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g5rv.nec

CM G5RV 80m,40m,20m,15m,10m CM (use auto-segmentation for higher bands) CE GW 1 29 0.0 -15.5 15.2 0.0 -.075 15.2 .0015 GW 2 29 0.0 15.5 15.2 0.0 .075 15.2 .0015 GW 3 1 0.0 -.075 5.0 0.0

td_print_g.m

function []=td_print_G(res,file_sal,op1) % PURPOSE: Generate output of temporal disaggregation methods. % Specific design for Guerrero method % --------------------------------------------

mess_g1.m

function results = mess_g1(y,x,options,ndraw,nomit,prior,start) % PURPOSE: Bayesian estimates of the matrix exponential spatial model (mess) % % [samples values of neighbors to produce a posterior dis

mess_g3.m

function results = mess_g3(y,x,options,ndraw,nomit,prior,start) % PURPOSE: Bayesian estimates of the matrix exponential spatial model (mess) % % [samples values of rho and #neighbors to produce a post

messv_g3.m

function results = messv_g3(y,x,options,ndraw,nomit,prior,start) % PURPOSE: Bayesian estimates of the matrix exponential spatial model (mess) % % [samples values of rho and #neighbors to produce a pos

mess_g2.m

function results = mess_g2(y,x,options,ndraw,nomit,prior,start) % PURPOSE: Bayesian estimates of the matrix exponential spatial model (mess) % % [samples values of rho to produce a posterior distribut

legend11_g.m

% legend11 - Gaussian high pass filter clear;clc; x=double(imread('Fig4.11(a).jpg')); r=input('Enter cutoff frequency = '); s=2; [m n]=size(x); for i=1:m; for j=1:n; w(i,j)=((i-(m