Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

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

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
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
See all related articles

Related Experiment Video

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

A Generalized Time Rescaling Theorem for Temporal Point Processes.

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
Summary
This summary is machine-generated.

A new generalized time rescaling theorem extends model evaluation for temporal point processes. This method works even when data is incomplete, improving analysis in fields like neuroscience and social media.

More Related Videos

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

Related Experiment Videos

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

Area of Science:

  • Statistics
  • Computational Neuroscience
  • Data Science

Background:

  • Temporal point processes model event dynamics in diverse fields.
  • The time rescaling theorem transforms point processes for model evaluation.
  • Current methods require non-terminating processes and complete observations, limiting practical application.

Purpose of the Study:

  • Introduce a generalized time-rescaling theorem.
  • Address limitations of existing methods for point process model evaluation.
  • Enable broader applicability of model assessment in real-world scenarios.

Main Methods:

  • Developed a generalized time-rescaling theorem.
  • The theorem accommodates terminating processes and incomplete observations.
  • Applied the framework to evaluate point process models.

Main Results:

  • The generalized theorem overcomes limitations of the standard approach.
  • Facilitates robust model fit assessment even with practical data constraints.
  • Demonstrates wider applicability for evaluating temporal point process models.

Conclusions:

  • The generalized time-rescaling theorem offers a more flexible and practical approach.
  • Enhances the evaluation of point process models in neuroscience, social media, and beyond.
  • Provides a valuable tool for assessing model performance in diverse, real-world settings.