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Related Concept Videos

Correlation of Experimental Data01:23

Correlation of Experimental Data

Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity, and...
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Empirical Method to Interpret Standard Deviation01:09

Empirical Method to Interpret Standard Deviation

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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

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Correlation01:09

Correlation

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Related Experiment Video

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Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
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Cross-correlations of diffuse noise in an ocean environment using eigenvalue based statistical inference.

Ravishankar Menon1, Peter Gerstoft, William S Hodgkiss

  • 1Marine Physical Laboratory, Scripps Institution of Oceanography, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093-0238, USA. rmenon@ucsd.edu

The Journal of the Acoustical Society of America
|November 14, 2012
PubMed
Summary
This summary is machine-generated.

Noise cross-correlations help environmental sensing, but ship noise causes bias. This study uses random matrix theory to isolate diffuse noise, yielding unbiased travel time estimates for better environmental monitoring.

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Area of Science:

  • Geophysics
  • Acoustics
  • Signal Processing

Background:

  • Diffuse noise fields are crucial for environmental sensing via cross-correlations.
  • Directional sources, like ships, introduce bias in travel time estimations.
  • Random matrix theory offers tools to analyze noise field components.

Purpose of the Study:

  • To develop a method for isolating diffuse noise from directional sources in ambient noise fields.
  • To mitigate bias in travel time estimates derived from cross-correlations.
  • To validate the method using experimental data.

Main Methods:

  • Utilizing an array of sensors to capture noise field data.
  • Applying random matrix theory to analyze eigenvalues of the sample covariance matrix (SCM).
  • Employing sequential hypothesis testing to identify and remove outliers (directional sources).

Main Results:

  • Successfully isolated the diffuse noise component by identifying and attenuating outlier eigenvalues.
  • Travel time estimates derived from the diffuse component converged to predicted values.
  • Demonstrated temporally stable and unbiased travel time estimates, validated by the Shallow Water 2006 experiment.

Conclusions:

  • The proposed method effectively removes bias caused by directional sources in noise cross-correlations.
  • Isolating diffuse noise leads to accurate and stable environmental information extraction.
  • The findings align with theoretical predictions regarding signal-to-noise ratio buildup over time.