Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

State Space to Transfer Function

State Space to Transfer Function

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:

Transfer Function to State Space

Transfer Function to State Space

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...

You might also read

Related Articles

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

Sort by

Same author

Quantitative System Risk Assessment From Incomplete Data With Belief Networks and Pairwise Comparison Elicitation.

Risk analysis : an official publication of the Society for Risk Analysis·2025

Same author

Discordance between fractional flow reserve and nonhyperemic index with a fiber-optic pressure wire. READI EPIC-14.

REC, interventional cardiology·2025

Same author

Use of a multistate model in survival predictions in cardiology studies.

REC, interventional cardiology·2025

Same author

Letter: Composite endpoints in clinical trials - simplicity or perfection?

EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology·2022

Same author

Design and interim results of a registry of left atrial appendage occlusion with the Watchman device in patients on hemodialysis: EPIC06-WATCH-HD.

Revista espanola de cardiologia (English ed.)·2021

Same author

Ticagrelor or Clopidogrel After an Acute Coronary Syndrome in the Elderly: A Propensity Score Matching Analysis from 16,653 Patients Treated with PCI Included in Two Large Multinational Registries.

Cardiovascular drugs and therapy·2021

Same journal

Decoding neuronal criticality firing patterns for large brain based EEG models.

NeuroImage·2026

Same journal

Segmentation of the parasagittal dura mater on multi-center 3D-FLAIR MRI.

NeuroImage·2026

Same journal

Spatial frequency channels implement a mental ruler in spatial vision.

NeuroImage·2026

Same journal

Exploring the Link Between Intravoxel Incoherent Motion Measured Brain Diffusivity During Wakefulness and Sleep Macrostructure in the Elderly.

NeuroImage·2026

Same journal

Closed-loop adaptation of transcranial magnetic stimulation intensity with electroencephalography feedback.

NeuroImage·2026

Same journal

Volumetric postmortem MRI of the medial temporal lobe in Alzheimer's disease and related disorders: methodological advances and implications for in vivo biomarker development.

NeuroImage·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 17, 2026

Functional Magnetic Resonance Imaging (fMRI) with Auditory Stimulation in Songbirds

Functional Magnetic Resonance Imaging (fMRI) with Auditory Stimulation in Songbirds

Published on: June 3, 2013

Bayesian spatiotemporal model of fMRI data using transfer functions.

Alicia Quirós¹, Raquel Montes Diez, Simon P Wilson

¹Departamento de Estadística e Investigación Operativa, Universidad Rey Juan Carlos, Madrid, Spain. alicia.quiros@urjc.es

|January 9, 2010

Summary

This summary is machine-generated.

This study introduces a novel Bayesian spatiotemporal model for analyzing Blood-Oxygen-Level-Dependent functional Magnetic Resonance Imaging (BOLD fMRI) data. The new model enhances hemodynamic response function estimation and offers computational benefits for fMRI analysis.

More Related Videos

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Published on: October 20, 2023

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training

Published on: August 24, 2017

Related Experiment Videos

Last Updated: Jun 17, 2026

Functional Magnetic Resonance Imaging (fMRI) with Auditory Stimulation in Songbirds

Functional Magnetic Resonance Imaging (fMRI) with Auditory Stimulation in Songbirds

Published on: June 3, 2013

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Published on: October 20, 2023

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training

A Protocol for the Administration of Real-Time fMRI Neurofeedback Training

Published on: August 24, 2017

Area of Science:

Neuroimaging
Statistical modeling
Biostatistics

Background:

Blood-Oxygen-Level-Dependent functional Magnetic Resonance Imaging (BOLD fMRI) is crucial for understanding brain activity.
Accurate modeling of the hemodynamic response function (HRF) and spatial patterns is essential for robust fMRI analysis.
Existing spatiotemporal models for fMRI data have limitations in flexibility and computational efficiency.

Purpose of the Study:

To develop a new Bayesian spatiotemporal model for BOLD fMRI data analysis.
To improve the flexibility in estimating the hemodynamic response function (HRF).
To achieve computational advantages through an improved Markov Chain Monte Carlo (MCMC) algorithm.

Main Methods:

A Bayesian spatiotemporal model incorporating a transfer function for the HRF shape.
A Gaussian Markov random field prior to model spatial continuity and local homogeneity of evoked responses.
Markov Chain Monte Carlo (MCMC) methods for parameter estimation and model inference.

Main Results:

Simulations demonstrated the model's performance in parameter estimation and sensitivity to signal-to-noise ratio.
The proposed model offers enhanced flexibility in HRF estimation compared to previous approaches.
Computational advantages were observed in the MCMC algorithm for the new model.

Conclusions:

The developed Bayesian spatiotemporal model provides a flexible and computationally efficient framework for BOLD fMRI analysis.
The model accurately estimates parameters and is robust to varying signal-to-noise ratios.
This approach advances the analysis of fMRI data, enabling more precise insights into brain function.