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Related Concept Videos

Transfer Function to State Space01:23

Transfer Function to State Space

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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...
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State Space to Transfer Function01:21

State Space to Transfer Function

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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.
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Transfer function and Bode Plots-II01:23

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In the standard form, the transfer function is shown in constant gain, poles/zeros at origin, simple poles/zeros, and quadratic poles/zeros; each contributing uniquely to the system's overall response. The term represents the magnitude of the simple zero:
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Transfer function and Bode Plots-I01:19

Transfer function and Bode Plots-I

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A transfer function presented in its standard form integrates elements' constant gain, the zeros, and poles at the origin, simple zeros and poles, and quadratic poles and zeros. The transfer function can be written as H(ω):
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Transfer Function in Control Systems01:21

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The transfer function is a fundamental concept in the analysis and design of linear time-invariant (LTI) systems. It offers a concise way to understand how a system responds to different inputs in the frequency domain. It serves as a bridge between the time-domain differential equations that describe system dynamics and the frequency-domain representation that facilitates easier manipulation and analysis.
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Pilot and Numeric Relaying01:21

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Pilot relaying is a type of differential protection used in power systems. It compares electrical quantities at the terminals of equipment via a communication channel instead of direct relay interconnection. This method is essential for transmission lines where the terminals are far apart, typically up to 80 km for lines with 69 to 115 kV ratings. Four types of communication channels are used for pilot relaying:
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Related Experiment Video

Updated: Feb 10, 2026

Multiscale Structures Aggregated by Imprinted Nanofibers for Functional Surfaces
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Multiscale Information Transfer in Functional Corticomuscular Coupling Estimation Following Stroke: A Pilot Study.

Xiaoling Chen1, Ping Xie1, Yuanyuan Zhang1

  • 1Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Institute of Electric Engineering, Yanshan University, Qinhuangdao, China.

Frontiers in Neurology
|May 17, 2018
PubMed
Summary
This summary is machine-generated.

This study reveals that functional corticomuscular coupling (FCMC) in stroke patients exhibits altered multiscale and directional properties compared to healthy individuals. These changes in brain-muscle communication may impact motor control and recovery after stroke.

Keywords:
functional corticomuscular couplinginformation flowmultiscalestroketransfer entropy

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Area of Science:

  • Neuroscience
  • Rehabilitation Medicine
  • Biomedical Engineering

Background:

  • Functional corticomuscular coupling (FCMC) assesses motor function post-stroke by examining brain-muscle connections.
  • Existing FCMC studies often overlook the complex, directional, and multiscale nature of sensorimotor systems.
  • Motor control involves intricate self-regulating mechanisms across multiple temporal and spatial scales.

Purpose of the Study:

  • To investigate the multiscale and directional properties of FCMC in stroke patients using a novel causal modeling approach.
  • To compare these multiscale characteristics between stroke survivors and healthy controls.
  • To explore the potential of multiscale FCMC analysis for clinical assessment in stroke rehabilitation.

Main Methods:

  • Employed a multiscale transfer entropy model to quantify FCMC between scalp electroencephalogram (EEG) and forearm electromyogram (EMG).
  • Recorded data during a steady-state grip task in eight stroke patients and eight healthy controls.
  • Analyzed coupling strength, directionality, and frequency band-specific characteristics across multiple time scales.

Main Results:

  • Healthy controls showed stronger descending FCMC at specific scales (1, 7, 12, 14) and higher overall coupling up to scale 12.
  • Stroke patients exhibited reduced FCMC in both descending and ascending directions across all scales.
  • The distinct directional differences observed in controls were absent in stroke patients, particularly affecting alpha and beta frequency bands.

Conclusions:

  • FCMC possesses complex, directional, and multiscale characteristics that are disrupted by stroke-related brain lesions.
  • These disruptions may impair sensorimotor coordination, feedback, and information transmission, affecting motor recovery.
  • Multiscale FCMC analysis offers a novel approach for evaluating neurological deficits and guiding rehabilitation strategies in stroke patients.