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m fmulf.m

function fout=fmulf(w,f1,f2) %FMULF Multiply two MVFR matrices. % FMULF(W,F1,F2) returns an MVFR matrix whose % component matrices = F1m*F2m, % where F1m and F2m are the compone
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m fms.m

function ms = fms(w,f); %FMS Measure of skewness of MIMO frequency response. % % MS = FMS(W,F) calculates a measure of skewness of an MVFR matrix, F. % W is the associated frequency r
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m fget.m

function x=fget(w,f,index) %FGET Get elements from MVFR matrix. % FGET(W,F,INDEX) returns the specified elements of % the component matrices of the MVFR matrix, F, % as column
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m mv2fr.m

function cout=mv2fr(a,b,c,d,w,iu) %MV2FR Frequency response of MIMO system % MV2FR(A,B,C,D,W) calculates the MVFR matrix of the system: % . % x = Ax + Bu
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h interleaver.h

/* File interleaver.h Description: Functions used to create the UMTS/3GPP and CCSDS interleavers. Copyright (C) 2005-2006, Matthew C. Valenti Last updated on June 24, 2006 F
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m m2m.m

function Mb=m2m(Ma,T) % Mb=m2m(Ma,T) generalized mass transformation. % Trasforms generalized mass with respect to coordinate frame a, % in the generalized mass with respect to coordinate frame b
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m rdct.m

function y=rdct(x,n) %RDCT Discrete cosine transform of real data Y=(X,N) % Data is truncated/padded to length N. % % This routine is equivalent to multiplying by the matrix % % rdct(eye(n
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m randvec.m

function x=randvec(n,m,c) %RANDVEC Generate gaussian random vectors X=(N,M,C) % generates a random matrix of size (n,p) where p is the maximum dimension of m or c % Each row of x is independent wi
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m ldbc.m

function [LDBC,ix]=ldbc(ECM,Y) % Linear discriminant based classifier % [LDBC] = ldbc(ECM); % LDBC is a multiple discriminator % % [LD] = ldbc(ECM,D); % calculates the LD to each class % % ECM
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m acorf.m

function [AUTOCOV,stderr,lpq,qpval] = acorf(Z,N); % Calculates autocorrelations for multiple data series. % Missing values in Z (NaN) are considered. % Also calculates Ljung-Box Q stats and p-v
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