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Related Concept Videos

Echo01:06

Echo

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The human ear cannot distinguish between two sources of sound if they happen to reach within a specific time interval, typically 0.1 seconds apart. More than this, and they are perceived as separate sources.
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Atomic Emission Spectroscopy: Lab01:29

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AES is a powerful analytical technique, especially effective when used with plasma sources, producing abundant spectra in characteristic emission lines. The Inductively Coupled Plasma (ICP), in particular, yields superior quantitative analytical data due to its high stability, low noise, low background, and minimal interferences under optimal experimental conditions. However, newer air-operated microwave sources are emerging as promising alternatives that could be more cost-effective than...
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Related Experiment Video

Updated: Oct 9, 2025

Sound Source Localization Testing in Single-sided Deafness Following Bone Conduction Intervention
04:32

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Published on: December 20, 2024

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Optimal Passive Source Localization for Acoustic Emissions.

Carlos A Prete1, Vítor H Nascimento1, Cássio G Lopes1

  • 1Department of Electronic Systems Engineering, University of São Paulo, São Paulo 3566-590, Brazil.

Entropy (Basel, Switzerland)
|December 24, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for pinpointing crack locations using acoustic emission testing. It improves the accuracy of passive source localization by addressing signal noise and bias in time-of-arrival measurements.

Keywords:
acoustic emissionssource localizationtime-of-arrival

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Area of Science:

  • Non-destructive testing
  • Signal processing
  • Structural health monitoring

Background:

  • Acoustic emission (AE) is a non-destructive testing (NDT) method utilizing sensors to detect elastic waves from sources like cracks.
  • Passive source localization relies on Time of Arrival (TOA) data from sensor signals to determine source positions.
  • Existing TOA estimation techniques can be affected by noise and bias, impacting localization accuracy.

Purpose of the Study:

  • To derive the probability distribution of TOAs obtained via the fixed threshold technique.
  • To develop an optimal source position estimator based on the derived TOA distribution.
  • To propose a bias-removal modification for Time Difference of Arrival (TDOA) measurements and validate the new estimator.

Main Methods:

  • Derived the probability distribution of TOAs using the fixed threshold technique, modeling them as a mixture of Gaussian distributions.
  • Developed an optimal source position estimator assuming known mixture parameters, achieving Mean Squared Error (MSE) matching the Cramér-Rao lower bound.
  • Presented an algorithm for estimating mixture parameters from noisy signals and proposed a bias-correction method for TDOAs.

Main Results:

  • Showed that TOAs can be approximated by a mixture of Gaussians, reducing to a Gaussian in low noise.
  • The proposed optimal estimator, with known parameters, achieves the Cramér-Rao lower bound.
  • The modified TDOA method removes bias, and the proposed estimator outperforms other TOA-based methods in simulations.

Conclusions:

  • The derived TOA probability distribution and optimal estimator offer improved passive source localization accuracy.
  • Addressing TDOA bias is crucial for reliable localization using the fixed threshold technique.
  • The proposed method demonstrates superior performance in simulated scenarios, enhancing structural health monitoring capabilities.