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

Deconvolution01:20

Deconvolution

129
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...
129
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

85
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....
85
Region of Convergence of Laplace Tarnsform01:20

Region of Convergence of Laplace Tarnsform

474
The Region of Convergence (ROC) is a fundamental concept in signal processing and system analysis, particularly associated with the Laplace transform. The ROC represents an area in the complex plane where the Laplace transform of a given signal converges, determining the transform's applicability and utility.
Consider a decaying exponential signal that begins at a specific time. When deriving its Laplace transform, the time-domain variable is replaced with a complex variable. This...
474
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

59
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,...
59
Convolution Properties II01:17

Convolution Properties II

166
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...
166
Convolution Properties I01:20

Convolution Properties I

134
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:
134

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

A functional amyloid matrix underpins the PDIM-architected corded superstructure of the <i>Mycobacterium tuberculosis</i> biofilm.

bioRxiv : the preprint server for biology·2026
Same author

Assessment of retinal neurodegeneration in metabolic syndrome and its vascular determinants.

Acta ophthalmologica·2026
Same author

Acupuncture in remodeling the tumor microenvironment: current status and challenges.

Frontiers in immunology·2026
Same author

SYN-OCT:A synthetic dataset of ocular optical coherence tomography images from healthy and glaucoma eyes.

Scientific data·2026
Same author

The optical coherence tomography and microperimetry biomarker evaluation in patients with geographic atrophy (OMEGA) study: Geographic atrophy progression in fundus autofluorescence - OMEGA report 3.

Acta ophthalmologica·2026
Same author

MAGNETIC INTRAOCULAR FOREIGN BODY INJURIES: Intravitreal Antibiotic Prophylaxis, Endophthalmitis Risk and Prognostic Factors.

Retina (Philadelphia, Pa.)·2026

相关实验视频

Updated: May 27, 2025

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography
11:21

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography

Published on: January 15, 2013

11.5K

在光学连贯断层扫描中使用相位感知生成对抗网络进行复杂的结合物去除.

Valentina Bellemo1,2,3, Richard Haindl4, Manojit Pramanik5

  • 1Nanyang Technological University, School of Chemistry, Chemical Engineering and Biotechnology, Singapore, Singapore.

Journal of biomedical optics
|February 18, 2025
PubMed
概括
此摘要是机器生成的。

一种新的深度学习方法使用生成对抗网络从光学连贯断层扫描 (OCT) 扫描中删除复杂的结合元件 (CCA). 这种基于软件的方法消除了对额外硬件的需求,为增强成像提供了具有成本效益的解决方案.

关键词:
复杂的结合物去除复杂的结合物去除.生成性的对抗性网络.光学连贯性断层扫描 (optical coherence tomography) 是一种光学连贯性断层扫描技术.

更多相关视频

Author Spotlight: Advancements in In Vivo and Ex Vivo Retinal Imaging for Improved Glaucoma Diagnosis and Treatment
07:02

Author Spotlight: Advancements in In Vivo and Ex Vivo Retinal Imaging for Improved Glaucoma Diagnosis and Treatment

Published on: June 30, 2023

1.4K
Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture
09:04

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Published on: February 23, 2018

9.4K

相关实验视频

Last Updated: May 27, 2025

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography
11:21

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography

Published on: January 15, 2013

11.5K
Author Spotlight: Advancements in In Vivo and Ex Vivo Retinal Imaging for Improved Glaucoma Diagnosis and Treatment
07:02

Author Spotlight: Advancements in In Vivo and Ex Vivo Retinal Imaging for Improved Glaucoma Diagnosis and Treatment

Published on: June 30, 2023

1.4K
Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture
09:04

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Published on: February 23, 2018

9.4K

科学领域:

  • 生物医学光学 生物医学光学
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 在频域光连贯断层扫描 (FD-OCT) 中,复杂的并联器件 (CCA) 需要额外的硬件,从而增加了系统的复杂性和成本.
  • 基于软件的CCA移除解决方案对于效率和成本效益来说是非常理想的.

研究的目的:

  • 开发一种深度学习方法,以便在OCT扫描中有效地去除CCA.
  • 在FD-OCT系统中消除了对额外硬件组件的需求.

主要方法:

  • 实施使用生成对抗网络 (GAN) 的深度学习方法.
  • 利用来自OCT扫描的强度和相位图像来改善文物移除.
  • 开发一个CCA去除-GAN模型.

主要成果:

  • 成功地将OCT扫描与CCA转换为各种样本 (幽灵,人类皮肤,老鼠眼睛) 的无文物扫描.
  • 使用相位稳定扫描源OCT原型进行体内成像的演示.
  • 通过相位图像的集成,在CCA去除方面显著提高了性能.

结论:

  • 开发的方法提供了一个低成本的,数据驱动的,基于软件的CCA去除解决方案.
  • 通过有效的工件减少,增强FD-OCT成像能力.
  • 为基于硬件的CCA移除技术提供了可行的替代方案.