Aiming at the correlation problem between sleep stages and sleep apnea hypopnea， a sleep physiological time series classification method based on adaptive multi-task learning was proposed. Single-channel electroencephalogram and electrocardiogram were used for sleep staging and Sleep Apnea Hypopnea Syndrome （SAHS） detection. A two-stream time dependence learning module was utilized to extract shared features under joint supervision of the two tasks. The correlation between sleep stages and sleep apnea hypopnea was modeled by the adaptive inter-task correlation learning module with channel attention mechanism. The experimental results on two public datasets indicate that the proposed method can complete sleep staging and SAHS detection simultaneously. On UCD dataset， the accuracy， MF1（Macro F1-score）， and Area Under the receiver characteristic Curve （AUC） for sleep staging of the proposed method were 1.21 percentage points， 1.22 percentage points， and 0.008 3 higher than those of TinySleepNet； its MF2 （Macro F2-score）， AUC， and recall of SAHS detection were 11.08 percentage points， 0.053 7， and 15.75 percentage points higher than those of the 6-layer CNN model， which meant more disease segments could be detected. The proposed method could be applied to home sleep monitoring or mobile medical to achieve efficient and convenient sleep quality assessment， assisting doctors in preliminary diagnosis of SAHS.

As the uncertainty of user requirements in the cloud-edge environment causes the microservice composition logic to be dynamically adjusted with the changes of user needs， a Dynamic Evolution method for Microservice Composition system （DE4MC） in the cloud-edge environment was proposed. Firstly， the user's operation was automatically recognized to implement the corresponding algorithm strategy. Secondly， in the deployment stage， the better node was selected by the system for deployment through the deployment algorithm in the proposed method after the user submitting the business process. Finally， in the dynamic adjustment stage， the dynamic evolution was performed by the system through the dynamic adjustment algorithm in the proposed method after the user adjusting the business process instances. In both algorithms in the proposed method， the migration cost of microservice instances， the data communication cost between microservices and users， and the data flow transmission cost between microservices were comprehensively considered to select better nodes for deployment， which shortened the running time and reduced the evolution cost. In the simulation experiment， in the deployment stage， the deployment algorithm in the proposed method has average running time of all scales 9.7% lower and total evolution cost 16.8% lower than those of the combination algorithm of Heuristic Algorithm （HA） with Non-dominated Sorting Genetic Algorithm-Ⅱ （NSGA-Ⅱ）； in the dynamic adjustment stage， compared with the combination algorithm of HA and NSGA-Ⅱ， the dynamic adjustment algorithm in the proposed method has the average running time of all scales 6.3% lower， and the total evolution cost 21.7% lower. Experimental results show that the proposed method ensures timely evolution of the microservice composition system in the cloud-edge environment with low evolution cost and short business process time， and provides users with satisfactory quality of service.

The complex structure and diverse imformation of stock markets make stock movement prediction extremely challenging. However， most of the existing studies treat each stock as an individual or use graph structures to model complex higher-order relationships in stock markets， without considering the hierarchy and dynamics among stocks， industries and markets. Aiming at the above problems， a Dynamic Macro Memory Network （DMMN） was proposed， and price movement prediction was performed for multiple stocks simultaneously based on DMMN. In this method， the market macro-environmental information was modeled by the hierarchies of “stock-industry-market”， and long-term dependences of this information on time series were captured. Then， the market macro-environmental information was integrated with stock micro-characteristic information dynamically to enhance the ability of each stock to perceive the overall state of the market and capture the interdependences among stocks， industries， and markets indirectly. Experimental results on the collected CSI300 dataset show that compared with stock prediction methods based on Attentive Long Short-Term Memory （ALSTM） network， GCN-LSTM （Graph Convolutional Network with Long Short-Term Memory）， Convolutional Neural Network （CNN） and other models， the DMMN-based method achieves better results in F1-score and Sharpe ratio， which are improved by 4.87% and 31.90% respectively compared with ALSTM， the best model among all comparison methods. This indicates that DMMN has better prediction performance and better practicability.

