# Created by Octave 3.4.2, Fri Sep 09 17:38:04 2011 EDT <mockbuild@fedora1.s390.bos.redhat.com>
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cl2bp


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 -- Loadable Function: H = cl2bp (M, W1, W2, UP, LO [, GRIDSIZE])
     Constrained L2 bandpass FIR filter design.  This is a fast
     implementation of the algorithm cited below.  Compared to "remez",
     it offers implicit specification of transition bands, a higher
     likelihood of convergence, and an error criterion combining
     features of both L2 and Chebyshev approaches.
     Inputs:
     M: degree of cosine polynomial, i.e. the number of output
     coefficients will be M*2+1
     W1, W2: bandpass filter cutoffs in the range 0 <= W1 < W2 <= pi,
     where pi is the Nyquist frequency
     UP: vector of 3 upper bounds for [stopband1, passband, stopband2]
     LO: vector of 3 lower bounds for [stopband1, passband, stopband2]
     GRIDSIZE: search grid size; larger values may improve accuracy,
     but greatly increase calculation time.
     Output:
     A vector of M*2+1 FIR coefficients, or an empty value if the
     solver failed to converge.
     Example:
          `h = cl2bp(30, 0.3*pi, 0.6*pi, [0.02, 1.02, 0.02], [-0.02, 0.98, -0.02], 2^11);'
     Original Paper:  I. W. Selesnick, M. Lang, and C. S. Burrus.  A
     modified algorithm for constrained least square design of
     multiband FIR filters without specified transition bands.  IEEE
     Trans. on Signal Processing, 46(2):497-501, February 1998.

   See also: remez




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Constrained L2 bandpass FIR filter design.



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medfilt1


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y = medfilt1(x [, n])

Apply a median filter of length n to the signal x.  A sliding window is
applied to the data, and for each step the median value in the window is
returned.  If n is odd then the window for y(i) is x(i-(n-1)/2:i+(n-1)/2).
If n is even then the window is x(i-n/2:i+n/2-1) and the two values in the
center of the sorted window are averaged. If n is not given, then 3 is used.
NaNs are ignored, as are values beyond the ends, by taking the median of
the remaining values.


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y = medfilt1(x [, n])




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remez


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b = remez(n, f, a [, w] [, ftype] [, griddensity])
Parks-McClellan optimal FIR filter design.
n gives the number of taps in the returned filter
f gives frequency at the band edges [ b1 e1 b2 e2 b3 e3 ...]
a gives amplitude at the band edges [ a(b1) a(e1) a(b2) a(e2) ...]
w gives weighting applied to each band
ftype is 'bandpass', 'hilbert' or 'differentiator'
griddensity determines how accurately the filter will be
    constructed. The minimum value is 16, but higher numbers are
    slower to compute.

Frequency is in the range (0, 1), with 1 being the nyquist frequency


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b = remez(n, f, a [, w] [, ftype] [, griddensity])
Parks-McClellan optimal FIR f



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sosfilt


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 -- Loadable Function: Y = sosfilt (SOS,X)
     Second order section IIR filtering of X.
     The second order section filter is described by the matrix SOS
     with:
             [ B1 A1 ]
     SOS =   [  ...  ],
             [ BN AN ]
     where `B1=[b0 b1 b2]' and `A1=[1 a1 a2]' for section 1, etc.
     b0 must be nonzero for each section.




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Second order section IIR filtering of X.



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upfirdn


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 -- Loadable Function: Y = upfirdn (X,H,P,Q)
     Upsample, FIR filtering and downsample.




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Upsample, FIR filtering and downsample.





