In the construction of effective and scalable overlay networks, publish/subscribe (pub/sub) network designers prefer to keep the diameter and maximum node degree of the network low. However, existing algorithms are not capable of simultaneously decreasing the maximum node degree and the network diameter. To address this issue in an overlay network with various topics, we present herein a heuristic algorithm, called the constant-diameter minimum–maximum degree (CD-MAX), which decreases the maximum node degree and maintains the diameter of the overlay network at two as the highest. The proposed algorithm based on the greedy merge algorithm selects the node with the minimum number of neighbors. The output of the CD-MAX algorithm is enhanced by applying a refinement stage through the CD-MAX-Ref algorithm, which further improves the maximum node degrees. The numerical results of the algorithm simulation indicate that the CD-MAX and CD-MAX-Ref algorithms improve the maximum node-degree by up to 64% and run up to four times faster than similar algorithms.
Today many systems are invented which have special ability that sense of recognition plays a vital role in them and reshaped our life dramatically. These kinds of systems can make a proper decision about data clustering. Image processing or recognition of patterns like signs and … are the systems that are the example of them. Till now many methods have been presented which used to design a recognition system that some of them haven't been improved completely. Nevertheless, most of them should follow some steps such as: how to identify and represent the classes, the manner of choosing and extracting and how to cluster and train samples. Although, some issues like orientation or location couldn't be solved by these systems but scholars have trying to find the best solution for them. In this article, primary goal is comparing the methods used in pattern recognition that neural network is one of the most important techniques among them.
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