Service description texts for Internet of Things （IoT） are short in length and sparse in text features， and direct modeling the IoT service by using traditional topic model has poor clustering effect， so that the best service cannot be discovered. To solve this problem， an IoT service discovery method based on Biterm Topic Model （BTM） was proposed. Firstly， BTM was employed to mine the latent topic of the existing IoT services， and the service document-topic probability distribution was calculated and deduced through global topic distribution and theme-word distribution. Then， K-means algorithm was used to cluster the services and return the best matching results of service requests. Experimental results show that the proposed method can improve the clustering effect of services for IoT and thus obtain the matched best service. Compared with the methods of HDP （Hierarchical Dirichlet Process） and LDA-K （Latent Dirichlet Allocation based on K-means）， the proposed method achieves better performance in terms of Precision and Normalized Discounted Cumulative Gain （NDCG） for best service discovery.

To solve the problem that it is difficult to design a controller by previous coordinate transformation because there is nonaffine structure in nonlinear pure feedback system, a new coordinate transformation was proposed and a first-order control input auxiliary system was introduced to deal with the nonlinear pure feedback systems. Firstly, a new state equation was calculated by combining the new coordinate transformation. Secondly, positive definite Lyapunov function was designed for each step based on the backstepping method. Finally, the derivatives of Lyapunov functions were made negative by designing virtual controllers and auxiliary controller, so the tracking problem of nonlinear pure feedback systems was theoretically solved. The experimental results show that the designed auxiliary controller is able to make the state of the nonlinear system bounded globally, and the control output can track the given signal, and the tracking error becomes asymptotically stable.

To improve the efficiency of graph cut algorithm and reduce user interaction, the graph cut and mean shift algorithm were combined to automatically segment 3D vertebra images. The image was pre-processed with the mean shift method and the region graph was obtained instead of pixel graph, which could reduce dozen times edge numbers in the graph. Furthermore, the edge-preserving property of mean shift algorithm was used. The experimental results show that the proposed method possesses nice properties of the mean shift and graph algorithm, so it is efficient and accurate, and it also reduces user interaction.