Information theory projects and source code

Udp sender and receiver c source code

The following C project contains the C source code and C examples used for udp sender and receiver. This program send udp datagram through defined IP and port number of the client. In this case, IP is 127.0.0.1 that continuously sends udp datagram at local host to udp_receiver.cpp. Input is hardcoded data (data = 100). Output is udp datagram sent by udp_sender.cpp.

Linear bbock code encoder and decoder. in matlab

The following Matlab project contains the source code and Matlab examples used for linear bbock code encoder and decoder. . The encoder function displays the linear block code matrix along with the minimum Hamming distance, minimum error detection and correction capability and Hamming weights and Hamming distances The input n and k are the dimensions of the block code eg (7,4) linear block code and pm is the parity sub matrix.

Spatial channel model for mimo simulations. a ray based simulator based on 3gpp tr 25.996 v.6.1.0 in matlab

The following Matlab project contains the source code and Matlab examples used for spatial channel model for mimo simulations. a ray based simulator based on 3gpp tr 25.996 v.6.1.0. The MIMO spatial channel model simulates a wireless propagation channel in various cases and applies the concept of diversity (spatial and polarization) assuming multiple antennas at both the transmitter and receiver, thus forming a Multiple Input Multiple Output antenna system.

Entropy, joint entropy and conditional entropy function for n variables in matlab

The following Matlab project contains the source code and Matlab examples used for entropy, joint entropy and conditional entropy function for n variables. for entropy H = entropy(S) this command will evaluate the entropy of S, S should be row matrix H = entropy([X;Y;Z]) this command will find the joint entropy for the 3 variables H = entropy([X,Y],[Z,W]) this will find H(X,Y/Z,W).

Calculates the sample entropy, in bits, of discrete variables. in matlab

The following Matlab project contains the source code and Matlab examples used for calculates the sample entropy, in bits, of discrete variables. . Entropy: Returns entropy (in bits) of each column of 'X'   by Will Dwinnell     H = Entropy(X)     H = row vector of calculated entropies (in bits)   X = data to be analyzed     Note 1: Each distinct value in X is considered a unique value.

Conditional entropy in matlab

The following Matlab project contains the source code and Matlab examples used for conditional entropy. ConditionalEntropy: Calculates conditional entropy (in bits) of Y, given X   H = ConditionalEntropy(Y,X)     H = calculated entropy of Y, given X (in bits)   Y = dependent variable (column vector)   X = independent variable(s)      Note 1: Each distinct value is considered a unique symbol.

Joint entropy in matlab

The following Matlab project contains the source code and Matlab examples used for joint entropy.   JointEntropy: Returns joint entropy (in bits) of each column of 'X'   Note: Each distinct value is considered a unique symbol.     H = JointEntropy(X)     H = calculated joint entropy (in bits)   X = data to be analyzed

Probability of error for sequence in awgn and awgn+rayleigh channel, confrontation in matlab

The following Matlab project contains the source code and Matlab examples used for probability of error for sequence in awgn and awgn+rayleigh channel, confrontation. Probability of error for sequence in AWGN and AWGN+Rayleigh channel, confrontation between the two probability. Rayleigh channel is considered as slow fading

Mutual information in matlab

The following Matlab project contains the source code and Matlab examples used for mutual information. MutualInformation: returns mutual information (in bits) of discrete variables 'X' and 'Y'   I = MutualInformation(X,Y);     I = calculated mutual information (in bits)   X = variable(s) to be analyzed (column vector)   Y = variable to be analyzed (column vector)     Note 1: Multiple variables may be handled jointly as columns in     matrix 'X'.

Total kullback leibler (tkl) divergence between multivariate normal probability density functions. in matlab

The following Matlab project contains the source code and Matlab examples used for total kullback leibler (tkl) divergence between multivariate normal probability density functions. . This program implements the tKL between two multivariate normal probability density functions following the references:     Baba C.

Information theory toolbox in matlab

The following Matlab project contains the source code and Matlab examples used for information theory toolbox. This toolbox contains functions for discrete random variables to compute following quantities: 1)Entropy 2)Joint entropy 3)Conditional entropy 4)Relative entropy (KL divergence) 5)Mutual information 6)Normalized mutual information 7)Normalized variation information This toolbox is a tweaked and bundled version of my previous submissions.

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