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

Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

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...
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system.
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

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, the...
Downsampling01:20

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
Basic Continuous Time Signals01:22

Basic Continuous Time Signals

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...

You might also read

Related Articles

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

Sort by
Same author

Correlation between ocular biological parameters and cycloplegic refractive shift in pediatric myopia.

International journal of ophthalmology·2026
Same author

Joint Estimation of States, Sparse Sensor Attacks, and Unknown Inputs in Discrete-Time Cyber-Physical Systems.

IEEE transactions on cybernetics·2026
Same author

Thymosin α1 improves the outcomes of patients with hepatitis B virus-related acute-on-chronic liver failure by restoring immune balance.

Immunopharmacology and immunotoxicology·2026
Same author

Single-cell transcriptomics reveals lipid metabolism reprogramming in macrophages in vitro during early stages of Leishmania donovani infection.

Parasites & vectors·2026
Same author

Mesenchymal stem cells-derived TGF-β1 promotes polarization of M2 macrophages in mice with acute-on-chronic liver failure via FOSL1/MERTK axis.

Stem cell research & therapy·2026
Same author

Improved Results for T-S Fuzzy Systems With Periodically Varying Delays via a Generalized Delay Derivative-Dependent Reciprocally Convex Matrix Inequality.

IEEE transactions on cybernetics·2026
Same journal

A Quantum Self-Attention Neural Network Model on Quantum Circuits.

IEEE transactions on cybernetics·2026
Same journal

Semi-Explicit Solution of Some Discrete-Time Higher-Order-Cost Mean-Field-Type Control.

IEEE transactions on cybernetics·2026
Same journal

A Novel One-Step Small Object Detector for Autonomous Aerial Vehicles.

IEEE transactions on cybernetics·2026
Same journal

Online Data-Driven-Based Optimal Output Tracking Control Without Initial Stabilizing Policy.

IEEE transactions on cybernetics·2026
Same journal

Output Prediction-Based Event-Triggered Interval Estimation for Continuous-Time Switched Systems.

IEEE transactions on cybernetics·2026
Same journal

Differentially Private Distributed Algorithms for Aggregative Games Over Directed Graphs With Linear Convergence.

IEEE transactions on cybernetics·2026
See all related articles

Related Experiment Video

Updated: Jun 26, 2026

Interactive and Visualized Online Experimentation System for Engineering Education and Research
08:35

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

Digital Redesign-Based Interval State Estimation for Continuous Systems With Aperiodic Discrete Measurements.

Zhi-Hui Li, Guang-Hong Yang

    IEEE Transactions on Cybernetics
    |June 24, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study presents a novel two-step interval state estimation method for continuous systems using aperiodic discrete measurements. The approach improves estimation accuracy and removes cooperative system constraints, offering superior performance for state estimation challenges.

    More Related Videos

    Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy
    08:25

    Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy

    Published on: April 27, 2021

    X-ray Beam Induced Current Measurements for Multi-Modal X-ray Microscopy of Solar Cells
    10:16

    X-ray Beam Induced Current Measurements for Multi-Modal X-ray Microscopy of Solar Cells

    Published on: August 20, 2019

    Related Experiment Videos

    Last Updated: Jun 26, 2026

    Interactive and Visualized Online Experimentation System for Engineering Education and Research
    08:35

    Interactive and Visualized Online Experimentation System for Engineering Education and Research

    Published on: November 24, 2021

    Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy
    08:25

    Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy

    Published on: April 27, 2021

    X-ray Beam Induced Current Measurements for Multi-Modal X-ray Microscopy of Solar Cells
    10:16

    X-ray Beam Induced Current Measurements for Multi-Modal X-ray Microscopy of Solar Cells

    Published on: August 20, 2019

    Area of Science:

    • Control Systems Engineering
    • State Estimation Theory
    • Signal Processing

    Background:

    • Interval state estimation is crucial for systems with uncertain parameters.
    • Aperiodic discrete measurements pose challenges for traditional estimation methods.
    • Existing methods often require cooperative error systems, limiting applicability.

    Purpose of the Study:

    • To develop a two-step interval state estimation method for continuous systems with aperiodic discrete measurements.
    • To design a discrete measurement observer with performance comparable to continuous observers.
    • To remove the cooperative system constraint found in prior interval estimation techniques.

    Main Methods:

    • Digital redesign techniques are employed to design a discrete measurement observer with exponential time-varying gain.
    • Interval state estimation is constructed by bounding continuous measurement estimation error and state matching error.
    • The proposed method avoids the need for cooperative error systems.

    Main Results:

    • The designed discrete observer achieves estimation performance close to optimal continuous observers.
    • The interval state estimation effectively bounds system states despite aperiodic measurements.
    • Simulation examples demonstrate the method's effectiveness and superiority over existing approaches.

    Conclusions:

    • The proposed two-step interval state estimation method is effective for continuous systems with aperiodic discrete measurements.
    • The removal of the cooperative system constraint broadens the applicability of interval state estimation.
    • The method offers improved performance and robustness in state estimation problems.