# Created by Octave 3.8.0, Fri Feb 14 18:59:25 2014 CET <marco@GE-MATZERI-EU>
# name: cache
# type: cell
# rows: 3
# columns: 5
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 5
cl2bp


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1478
 -- Loadable Function: H = cl2bp (M, W1, W2, UP, LO)
 -- 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.




# name: <cell-element>
# type: sq_string
# elements: 1
# length: 42
Constrained L2 bandpass FIR filter design.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 8
medfilt1


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 594
 -- Loadable Function: Y = medfilt1 (X)
 -- Loadable Function: 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.




# name: <cell-element>
# type: sq_string
# elements: 1
# length: 50
Apply a median filter of length N to the signal X.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 5
remez


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 852
 -- Loadable Function: B = remez (N, F, A)
 -- Loadable Function: B = remez (N, F, A, W)
 -- Loadable Function: B = remez (N, F, A, W, FTYPE)
 -- Loadable Function: 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.




# name: <cell-element>
# type: sq_string
# elements: 1
# length: 42
Parks-McClellan optimal FIR filter design.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 7
sosfilt


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 358
 -- 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.
     The b0 entry must be nonzero for each section.




# name: <cell-element>
# type: sq_string
# elements: 1
# length: 40
Second order section IIR filtering of X.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 7
upfirdn


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 95
 -- Loadable Function: Y = upfirdn (X, H, P, Q)
     Upsample, FIR filtering, and downsample.




# name: <cell-element>
# type: sq_string
# elements: 1
# length: 40
Upsample, FIR filtering, and downsample.





