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Comparison of Two ICA Algorithms in BSS Applied to Non-destructive Vibratory Tests

  • Juan-José González de-la-Rosa
  • Carlos G. Puntonet
  • Rosa Piotrkowski
  • Antonio Moreno
  • Juan-Manuel Górriz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4247)

Abstract

Two independent component analysis (ICA) algorithms are applied for blind source separation (BSS) in a synthetic, multi-sensor situation, within a non-destructive pipeline test. CumICA is based in the computation of the cross-cumulants of the mixtures and needs the aid of a digital high-pass filter to achieve the same SNR (up to –40 dB) as Fast-ICA. Acoustic Emission (AE) sequences were acquired by a wide frequency range transducer (100-800 kHz) and digitalized by a 2.5 MHz, 8-bit ADC. Four common sources in AE testing are linearly mixed, involving real AE sequences, impulses and parasitic signals modelling human activity.

Keywords

Acoustic Emission Independent Component Analysis Acoustic Emission Signal Independent Component Analysis Blind Source Separation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Juan-José González de-la-Rosa
    • 1
  • Carlos G. Puntonet
    • 2
  • Rosa Piotrkowski
    • 1
  • Antonio Moreno
    • 1
  • Juan-Manuel Górriz
    • 1
  1. 1.Research Group TIC168 – Computational Electronics Instrumentation and Physics Engineering, EPSAUniversity of CádizAlgeciras-CádizSpain
  2. 2.Department of Architecture and Computers Technology, ESII, C/Periodista Daniel SaucedoUniversity of GranadaGranadaSpain

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