Focused on the issue that the current large-scale networks are not suitable to be applied on resource-starved mobile devices like smart phones and tablet computers， and the pooling layer will lead to the sparsity of feature map， which ultimately affect the recognition accuracy of the neural network， a lightweight face recognition neural network namely ShuffaceNet was proposed， a smooth nonlinear Log-Mean-Exp function ThetaMEX was designed， and an end-to-end trainable ThetaMEX Global Pool Layer （TGPL） was proposed， so as to reduce network parameters and improve computing speed while ensuring the accuracy of the algorithm， achieving the purpose that the network can be effectively deployed on mobile devices with limited resources. ShuffaceNet has about 3 600 parameters， and the model size is only 3.5 MB. The recognition test results on LFW （Labled Faces in the Wild）， AgeDB-30 （Age Database-30） and CFP （Celebrities in Frontal Profile） face datasets show that the accuracy of ShuffaceNet reaches 99.32%， 93.17%， 94.51% respectively. Compared with the traditional networks such as MobileNetV1， SqueezeNet and Xception， the proposed network has the size reduced by 73.1%， 82.1% and 78.5% respectively， and the accuracy on AgeDB-30 dataset improved by 5.0%， 6.3% and 6.7% respectively. It can be seen that the proposed network based on ThetaMEX global pooling can improve the model accuracy.

Traditional spectral clustering algorithms are difficult to be applied to large-scale hyperspectral images， and the existing improved spectral clustering algorithms are not effective in processing large-scale hyperspectral images. To address these problems， a hyperspectral clustering algorithm based on double dimension-reduction of super-pixel and anchor graph was proposed to reduce the complexity of clustering data that is to reduce the computational cost of clustering process， thereby improving the clustering performance in many aspects. Firstly， Principal Component Analysis （PCA） was performed to the hyperspectral image data， and dimension-reduction was carried out to the data based on super-pixel segmentation according to the regional characteristics of hyperspectral image. Then， the anchor points of the data obtained in previous step were selected with the idea of constructing anchor graph. And the adjacent anchor graph was constructed to achieve double dimension-reduction for spectral clustering. At the same time， in order to remove the artificial adjustment of parameters in the operation of the algorithm， a kernel-free anchor graph construction method with the Gaussian kernel removed was used in the construction of anchor graph to achieve automatic graph construction. Experimental results on Indian Pines dataset and Salinas dataset show that the proposed algorithm can improve the overall effects of clustering with guaranteeing availability and low time consumption， thus verifying that the proposed algorithm can improve the quality and performance of clustering.

In the virtual machine migration process, one of the most problems is how to select the candidate migrating virtual machine list from the abnormal physical hosts in cloud data center. Therefore, a Greedy Algorithm Optimization based Virtual Machine Selection algorithm (GAO-VMS) was proposed. In GAO-VMS, the virtual machines with the optimal objective functions would be selected to perform the migration and the candidate migration virtual machine list was formed subsequently. There are three kinds of greedy modes in GAO-VMS: Maximum Power Reduction Policy (MPR), minimum migration Time and Power Tradeoff policy (TPT) and Violated million instructions per second-Virtual Machines policy (VVM). GAO-VMS was evaluated on CloudSim simulator. Simulation results show that compared to the common virtual machine migration strategy, GAO-VMS reduces the energy consumption of cloud data center by 30% - 35%, and reduces the number of virtual machine migrations by 40% - 45% with 5% increment of the Service Level Agreement (SLA) violation rate and the joint index of SLA violation and energy. The proposed GAO-VMS strategy can be used for enterprises to construct green cloud computing center.

PICO proposed in recent years is a bit-based ultra lightweight block cipher algorithm. The security of this algorithm to resist integral cryptanalysis was evaluated. Firstly, by analyzing the structure of PICO cipher algorithm, a Mixed-Integer Linear Programming (MILP) model of the algorithm was established based on division property. Then, according to the set constraints, the linear inequalities were generated to describe the propagation rules of division property, and the MILP problem was solved with the help of the mathematical software, the success of constructing the integral distinguisher was judged based on the objective function value. Finally, the automatic search of integral distinguisher of PICO algorithm was realized. Experimental results showed that, the 10-round integral distinguisher of PICO algorithm was searched, which is the longest one so far. However, the small number of plaintexts available is not conducive to key recovery. In order to obtain better attack performance, the searched 9-round distinguisher was used to perform 11-round key recovery attack on PICO algorithm. It is shown that the proposed attack can recover 128-bit round key, the data complexity of the attack is 2 ^63.46, the time complexity is 2 ⁷⁶ 11-round encryptions, and the storage complexity is 2 ²⁰.

