Catastrophic forgetting poses a significant challenge to Federated Class-Incremental Learning （FCIL）， leading to performance degradation of continuous tasks in FCIL. To address this issue， an FCIL method of Label Semantic Embedding （LSE） with Multi-Head Self-Attention （MHSA） — ATTLSE （ATTention Label Semantic Embedding） was proposed. Firstly， an LSE with MHSA was integrated with a generator. Secondly， during the stage of Data-Free Knowledge Distillation （DFKD）， the generator with MHSA was used to produce more meaningful data samples， which guided the training of client models and reduced the influence of catastrophic forgetting problem in FCIL. Experiments were carried out on the CIFAR-100 and Tiny_ImageNet datasets. The results demonstrate that the average accuracy of ATTLSE is improved by 0.06 to 6.45 percentage points compared to LANDER （Label Text Centered Data-Free Knowledge Transfer） method， so as to solve the catastrophic forgetting problem to certain extent of continuous tasks in FCIL.

Aiming at the problems of lack of satellite positioning signal， limited communication and weak ambient light of Unmanned Surface Vehicle （USV） in subterranean closed water body， a cooperative visual positioning method of multiple USVs in subterranean closed water body was proposed. Firstly， a vehicle-borne light source cooperative marker was designed， and the marker structure was optimized according to the vehicle structure and application scene. Secondly， monocular vision was used to collect the marker images， and the image coordinates of the feature points were solved. Thirdly， on the basis of camera imaging model， by using the relationship between the spatial coordinates of feature points of the markers and the corresponding image coordinates， the relative positions between adjacent vehicles were calculated through improving direct linear transformation method. Fourthly， the cameras of the front and rear vehicles were used to make look face to face between the vehicles. Through the minimum variance algorithm， the relative positions calculated on the basis of the camera images of the front and rear vehicles were fused to improve the relative positioning accuracy. Finally， the absolute location of each USV was obtained by using the known absolute coordinates in the scene. The factors influencing positioning error were analyzed through simulation， and the proposed method was compared with the traditional direct linear transformation method. The results show that as the distance increases， the effect of this method becomes more obvious. At a distance of 15 m， the position variance solved by the proposed method is stable within 0.2 m²， verifying the accuracy of this method. Static experimental results show that the proposed method can stabilize the relative error within 10.0%； dynamic experimental results in underground river courses show that the absolute positioning navigation trajectory solved by the proposed method achieves accuracy similar to satellite positioning， which verifies the feasibility of this method.

Accurate background model is the paramount base for object extracting and tracing. In response to swing objects which part quasi-periodically changed in intricate scene, based on multi-Gaussian background model, a new Quasi-Periodic Background Algorithm (QPBA) was proposed to suppress the swing objects and establish an accurate and stable background model. The specific process included: According to multi-Gaussian background model, the object classification in scene was set up, and the effect on Gaussian model's parameters caused by swing objects was analyzed. By using color distribution values as samples to establish Gaussian model to keep swing pixels, the swing model in swing pixels was integrated into background model with weight factors of occurrence frequency and time interval. Comparison among QPBA and the classical background modeling algorithms such as GMM (Gaussian Mixture Model), ViBe (Visual Background extractor) and CodeBook was put forward, and the results were assessed in aspects of quality, quantity and efficiency. It shows that QPBA has a more obvious suppression on swing objects, and its fall-out ratio is less than 1%, so that it can handle the scene with swing objects. At the same time, its correct detection number is consistent with other algorithms, thus the moving objects can be reserved perfectly. In addition, the efficiency of QPBA is high, and its resolving time is approximate to CodeBook, which can satisfy the requirements of real-time computation.

To keep the trade-off of time complexity and accuracy of community detection in complex networks, Community Detection Algorithm based on Clustering Granulation (CGCDA) was proposed in this paper. The granules were regarded as communities so that the granulation for a network was actually the community partition of a network. Firstly, each node in the network was regarded as an original granule, then the granule set was obtained by the initial granulation operation. Secondly, granules in this set which satisfied granulation coefficient were merged by clustering granulation operation. The process was finished until granulation coefficient was not satisfied in the granule set. Finally, overlapping nodes among some granules were regard as isolated points, and they were merged into corresponding granules based on neighbor nodes voting algorithm to realize the community partition of complex network. Newman Fast Algorithm (NFA), Label Propagation Algorithm (LPA), CGCDA were realized on four benchmark datasets. The experimental results show that CGCDA can achieve modularity 7.6% higher than LPA and time 96% less than NFA averagely. CGCDA has lower time complexity and higher modularity. The balance between time complexity and accuracy of community detection is achieved. Compared with NFA and LPA, the whole performance of CGCDA is better.

The analysis results on PPStream-VOD System client behavior characteristics were presented in this paper. This study began from researching on peer-distributing protocol and the architecture of Buffer-Map based on passive measurement. A dedicated PPS-VOD crawler was deployed to capture clients’ Buffer-Map and study the characteristics of client watching behavior. By accurate data analysis, the client behavior was classified as Long-Smoother, Short-Smoother and Jumper. Then the proportion of three kinds of clients and their different watching behaviors were proposed. The concept watching viscosity was put forward to reveal the attraction of program to users, which is in direct proportion to average watching time, and in inverse proportion to slope of probabillty accumulation curve.

Based on certain detection characteristics of process, hidden process could be uncovered by memory searching. However, malware, with the help of developing Rootkit, could hardly be detected because its feature has been manipulated or virtual memory scan could be invalid, thus increasing the difficulty of detection. In order to address this issue, a new multi-characteristics matching approach was proposed. It was to obtain the whole physical memory image by Page Table Entry (PTE) patching, to extract the key fields from process data structure and construct a template to improve the reliability of characteristics, and to introduce similarity for preventing the detection leakage. The results show that the new detection is effective in the hidden process searching.

The Scale-Invariant Feature Transform (SIFT) algorithm for trademark retrieval may fail to detect some reversal and highly similar images. To solve this problem, a new algorithm based on SIFT descriptor and corner feature was proposed. This method used corner feature to make up for the lack of SIFT. The experimental results show that this method not only contains the character of SIFT descriptor like robustness against noise, but also improves the ability for description of image shape, so the method shows a better retrieval performance than other methods.

It is very difficult to get the security in sensor networks because of the limited resources of nodes and the wireless communication between nodes. A double security-based routing algorithms was proposed based on the existed security arrangement and low power consumption multipath.The existed security arrangement can provide security to some certain case and multipath can provide more security even when security arrangement failed. Theoretic analysis and simulation results show that the new algorithm has better performances.