In order to solve the problem of safety caused by multi-party data sharing in autonomous driving simulation testing， a blockchain-based model for data notarization of autonomous driving simulation testing was proposed to ensure secure storage and traceability of the data， thereby providing reliable support for auditing work. Firstly， the semi-public characteristics of consortium blockchain were utilized to ensure that on-chain data were only visible to authorized organizations， while a permission verification mechanism based on RBAC （Role-Based Access Control） model was employed to implement access control for these organizations. Secondly， a smart contract template was defined to standardize the data access process， and process extension points were open to support customized functions， for example， allowing extension of associated smart contracts to ensure automatic execution of simulation resource trading activities. Finally， optimization strategies， including on-chain and off-chain hybrid storage of InterPlanetary File System （IPFS）， data batch processing， and resource data caching， were proposed to address limitations of blockchain storage resources and processing capabilities. Tests were conducted to evaluate the efficiency of data notarization for 500 data simulation scenarios generated by large language models. Experimental results show that compared to the direct access method， the notarization process applying batch processing strategy reduces the total transaction number by 72.00%， decreasing the performance consumption caused by smart contract calls significantly， and has the average time for writing and reading all data reduced by 85.36% and 52.67%， respectively. It can be seen that the proposed model provides reliable technical support for the data security of multi-party data sharing in autonomous driving simulation testing， while the proposed optimization strategies improve the data memory access efficiency significantly.

Multimedia forensics tasks based on cell-phone speech has always been a key research hotspot. However， the existing speech-based cell-phone identification tasks are all confined to the closed-set mode， which means that the training set and the test set share the same category set， which cannot guarantee the recognition accuracy for cell-phones of unknown categories， leading to the difficulty in applications of the existing methods to the unknown cell-phones. Therefore， an Open-set Source Cell-phone Identification method based on Feature interaction and representation enhancement （FireOSCI） was proposed. Firstly， a global information extraction block named GlobalBlock was designed on the basis of the multi-head attention block Fastformer for better capturing the global information from the whole speech sample and obtaining rich device feature information. Secondly， a local feature extraction block named LocalBlocks was presented on the basis of SE-Res2Block （Squeeze-Excitation Res2Block） to focus on enhancing cell-phone information related features and suppressing the features that are not related to the source cell-phone identification. Thirdly， an attention mechanism based feature fusion mechanism was designed to fuse global features with multi-layer local features deeply. Finally， a source cell?phone confirmation network was designed on the basis of attention pooling to improve the recognition accuracy in open-set mode. Comparison experimental results on cell-phone speech dataset with 13 different cell-phone brands and 86 different cell-phone models show that the proposed method can achieve identification of unknown categories of cell-phones， and provide a referable technical solution for the open-set recognition of speech-based source cell-phones.

Working memory complex network analysis methods mostly use channels as nodes to analyze from the perspective of space, while rarely analyze channel networks from the perspective of time. Focused on the high time resolution characteristics of ElectroEncephaloGraph (EEG) and the difficulty of time series segmentation, a method of constructing and analyzing network from the time perspective was proposed. Firstly, the microstate was used to divide EEG signal of each channel into different sub-segments as nodes of the network. Secondly, the effective features in the sub-segments were extracted and selected as the sub-segment effective features, and the correlation between sub-segment feature vectors was calculated to construct channel time sequence complex network. Finally, the attributes and similarity analysis of the constructed network were analyzed and verified on the schizophrenic EEG data. The experimental results show that the analysis of schizophrenia data by the proposed method can make full use of the time characteristics of EEG signals to understand the characteristics of time series channel network constructed in working memory of patients with schizophrenia from a time perspective, and explain the significant differences between patients and normals.

Aiming at the problem that a large amount of legacy code can not be reused, a new compilation tool was designed to convert the serial code of C into a hybrid parallel programming code based on MPI+OpenMP, which can reduce the development cost of parallel programming. First of all, by optimizing Java Compiler Compiler (JavaCC), a lexical and syntax analyzer which can parse the C language was implemented, then the source code analysis was conducted and the abstract syntax tree was generated. Secondly, according to the abstract syntax tree, the control dependence and data dependence of the source code were analyzed to produce the parallelizable statement block partitions. Thirdly, the object code was obtained according to the proposed parallel code generation method. Finally, the target code simulation environment was built based on Visual Studio 2010. The experimental results show that the tool can effectively achieve automatic parallelization of the serial code, and compared with the code written by hand, its speedup of the error is between 8.2% to 18.4%.

