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

Properties of DTFT I01:24

Properties of DTFT I

341
In signal processing, Discrete-Time Fourier Transforms (DTFTs) play a critical role in analyzing discrete-time signals in the frequency domain. Various properties of the DTFTs such as linearity, time-shifting, frequency-shifting, time reversal, conjugation, and time scaling help understand and manipulate these signals for different applications.
The linearity property of DTFTs is fundamental. If two discrete-time signals are multiplied by constants a and b respectively, and then combined to...
341
Properties of the z-Transform I01:17

Properties of the z-Transform I

150
The z-transform is a fundamental tool in digital signal processing, enabling the analysis of discrete-time systems through its various properties. It is an invaluable tool for analyzing discrete-time systems, offering a range of properties that simplify complex signal manipulations. One fundamental property is linearity. For any two discrete-time signals, the z-transform of their linear combination equals the same linear combination of their individual z-transforms. This property is essential...
150
Properties of the z-Transform II01:16

Properties of the z-Transform II

95
The property of Accumulation in signal processing is derived by analyzing the accumulated sum of a discrete-time signal and using the time-shifting property to determine its z-transform. This principle reveals that the z-transform of the summed signal is related to the z-transform of the original signal by a multiplicative factor.
Moreover, the convolution property indicates that the convolution of two signals in the time domain corresponds to the product of their z-transforms in the frequency...
95
Properties of Laplace Transform-I01:15

Properties of Laplace Transform-I

331
The Laplace transform is a powerful mathematical tool used to convert functions from the time domain into the frequency domain, greatly simplifying the analysis and solution of linear time-invariant systems. This transformation is facilitated by several universal properties: Linearity, Time-Scaling, Time-Shifting, and Frequency Shifting.
The Linearity property is foundational to the Laplace transform. It states that the transform of a linear combination of functions is equivalent to the same...
331
Properties of DTFT II01:24

Properties of DTFT II

174
In the study of discrete-time signal processing, understanding the properties of the Discrete-Time Fourier Transform (DTFT) is crucial for analyzing and manipulating signals in the frequency domain. Several properties, including frequency differentiation, convolution, accumulation, and Parseval's relation, offer powerful tools for signal analysis.
The frequency differentiation property is illustrated by considering a DTFT pair and differentiating both sides with respect to ω.
174
Reynolds Transport Theorem01:24

Reynolds Transport Theorem

783
The Reynolds transport theorem provides a framework to relate the time rate of change of an extensive property within a system to that in a control volume, which is crucial for analyzing fluid dynamics. Extensive properties, such as mass, velocity, acceleration, temperature, and momentum, can be expressed in terms of the mass of a fluid portion. These properties are called extensive because they depend on the system's size, while intensive properties are their corresponding values per unit...
783

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 multi-firearm, multi-orientation audio dataset of gunshots.

Data in brief·2023
Same author

Pareto-Optimal Model Selection via SPRINT-Race.

IEEE transactions on cybernetics·2017
Same author

Multi-Objective Model Selection via Racing.

IEEE transactions on cybernetics·2015
Same author

A Simple Method for Solving the SVM Regularization Path for Semidefinite Kernels.

IEEE transactions on neural networks and learning systems·2015
Same author

Pareto-path multitask multiple kernel learning.

IEEE transactions on neural networks and learning systems·2014
Same author

Multitask Classification Hypothesis Space With Improved Generalization Bounds.

IEEE transactions on neural networks and learning systems·2014
Same journal

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Neural computation·2026
Same journal

DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning.

Neural computation·2026
Same journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026
Same journal

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators.

Neural computation·2026
Same journal

A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing.

Neural computation·2026
Same journal

Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

Neural computation·2026
查看所有相关文章

相关实验视频

Updated: May 24, 2025

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

5.9K

一个关于时间点过程的通用时间调整定理.

Xi Zhang1, Akshay Aravamudan2, Georgios C Anagnostopoulos3

  • 1Electrical Engineering and Computer Science, Florida Institute of Technology, Melbourne, FL 32901, U.S.A. zhang2012@my.fit.edu.

Neural computation
|March 3, 2025
PubMed
概括
此摘要是机器生成的。

一个新的通用时间调整定理扩展了对时间点过程的模型评估. 这种方法即使在数据不完整的情况下也起作用,改善了神经科学和社交媒体等领域的分析.

更多相关视频

Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia
10:05

Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia

Published on: January 27, 2018

9.7K
Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
08:42

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

3.0K

相关实验视频

Last Updated: May 24, 2025

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

5.9K
Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia
10:05

Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia

Published on: January 27, 2018

9.7K
Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
08:42

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

3.0K

科学领域:

  • 统计 统计 统计 统计
  • 计算神经科学是一种神经科学.
  • 数据科学数据科学数据科学

背景情况:

  • 时间点过程模拟了各种领域的事件动态.
  • 时间调整定理转换模型评估的点过程.
  • 目前的方法需要非终止的过程和完整的观察,限制实际应用.

研究的目的:

  • 介绍一个通用的时间调整定理.
  • 解决点过程模型评估现有方法的局限性.
  • 实现模型评估在现实场景中的更广泛应用.

主要方法:

  • 开发了一个通用的时间调整定理.
  • 该定理适用于终止过程和不完整的观察.
  • 应用框架来评估点过程模型.

主要成果:

  • 一般定理克服了标准方法的局限性.
  • 即使在实际数据约束的情况下,也可以进行可靠的模型合适性评估.
  • 证明了用于评估时间点过程模型的更广泛的适用性.

结论:

  • 一般化的时间调整定理提供了一个更灵活,更实用的方法.
  • 增强神经科学,社交媒体及其他领域点过程模型的评估.
  • 提供了一种有价值的工具,用于评估模型在各种现实环境中的性能.