The rapid development of Internet leads to the explosive growth of news data. How to capture the topic evolution process of current popular events from massive news data has become a hot research topic in the field of document analysis. However， the commonly used traditional dynamic clustering models are inflexible and inefficient when dealing with large-scale datasets， while the existing deep document clustering models lack a general method to capture the topic evolution process of time series data. To address these problems， a Deep Dynamic Document Clustering （DDDC） model was designed. In this model， based on the existing deep variational inference algorithms， the topic distributions incorporating the content of previous time slices on different time slices were captured， and the evolution process of event topics was captured from these distributions through clustering. Experimental results on real news datasets show that compared with Dynamic Topic Model （DTM）， Variational Deep Embedding （VaDE） and other algorithms， DDDC model has the clustering accuracy and Normalized Mutual Information （NMI） improved by at least 4 percentage points averagely and at least 3 percentage points respectively in each time slice on different datasets， verifying the effectiveness of DDDC model.

For the problems that third party counting institution does not meet the decentralization and de-trusting characteristics of blockchain and is lack of credibility,a blockchain electronic counting scheme based on the Practical Byzantine Fault Tolerance (PBFT) algorithm was proposed. Firstly,the centerless counting model was built in the distributed environment,and the counting node was determined by the credibility level of the node. Secondly,the consensus of pending ballots was formed based on PBFT. Thirdly,the minimum number of honest nodes in PBFT was set as the threshold for threshold signature,and the threshold signature was only formed by results satisfying the threshold. Finally, the results satisfying the trusted state were recorded in the blockchain account book. Test and analysis results show that only when the honest nodes exceed two-thirds,the PBFT is satisfied,and the obtained counting result is credible.

The seizure detection is important for the localization and classification of epileptic seizures. In order to solve the problem brought by large amount of data and high feature space in EEG (Electroencephalograph) for quickly and accurately detecting the seizures, a method based on max-Relevance and Min-Redundancy (mRMR) criteria and Extreme Learning Machine (ELM) was proposed. The time-frequency measures by Short-Time Fourier Transform (STFT) were extracted as features, and the large set of features were selected based on max-relevance and min-redundancy criteria. The states were classified using the extreme learning machine, Support Vector Machine (SVM) and Back Propagation (BP) algorithm. The result shows that the performance of ELM is better than SVM and BP algorithms in terms of computation time and classification accuracy. The classification accuracy rate of interictal durations and seizures can reach more than 98%, and the computation efficiency is only 0.8s. This approach can detect epileptic seizures accurately in real-time.

The special nature of sensor network makes it vulnerable to Radio Frequency Jamming Attacks (RF JA) and other attacks. To implement and deploy the security mechanism of the next step, and determine the location of the Jamming attacker called jammer in Wireless Sensor Network (WSN), α-hull was applied to calculate the Minimum Circumscribed Circle (MCC) of point set. An effective and accurate method for MCC detection was established through finding the least square circle of the point set and iteratively approaching the MCC with recursive subdivision. All vertices of the α-hull will be on the same circle, if 1/α is equal to the radius of points' MCC. On the basis of those rules, an algorithm for detecting MCC named α-MCC was developed. The simulation results show that, compared with the existing incremental algorithm, α-MCC is able to achieve higher accuracy in most cases. With the network node density, time consumption of α-MCC does not grow exponentially, but with only a slight linear increase.

To resolve the limitations in the integrality of evidence used for the trust judgment in pervasive environments, a new trust model was proposed based on the analysis of the four trust evidence. In this model, the trust was classified into two groups: namely static trust and dynamic trust. Furthermore, the workflow of the model as well as the method of updating trust was described. By using this model, the reliable trust relationship can be set up between pervasive entities, which can enhance the security performance of the pervasive environments.