In Digital Library (DL) applications, digital book clustering is an important and urgent research task. However, it is difficult to conduct effectively because of the great length of digital books. To do the correct clustering for digital books, a novel method based on probabilistic topic model is proposed. Firstly, we build a topic model named LDAC. The main goal of LDAC topic modeling is to effectively extract topics from digital books. Subsequently, Gibbs sampling is applied for parameter inference. Once the model parameters are learned, each book is assigned to the cluster which maximizes the posterior probability. Experimental results demonstrate that our approach based on LDAC is able to achieve significant improvement as compared to the related methods.

In this letter, we focus on the selection of the BEM order in the doubly-selective channel estimation. Based on the Jakes' channel model, we take into account the channel spectrum spread caused by observation window effects and the channel estimation error, and propose a method of selecting the optimal BEM order in the sense of minimum mean square error.