To effectively extract the temporal information between consecutive video frames， a prediction network IndRNN-VAE （Independently Recurrent Neural Network-Variational AutoEncoder） that fuses Independently Recurrent Neural Network （IndRNN） and Variational AutoEncoder （VAE） network was proposed. Firstly， the spatial information of video frames was extracted through VAE network， and the latent features of video frames were obtained by a linear transformation. Secondly， the latent features were used as the input of IndRNN to obtain the temporal information of the sequence of video frames. Finally， the obtained latent features and temporal information were fused through residual block and input to the decoding network to generate the prediction frame. By testing on UCSD Ped1， UCSD Ped2 and Avenue public datasets， experimental results show that compared with the existing anomaly detection methods， the method based on IndRNN-VAE has the performance significantly improved， and has the Area Under Curve （AUC） values reached 84.3%， 96.2%， and 86.6% respectively， the Equal Error Rate （EER） values reached 22.7%， 8.8%， and 19.0% respectively， the difference values in the mean anomaly scores reached 0.263， 0.497， and 0.293 respectively. Besides， the running speed of this method reaches 28 FPS （Frames Per Socond）.

To analyze the abnormal behavior of program induced by software defects, a characterization method of program behavior was proposed firstly, and then the baseline behavior and fault behavior of program got defined and formally described. A quantitative approach to represent the fault behavior of program was proposed afterwards. Furthermore, a Program Fault Behavior Analysis (PFBA) was delivered and implemented, which selected system-call as state granularity of program behavior. Based on specific embedded benchmarks, the experiment was followed through with fault injection method to obtain early-described indices of fault behavior. The experimental results show that there exists a difference among program behaviors under each individual fault type. Based on an in-depth analysis, it is demonstrated that the diversity of fault behaviors is induced by algorithm implementations and structural characteristics of embedded program themselves. Therefore, the analysis of fault behavior presented here can reveal the characteristics of embedded program response behavior under specific software defects, as well as providing important feedback to the process of program development.

The solution of "semantic gap" between the low-level features describing and the high-level semantic knowledge has become the key in problems of the Cross-Media Retrieval (CMR), a CMR model based on multimodal fusion and temporal-spatial context semantic was designed. The Independent Component Analysis (ICA) and Principal Component Analysis (PCA) were applied to dimension reduction of multimodal fusion features. The classifier of Support Vector Machine (SVM) and Hidden Markov Model (HMM) was designed to map semantic relationship in the model; meanwhile, methods of temporal-spatial fuzzy cluster and relevance feedback were used to improve the effect of CMR system. A prototype based on the model had been developed, and validated the correctness of the new model, which can provide enlightenment to the designers who work at CMR system.