Research on Weibo Hotspot Finding Based on Self-Adaptive Incremental Clustering

Weibo,also known as micro-blog,with its extremely low threshold of information release and interactive communication mode,has become the primary source and communication form of Internet hotspots.However,characterized as a kind of short text,the sparsity in semantic features,plus its colloquial and diversified expressions makes clustering analysis more difficult.In order to solve the above problems,we use the Biterm topic model(BTM)to extract features from the corpus and use vector space model(VSM)to strengthen the features to reduce the vector dimension and highlight the main features.Then,an improved Weibo feature-incorporated incremental clustering algorithm and the Weibo buzz calculation formula are proposed to describe the buzz of Weibo,and then the discovery of hotspots can be reasonably made.The experimental results show that the incremental clustering algorithm presented in this paper can effectively improve the accuracy of clustering in different dimensions.Meanwhile,the calculation formula of Weibo buzz reasonably describes the evolution process of Weibo buzz from a qualitative point of view,which can help discover the hotspots effectively.     

基于TD-LTE UE侧随机接入过程的研究与分析

随机接入过程是LTE系统中必不可少的一个环节,高效快速的随机接入过程对于提高LTE的整体性能具有重要意义。基于TD-LTE系统,从UE的角度对随机接入过程中物理层以及高层协议栈的关键技术和实现细节进行了研究,对提高UE在TD-LTE系统中,随机接入过程的实现效率有很大的帮助和参考意义。