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Embedding a wavelet transform within a neural network

  • US 20030018599A1
  • Filed: 04/18/2002
  • Published: 01/23/2003
  • Est. Priority Date: 04/23/2001
  • Status: Abandoned Application
First Claim
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1. An artificial neural network configured to perform a discrete wavelet transform, comprising:

  • an input interface having a plurality of j inputs;

    a low-pass filter comprising at least j/2 low-pass neural processing elements, an nth one of the low-pass neural processing elements providing a low-pass first-octave output (L0,n) comprising the sum of;

    the product of a first low-pass filter coefficient and input 2n

    k, the product of a second low-pass filter coefficient and input 2n

    (k−

    1), the product of a third low-pass filter coefficient and input 2n

    (k−

    2), continuing this process until the kth low-pass filter coefficient is multiplied by input 2n, where k is the number of filter coefficients;

    a high-pass filter comprising at least j/2 high-pass neural processing elements, an nth one of the high-pass neural processing elements providing a high-pass first-octave output (H0,n) comprising the sum of;

    a first high-pass filter coefficient and the product of input 2n

    k, the product of a second high-pass filter coefficient and input 2n

    (k−

    1), the product of a third high-pass filter coefficient and input 2n

    (k−

    2), continuing this process until the kth high-pass filter coefficient is multiplied by input 2n; and

    an output interface having at least j/2 low-pass outputs and at least j/2 high-pass outputs, a low-pass output providing the low-pass first-octave output (L0,n) of the nth one of the low-pass neural processing elements, and a high-pass output providing the high-pass first-octave output (H0,n) of the nth one of the high-pass neural processing elements.

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