Handover is a very common event in Cellular Mobile network; however QoS could be severely affected by the handover performance. More handover cause more signaling traffic. Therefore, it is desired that handover should be done only when it is necessary. Besides, handover decision should be precise by taking account all possible options and considering the best one. For moving Mobile Station (MS) handover takes place more frequently. Some fuzzy logic based methodologies have already been proposed in literature to provide handover decision. In this paper, a method has been proposed to calculate speed and direction of MS relative to base station as a single metric using measurement data. Accordingly, a fuzzy logic based handover algorithm is implemented to avoid ping pong effect. By taking relative speed and direction, traffic load, signal strength and distance, the fuzzy inference system determines the best candidate neighbor based on the measurement reports from MS. Simulation has been carried out in Matlab environment and a comparison of different approaches has been performed. Simulation results demonstrate that proposed algorithm provides prediction based handover decision more accurately and avoid unnecessary handover and ping pong effect.
Ribonucleic Acid (RNA) plays a vital role in the transcription process. Since the information stored in DNA is converted into sequences of a chemical compounds named amino acids through mRNA in order to produce the ultimate gene product i.e., protein.The importance of RNA in the transcription process gives a better justification to analyze it. RNA cannot exist stably in its primary structure, thus, to attain a stable structure, it folds back on itself to form secondary structure (2o RNA) and further folding of RNA nucleotides gives rise to the tertiary structure (3o RNA). In this paper, a new model using neural network for RNA secondary structure prediction is proposed. Our computational model predicts multiple secondary structures of a single RNA by applying a parallel algorithm for finding near maximum independent set in the circle graph proposed by Takefuji Y. et al (1990). Based on frequency density analysis of the predicted RNA secondary structures, we proposed an optimized secondary structure of RNA among all the possibilities using statistical probability distributions. The paper concludes by discussing the nature and behavior of 2o RNA predicted by our method and a comparison with the results of other researchers. We have shown that the proposed model has better accuracy as compared to the other researches.
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