Internet of Things (IoT) communication system has the characteristics of small active user number and short data frame, while the pilot and user identification code required by channel estimation and multi-user detection will greatly reduce the communication efficiency and response speed of IoT system. To solve these problems, a blind channel estimation and multi-user detection algorithm based on Non-Orthogonal Multiple Access (NOMA) was proposed. Firstly, the spread spectrum matrix in Code Division Multiple Access (CDMA) system was used to allocate the carrier to each user, and the constellation rotation problem caused by blind estimation was solved by differential coding. Secondly, according to the sparsity of carriers allocated to users, the Bernoulli-Gaussian (B-G) distribution was introduced as a prior distribution, and the hidden Markov characteristic between the variables was used to perform the factor decomposition and modeling, and the multi-user identification was carried out based on sparse features of user data. Finally, the above model was deduced by message passing algorithm to solve multi-user interference caused by NOMA, and the joint channel estimation and detection receiver algorithm for IoT environment was obtained. The simulation results show that, compared with Block Sparse Single Measurement Vector (BS-SMV) algorithm and Block Sparse Adaptive Space Pursuit (BSASP) algorithm, the proposed algorithm can achieve a performance gain of about 1 dB without increasing the complexity.

In view of performance flaws in the case of sparse data and the lack of similarity measurement methods in traditional collaborative filtering recommendation algorithm, a collaborative filtering recommendation algorithm based on multi-level hybrid similarity was proposed to improve the recommendation accuracy. The algorithm is divided into three different levels. Firstly, the concept of fuzzy set was used to fuzzify the user rating and then to calculate the user's fuzzy preferences, and the adjusted cosine-based similarity of the user rating and the Jarccad similarity of the user rating were combined as the user rating similarity. Secondly, the use rating was classified to predict the degree of interest of the user to the item category so that the user's interest similarity was calculated. Thirdly, the user's characteristic similarity was predicted by the characteristic attributes between users. Then, the user's interest similarity and user's characteristic similarity were dynamically integrated by the number of user ratings. Finally, the similarities of three levels were fused as the result of user similarity. The experimental results show that the improved hybrid algorithm has a decrease of 5% in Mean Absolute Error (MAE) compared to the adjusted cosine-based similarity algorithm when the number of neighbors is small. Compared with the improved MKJCF (Modified K-pow Jaccard similarity Cooperative Filtering) algorithm, the improved hybrid algorithm has a slight advantage, and the MAE fell by an average of about 1% with the increase of neighbor number. The proposed algorithm uses a multi-level recommendation strategy to improve the user's recommendation accuracy, effectively alleviates the sparseness of data and the impact of single measurement method.

To deal with high computing complexity and bad anti-CFO (anti-Carrier Frequency Offset) performance of conventional time synchronization algorithms for Time Division Long Term Evolution (TD-LTE) system, an improved algorithm based on Secondary Synchronization Signal (SSS) conjugate-symmetric in time domain was proposed in this paper. For the algorithm, SSS location was estimated as the peak of cross-correlation of received signal and its time reversal. And by combining SSS location with the detection of cell group ID, CP (Cyclic Prefix) type could also be judged. Analysis and simulation results demonstrate that the improved algorithm has low computing complexity, good performs on anti-CFO and better reliability compared with normal methods, especially, it also has good performs in multi-path channels. By applying to the third party TD-LTE UE detecting system, the algorithm is proved to be effective and feasible.

In the RFID system, the tags collision resulting from the data transaction between several tags and the reader at the same time is unavoidable. Focusing on the tags collision problem of the RFID system, analyzing the theories of binary, dynamic binary and retrusive binary algorithm, and considering the identifying times and the bits of transmitting data together, an improved algorithm which effectively reduced the times of identification and transmission of redundant information via preprocessing the tags’ ID and setting the stack on reader was presented. The simulation results indicate that this algorithm can obtain a better performance in terms of timesefficiency and bitsefficiency.

Concerning the complex dependence between classes in the object-oriented software, through the analysis of all kinds of static relations in UML Class Diagram (UCD), a dependence analysis model was proposed. In order to implement automatic analysis, the concept of Class Dependence Relation Graph (CDG) was introduced. Meanwhile, a CDG creation algorithm based on the XMI file exported from UCD was proposed. On the basis of this model, the test path search algorithm was proposed by the impact analysis of UCD. With the help of test path search algorithm, class set and test paths that need regression testing can be obtained.

The research about concept similarity is very important in knowledge representation and information retrieval. After studying the current classic distance-based semantic similarity algorithm, a more standardized similarity algorithm was proposed by analyzing the other key factors of semantic concept and increasing the impact of the node density and attributes of the concept for the semantic similarity. Through the experimental analysis, the similarity value of the improved algorithm is more reasonable; and compared with human subjective judgements under certain condition of the mediation parameter, the compatibility of the improved algorithm increases about 15% than that of the original algorithm.

Presently,histogram is the most effective technique that is used to estimate the query selectivity.Histogram can also be used in spatio-temporal query optimization after some improvements.TPR-tree is more predominant than other indices in predictive spatio-temporal database indexing.With time-parameter R-tree, a novel spatio-temporal histogram was proposed to estimate the selectivity for spatio-temporal querying.