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Related Concept Videos

Transfer Function to State Space01:23

Transfer Function to State Space

774
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...
774
State Space to Transfer Function01:21

State Space to Transfer Function

565
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:
565
Transfer function and Bode Plots-II01:23

Transfer function and Bode Plots-II

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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:
728
Voltammetry: Factors Affecting Measurements01:21

Voltammetry: Factors Affecting Measurements

534
A current produced due to the redox reactions of the analyte at the working and auxiliary electrodes is called a faradaic current. The reaction can be divided into two types. The current generated due to the reduction of the analyte is called cathodic current, and it carries a positive charge. In contrast, the current produced by analyte oxidation is known as an anodic current, and it has a negative charge. The applied potential at the working electrode determines the faradaic current flow, and...
534
Transfer Function in Control Systems01:21

Transfer Function in Control Systems

1.5K
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...
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Transfer function and Bode Plots-I01:19

Transfer function and Bode Plots-I

712
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):
712

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Factors affecting modulation transfer function measurements in cone-beam computed tomographic images.

Jin-Woo Choi1

  • 1Department of Oral and Maxillofacial Radiology, Dankook University College of Dentistry, Cheonan, Korea.

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|July 9, 2019
PubMed
Summary

For accurate modulation transfer function (MTF) measurements, prioritize small-voxel images and MTF 10 values. Large-voxel images require specific oversampling techniques for reliable results.

Keywords:
Cone-Beam Computed TomographyQuality ControlRadiography, Dental

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Area of Science:

  • Medical Imaging Physics
  • Radiological Quality Assurance

Background:

  • Accurate Modulation Transfer Function (MTF) measurement is crucial for evaluating imaging system performance.
  • Optimizing MTF measurement protocols can enhance diagnostic image quality and reduce radiation dose.

Purpose of the Study:

  • To determine the optimal parameters for Modulation Transfer Function (MTF) measurement.
  • Investigate the impact of voxel size, oversampling, and measurement area/direction on MTF values.

Main Methods:

  • Acquired CT images of the SedentexCT IQ phantom.
  • Calculated MTF values under varying conditions: voxel sizes (0.1-0.3 mm), 5 oversampling techniques, simulated location errors, and different measurement directions/areas.

Main Results:

  • MTF 10 values exhibited lower standard deviations compared to MTF 50 values.
  • 0.1-mm voxel images yielded stable and accurate MTF.
  • In 0.3-mm voxel images, oversampling with 11+ lines minimized differences due to location errors.
  • MTF 10 values varied significantly based on measurement direction and area.

Conclusions:

  • Measuring MTF 10 in small-voxel images (0.1 mm) provides the most accurate and stable results.
  • For large-voxel images, appropriate oversampling techniques are essential.
  • MTF values can differ between radial and tangential directions and across measurement areas.