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Abstract-In this paper, we present a class of low-hit-zone (LHZ) frequency hopping sequence set (LHZ FHS set) with periodic partial Hamming correlation property from the Cartesian product of known FHS sets. It is shown that the sequence set is near optimal with respect to the partial Hamming correlation bound of the LHZ FHS set.

Based on the principle that the same class is adjacent, an anomaly intrusion detection method based on K-means and Support Vector Machine (SVM) is presented. In order to overcome the disadvantage that k-means algorithm requires initializing parameters, this paper proposes an improved K-means algorithm with a strategy of adjustable parameters. According to the location of wireless sensor networks (WSN), we can obtain clustering results by applying improved K-means algorithm to WSN, and then SVM algorithm is applied to different clusters for anomaly intrusion detection. Simulation results show that the proposed method can detect abnormal behaviors efficiently and has high detection rate and low false positive rate than the current typical intrusion detection schemes of WSN.

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Abstract-This paper presents a method of query expansion based on lexical time distribution for micro-blog search. Similarity of distribution is used to measure the correlation between the extended words and the query words. A query model based on the time distribution of the words is established. The query expansion method is unable to accurately estimate the expansion words due to the short micro-blog content. The real graph on the evaluation data is retrieved by the TREC2011 and TREC2012 micro-blog. The results show that the model can effectively improve the performance of micro-blog search extension vocabulary query based on time distribution, not only significantly outperforms the classical content based query expansion model, which is better than other time using the method of query expansion.