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Related Experiment Videos

A novel eddy current compensation scheme for pulsed gradient systems

R E Wysong1, D P Madio, I J Lowe

  • 1Department of Physics and Astronomy, University of Pittsburgh, Pennsylvania.

Magnetic Resonance in Medicine
|May 1, 1994
PubMed
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A new pre-emphasis network with fixed time constants is more effective and easier to adjust than variable time constant networks. A mathematical method using linear least squares optimizes this fixed time constant network.

Area of Science:

  • Signal processing
  • Electrical engineering

Background:

  • Pre-emphasis networks are crucial for signal processing, often employing variable time constants.
  • Adjusting these networks in real-time can be challenging and less effective.

Purpose of the Study:

  • To introduce a modified pre-emphasis network utilizing fixed time constants with adjustable amplitudes.
  • To compare its efficacy and ease of use against traditional variable time constant networks.
  • To develop a mathematical method for optimizing the fixed time constant network.

Main Methods:

  • Implementation of a modified pre-emphasis network with multiple fixed time constants and adjustable amplitudes.
  • Comparative analysis against previously used variable time constant pre-emphasis networks.
  • Development of a linear least squares technique for mathematical setting of the network parameters.

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Main Results:

  • The fixed time constant pre-emphasis network demonstrated superior effectiveness compared to variable time constant networks.
  • The modified network proved to be simpler to adjust in real-time ('on the fly').
  • The linear least squares method provides a robust mathematical approach for network optimization.

Conclusions:

  • Modified fixed time constant pre-emphasis networks offer significant advantages in performance and usability.
  • Mathematical optimization using linear least squares enhances the practical application of these networks.
  • This approach simplifies signal processing adjustments in dynamic environments.