Aiming at the problem that the existing differential privacy high-dimensional data publishing methods are difficult to take into account both the complex attribute correlation between data and computational cost, a differential privacy high-dimensional data publishing method based on clustering analysis technology, namely PrivBC, was proposed. Firstly, the attribute clustering method was designed based on the K-means++, the maximum information coefficient was introduced to quantify the correlation between the attributes, and the data attributes with high correlation were clustered. Secondly, for each data subset obtained by the clustering, the correlation matrix was calculated to reduce the candidate space of attribute pairs, and the Bayesian network satisfying differential privacy was constructed. Finally, each attribute was sampled according to the Bayesian networks, and a new private dataset was synthesized for publishing. Compared with PrivBayes method, PrivBC method had the misclassification rate and running time reduced by 12.6% and 30.2% averagely and respectively. Experimental results show that the proposed method can significantly improve the computational efficiency with ensuring the data availability, and provides a new idea for the private publishing of high-dimensional big data.

Aiming at the problem of wireless resource scarcity and errors caused by imperfect channel in Internet of Things (IoT) sensor network, a method for allocating bandwidth and power to Secondary IoT Device (SID), under the condition of imperfect Channel State Information (CSI), was proposed. Firstly, considering the secondary and primary situations, the imperfect CSI of links between secondary transmitter side and primary receiving end, secondary transmitter side and secondary receiving end were modeled. Secondly, a mechanism for allocating bandwidth and power to secondary IoT devices was proposed. Meanwhile, the measurement and corresponding penalty policy were considered when current bandwidth is not enough to allocate. The idea is to maximize the secondary Energy Efficiency (EE) while fully using resources in extent. Finally, Particle Swarm Optimization (PSO) algorithm and weighted Chebyshev method were implemented to solve the problem, which made it possible to obtain the optimal allocation policy while lowering the problem complexity. The simulation results show that compared with equal allocation method and random allocation method, the proposed method improves the overall system transmission rate and minimizes the average secondary IoT base station transmit power by 75%, which can effectively enhance the energy efficiency of the entire network.

More than half of the earth's surface is covered by cloud. Current cloud detection methods from satellite remote sensing imageries are mainly manual or semi-automatic, depending upon manual intervention with low efficiency. Such methods can hardly be utilized in real-time or quasi real-time applications. To improve the availability of satellite remote sensing data, an automatic cloud detection method based on Deep Belief Network (DBN) and Otsu's method was proposed, named DOHM (DBN-Otsu Hybrid Model). The main contribution of DOHM is to replace the empirical fixed thresholds with adaptive ones, therefore achieve full-automatic cloud detection and increase the accuracy to greater than 95%. In addition, a 9-dimensional feature vector is adopted in network training. Diversity of the input feature vector helps to capture the characteristics of cloud more effectively.

The altitude information extracted from a single remote sensing image can be applied to detect the natural disaster, such as landslide or mud-rock flow. An approach based on dark channel prior was proposed for the altitude extraction from a single remote sensing image, which considers the influence of shadow. The approach was based on dark channel prior, and meanwhile a solution to overcome the effect of mountain shadow was given. The quantitative and qualitative analysis of a large number of mountain remote sensing images demonstrates that the proposed algorithm can obtain the accurate relative altitude information. In conclusion, the improved algorithm is effective for the extraction of the relative altitude from single remote sensing image of mountain with shadows.

Focusing on the issue that feature selection for the usually encountered large scale data sets in the "big data" is too slow to meet the practical requirements, a fast feature selection algorithm for unsupervised massive data sets was proposed based on the incremental absolute reduction algorithm in traditional rough set theory. Firstly, the large scale data set was regarded as a random object sequence and the candidate reduct was set empty. Secondly, random object was one by one drawn from the large scale data set without replacement; next, each random drawn object was checked if it could be distinguished with the other objects in the current object set and then merged with current object set, if the new object could not be distinguished using the candidate reduct, a new attribute that can distinguish the new object should be added into the candidate reduct. Finally, if successive I objects were distinguishable using the candidate reduct, the candidate reduct was used as the reduct of the large scale data set. Experiments on five unsupervised large-scale data sets demonstrated that a reduct which can distinguish no less than 95% object pairs could be found within 1% time needed by the discernibility matrix based algorithm and incremental absolute reduction algorithm. In the experiment of the text topic mining, the topic found by the reducted data set was consistent with that of the original data set. The experimental results show that the proposed algorithm can obtain effective reducts for large scale data set in practical time.

