Using face images in the diagnosis of Obstructive Sleep Apnea （OSA） in children can reduce the burden of doctors and improve the accuracy of diagnosis. Firstly， the current methods and their limitations of OSA in children clinical diagnosis were briefly described. Then， on the basis of studying the existing methods， combining with the methods of computer-aided face diagnosis of other diseases， the computer-aided face diagnosis methods of OSA in children were divided into three types： traditional computer-aided face diagnosis methods， transfer learning based diagnosis methods， and 3D face data based diagnosis methods. The key steps of the three types of methods were summarized， and the methods used in these key steps were compared， which provides different entry points for the future research of computer-aided face diagnosis for OSA in children. Finally， the opportunities and challenges in the future research of OSA in children diagnosis were analyzed.

In allusion to the problem that the computational complexity of Volterra for nonlinear adaptive filtering algorithm increases in power series, a second-order Volterra adaptive filter algorithm based on Set-Membership-Filtering (SMF) under the α-stable distributions noise was proposed. As the object function of SMF involved all signal pairs of input and output, through the threshold judgment of the p square of output errors amplitude the weight vectors of Volterra filter were updated, not only reducing the complexity of filtering algorithm, but also improving the robustness of the adaptive algorithm for input signal correlation. And the update formula of the weight vectors was derived. The simulation results show that the proposed algorithm has lower computational complexity, faster convergence rate, and better robustness against the noise and the input signal correlation.

The research about concept similarity is very important in knowledge representation and information retrieval. After studying the current classic distance-based semantic similarity algorithm, a more standardized similarity algorithm was proposed by analyzing the other key factors of semantic concept and increasing the impact of the node density and attributes of the concept for the semantic similarity. Through the experimental analysis, the similarity value of the improved algorithm is more reasonable; and compared with human subjective judgements under certain condition of the mediation parameter, the compatibility of the improved algorithm increases about 15% than that of the original algorithm.