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

Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

251
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
251
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

107
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
107
Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

318
The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
318
Basic Continuous Time Signals01:22

Basic Continuous Time Signals

215
Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
215
Multiple Regression01:25

Multiple Regression

3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K
Classification of Signals01:30

Classification of Signals

472
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
472

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

Single twistable tendon-driven continuum robots.

Nature communications·2026
Same author

ESD-VesNet: uncertainty-aware vessel segmentation network for endoscopic submucosal dissection with hard negative mining.

International journal of computer assisted radiology and surgery·2026
Same author

High-performance quantum interconnect between bosonic modules beyond transmission loss constraints.

Science bulletin·2026
Same author

Transferable Deep Reinforcement Learning With Edge-Contour-Depth Fusion for Autonomous Wireless Capsule Endoscopy Navigation.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

High-Fidelity Controlled-Phase Gate for Binomial Codes via Geometric Phase Engineering.

Physical review letters·2026
Same author

How can reasoning capability empower the AI copilot robot in endoscopic surgery.

NPJ digital medicine·2026

相关实验视频

Updated: Jul 9, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

594

使用连续条件生成对抗网络进行回归的数据增强,并将其应用于改进的光谱传感.

Yuhao Zhu, Haoyu Su, Pengsheng Xu

    Optics express
    |November 29, 2023
    PubMed
    概括

    一个新的连续条件生成对抗网络 (CcGAN) 从有限的数据生成高质量的合成频谱. 这种方法通过数据增强显著改善深度神经网络 (DNN) 回归模型的性能.

    更多相关视频

    ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
    07:11

    ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

    Published on: August 19, 2021

    2.5K
    Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
    03:37

    Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

    Published on: March 1, 2024

    767

    相关实验视频

    Last Updated: Jul 9, 2025

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    594
    ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
    07:11

    ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

    Published on: August 19, 2021

    2.5K
    Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
    03:37

    Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

    Published on: March 1, 2024

    767

    科学领域:

    • 频谱学是一种光谱学.
    • 机器学习 机器学习
    • 数据科学数据科学数据科学

    背景情况:

    • 在光谱学中的机器学习受限于缺乏光谱样本.
    • 对于连续回归模型,缺乏有效的算法来模拟有限的真实光谱中的合成光谱.

    研究的目的:

    • 引入一个连续的条件生成对抗网络 (CcGAN),用于自主合成频谱的生成.
    • 为应对有限的光谱数据在机器学习辅助光谱分析的挑战.

    主要方法:

    • 开发了一个连续的条件生成对抗网络 (CcGAN) 来生成合成频谱.
    • 利用来自基于自我干扰微波振器 (SIMRR) 传感器的小型数据集.
    • 使用主要组件分析 (PCA) 评估生成的光谱,并将其纳入深度神经网络 (DNN) 回归模型以进行数据增强.

    主要成果:

    • 从有限的真实光谱数据中,CCGAN成功生成了高质量的合成光谱.
    • 主要成分分析无法区分真实和CcGAN生成的合成光谱.
    • 整合合成光谱显著提高了DNN回归模型的预测性能.

    结论:

    • 对于产生高质量的合成光谱来说,CCGAN是一种有前途的方法.
    • 开发的方法为光谱学中的回归任务提供了卓越的数据增强效应.
    • 这种技术有效地克服了机器学习应用中频谱样本不足的局限性.