The current personalized recommendation platform has the characteristics of a wide range of data sources and many data types. With the data sparsity of the platform as an important reason for affecting the performance of the recommendation system, there are many challenges faced by the recommendation system:how to mine structured data and unstructured data of the platform to discover more features, improve the accuracy of recommendations in data-sparse scenarios, alleviate the cold start problem, and make recommendations interpretable. Therefore, for the personalized scenario of recommending Items for Users, the Heterogeneous Information Network (HIN) was used to build the association relationships between objects in the recommendation platform, and the Meta-Graph was used to describe the association paths between objects and calculate the User-Item similarity matrices under different paths; the FunkSVD matrix decomposition algorithm was adopted to calculate the implicit features of Users and Items, and for the unstructured data with text as an example, the Convolutional Neural Network (CNN) technology was used to mine the text features of the data; after splicing the features obtained by the two methods, a Factorization Machine (FM) incorporating historical average scores of Users and Items was used to predict Users' scores for Items. In the experiment, based on the public dataset Yelp, the proposed hybrid recommendation model, the single recommendation model based on Meta-Graph, the FM Recommendation model (FMR) and the FunkSVD based recommendation model were established and trained. Experimental results show that the proposed hybrid recommendation model has good validity and interpretability, and compared with the comparison models, the recommendation accuracy of this model has been greatly improved.

It is of great significance to apply the blockchain technology to the field of digital music copyright management in view of the difficulties in copyright confirmation, infringement monitoring, right protection and evidence collection, royalty settlement and other problems in the traditional music copyright industry. A digital music copyright management system was designed and constructed by using the VNT Chain blockchain platform. In the system, the blockchain technology was used to provide the proof of music copyright and realize the evidence solidification, the Shazam algorithm was used to provide the proof of originality for music copyright, and the smart contract was used to guarantee the security and reliability of transactions. This system included six function modules:user management, copyright registration, copyright trading, infringement monitoring, evidence solidification and music ecology, covering the main parts of copyright management. According to different needs of business data, blockchain, InterPlanetary File System(IPFS) and MySQL were adopted as storage engines respectively for the system. Experimental results show that the copyright registration time of each music increases by about 1.9 s, and the storage of music feature fingerprint data of one song on IPFS costs about 8 MB averagely, which meets the expected system performance requirements.

Aiming at the problems of high time complexity, slow convergence speed and error-prone matching of low coverage point cloud registration, a point cloud registration algorithm based on region segmentation was proposed. Firstly, the volume integral invariant was used to calculate the concavity and convexity of points on the point cloud, and then the concavity and convexity feature point sets were extracted. Secondly, the regions of the feature points were partitioned by the segmentation algorithm based on the mixed manifold spectral clustering, and the regions were registered by the Iterative Closest Point (ICP) algorithm based on Singular Value Decomposition (SVD), so that the accurate registration of point clouds could be achieved. The experimental results show that the proposed algorithm can greatly improve the coverage of point clouds by region segmentation, and the optimal rotation matrix of rigid body transformation can be calculated without iteration. The algorithm has the registration accuracy increased by more than 10% and the registration time reduced by more than 20%. Therefore, the proposed algorithm can achieve fast and accurate registration of point clouds with low coverage.

Focusing on low the channel estimation accuracy is in virtual angular domain channel for Frequency Division Duplex based MASSIVE Multi-Input Multi-Output (MASSIVE MIMO) systems, a new algorithm Based on Threshold Sparsity Adaptive Matching Pursuit (BT-SAMP) was proposed. The algorithm combined the atomic selection characteristics of BAOMP algorithm and the adaptive characteristics of Sparsity Adaptive Matching Pursuit (SAMP) algorithm. The Backtracking-based Adaptive Orthogonal Matching Pursuit (BAOMP) rule of the "adding atom" algorithm was used as the atomic selection preprocessing of the SAMP algorithm, the fixed atom was added by reasonable threshold, and then the step size of the SAMP algorithm was extended to find the maximum approximation coefficient of the channel matrix, which can improve the accuracy of SAMP algorithm and accelerate the convergence speed of the algorithm. The simulation results show that the channel estimation accuracy is improved compared with the SAMP algorithm in the case of low Signal-to-Noise Ratio (SNR), especially when the SNR is 0 to 10 dB, the estimation accuracy is improved by 4 dB, and the running time of the algorithm is reduced by about 61%.

