function[y,q]=segments(x,L,M)%      [y,Q]=segments(X,L,M)%%      Given the data samples X=(x_1,x_2,...,x_N),     %      the program obtains Q overlapping (M<L) or %      non-overlapping (M>=L) segments, each of L samples %      in the form of a matrix "y" of L rows and Q columns. %        _______________    %       |______ L ______| .....                     %       |___ M ___|______ L ______| .....         %       |___ M ___|___ M ___|______ L ______| .....%%      The procedure is used for the block of blocks bootstrap.%        %     Inputs:%          X - input vector data %          L - number of elements in a segment%          M - shift size (i.e. L-M is the size of overlap)               %     Output:%          y - the output matrix of the data%          Q - number of output segments%  Created by A. M. Zoubir and  D. R. Iskander%  May 1998%%  References:% %% Zhang, Y. et. al. Bootstrapping Techniques in the Estimation of Higher%           Order Cumulants from Short Data Records. (Proceedings of the%           International Conference on  Acoustics,  Speech  and  Signal %           Processing, ICASSP-93, Vol. IV, pp. 200-203.%%  Zoubir, A.M. Bootstrap: Theory and Applications. Proceedings %               of the SPIE 1993 Conference on Advanced  Signal %               Processing Algorithms, Architectures and Imple-%               mentations. pp. 216-235, San Diego, July  1993.%%  Zoubir, A.M. and Boashash, B. The Bootstrap and Its Application%               in Signal Processing. IEEE Signal Processing Magazine, %               Vol. 15, No. 1, pp. 55-76, 1998.x=x(:); N=length(x);q=fix((N-L)/M)+1;y=zeros(L,q);for ii=1:q,     y(:,ii)=x((ii-1)*M+1:(ii-1)*M+L);end;