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Abstract  Large-scale mobile ad-hoc networks require flexible and stable clustered network structure for efficient data collection and dissemination. In this paper, a technique is presented to construct multi-hop clusters with balanced sizes, based on the neighborhood benchmark (NB) to quantify the connectivity and link stability of mobile nodes. By exploiting autonomous clusterhead selection and a specialized handshake process with the clusterheads, the nodes with highest NB scores are selected as clusterheads and all the clusters constructed are connected. The deviation of cluster sizes is kept small using a partial probability-based approach. Our technique generates highly stable multi-hop clusters with low overhead, and provides the flexibility of controlling the cluster radius adaptively for various network applications. [ABSTRACT FROM AUTHOR]

The inhomogeneous Markov chain model is used to discriminate acceptor and donor sites in genomic DNA sequences. It outperforms statistical methods such as homogeneous Markov chain model, higher order Markov chain and interpolated Markov chain models, and machine-learning methods such as k-nearest neighbor and support vector machine as well. Besides its high accuracy, another advantage of inhomogeneous Markov chain model is its simplicity in computation. In the three states system (acceptor, donor, and neither), the inhomogeneous Markov chain model is combined with a three-layer feed forward neural network. Using this combined system 3175 primate splice-junction gene sequences have been tested, with a prediction accuracy of greater than 98%. [ABSTRACT FROM AUTHOR]