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

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

113
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,...
113
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

985
The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the...
985
Time and frequency -Domain Interpretation of Phase-lag Control01:21

Time and frequency -Domain Interpretation of Phase-lag Control

134
Phase-lag controllers are widely used in control systems to improve stability and reduce steady-state errors. A dimmer switch controlling the brightness of a light bulb serves as a practical example of phase-lag control, gradually adjusting the bulb's brightness. Mathematically, phase-lag control or low-pass filtering is represented when the factor 'a' is less than 1.
Phase-lag controllers do not place a pole at zero, but instead influence the steady-state error by amplifying any...
134
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

315
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...
315
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

766
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
766
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

121
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....
121

You might also read

Related Articles

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

Sort by
Same author

Adhesive loose packing limit of axisymmetric nonspherical particles.

Physical review. E·2026
Same author

Gallic Acid Protects Against DSS-Induced Colitis by Modulating Gut Microbiota and Suppressing the Activation of NF-κB/MAPK Signaling Pathway.

Molecular nutrition & food research·2026
Same author

Gallic acid alleviates colitis by restoring intestinal barrier function and enriching butyrate-producing bacteria.

International immunopharmacology·2026
Same author

Influence of particle-wall electrostatic interactions on packing of charged micron particles.

Physical review. E·2026
Same author

Electrocatalytic Hydrogen Peroxide Production: Advances, Challenges, and Future Perspectives.

Chemical record (New York, N.Y.)·2025
Same author

Does Frequency of the Mechanical Vibration Matter? Evaluating the Impact of Whole-Body Vibration Training on Older Adults Strength, Balance, and Gait Performance: A Systematic Review and Network Meta-analysis.

Archives of physical medicine and rehabilitation·2025

Related Experiment Video

Updated: Aug 13, 2025

Image-based Lagrangian Particle Tracking in Bed-load Experiments
10:32

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Published on: July 20, 2017

9.0K

Network Delay and Cache Overflow: A Parameter Estimation Method for Time Window Based Hopping Network.

Zhu Fang1, Zhengquan Xu2

  • 1School of Electronic Information, Wuhan University, Wuhan 430064, China.

Entropy (Basel, Switzerland)
|January 21, 2023
PubMed
Summary
This summary is machine-generated.

Understanding delayed packet loss is crucial for multi-node hopping networks. This study introduces a novel delay time window estimation method, improving data reception in complex network environments.

Keywords:
delayed time windowshopping networknetwork securitytime window compensationtime window parameter estimation

More Related Videos

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

632
Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
16:23

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction

Published on: February 26, 2014

14.4K

Related Experiment Videos

Last Updated: Aug 13, 2025

Image-based Lagrangian Particle Tracking in Bed-load Experiments
10:32

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Published on: July 20, 2017

9.0K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

632
Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
16:23

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction

Published on: February 26, 2014

14.4K

Area of Science:

  • Computer Science
  • Network Engineering

Background:

  • Delayed data loss is a significant challenge in multi-node hopping networks.
  • Existing time windowing approaches often fail in general hopping network environments due to intermediate node forwarding.

Purpose of the Study:

  • To address delayed packet loss in multi-node hopping networks.
  • To propose a generalized delay time window estimation method.

Main Methods:

  • Review of existing time windowing techniques.
  • Development of a delay time window estimation method based on network delay examination.
  • Validation of estimates against delay distribution laws.
  • Simulation tests to optimize delay grouping reception.

Main Results:

  • The proposed method provides accurate network delay estimates.
  • Estimated delay time windows satisfy the delay distribution law.
  • Simulation results demonstrate maximized reception of delay groupings.

Conclusions:

  • The novel delay time window estimation method is more general and applicable to multi-node hopping networks.
  • This approach enhances data transmission reliability in complex network scenarios.