In the era of big data, research in topic evolution is mostly based on the classical probability topic model, the premise of word bag hypothesis leads to the lack of semantic in topic and the retrospective process in analyzing evolution. An online incremental feature ontology based topic evolution algorithm was proposed to tackle these problems. First of all, feature ontology was built based on word co-occurrence and general WordNet ontology base, with which the topic in text stream was modeled. Secondly, a text stream topic matrix construction algorithm was put forward to realize online incremental topic evolution analysis. Finally, a text topic ontology evolution diagram construction algorithm was put forward based on the text steam topic matrix, and topic similarity was computed using sub-graph similarity calculation, thus the evolution of topics in text stream was obtained with time scale. Experiments on scientific literature showed that the proposed algorithm reduced time complexity to O(nK+N), which outperformed classical probability topic evolution model, and performed no worse than sliding-window based Latent Dirichlet Allocation (LDA). With ontology introduced, as well as the semantic relations, the proposed algorithm can demonstrate the semantic feature of topics in graphics, based on which the topic evolution diagram is built incrementally, thus has more advantages in semantic explanatory and topic visualization.

In recent years, with the rapid development of Vehicular Adhoc NETwork (VANET), wellclassified routing protocol and comparative analysis are important for future study. This paper dealt with the ambiguous classification and deficient analysis of the routing protocol in VANET, classifying and summarizing the routing protocols on the basis of topology, geography and hybrid route in turn. Mainly introducing some classical routing protocols, it analyzed their characteristics and performance, and put forward the advantages and disadvantages as well as improvements. Special stress was put on the comparative analysis of Delay Tolerant Network (DTN) and opportunity routing protocol which would become hot spots for future geographic routing study. Besides, the major challenges and potential opportunities that VANET was facing were pointed out, which had laid the foundation of future protocol study and suggested the path to research clearly.

This paper investigated the limited feedback technology based on the Multiple Input Multiple Output Orthogonal Frequency Division Multiple (MIMO-OFDM) system in LTE-Advanced applications. A new limited feedback method based on recursive feedback was proposed. The method utilized the correlation between the subcarrier channels. The codewords for the current subcarrier could be determined by ordering the result of the inner product with respect to the codewords of the previous subcarrier. Then the variable length encoding method was used to code the index of the new codewords. The experimental results show that the recursive feedback algorithm based on variable length coding can reduce the feedback bits effectively in LTE-Advanced systems.

The sentiment analysis has aroused the interest of many researchers in recent years，since the subjective texts are useful for many applications. Sentiment analysis is to mine and analyze the subjective text, aiming to acquire valuable knowledge and information. This paper surveyed the status of the art of Chinese sentiment analysis. Firstly, the technique was introduced in detail, according to different granularity levels, namely word, sentence, and document; and the research of product review and news review were presented respectively. Then evaluation and corpus for Chinese text sentiment analysis were introduced. The difficulty and trend of Chinese text sentiment analysis were concluded finally. This paper focuses on the major methods and key technologies in this field, making detailed analysis and comparison.

To study the fairness-optimization problem with different user-rate in non-regenerative relay network, a power allocation algorithm based on user-required rate proportional fairness was proposed. Because the source could not receive the messages of the user's hope rate, a pre-average allocation fairness algorithm was proposed to overcome the disadvantage of the prior algorithm. The simulation results show that the two algorithms realize goal of minimizing the difference between user-rate and required rate and save the resource in network with assuring the users' requirement of Quality of Service (QoS).

The integration of MPLS and traditional mobile IP has many limitations. In order to improve its performance, many researches on integrating MPLS into micro-mobility were carried out. Based on these researches, especially HMPLS and MMPLS, this paper analysed their problems and then proposed a solution. Through LSP extension within a domain, this scheme overcomes some defects of current schemes of micro-mobility integrated with MPLS,supports fast and smooth handover and has lower signalling overhead.