Focused on the issue that the channel estimation accuracy of multi-cell multi-user MASSIVE Multi-Input Multi-Output (MASSIVE MIMO) system was poor in the case of low Signal-to-Noise Ratio (SNR), a compressive sensing algorithm named Fruit Fly Stagewise Orthogonal Matching Pursuit (FF-StOMP) based on group intelligent search was proposed. Based on the Stagewise Orthogonal Matching Pursuit (StOMP) solution to the channel matrix parameters and the normalized minimum mean square error under different thresholds, the algorithm was used to search the minimum normalized mean square error and its corresponding threshold by the fruit fly optimization algorithm to achieve the adaptive parameter setting. The simulation results show that the channel estimation performance of FF-StOMP algorithm can be improved by 0.5 to 1 dB when the SNR is 0 to 10 dB compared with the StOMP algorithm. When the SNR is 11 to 20 dB, the channel estimation performance can be improved by 0.2 to 0.3 dB. When the number of cell users changes, the proposed algorithm can realize the adaptive channel estimation, which can effectively improve the channel estimation accuracy in the case of MASSIVE MIMO system with low SNR.

In order to effectively master protocol interactive behavior, a method to automatically build protocol interactive process based on message sequence chart was proposed. Firstly, according to the characteristics of the protocol interactive process, the dependency graph was defined to represent the partial order of events in message sequence, and the network flows were converted to dependency graphs. Secondly, the basic message sequences were used to describe protocol interactive behavior fragments, and the basic message sequences were mined by defining event maximum suffix. Finally, the maximum dependency graphs that were found out were connected and merged to build a message sequence chart. The experimental results show that the proposed method has a high accuracy and the built message sequence chart can visually represent the protocol interactive process.

In this paper, an novel virtual EEPROM based on flash device was designed, which focused on the current urgent need for low-cost in embedded systems especially, The virtual EEPROM was used as NAND device part in embedded Operating System (OS), which used data backup mechanism to ensure data security. Considering that NAND only supports page programming, a method of log update based on memory buffer was employed to realize byte programming in Virtual EEPROM. Finally, to achieve unified management of NAND block, a wear leveling was designed, meanwhile, issues of bad block and Program/Erase cycle were greatly optimized. The result shows that the Virtual EEPROM has fast programming characteristics of NAND, as well as a great advantage of endurance over the EEPROM. The design has good compatibility and can be widely transplanted in various embedded platforms.

Protocol state machine can describe the behavior of a protocol, which can help to understand the behavior logic of protocol. Oriented towards text protocols, a statistical method was firstly used to extract the semantic keyword of representative message type, and an adjacency matrix was used to describe the sequential relationship between the message types, based on which the protocol states were labeled and a state transition diagram was built. The experimental results show that the method can accurately describe the sequential relationship between the message types and abstract state machine model accurately.

In order to meet the needs of modern society for weather forecast and early warning service, a real-time weather forecast and abnormal weather early warning system was researched and implemented in the Android system. Based on the decision tree algorithm of C4.5 algorithm, the warning classification problem was resolved. By means of extracting the attributes with maximum gain rate as the features of training sample, a decision tree was built. A model of decision tree was got by the pruning weather warning evaluation and analysis and application were made on this model. The experimental results show that this method has advantages in the assessment of classification accuracy, with correct classification rate up to 85.8%.

In the network performance measurement, location error is one of the main factors which influence the accurate network delay measurement. To reduce location error, an improved method for delay measurement was proposed based on Windows Network Driver Interface Specification (NDIS). By using this method, the timestamp position was removed from application to NDIS Intermediate Driver (ID) which was embedded between Miniport Driver (MD) and Protocol Driver (PD), and then the measurement program could calculate the network delay according to those timestamps. Compared with the traditional method, the experimental results show that the proposed method can nearly eliminate location error, and make sure the measurement standard deviation to be lowered than 10μs under different packet-lengths and host-loads. The improved measurement method does not need additional software and hardware, so it has lower measurement cost and can be applied widely.

To solve the problems of the ringing, blocking artifacts and the excessive noise amplification in local image enhancement, an object-based multilevel contrast stretching method was proposed. First, segment the image into its constitent objects by using morphological watersheds and regional merging; then, separately stretch the image contrast at inter-object level and intra-object level in different ways. At inter-object level, an approach of stretching between adjacent extrema was adopted to adequately enlarge the local dynamic range of gray levels between objects; at intra-object level, the linear stretching approach was adopted to enhance the textural feature of the object and keep its appearance. Experimental results show, besides enhancing the image structure, the proposed method can effectively avoid ringing, blocking artifacts, restrict excessive noise amplification in smooth areas and preserve the overall brightness of the image, thus can provide the enhanced image with natural appearance.

The standard particle swarm optimization in the evolution formula only considers both the colony's best fitness value and the individual's fitness values. Therefore, it leads to the low accuracy of the convergence on account of being lack of the diversity in later evolution period. In order to improve the accuracy of the algorithm, the paper proposed the core master group particle swarm, and combined a core master group particle swarm with the improved formula. The improved algorithm is proved through experiments to be more accurate.

Low expandability and overweight of telecom backbone in mainstream P2P network model have been hampering the development of P2P system. Though structured overlay network model solves part of problems, it is difficult to be applied into Internet due to its limitations. By analyzing other P2P models, this paper provided a hybrid model based P2P network model, and described some important processes in the model. A manager protocol and new key matching algorithm were also proposed to enhance the management and practicability of P2P network.