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function [gn,bn,en,Nuse,btot] = waterfill(H,Ex_bar,Ntot,gap,Noise_var) % Pn is the channel pulse response % Ex_bar is the normalized energy % Ntot is the total number of subchannels % gap is the

function [E,sE]=energies(P,dt,ind,wid); % ENERGIES calculates energy from time-dependent powerspectrum % % [E,sE]=energies(P,dt,ind,wid); % % in: P time-dependent power spectrum matri

%% Discrete Energy Separation Algorithm 1(DESA-1)for estimation purpose on an AM signal. %we are considering 512 samples of AM Signal given by the following equation: k=0.8; w=pi/128; q=pi/6; f

% % Figure 5.10 reproduction % clear; beta = 50; Nu = [1:10,20:10:100,200:100:1000]; % Number of Users M = 32; %Number of PPM positions Ep = 1; % Energy of pulse No = 0.05; % Noise va

% % Figure 5.10 reproduction % clear; beta = 50; Nu = [1:10,20:10:100,200:100:1000]; % Number of Users M = 32; %Number of PPM positions Ep = 1; % Energy of pulse No = 0.05; % Noise va

function [ur,uy] = green(freq,vr,group,energy,z,r,offsets,Fx,Fy,Fz,Phi,s_depth,r_depth) % This function calculates the Green's function of the horizontal and vertical % Rayleigh wave displacement

%EIGEN Solves the eigenvalue problem in design of the unit-energy % constrained minimum mean-squared error time domain equalizer. % [B, W, D, MSE, R, Dv] = EIGEN(RXX, RYY, RXY, Dmin, Dmax, Nb,

%EIGEN Solves the eigenvalue problem in design of the unit-energy % constrained minimum mean-squared error time domain equalizer. % [B, W, D, MSE, R, Dv] = EIGEN(RXX, RYY, RXY, Dmin, Dmax, Nb,

package emp; import java.util.Random; import java.util.Vector; public class Motor implements Consts{ private class Energy2D{ public double OriginX; //x point of origin public double

/* fe.h fe.c handles purely the inference problem A s + n = z by free energy minimization fe6.h differs by having most c-> variables specified in fe_var6