Aiming at the energy efficiency and scene adaptability problems of synchronization topology， a Greedy Synchronization Topology algorithm based on Formal Concept Analysis for traffic surveillance based sensor network （GST-FCA） was proposed. Firstly， scene adaptability requirements and energy efficiency model of the synchronization topology in traffic surveillance based sensor network were analyzed. Secondly， correlation analysis was performed on the adjacent features of sensor nodes in the same layer and adjacent layers by using Formal Concept Analysis （FCA）. Afterward， Broadcast Tuples （BT） were built and synchronization sets were divided according to the greedy strategy with the maximum number of neighbors. Thirdly， a backtracking broadcast was used to improve the broadcast strategy of layer detection in Timing-synchronization Protocol of Sensor Network （TPSN） algorithm. Meanwhile， an upward hosting mechanism was designed to not only extend the information sharing range of synchronous nodes but also further alleviate the locally optimal solution problem caused by the greedy strategy. Finally， GST-FCA was verified and tested in terms of energy efficiency and scene adaptability. Simulation results show that compared with algorithms such as TPSN， Linear Estimation of Clock Frequency Offset （LECFO）， GST-FCA decreases the synchronization packet overhead by 11.54%， 24.59% and 39.16% at lowest in the three test scenarios of deployment location， deployment scale and road deployment. Therefore， GST-FCA can alleviate the locally optimal solution problem and reduce the synchronization packet overhead， and it is excellent in energy efficiency when the synchronization topology meets the scene adaptability requirements of the above three scenarios.

Cross-project software defect prediction can solve the problem of few training data in prediction projects. However， the source project and the target project usually have the large distribution difference， which reduces the prediction performance. In order to solve the problem， a new Cross-Project Defect Prediction method based on Feature Selection and TrAdaBoost （CPDP-FSTr） was proposed. Firstly， in the feature selection stage， Kernel Principal Component Analysis （KPCA） was used to delete redundant data in the source project. Then， according to the attribute feature distribution of the source project and the target project， the candidate source project data closest to the target project distribution were selected according to the distance. Finally， in the instance transfer stage， the TrAdaBoost method improved by the evaluation factor was used to find out the instances in the source project which were similar to the distribution of a few labeled instances in the target project， and establish a defect prediction model. Using F1 as the evaluation index， compared with the methods such as cross-project software defect prediction using Feature Clustering and TrAdaBoost （FeCTrA）， Cross-project software defect prediction based on Multiple Kernel Ensemble Learning （CMKEL）， the proposed CPDP-FSTr had the prediction performance improved by 5.84% and 105.42% respectively on AEEEM dataset， enhanced by 5.25% and 85.97% respectively on NASA dataset， and its two-process feature selection is better than the single feature selection process. Experimental results show that the proposed CPDP-FSTr can achieve better prediction performance when the source project feature selection proportion and the target project labeled instance proportion are 60% and 20% respectively.

In terms of the issue of reversible data hiding algorithm in videos, a novel algorithm based on histogram-pair method was proposed, which embedded data by selecting reasonable fluctuation value, freguercy range and area in I frames Discrete Cosine Transform (DCT) field, achieved high quality embedded MPEG-4 video. By embedding data in the macroblocks (8×8) in the optimum area, optimum frequency range in the macroblocks, and optimum DCT fluctuation, optimum reversible data hiding was completed. Higher Peak Signal-to-Noise Ratio (PSNR) was achieved in the experiments of 6 sequences videos. For akiyo, the PSNR of embedded I frame reached 45.33dB （1000b/frame），43.58dB （2000b/frame）和40.28dB (4000b/frame)。In the cases of high capacity, the increase of bit rate is relatively low, approximately 6% on average. The proposed method embedded data in DCT coefficient, achieves higher PSNR than the method based on DCT quantization table. The method embedded information in I frame beter than in B frame, which has formed relatively completed reversible data hiding method in video.