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i i i use software ean13 creation toencode ean-13 supplement 2 in software qrcode i i Trante Software ean13+5 rBook --- 2003/11/18 --- 16:12 --- page 633 --- #651. Section 15.13. Appendix C: The Semi-Hidden Markov Model +partition(2 )); A{4} = A_matrix(partition(1)+1:partition(1)+...

partition(2),partition(1)+1:partition(1)+partition(2)); % for iterations = 1:cycles % % alpha generation % alpha{1} = pi_u1*(A{u(1)+1,u(1)+1}.^(m(1)-1)); scale(1)= ..

. sum(alpha{1}); alpha{1}= alpha{1}/scale(1); % normalization for c = 2:C alpha{c}= alpha{c-1}*A{u(c-1)+1,u(c)+1}*A{u(c)+1,..

. u(c)+1}^(m(c)-1); scale(c)= sum(alpha{c}); % scaling factor alpha{c}= alpha{c}/scale(c); % normalize alpha end; % % beta generation % beta{C}= ones(partition(u(C)+1),1)/scale(C); % last element of % beta for(c= C-1:-1:1) beta{c}= A{u(c)+1,u(c+1)+1}*(A{u(c+1)+1,u(c+1)+1}..

. ^(m(c+1)-1))*beta{c+1}/scale(c); end; % % gamma generation % Gamma{1} = alpha{1}.*beta{1} ; Gamma{2} = alpha{2}.

*beta{2} ; % sum_Tii_00s = diag(zeros(partition(1),1)); % initialization % of A00 sum_Tii_11s = diag(zeros(partition(2),1)); % initialization % of A11 sum_Tij_01s = zeros(partition(1),partition(2)); % initialization % of A01 sum_Tij_10s = zeros(partition(2),partition(1)); % initialization % of A10 % % re-estimation for the A00 matrix % for c=1:2:C-1 if ( c == 1). i i i i i Trante rBook --- 2003/11/18 --- 16:12 --- page 634 --- #652. Discrete Channel Models 15 . Tii_00s = di ean13 for None ag((m(1)-1)*(pi_u1) .*(diag(A{u(1)+1,..

. u(1)+1}).^(m(1)-1)).

*beta{1}); else Tii_00s = diag((m(c)-1)*((alpha{c-1}*A{u(c-1)+1,...

u(c)+1}) .*(diag(A{u(c)+1,u(c)+1}^(m(c)-1)))). .

.. *beta{c}); end sum_Tii_00s = sum_Tii_00s + Tii_00s; % sum elements % of A00 end % % re-estimation for the A11 matrix % for c=2:2:C-1 Tii_11s = diag((m(c)-1)*((alpha{c-1}*A{u(c-1)+1,u(c)+1}) .

.. .

*(diag(A{u(c)+1,u(c)+1}^(m(c)-1)))).*beta{c}); sum_Tii_11s = sum_Tii_11s + Tii_11s; % sum elements % of A11 end % % re-estimation for the A01 matrix % for c=1:2:C-1 Tij_01s = (alpha{c} *((A{u(c+1)+1,u(c+1)+1}^(m(c+1)-1))..

. *beta{c+1}) ).*A{u(c)+1,u(c+1)+1}; sum_Tij_01s = sum_Tij_01s + Tij_01s; % sum elements % of A01 end % % re-estimation for the A10 matrix % for c=2:2:C-1 Tij_10s = (alpha{c} *((A{u(c+1)+1,u(c+1)+1}^(m(c+1)-1)).

.. *beta{c+1}) ).

*A{u(c)+1,u(c+1)+1}; sum_Tij_10s = sum_Tij_10s + Tij_10s; % sums elements of A10 end % A_matrix = [sum_Tii_00s sum_Tij_01s; sum_Tij_10s sum_Tii_11s]; % for i = 1:sum(partition) A_matrix(i,:) = A_matrix(i,:)/sum(A_matrix(i,:)); % normalize % A end % A{1} = A_matrix(1:partition(1),1:partition(1));. i i i i i TranterBook Software EAN13 2003/11/18 16:12 page 635 #653. Section 15.13. Appendix C: The Semi-Hidden Markov Model A{2} = A_matrix(par tition(1)+1:partition(1)+partition(2),1:...

partition(1)); A{3} = A_matrix(1:partition(1),partition(1)+1:partition(1)...

+partition(2)); A{4} = A_matrix(partition(1)+1:partition(1)+partition(2),...

partition(1)+1:partition(1)+partition(2)); % pi_est = [Gamma{1} Gamma{2}]; % re-estimated initial state vector pi_est = pi_est/sum(pi_est); % normalized initial state vector pi_rec(iterations,:) = pi_est; pi_u1 = pi_est(1:partition(1)); iterations % display current iteration A_matrix % display estimated A matrix end % End of function file.. i i i i i TranterBook 2003/11/18 16:12 page 636 #654. Discrete Channel Models 15 . Appendix D: Run-Length Code Generation % File: c15_segleng th.m function runcode=c15_seglength(errvect) % Produces a two-row matrix of error intervals and error-free % intervals. Row 1 specifies the interval length and row 2 % specifies the interval class (error(1) or no error(0)).

% len = length(errvect); % length of input vector j = 1; % initialize index of m count = 1; % initialize counter for i=1:(len-1) if errvect(i+1) == errvect(i); % compare elements count = count+1; % on match increment count else m(j) = count; % record count j = j+1; % increment index of m count = 1; % reset counter end end % runcode = zeros(2,length(m)); % allocate memory runcode(1,:) = m; % assign counts to row 1 % if errvect(1)==0 runcode(2,2:2:length(m)) = 1; % even index error count else runcode(2,1:2:length(m)) = 1; % odd index error count end % End of function file.. i i i i i TranterBook --- 2003/11/18 --- 16:12 --- page 637 --- #655. Section 15.15. Appendix E: Determination of Error-Free Distribution Appendix E: Determination of Error-Free Distribution In this appendix tw o MATLAB programs are given for determing a plotting the error-free distribution. The rst program is designed for a single data le, and the second program is for the comparison of two data les..

c15 intervals1.m % index of first % interval maxLength_1 = max(r1(1,start(1):2:length(r1))); % maximum length % of interval interval_1 = r1(1,start(1):2:length(r1)); % get the intervals for i = 1:maxLength_1 rec_1(i) = length(find(interval_1>=i)); % record the % intervals end int1out = rec_1/max(rec_1); figure; plot(1:maxLength_1,int1out) grid; ylabel( Pr(0m. 1) ); xlabel( Length of intervals m ); % End of function file. % File: c15_interva ls1.m function [] = c15_Intervals1(r1); start = find(r1(2,:)==0);.
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