Grid computing hit its stride in week 9 😀 haha, had to happen sooner or later.
Distributed Pattern Recognition was put forth as a prime application for computing grids. Purposes could include data mining, medical research and an array of scientific and engineering pursuits. Asad Khan proposed the use of the Distributed Pattern Recognition Architecture in conjunction with distributed pattern recognition algorithms to enable recognition computation on scales far greater than current solutions permit.
In the modern world where data held by the human race is growing exponentially, the ability to draw valuable information from the mass of data is quite exciting.
The Distributed Hierarchical Graph Neuron algorithm in conjunction with single cycle learning derived from the Associative memory algorithm is a method for achieving Distributed Pattern recognition.
We spoke in the lecture briefly on the topic of cross talk and the false flags within a sensor network where rudimentary filtering occurs. The conclusion of which was that DHGN must be implemented in part prior to the transmission gateway.
I will make some more detailed analysis of this topic in the coming weeks.