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m se2.m

%SE2 Problem 4.2 % % 'ifile.mat' - input file containing: % I - members of ensemble % K - iterations % s - deterministic part of reference signal % sigman - standard devi
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m nlms2.m

%NLMS2 Problem 4.4 % % 'ifile.mat' - input file containing: % I - members of ensemble % K - iterations % s - deterministic part of reference signal % sigman - standard de
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m eflrls2.m

%EFLRLS2 Problem 1.1.1.2.6 % % 'ifile.mat' - input file containing: % I - members of ensemble % K - iterations % a1 - coefficient of input AR process % sigmax - standard
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m pte2.m

%PTE2 Problem 4.3 % % 'ifile.mat' - input file containing: % I - members of ensemble % K - iterations % s - deterministic part of reference signal % sigman - standard dev
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m eflrls3.m

%EFLRLS3 Problem 2.2 % % 'ifile.mat' - input file containing: % K - iterations % H - FIR channel % Neq - equalizer order % sigman - standard deviation of noise at channel
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m lms6.m

%LMS6 Problem 4.1 % % 'ifile.mat' - input file containing: % I - members of ensemble % K - iterations % s - deterministic part of reference signal % sigman - standard dev
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m pte1.m

%PTE1 Problem 3.2 % % 'ifile.mat' - input file containing: % I - members of ensemble % K - iterations % s - deterministic part of signal to predict % sigman - standard de
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c dlp.c

/* Author: Pate Williams (c) 1997 The following program implements and tests two algorithms for solving the discrete logarithm problem. The algorithms are baby-step giant-step and Po
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cs svm.cs

/* * Conversion notes: * Using JLCA 3.0, the only problem was with the save and loads. I changed them both to be * StreamR/W around a FileStream. Originally, the save was a BinaryWriter over a F
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m demev1.m

%DEMEV1 Demonstrate Bayesian regression for the MLP. % % Description % The problem consists an input variable X which sampled from a % Gaussian distribution, and a target variable T generated by compu