Concerning the high time-complexity of shared resource matrix method based on array storage in the detection of system covert channel, an improved algorithm based on double cross linked list was proposed. Firstly, traditional array storage was improved by double cross linked list storage in transitive closure operation. Secondly, a probability model for shared resource matrix method was constructed. Finally, the time-complexity of the improved algorithm and features of shared resource matrix were analyzed under the probability model. When the shared resource matrix was a sparse matrix, using the improved algorithm based on double across linker storage could prompt 67% time efficiency of shared resource matrix compared to traditional realization based on array storage. When the scale of shared resource matrix was quite great, the property of transitive closure operation would cause quick filling of elements in shared resource matrix, then the time efficiency advantage of improved algorithm based on double cross linker was declined compared to traditional algorithm based on array storage. This property of transitive closure operation was proven through theoretical deduction under the probability model.

Focusing on the drawback that Discovering Maximum Frequent Itemsets Algorithm (DMFIA) has to generate lots of maximum frequent candidate itemsets in each dimension when given datasets with many candidate items and each maximum frequent itemset is not long, an improved Algorithm for mining Maximum Frequent Itemsets based of Frequent-Pattern tree (FP-MFIA) for mining maximum frequent itemsets based on FP-tree was proposed. According to Htable of FP-tree, this algorithm used bottom-up searches to mine maximum frequent itemsets, thus accelerated the count of candidates. Producing infrequent itemsets with lower dimension according to conditional pattern base of every layer when mining, cutting and reducing dimensions of candidate itemsets can largely reduce the amount of candidate itemsets. At the same time taking full advantage of properties of maximum frequent itemsets will reduce the search space. The time efficiency of FP-MFIA is at least two times as much as the algorithm of DMFIA and BDRFI (algorithm for mining frequent itemsets based on dimensionality reduction of frequent itemset) according to computational time contrast based on different supports. It shows that FP-MFIA has a clear advantage when candidate itemsets are with high dimension.

Noisy data and disease misjudgment in Region of Interest (ROI) of medical image is a typical inconsistent decision question of Inconsistent Decision System (IDS), and it is becoming huge challenge in clinical diagnosis. Focusing on this problem, based on certainty degree, inclusion degree and cover degree, a decision algorithm named ItoC-CIC was proposed for ROI of prostate tumor Magnetic Resonance Imaging (MRI) combined with macro-and-micro characteristics and global-and-local characteristics. Firstly, high-dimensional features for ROI of prostate tumor MRI were extracted to construct complete inconsistent decision table. Secondly, the equivalent classes possessing inconsistent samples were found by calculating certainty degree. Thirdly, the Score value was obtained by calculating inclusion degree and cover degree of inconsistent equivalent classes respectively, which was used to filter inconsistent samples, making inconsistent decision convert to consistent decision. Finally, test experiments of inconsistent decision tables were conducted on typical examples, UCI data and 102 features of MRI prostate tumor ROI. The experimental results illustrate that this algorithm is effective and feasible, and the conversion rate can reach 100% from inconsistent decision to consistent decision.

The existing antivirus methods take too much system overhead, consume a lot of network bandwidth and can not detect the unknown programs in time. Therefore, this paper improved the previous work and presented a new virtual desktop antivirus model regarding virtual desktop infrastructure. It supported active antivirus and passive antivirus moves. Privileged virtual machines were used to scan viruses, manage the trust-list and transmit signatures of every virtual machine to others. Agents were used to analyze the signatures and characteristics of files, optimize the bytes to be uploaded and scanned, and scan the programs timely when being loaded. The experimental results show that model can detect viruses in real-time, in the meantime reduce system overhead and network bandwidth usage.

By analyzing the principle and the existing problems in the ray method and the extreme coordinate value method, the existing algorithms which determined contour direction were simplified. Then an improved algorithm on the contour line position relation was proposed combining the advantages of the ray method and the extreme coordinate method. The algorithm mainly included four parts: distinction among the internal and external contours, adjustment of the profile direction, inclusive identification of contours and the construction of a contour tree. The experimental results show that the improved algorithm can correct the problems in the existing algorithms and achieve good efficiency.

As the session controlling protocol of application-layer, SIP has the features of simple, expansibility and dilatancibility. At the basis of simple introduction of SIP protocol, the JAIN SIP exploring construction for the fulfillment of SIP communication of SUN Co was discussed in detail. To use Java language and take JAIN SIP as the core, all kinds of communication entity basic method in the fulfillment of SIP communication were described and simple model for SIP communication was built.