Computing Power Network （CPN） is a new network system that solves the contradiction between computing power supply and demand， network transmission problems， and the issue of universal access to computing resources. According to the supply capacity of computing power resource providers and the dynamic resource requirements of application demanders， the computing， storage， network and other multi-dimensional resources of the underlying computing power infrastructure in the region are integrated to provide users with personalized computing power resource services and realize efficient management and on-demand allocation of computing power resources. To enhance the utilization and reliability of CPN resource matching and scheduling， a highly reliable matching method was proposed， namely Resource Measurement and Rescheduling Matching Method （RMRMM）. To achieve high-utilization resource scheduling， RMRMM designed a resource measurement matching scheme based on entropy weighted Technique for Order Preference by Similarity to Ideal Solution （entropy weighted TOPSIS） method and Deep Reinforcement Learning （DRL）， comprehensively measured the Structural Feature Value （SFV）， computing power， storage capacity， and network communication capacity of the node， and narrowed the resource matching range to improve the matching accuracy and resource utilization. Additionally， RMRMM considered the failure of nodes due to attacks， and designed a rescheduling module based on the Adaptive Large Neighborhood Search （ALNS） algorithm. When matches failed， nodes and tasks were rescheduled to improve the acceptance rate of tasks and enhance the overall reliability. Simulation experimental results on OMNet++ platform demonstrate that average BandWidth （BW） utilization， average Random Access Memory （RAM） utilization， average STORAGE utilization， and task request reception rate of RMRMM reach 69.7%， 66.4%， 68.5%， and 75.5%， respectively. Both resource utilization and request reception rate of RMRMM outperform other matching strategies， improving the efficiency and reliability of RMRMM.

The Alzheimer's Disease (AD) usually leads to atrophy of hippocampus region. According to the characteristic, a Convolutional Neural Network (CNN) based method was proposed for the diagnosis of AD by using the hippocampu region in brain Magnetic Resonance Imaging (MRI). All the test data were got from the ADNI database including 188 AD and 229 Normal Control (NC). Firstly, all the brain MRI were preprocessed by skull stripping and aligned to a template space. Secondly, a linear regression model was used for age correction of brain aging atrophy. Then, after preprocessing, multiple 2.5D images were extracted from the hippocampus region in the 3D brain image for each object. Finally, the CNN was used to train and recognize the extracted 2.5D images, and the recognition results of the same object were used for the joint diagnosis of AD. The experiments were carried out by using multiple ten-fold cross validation methods. The experimental results show that the average recognition accuracy of the proposed method reaches 88.02%. The comparison results show that, compared with Stacked Auto-Encoder (SAE) method, the proposed method has improved the diagnosis effect of AD in the case of only using MRI.

Time overhead of the taint propagation analysis in the off-line taint analysis is very large, so the research on efficient taint propagation has important significance. In order to solve the problem, an optimization method of taint propagation analysis based on semantic rules was proposed. This method defined semantic description rules for the instruction to describe taint propagation semantics, automatically generated the semantics of assembly instructions by using the intermediate language, and then analyzed taint propagation according to the semantic rules, to avoid the repeated semantic parsing caused by repeating instructions execution in the existing taint analysis method, thus improving the efficiency of taint analysis. The experimental results show that, this method can effectively reduce the time cost of taint propagation analysis, only costs 14% time of the taint analysis based on intermediate language.

Douglas-Peucker (DP) compression algorithm of vector data compression algorithm was introduced to wireless sensor networks, at the same time for the number of scans of the data compression process, the paper put forward an improved cluster head extraction for data compression algorithm, and the cluster head was called data cluster head. Cluster head extraction compression algorithm reduced the number of data scan in compression process by setting step, and used the optimum curve fitting method for monitoring data point to do linear optimization fitting, according to the attachment relationship of the data, and extracted the cluster head data that reflected the overall characteristics; meanwhile, the subgroups of non-cluster head data subgroups were divided. The simulation results show that, the process of cluster head extraction compression algorithm is simpler; for the large fluctuation data it has a better cluster head extraction effect; besides, it reduces the amount of network data transmission, and effectively saves the energy consumption across the network.

Hyperspectral image has high spectral dimension, vast data and altitudinal interband redundancy, which brings problems to image classification. To effectively reduce dimensionality and improve classification precision, a new extraction method of nonlinear manifold learning feature based on Supervised Local Linear Embedding (SLLE) for classification of hyperspectral image was proposed in this paper. A data point's k Nearest Neighbours (NN) were found by using new distance function which was proposed according to prior class-label information. Because the intra-class distance is smaller than inter-class distance, classification is easy for SLLE algorithm. The experimental results on hyperspectral datasets and UCI data set demonstrate the effectiveness of the presented method.

Problems such as high delay, high packet loss rate, low stability and reliability exist in current handoff scheme. To solve these problems, a dual-link communication mechanism and a dual-link selection were proposed, and the data transmission algorithm was presented in this paper to acquire accurate signal quality by smooth processing and control the handoff between two communication links at appropriate time by threshold of difference value and packet forwarding using dual-thread. The experimental results show that compared with the single link mechanism, no delayed pulse exists in dual-link mechanism, the packet loss rate is close to zero and the average throughput is increased by 20%. This mechanism can be applied to many environments owing high-speed mobile subnet such as metro, highway, etc.

Optimizing interactive response time is an important measure for improving quality of users’ experience. To reduce end-points process delay, client caching delay and acquisition delay of Random Access Point (RAP), a novel method based on building RAP index table was proposed. Media RAP acquisition and key frame abstraction can be quickly done by searching index table. With the analysis of MPEG-2 system layer and H.264 network abstract layer, two methods of key frame abstraction were given. Whereas there are so many media files stored in media server, program information index built by Hash method was discussed, then program information could be acquired quickly. Based on the index tables of RAP and program information built in pre-processing phrase, trick-play implementation was depicted in detail. Furthermore, the feasibility was verified in the IMS-based IPTV prototype developed by Huawei Technologies Co., Ltd and our laboratory.