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m ss_tbl31.m

Gold sequences for the table of problem 3.1 of Spread Spectrum Chapter Note that the sequences are the columns of the below matrix not the rows, so we have to take the transpose of the following m
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pas testmcmc.pas

{ ********************************************************************** * Program TESTMCMC.PAS * * Version 1.3d
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pas syseq.pas

{ ********************************************************************** * Program SYSEQ.PAS * * Version 1.5d
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pas eigensym.pas

{ ********************************************************************** * Program EIGENSYM.PAS * * Version 1.5d
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pas syseqsvd.pas

{ ********************************************************************** * Program SYSEQSVD.PAS * * Version 1.4d
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asm conv2d5x5_spl.asm

/******************************************************************************* Copyright(c) 2000 - 2002 Analog Devices. All Rights Reserved. Developed by Joint Development Software Application Tea
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m acorf.m

function [ACF,stderr,lpq,qpval] = acorf(Z,N); % Autocorrelation function % Calculates autocorrelations for multiple data series. % Also calculates Ljung-Box Q stats and p-values. % % [ACF,s
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m arfit.m

function [w, A, C, sbc, fpe, th]=arfit(v, pmin, pmax, selector, no_const) %ARFIT Stepwise least squares estimation of multivariate AR model. % % [w,A,C,SBC,FPE,th]=ARFIT(v,pmin,pmax) produces esti
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m adjph.m

function ox=adjph(x) %ADJPH Normalization of columns of a complex matrix. % % Given a complex matrix X, OX=ADJPH(X) returns the complex matrix OX % that is obtained from X by multiplying column
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m arsim.m

function [v]=arsim(w,A,C,n,ndisc) %ARSIM Simulation of AR process. % % v=ARSIM(w,A,C,n) simulates n time steps of the AR(p) process % % v(k,:)' = w' + A1*v(k-1,:)' +...+ Ap*v(k-p,:)' + eta(
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