Information Theory, Inference & Learning Algorithms

ISBN: 0521642981

Category: Technical

Tag: Programming

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Description

David J. C. MacKay “Information Theory, Inference & Learning Algorithms" 
Cambridge University Press | 2002-06-15 | ISBN: 0521642981 | 550 pages | PDF | 6,7 MB

Information theory and inference, often taught separately, are here united in one entertaining textbook. These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for Undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

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Conditionally colored line plot

coloring partialy matlab code


Plots vectors x & y using one color for when y is greater than a given threshold value, and another color when y is less than the threshold.


You can optionally specify any standard formatting string that PLOT accepts (eg 'rx:' for a red dotted line with cross markers).


The plotting follows the standard MATLAB PLOT approach of linearly interpolating between data values. The coloring/linestyle changes at y = threshold, even on the interpolated line.

Time division multiplexing

Time division multiplexing in matlab
This program perform TDM for as many signals as you want

function [y]=TDM_nik(x)
% x contains all the signals to be multiplexed
% y is multiplexed signal
%----------------------------------------
% Example: if you have to mutiplex two signals e.g. x1 and x2 (of course both of same length)
% if length is not same append zeros in smaller one to make it equal to larger on
% then make x(1,:)=x1, x(2,x2)...x(r, xr) (if you have r signals to be multiplexed)
% then simple run y=TDM_nik(x)
%-Do it x1=1:10, x2=10:-1:1, x3(1:5)=4, x3(6:10)=-4, x(1,:)=x1, x(2,:)=x2,
% x(3,:)=x3 amd y=TDM_nik(x)
%If you have any problem or feedback please contact me @
%%===============================================
% NIKESH BAJAJ
% Asst. Prof., Lovely Professional University, India
% Almameter: Aligarh Muslim University, India
% +919915522564, bajaj.nikkey@gmail.com
%%===============================================
[r c]=size(x);
k=0;
% Multiplexing
for i=1:c
for j=1:r
k=k+1;
y(k)=x(j,i);
end
end
% Ploting
color='ybrgmkc';
figure(1)
sig='x1';
for i=1:r
sig(2)=i+48;
j=mod(i,7)+1;
subplot(r,1,i)
stem(x(i,:),color(j),'linewidth',2)
title(sig)
ylabel('Amplitude')
grid
end
xlabel('Time')
t=1/r:1/r:c;
figure(2)
for i=1:r
j=mod(i,7)+1;
stem(t(i:r:r*c),y(i:r:r*c),color(j),'linewidth',2)
hold on
grid
end
hold off
title('Time Division Multiplexed Sequence')
xlabel('Time')
ylabel('Amplitude')