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

Space Trusses01:25

Space Trusses

1.3K
A space truss is a three-dimensional counterpart of a planar truss. These structures consist of members connected at their ends, often utilizing ball-and-socket joints to create a stable and versatile framework. The space truss is widely used in various construction projects due to its adaptability and capacity to withstand complex loads.
At the core of a space truss lies the fundamental unit known as the tetrahedron. This structure is composed of six members that form a three-dimensional shape...
1.3K
State Space Representation01:27

State Space Representation

573
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
573
Space Trusses: Problem Solving01:29

Space Trusses: Problem Solving

909
A space truss is a three-dimensional counterpart of a planar truss. These structures consist of members connected at their ends, often utilizing ball-and-socket joints to create a stable and versatile framework. Due to its adaptability and capacity to withstand complex loads, the space truss is widely used in various construction projects.
Consider a tripod consisting of a tetrahedral space truss with a ball-and-socket joint at C. Suppose the height and lengths of the horizontal and vertical...
909
Transfer Function to State Space01:23

Transfer Function to State Space

803
State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an RLC...
803
State Space to Transfer Function01:21

State Space to Transfer Function

581
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:
581
Rocket Propulsion in Empty Space - I01:13

Rocket Propulsion in Empty Space - I

3.8K
The driving force for the motion of any vehicle is friction, but in the case of rocket propulsion in space, the friction force is not present. The motion of a rocket changes its velocity (and hence its momentum) by ejecting burned fuel gases, thus causing it to accelerate in the direction opposite to the velocity of the ejected fuel. In this situation, the mass and velocity of the rocket constantly change along with the total mass of ejected gases. Due to conservation of momentum, the...
3.8K

You might also read

Related Articles

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

Sort by
Same author

Neurophysiological correlates of delayed recovery of consciousness in a critically ill patient with COVID-19 with repeated cardiac arrest.

British journal of anaesthesia·2026
Same author

Histology-guided 3D virtual staining of microCT-imaged lung tissue via deep learning.

Journal of the Royal Society, Interface·2026
Same author

Determinants of Delayed Recovery of Consciousness After Analgosedation Discontinuation in the ICU: Insights From Patients With COVID-19 Hypoxemic Respiratory Failure.

Critical care medicine·2026
Same author

Electroencephalographic Monitoring in the Recovery Room for Identification of Patients at Risk for Postoperative Delirium.

Anesthesiology·2026
Same author

Similar destabilization of neural dynamics under different general anesthetics.

Cell reports·2026
Same author

Time-frequency embedding with contrastive pre-training allows sub-second seizure detection.

bioRxiv : the preprint server for biology·2026
Same journal

Analysis of End-Tidal CO2 Variability During Plateau Waves Episodes: An Information Theoretic Approach<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

AI and Tomosynthesis for Breast Cancer Molecular Subtyping: A step toward precision medicine<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Towards Sustainable Protein Recovery from Biological Waste: Assessing Polyethersulfone-based Microfiltration.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Analysis of the cardiovascular response to standardized polymicrobial peritonitis experimental model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Automated Wrist Ultrasound Image Bone Enhancement and Segmentation Using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
See all related articles

Related Experiment Video

Updated: Feb 2, 2026

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data
04:13

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Published on: November 13, 2019

12.8K

A Smoother State Space Multitaper Spectrogram.

Andrew H Song, Sourish Chakravarty, Emery N Brown

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |November 17, 2018
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a smoother statistical model for analyzing non-stationary time series. The Integrated Wiener Process State-Space Multitaper (IWP-SSMT) method provides improved spectral density estimates for complex data like EEG signals.

    More Related Videos

    In vitro Synthesis of Native, Fibrous Long Spacing and Segmental Long Spacing Collagen
    07:54

    In vitro Synthesis of Native, Fibrous Long Spacing and Segmental Long Spacing Collagen

    Published on: September 20, 2012

    14.2K
    Dissecting the Non-human Primate Brain in Stereotaxic Space
    09:09

    Dissecting the Non-human Primate Brain in Stereotaxic Space

    Published on: July 16, 2009

    10.6K

    Related Experiment Videos

    Last Updated: Feb 2, 2026

    Computer-based Multitaper Spectrogram Program for Electroencephalographic Data
    04:13

    Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

    Published on: November 13, 2019

    12.8K
    In vitro Synthesis of Native, Fibrous Long Spacing and Segmental Long Spacing Collagen
    07:54

    In vitro Synthesis of Native, Fibrous Long Spacing and Segmental Long Spacing Collagen

    Published on: September 20, 2012

    14.2K
    Dissecting the Non-human Primate Brain in Stereotaxic Space
    09:09

    Dissecting the Non-human Primate Brain in Stereotaxic Space

    Published on: July 16, 2009

    10.6K

    Area of Science:

    • Statistical Modeling
    • Time Series Analysis
    • Signal Processing

    Background:

    • Non-stationary time series data analysis presents challenges for traditional spectral estimation methods.
    • The State-Space Multitaper (SSMT) method offers a framework for time-varying spectral analysis by integrating multitaper spectral analysis with state-space models.
    • Existing methods may not always provide sufficiently smooth estimates for underlying processes.

    Purpose of the Study:

    • To develop and evaluate a variant of the SSMT framework that enhances the smoothness of spectral density estimates for non-stationary data.
    • To introduce a smoothness-promoting state-space model using Integrated Wiener Processes (IWPs) within the SSMT framework.
    • To compare the performance of the proposed IWP-SSMT method against the original SSMT using both synthetic and real-world electroencephalography (EEG) data.

    Main Methods:

    • Implementation of a novel smoothness-promoting state-space model assuming independent Integrated Wiener Processes (IWPs) for frequency domain observations.
    • Application of the proposed Integrated Wiener Process State-Space Multitaper (IWP-SSMT) method to estimate time-varying spectral representations.
    • Comparative analysis using synthetic datasets and human electroencephalography (EEG) data from a subject under anesthesia.

    Main Results:

    • The IWP-SSMT method demonstrates the ability to generate smoother estimates of power spectral densities compared to the original SSMT method.
    • Validation of the IWP-SSMT's utility in providing more refined descriptions of underlying spectral processes in non-stationary data.
    • Successful application to EEG data, highlighting its potential for analyzing complex biological signals.

    Conclusions:

    • The IWP-SSMT method offers a valuable enhancement to the SSMT framework for analyzing non-stationary time series.
    • Smoother spectral density estimates can lead to more accurate interpretations of underlying dynamic processes.
    • Both SSMT and IWP-SSMT can serve as complementary tools within a broader model selection toolkit for non-stationary time series analysis.