Software vulnerabilities pose a great threat to network and information security， and the root of vulnerabilities lies in software source code. Existing traditional static detection tools and deep learning based detection methods do not fully represent code features， and simply use word embedding method to transform code representation， so that their detection results have low accuracy and high false positive rate or high false negative rate. Therefore， a source code vulnerability detection method based on hybrid code representation was proposed to solve the problem of incomplete code representation and improve detection performance. Firstly， source code was compiled into Intermediate Representation （IR）， and the program dependency graph was extracted. Then， structural features were obtained through program slicing based on data flow and control flow analysis. At the same time， unstructural features were obtained by embedding node statements using doc2vec. Next， Graph Neural Network （GNN） was used to learn the hybrid features. Finally， the trained GNN was used for prediction and classification. In order to verify the effectiveness of the proposed method， experimental evaluation was performed on Software Assurance Reference Dataset （SARD） and real-world datasets， and the F1 score of detection results reached 95.3% and 89.6% respectively. Experimental results show that the proposed method has good vulnerability detection ability.

Towards the large frequency offset caused by Doppler effect in high speed moving environment, a dynamic state space model of Orthogonal Frequency Division Multiplexing (OFDM) was built, and a kind of frequency offset tracking and estimation algorithm in OFDM based on improved Strong Tracking Unscented Kalman Filter (STUKF) was proposed. By combining strong tracking filter theory and UKF together, the fading factor was introduced during the process of calculating the measurement predictive covariance and cross covariance. The frequency offset estimation error covariance was adjusted; meanwhile, the process noise covariance was also controlled, and the gain matrix was adjusted in real-time. So the tracking ability to time-varying frequency offset was enhanced and the estimated accuracy was raised. The simulation test was carried out in time-invariant and time-varying frequency offset models. The simulation results show that the proposed algorithm has better tracking and estimation performance than the UKF frequency offset estimation algorithm, the Signal-to-Noise Ratio (SNR) raises about 1dB under the same Bit Error Rate (BER).

To meet the requirements of Remote Medical Information System (RMIS) application, a proposal about how to design and develop Android applications in RMIS combined with the characteristic of Android system was proposed. Firstly, the framework of RMIS and the architecture of Android Operating System (OS) were introduced, and then the development method of serial port, bluetooth, socket, HTTP and other means of communication and the application of these technologies in medical data acquisition and mobile data processing were analyzed, the Representational State Transfer (REST) Web service’s realization was introduced as well. Finally, data acquisition application and mobile information management application based on Android were carried out. The results show that Android can be used in RMIS in many aspects.

This paper proposed a domain ontology construction method based on knowledge graphs. This domain ontology was constructed based on the mature idea of software engineering by using the "HowNet" as the semantic knowledge resources, and knowledge graphs as a semantic representation. It has a clear structure with clear semantic meaning. It can provide effective supports for some applications such as semantic Web and information extraction. This paper introduced the concept of ontology, the standard of design, the process of modeling, and future prospects of ontology portability. This method was proved to be more effective than traditional ontology construction method in uncertain knowledge processing by experimental results.