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: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

218
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...
218
Deconvolution01:20

Deconvolution

123
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...
123
Convolution Properties I01:20

Convolution Properties I

126
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:
126
Convolution Properties II01:17

Convolution Properties II

155
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...
155
Inverse z-Transform by Partial Fraction Expansion01:20

Inverse z-Transform by Partial Fraction Expansion

254
The inverse z-transform is a crucial technique for converting a function from its z-domain representation back to the time domain. One effective method for finding the inverse z-transform is the Partial Fraction Method, which involves decomposing a function into simpler fractions with distinct coefficients. These fractions correspond to known z-transform pairs, facilitating the inverse transformation process.
To begin the process, the poles of the function are identified and the function is...
254
Inverting and Non-inverting OpAmps01:20

Inverting and Non-inverting OpAmps

508
In an inverting amplifier, the input voltage is connected through a resistor to the inverting terminal. Meanwhile, the non-inverting terminal is grounded and a feedback resistor is established between the inverting and output terminal, as depicted in Figure 1.
508

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

Pharmacological SIRT2 Inhibition by AGK2 Attenuates Hapten-Induced Keratinocyte Inflammation via NF-κB/NLRP3 Suppression and Nrf2 Reactivation.

Iranian journal of pharmaceutical research : IJPR·2026
Same author

Generation of High-Brilliance Polarized γ-Rays Via Vacuum Dichroism-Assisted Vacuum Birefringence.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025
Same author

Compact spin-polarized positron acceleration in multilayer microhole-array films.

Physical review. E·2025
Same author

Potential role of coagulation markers in early detection of bone metastasis in gastric cancer: A critical review.

World journal of gastrointestinal oncology·2025
Same author

Clinical significance and potential mechanism of AEBP1 in glioblastoma.

Journal of neuropathology and experimental neurology·2024
Same author

Manipulation of Giant Multipole Resonances via Vortex γ Photons.

Physical review letters·2023

相关实验视频

Updated: May 20, 2025

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

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

8.9K

大约可逆神经网络用于学习图像压缩.

Yanbo Gao, Shuai Li, Meng Fu

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |May 14, 2025
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了一种用于学习图像压缩的近似可逆神经网络 (A-INN) 框架. A-INN框架有效地减少了量子化噪声,并增强了高频组件,以获得更优质的图像重建质量.

    更多相关视频

    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

    442
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    335

    相关实验视频

    Last Updated: May 20, 2025

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

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    8.9K
    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

    442
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    335

    科学领域:

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

    背景情况:

    • 学习图像压缩使用合变换来编码和解码图像.
    • 可逆神经网络 (INN) 显示出构建这些转换的前景.
    • 量子化噪声挑战了INN在压缩中的可逆性.

    研究的目的:

    • 为学习图像压缩提出一个新的框架,即近似可逆神经网络 (A-INN).
    • 为了应对基于INN的压缩中量子化噪声和高频信息丢失的挑战.
    • 为基于INN的损耗图像压缩方法提供理论基础.

    主要方法:

    • 开发了一个大致可逆神经网络 (A-INN) 框架.
    • 包含一个渐进式消噪模块 (PDM) 来减轻量子化噪声.
    • 设计了一个级联特征恢复模块 (CFRM) 用于特征通道压缩.
    • 引入了一种频率增强的分解和合成模块 (FDSM),以保存高频细节.

    主要成果:

    • 在解码过程中,A-INN框架有效地减少了量子化噪声.
    • CFRM 改善了从低维表示中恢复特征.
    • FDSM 增强了高频图像组件的保存.
    • 实验结果显示了具有竞争力或优越的压缩效率.

    结论:

    • 拟议的A-INN框架提供了一个强大的方法来学习图像压缩.
    • 综合模块 (PDM,CFRM,FDSM) 显著提高了重建质量.
    • A-INN为未来基于INN的压缩研究提供了坚实的理论和实践基础.