Ph.D Research Scholar Muhammad Irshad Nazeer delivered his PhD Open Defense Seminar on the topic 'Secure Network Coding
Using Computational Intelligence', under the supervision of Prof.
 studied the energy consumption and network throughput of multicast transmission in wireless networks and concluded that network coding
could solve such problems in a better way.
Recently, a variant of NC called Random Network Coding
(RNC) has been applied in the latest generation of smart-phones at the application layer to ensure reliable multimedia delivery over wireless links .
(2) Partial network coding
is introduced when routing data from source cluster head to the sink node.
(NC) is a promising technology to be used in the next-generation network because it can maximize the throughput capacity of a network [3, 4].
For a reliable data transmission between transmitters and receivers, one effective tool which helps to prevent data loss, to reduce the data transmission time and to increase spectral efficiency in wireless transmission systems with the use of relay (intermediate) nodes, is network coding
The polynomial time algorithm for multicast to heterogeneous receivers using network coding
. It has been proved to be an effective technology in solving network information flow problem, which is derived from traditional multi-commodity flow problems and have recently absorbed some ideas from information theory and coding theory.
, as introduced by the pioneers Ahlswede et al.
that emerged as a promising technique that can provide significant improvements with low cost, on the other hand, has attracted much interest.
(5) The proposed algorithm is analyzed with single relay participation for cooperative scheme and further extended for supporting network coding
The topics include the sum of squares basis pursuit with linear and second order cone programming, representation theory of the symmetric group in voting theory and game theory, geometric combinatorics and computational molecular biology: branching polytopes for RNA sequences, the neural network coding
of natural images with applications to pure mathematics, proving Tucker's Lemma with a volume argument, and a survey of discrete methods in (algebraic) statistics for networks.