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The (Spatial) Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
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Sampling considerations for multilevel crossing analysis.

R E Woods1, R C Gonzalez

  • 1MEMBER, IEEE, Department of Electrical Engineering, University of Tennessee, Knoxville, TN 37919.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new sampling rate for band-limited functions to accurately estimate level crossing profiles. This method ensures minimal missed level crossings, improving pattern recognition capabilities.

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Area of Science:

  • Signal Processing
  • Information Theory
  • Applied Mathematics

Background:

  • Level crossings are crucial for pattern recognition, offering insights into amplitude and frequency.
  • Estimating level crossing profiles of band-limited functions with bounded zeroth absolute moments presents challenges.
  • Amplitude fluctuations and their impact on signal analysis are key research areas.

Purpose of the Study:

  • To analyze amplitude fluctuations in band-limited functions.
  • To develop a method for accurately estimating level crossing profiles.
  • To establish a precise sampling rate for signal analysis.

Main Methods:

  • Analysis of the average rate of change of band-limited functions.
  • Derivation of the least upper bound for bounded zeroth absolute moments.
  • Development of a theorem for optimal sampling rate determination.

Main Results:

  • A bound on the average rate of change of functions with bounded zeroth absolute moments was derived.
  • A novel sampling rate was established to limit amplitude deviation between samples.
  • A theorem was developed to minimize missed level crossings during profile estimation.

Conclusions:

  • The proposed sampling rate ensures accurate estimation of level crossing profiles for band-limited functions.
  • The derived sampling rate is adaptable and reduces to the Nyquist rate for zero crossing analysis.
  • This research enhances pattern recognition by improving signal feature extraction.