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      • Faculty of Engineering
      • Department of Electrical and Electronics Engineering
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      Wheat and hazelnut inspection with impact acoustics time-frequency patterns

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      Author
      Ince, N.F.
      Onaran I.
      Tewfik, A.H.
      Kalkan H.
      Pearson, T.
      Cetin, A.E.
      Yardimci, Y.
      Date
      2007
      Journal Title
      2007 ASABE Annual International Meeting, Technical Papers
      Volume
      10 BOOK
      Language
      English
      Type
      Conference Paper
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      Please cite this item using this persistent URL
      http://hdl.handle.net/11693/27084
      Abstract
      Kernel damage caused by insects and fungi is one of the most common reason for poor flour quality. Cracked hazelnut shells are prone to infection by cancer producing mold. We propose a new adaptive time-frequency classification procedure for detecting cracked hazelnut shells and damaged wheat kernels using impact acoustic emissions recorded by dropping wheat kernels or hazelnut shells on a steel plate. The proposed algorithm is based on a flexible local discriminant bases (F-LDB) procedure. The F-LDB method combines local cosine packet analysis and a frequency axis clustering approach which supports individual time and frequency band adaptation. Discriminant features are extracted from the adaptively segmented acoustic signal, sorted according to a Fisher class separability criterion, post processed by principal component analysis and fed to linear discriminant. We describe experimental results that establish the superior performance of the proposed approach when compared with prior techniques reported in the literature or used in the field. Our approach achieved classification accuracy in paired separation of undamaged wheat kernels from IDK, Pupae and Scab damaged kernels with 96%, 82% and 94%. For hazelnuts the accuracy was 97.1%.
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      • Department of Electrical and Electronics Engineering 2964

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