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function [pb,ps]=bper8pskG2(snr_in_dB,N) E=1; %energy per symbol snr=10^(snr_in_dB/10); numan=2; %the number of trans

function [pb,ps]=bper2pskG2(snr_in_dB,N) E=1; %energy per symbol snr=10^(snr_in_dB/10); numan=2; %the number of trans

function [pb,ps]=bper16QAMH3(snr_in_dB,N) echo on E=1; %energy per symbol; snr=10^(snr_in_dB/10); numan=3; %the number

function [pb,ps]=bper16QAMG3(snr_in_dB,N) echo on E=1; %energy per symbol; snr=10^(snr_in_dB/10); numan=3; %the number

function [pb,ps]=bper16QAMG4(snr_in_dB,N) echo on E=1; %energy per symbol; snr=10^(snr_in_dB/10); numan=4; %the number

function [pb,ps]=bper4pskG2(snr_in_dB,N) E=1; %energy per symbol snr=10^(snr_in_dB/10); numan=2; %the number of trans

function [pb,ps]=bper4pskG2(snr_in_dB,N) E=1; %energy per symbol snr=10^(snr_in_dB/10); numan=2; %the number of trans

function D = RCE(train_features, train_targets, lambda_m, region) % Classify using the reduced coulomb energy algorithm % Inputs: % features - Train features % targets - Train targets % la

function Ew = PROBLEMNAME_cost(X,W) % Ew = PROBLEMNAME_cost(X,W) % % X = behaviorally constant application data % % W = specific data about current state % % Ew = energy corresponding to

% delay = findchanneldelay(h, win_len) % % h: channel impulse response % window_length: window length for sliding energy calculation % delay: channel delay % %Copyright (c) 1999-2002 The Unive