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

Kaplan-Meier Approach01:24

Kaplan-Meier Approach

212
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
212
Transient and Steady-state Response01:24

Transient and Steady-state Response

230
In control systems, test signals are essential for evaluating performance under various conditions. The ramp function is effective for systems undergoing gradual changes, while the step function is suitable for assessing systems facing sudden disturbances. For systems subjected to shock inputs, the impulse function is the most appropriate test signal.
These test signals are integral in designing control systems to exhibit two key performance aspects: transient response and steady-state...
230
State Space to Transfer Function01:21

State Space to Transfer Function

255
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
255

You might also read

Related Articles

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

Sort by
Same author

The relationship between cognitive function and quality of life in older hypertension patients: a parallel mediation effect of frailty and medication discrepancies.

BMC geriatrics·2025
Same author

Unprecedented Energy Density of Polyimide Dielectrics at Elevated Temperatures Utilizing Atomic Engineering to Decouple π-Conjugation.

Advanced materials (Deerfield Beach, Fla.)·2025
Same author

Balancing mechanical-thermal-electrical properties in cellulose ionogels via crystallization-induced molecular assembly.

Nature communications·2025
Same author

Smart pyraclostrobin nanopesticides based on chitosan- and pectin-gated metal-organic framework PCN-777 for boosted efficacy and biosafety.

International journal of biological macromolecules·2025
Same author

Promising Synthesis Route of Ultra-Conductive Porous Polypyrrole via Template-Free Polymerization.

Macromolecular rapid communications·2025
Same author

An All-Solid-State Li-Cu Battery via Cuprous/Lithium-Ion Halide Solid Electrolyte.

Angewandte Chemie (International ed. in English)·2025
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Aug 8, 2025

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
12:03

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

8.5K

Event-Triggered Kalman Filter and Its Performance Analysis.

Xiaona Li1, Gang Hao1,2

  • 1School of Electronic Engineering, Heilongjiang University, Harbin 150080, China.

Sensors (Basel, Switzerland)
|February 28, 2023
PubMed
Summary
This summary is machine-generated.

An efficient event-triggered Kalman filter reduces communication costs for linear systems. This algorithm accurately sets thresholds for improved estimation accuracy in both steady and time-varying systems.

Keywords:
accuracy comparisonevent triggerstate estimationthreshold setting

More Related Videos

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

13.8K
Author Spotlight: A Streamlined and Accessible Analysis Method to Quantify Optokinetic Reflex Tracking Responses
05:26

Author Spotlight: A Streamlined and Accessible Analysis Method to Quantify Optokinetic Reflex Tracking Responses

Published on: April 12, 2024

813

Related Experiment Videos

Last Updated: Aug 8, 2025

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
12:03

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

8.5K
An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

13.8K
Author Spotlight: A Streamlined and Accessible Analysis Method to Quantify Optokinetic Reflex Tracking Responses
05:26

Author Spotlight: A Streamlined and Accessible Analysis Method to Quantify Optokinetic Reflex Tracking Responses

Published on: April 12, 2024

813

Area of Science:

  • Control Systems Engineering
  • Signal Processing
  • Estimation Theory

Background:

  • Kalman filters are crucial for estimating states in linear systems.
  • Traditional Kalman filters require frequent data transmission, increasing communication load.
  • Event-triggered mechanisms offer a solution to reduce communication frequency.

Purpose of the Study:

  • To propose an efficient event-triggered Kalman filter algorithm.
  • To accurately set the trigger frequency based on a significance test.
  • To calculate approximate estimation accuracy for the proposed filter.

Main Methods:

  • Utilizing hypothesis testing of Gaussian distribution to determine the event-triggered threshold significance.
  • Developing an event-triggered mechanism combined with the calculated threshold.
  • Calculating approximate estimation accuracy for the proposed event-triggered Kalman filter.

Main Results:

  • The proposed algorithm effectively reduces communication costs while maintaining high estimation accuracy.
  • The algorithm allows for pre-setting thresholds based on desired accuracy for steady and time-varying systems.
  • Simulation examples validate the algorithm's correctness and effectiveness.

Conclusions:

  • The developed event-triggered Kalman filter offers an efficient approach to state estimation.
  • It balances communication reduction with high accuracy requirements.
  • The method is applicable to both steady and time-varying linear systems.