG压缩的稳定性.
  • 引入一种新的量子化激活函数,可以准确地模仿JPEG量子化.
  • 通过更有效的水印技术来提高数字图像安全性.

主要方法:

  • 提出了一种新型的量子化激活函数,该函数由形 hyperbolic 函数组成.
  • 将量子化激活功能集成到ReDMark网络的攻击层中.
  • 与使用标准和定量化激活功能处理图像的传统ReDMark相比,性能进行了比较.

主要成果:

  • 拟议的量子化激活函数准确地接近JPEG压缩.
  • 使用量子化激活功能的网络表现出对JPEG压缩的优越稳定性.
  • 与ReDMark.com相比,新方法实现了较低的比特错误率 (BER).

结论:

  • 新的量子化激活功能显著提高了神经网络水印的稳定性,对抗JPEG压缩.
  • 这种方法提供了比现有技术更好的图像质量和更低的BER.
  • 这种方法代表了安全的数字图像水印的重大进步.