When Augmented Reality (AR) browser running in the Point of Interest (POI) dense region, there are some problems like data loading slowly, icon sheltered from the others, low positioning accuracy, etc. To solve above problems, this article proposed a new calculation method of the Global Positioning System (GPS) coordinate mapping which introduced the distance factor, improved the calculating way of coordinates based on the angle projection, and made the icon distinguished effectively after the phone posture changed. Secondly, in order to improve the user experience, a POI labels focus display method which is in more accord with human visual habits was proposed. At the same time, aiming at the low positioning accuracy problem of GPS, the distributed mass scene visual recognition technology was adopted to implement high-precision positioning of scenario.

In order to apply reasoning rules of the description logic to analyze and solve the simple contradiction problem, the extension set was introduced to be the set theory foundation of the description logic SHOQ, and a new description logic named D-SHOQES (Dynamic Description Logic SHOQ Based on Extension Set) was proposed. The cut sets of extension concepts and extension roles were defined as atomic concepts and atomic roles, and the action theory was injected to obtain the qualitative change domain and the quantitative change domain of the concepts and roles. The semantics of concepts, roles and actions in D-SHOQES were given, as well as the Tableau-algorithm reasoning rules. Finally, the method of solving contradiction problem was researched, which offered a strategy for the solution to contradiction problem.

Designated Verifier Signature (DVS) eliminates the publicly verifiable feature of traditional digital signature to prevent a verifier from proving to a third party the fact that a particular digital signature held by him is issued by a signer. Traditionally, security of digital signature schemes relies on the ideal assumption that an adversary has no access to the information of a secret key. However, side-channel attacks show that the physical implementation of a cryptographic algorithm will leak partial secret information to an adversary. Hence it is necessary to design a leakage-resilient designated verifier signature. Okamoto authentication scheme was converted into a designated verifier signature scheme by using the OR proof technique. Given the leakage bound, the security of the presented scheme was proved under the relative leakage model.

Heavy land clutter with antenna scan is the main interference for Foreign Object Debris (FOD) detection. However, traditional Constant False Alarm Rate (CFAR) in space-domain is ineffective to detect targets on runways. To solve this problem, a cell-average clutter-map CFAR was proposed. First of all, an echo model based on the characteristics of FOD surveillance radar system was built. Then, the range-bearing two-dimensional CFAR detection could be realized by using clutter-map cells dividing, cell averaging and recursive filtering. Further analysis of the main factors that affected the detection performance of this method was studied in the end. The simulation results show that, the proposed algorithm can effectively detect the weak target and obtain high detection probability with a low signal-to-clutter ratio.

The Artificial Fish Swarm Algorithm (AFSA) has low search speed and it is difficult to obtain accurate value. To solve the problems, an improved algorithm based on social learning mechanism was proposed. In the latter optimization period, the authors used convergence and divergence behaviors to improve the algorithm. The two acts had fast search speed and high optimization accuracy, meanwhile, the divergence behavior enhanced the population diversity and the ability of skipping over the local extremum. To a certain extent, the improved algorithm enhanced the search performance. The experimental results show that the proposed algorithm is feasible and efficacious.

Through analyzing the application status of Vehicle Ad Hoc NETwork (VANET) in road transportation field, according to the characteristics of VANET and challenges in news transmission process, concerning the problems of previous algorithms being difficult to establish spatial model accurately and hardly considering the regularity characteristics of social behavior, a routing scheme named HBSR was proposed based on the historical behavior statistics of vehicles, including nodes connected algorithm calculating the connectivity between vehicles, topological overlap algorithm calculating the number of periods between the source node and destination node, paths selected algorithm selecting messages forwarding paths and loss strategy. Compared with several typical routing algorithms on ONE simulation platform, the simulation results prove that HBSR can find news forwarding paths more effectively, and reduces message delivery delay obviously while delivery rate increases significantly, and performance is relatively stable in VANET.

For the dynamic characteristic of vehicle Ad-Hoc network's topology, the file is difficult to completely downloaded within the communication of a single vehicle road Access Point (AP) using the existing schemes of information dissemination, having the long time delay limitation of waiting for the next AP to document communication. A method that downloaded and spread the file fragmentation using multiple vehicles within the range of some free APs was proposed, the message delivery delay was divided into direct and indirect encounter delays, and discussion was made on them respectively, and a specific option was given to choose message carried vehicles. The experimental results of the message loss rate and delay show that the environment joined with the proposed scheme can effectively improve the reliability of message downloading, shorten the delay of downloading message to the purpose vehicle, without significant additional load to the network.