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Related Concept Videos

Network Function of a Circuit01:25

Network Function of a Circuit

Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
Frequency Response of a Circuit01:20

Frequency Response of a Circuit

Inductive circuits present intriguing challenges in electrical engineering, particularly during the transition from the time domain to the frequency domain. This transformation involves converting inductors into impedances and utilizing phasor representation.
The transfer function is pivotal in characterizing how these circuits react to various frequencies, facilitating a profound understanding of their behavior. An essential parameter is the time constant, signifying the...
Linear time-invariant Systems01:23

Linear time-invariant Systems

A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be calculated...
Transfer Function in Control Systems01:21

Transfer Function in Control Systems

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.
To derive the transfer function, consider a general nth-order linear time-invariant...
Transfer Function to State Space01:23

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...
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...

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A Tactile Automated Passive-Finger Stimulator (TAPS)
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Published on: June 3, 2009

Robust algorithm for estimation of time-varying transfer functions.

Rui Zou1, Ki H Chon

  • 1Department of Neurosurgery, Children's Hospital, Boston, MA 02115, USA.

IEEE Transactions on Bio-Medical Engineering
|February 10, 2004
PubMed
Summary
This summary is machine-generated.

We developed a new method for estimating time-varying (TV) transfer functions, outperforming existing techniques in accuracy and robustness. This advancement reveals complex TV autoregulation in hypertensive rats, previously undetectable.

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Area of Science:

  • Physiology
  • System Identification
  • Biomedical Engineering

Background:

  • Accurate estimation of time-varying (TV) system dynamics is crucial in physiological studies.
  • Existing methods like recursive least-squares (RLS) have limitations in accuracy, robustness to noise, and tracking abrupt changes.

Purpose of the Study:

  • To introduce a novel method for reliable estimation of TV transfer functions (TFs) and impulse response functions.
  • To demonstrate the method's superiority over RLS, particularly in noisy conditions and with abrupt dynamic changes.

Main Methods:

  • Utilizing TV autoregressive moving average models with TV parameters obtained via an optimal parameter search.
  • Comparing the new method against RLS for accuracy, noise robustness, and ability to track dynamic changes.

Main Results:

  • The new method provides more accurate TF and impulse response estimations than RLS.
  • It demonstrates robustness in the presence of significant noise contamination.
  • The method successfully tracks abrupt dynamic changes, a limitation of RLS.
  • Application to renal blood pressure and flow revealed more complex TV autoregulation in hypertensive rats compared to normotensive rats.

Conclusions:

  • The developed method offers a more accurate and robust approach to TV system identification.
  • It enables the detection of complex physiological dynamics, such as TV autoregulation in hypertension, previously unobserved.
  • This approach may facilitate broader application of TV modeling in physiological research, including previously intractable linear and nonlinear cases.