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−% FREQCORR Compute correlation between frequency bins
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−%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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−% FREQCORR Compute correlation between frequency bins of a spectral
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−% estimation given the data window
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−% 
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−% CALL 
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−% 
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−% Gf = utils.math.freqCorr(w,eta,T)
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−% 
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−% INPUT
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−% 
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−% - w, window samples, Nx1 double, N must be the effective length of the
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−% segments used for spectral estimation. E.g. For a periodogram N is equal
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−% to the length of the data series. For a WOSA estimation N is the length
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−% of each averaging segment.
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−% - eta, frequency lag in Hz, 1x1 double
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−% - T, sampling time in seconds, 1x1 double
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−% 
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−% REFERENCES
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−% 
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−% D. B. Percival and A. T. Walden, Spectral Analysis for Physical
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−% Applications (Cambridge University Press, Cambridge, 1993) p 231.
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−% 
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−% L Ferraioli 09-03-2011
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−%
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−% $Id: freqCorr.m,v 1.1 2011/03/28 16:37:23 luigi Exp $
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−%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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−function R = freqCorr(w,eta,T)
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−  
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−  Ns = numel(w);
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−  % willing to work with columns
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−  [nn,mm] = size(w);
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−  if nn<mm
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−    w = w.';
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−  end
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−  % make suqre integrable
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−  a = sqrt(sum(w.^2));
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−  w = w./a;
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−
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−  t = 1:Ns;
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−
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−  ww = w.*w;
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−  hh = exp(-1i.*2.*pi.*t.*T.*eta)*ww;
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−  R = abs(hh)^2;
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−  
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−
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−end