In order to retrieve images of traffic violations from a large number of road traffic images， a semantic retrieval method based on specific object self-recognition was proposed. Firstly， road traffic domain ontology as well as road traffic rule description were established by experts in traffic domain. Secondly， traffic image features were extracted by Convolutional Neural Network （CNN）， and combined with the strategy for classifying image features which is based on the proposed improved Support Vector Machine based Decision Tree （SVM-DT） algorithm， the specific objects and the spatial positional relationship between the objects in the traffic images were automatically recognized and mapped into the association relationship （rule instance） between the corresponding ontology instance and its objects. Finally， the image semantic retrieval result was obtained by reasoning based on ontology instances and rule instances. Experimental results show that the proposed method has higher accuracy， recall and retrieval efficiency compared to keyword and ontology traffic image semantic retrieval methods.

Today, SIMD (Single Instruction Multiple Data) technology has been widely used in Digital Signal Processor (DSP), and most of the existing compilers realize automatic vectorization functions. However，the compiler cannot support SIMD auto-vectorization with the feature of DSP, because of DSP complex instruction set, the specific addressing model, the obstacle of dependence relation to vectorization non-aligned data or other reasons. In order to solve this problem, in this paper, for the automatic vectorization in the Superword Level Parallelism (SLP) based on the Open64 compiler back end, the instruction analysis and redundancy optimization algorithm were improved, so as to transform more efficient vectorized source program. The experimental results show that the proposed method can improve DSP performances and reduce power consumption efficiently.

Due to the limited resource of security chip, the scheme resisting power analysis attack was researched from two aspects of operation efficiency and withstanding multiple power analysis attacks. A scheme based on Signed Double-based Number System (SDBNS) was presented by coding the key renewably and basic point masking algorithm. According to security analysis, the result shows that the scheme could resist multiple power analysis attacks and promote operation efficiency.

A risk assessment model based on Bayesian network was proposed. In this model, each risk event influencing the Trusted Platform Control Module (TPCM)'s trust was analyzed. According to the relation among risks, the Bayesian network evaluation model was built. According to the evaluation from expert, Bayesian network inferring method was used to evaluate the risk probability and its influence. The whole system's risk value and risk priority were determined. An example was given to verify the model's correctness and validation.