Accurate segmentation of the polyp region in the colonoscopic images can assist doctors in diagnosing intestinal diseases. However， the structure information of polyp region is missing in the down sampling process， and the existing methods have the problems of over segmentation and under segmentation.Aiming at the problems above， a Fusing Filter enhancement and Reverse attention segmentation Network （FFRNet） was proposed. Firstly， Filter Enhancement Module （FEM） was added to the skip-connection to enhance the structure information of local lesion region in the down-sampling features. Secondly， the global features were obtained by aggregating the shallow features. Finally， Multiscale reverse Attention Fusion Mechanism （MAFM） was adopted in the up-sampling process， by combining the global features and up-sampling features to generate the reverse attention weight， the polyp region information was mined in the features layer by layer， and the relationship between the target region and the boundary was established by the guidance network to improve the integrity of the model on polyp region segmentation. On Kvasir and CVC-ClinicDB datasets， compared with Uncertainty Augmented Context Attention Network （UACANet）， FFRNet has Dice Similarity Coefficient （DSC） increased by 0.22% and 0.54% respectively. Experimental results show that FFRNet can effectively improve the accuracy of polyp image segmentation and has good generalization ability.

Due to the limitation of the computational power of embedded processors,the poor real-time performance has always been an urgent problem to be solved in the practical applications of Visual Inertial Simultaneous Localization And Mapping(VI-SLAM). Therefore,a real-time Simultaneous Localization And Mapping(SLAM)with keyframes determined by Inertial Measurement Unit(IMU)was proposed,which was mainly divided into three threads:tracking,local mapping and loop closing. Firstly,the keyframes were determined adaptively by the tracking thread through the IMU pre-integration, and the adaptive threshold was derived from the result of the visual inertia tight coupling optimization. Then,only the keyframes were tracked,thereby avoiding the feature processing to all frames. Finally,a more accurate Unmanned Aerial Vehicle(UAV)pose was obtained by the local mapping thread through the visual inertial bundle adjustment in the sliding window,and the globally consistent trajectory and map were output by the loop closing thread. Experimental results on the dataset EuRoC show that the algorithm can significantly reduce the tracking thread time consumption without loss of precision and robustness,and reduce the dependence of VI-SLAM on computing resources. In the actual flight test,the true trajectory of the drone with scale information can be estimated accurately by the algorithm in real time.

Concerning the problems of Attribute-Based Encryption (ABE) such as high computational consumption and lack of flexibility in mobile Internet, a dynamic trust level based Ciphertext-Policy ABE (CP-ABE) scheme was proposed. Firstly, the "trust level" attribute was defined to indicate user's trusted level and divide users into different classes. User with high "trust level" was be able to decrypt the message in a constant computational overhead. Meanwhile, Central Authority (CA) was allowed to evaluate user's access behavior within the certain time threshold. Only the user's "trust level" was updated dynamically by the updating algorithm instead of complete re-generating of secret key. Theoretical analysis and experimental results show that, with the growing proportion of high "trust level" user, the total time consumption of the proposed scheme was decreased until being stable and finally was superior to the traditional scheme. The proposed scheme can improve the access control efficiency in mobile Internet on the premise of keeping the security standard.

Cross-Project Defect Prediction (CPDP), which uses data from other projects to predict defects in the target project, provides a new perspective to resolve the shortcoming of limited training data encountered in traditional defect prediction. The data more similar to target project should be given priority in the context, because the quality of train cross-project data will directly affect the performance of cross-project defect prediction. In this paper, to analyze the impact of different similarity measures on the selection of training data for cross-project defect prediction, experiments were performed on 34 datasets from the PROMISE repository. The results show that the quality of training data selected by different similarity measure methods is various, and cosine similarity and correlation coefficient can achieve better performance as a whole. The greatest improvement rate is up to 6.7%. According to defect rate of target project, cosine similarity is seem to be more suitable when the defect rate is more than 0.25.

The traditional 3D face recognition and classification algorithms require multiple samples for training. However, the recognition performance will be seriously degraded on single sample training. To resolve the above problem, Fuzzy Adaptive Resonance theory MAP (Fuzzy ARTMAP) algorithm was used to classify the 3D face database. Firstly, the features of the 3D face deep image were extracted by Local Binary Pattern (LBP). Then the frequency-domain features of LBP features extracted by Log-Gabor wavelet were used as the input vectors for training. Finally the set of feature vectors were sent to Fuzzy ARTMAP classifier for recognition. The experiments compared with Probabilistic Neural Network (PNN) and Extreme Learning Machine (ELM) were conducted on FRGC v2.0 database, the recognition rate of the proposed algorithm reached 87.15%, the classifier training time was 24.88s, the matching time of single sample to single registered face was 0.0015s, and the searching time of a new face sample in the database was 1.08s. The experimental results show that the proposed method outperforms to PNN and ELM, it achieves a higher recognition rate with shorter training time, and has stable time performance with strong controllability.

With serious sidelight, it is difficult for the traditional algorithm to eliminate shadows. To improve the illumination compensation effect, a logarithmic transformation function was presented. In order to improve the performance of face recognition, by taking this problem as a classic pattern classification problem, a new method combining Local Binary Pattern (LBP) and Support Vector Machine (SVM) was proposed. One-against-one was used to convert multi-class problem to two-class problem, that can be used by SVM. Simulation experiments were conducted on the database of CMU PIE, AR, CAS-PEAL and one face database collected by the authors. The results show that lighting effects can be well eliminated and the proposed method performs better than the traditional ones.

The data model, business model, execution schema and performance metric of TPC-DS benchmark for next generation Decision Support System (DSS) application performance evaluation were introduced. The implementation architecture and key technologies for a configurable TPC-DS performance testing tool were put forward, including configuration file, query execution control and data maintenance mechanism. By testing practices in different Database Management Systems (DBMSs), the configurability and usability of the proposed tool for implementation strategies were verified.