In view of the high cost as well as the limitation of retrieval function of the existing searchable encryption schemes based on blockchain to realize result verification and fair payment， a multi-keyword ranked search scheme supporting verification and fair payment was proposed based on blockchain. In the proposed scheme， the Cloud Service Provider （CSP） was used to store the encrypted index tree and perform search operations， and a lookup table including verification certificates was constructed to assist the smart contract to complete the verification of retrieval results and fair payment， which reduced the complexity of smart contract execution and saved time as well as expensive cost. In addition， the index of balanced binary tree structure was constructed by combining vector space model and Term Frequency-Inverse Document Frequency （TF-IDF）， and the index and query vectors were encrypted by using secure K -nearest neighbor， which realized the multi-keyword ranked search supporting dynamic update. Security and performance analysis show that the proposed scheme is secure and feasible in the blockchain environment and under the known ciphertext model. Simulation results show that the proposed scheme can achieve result verification and fair payment with acceptable cost.

The traditional methods for prediction of oil reservoir utilize the seismic attributes generated when seismic waves passing through the stratum and geologic drilling data to make a comprehensive judgment in combination with the traditional geophysical methods. However, this type of prediction methods has high cost of research and judgement and its accuracy strongly depends on the prior knowledge of the experts. To address the above issues, based on the seismic data of the Subei Basin of Jiangsu Oilfield, and considering the sparseness and randomness of oil-labeling samples, a multi-granularity temporal structure representation based outlier detection algorithm was proposed to perform the prediction by using the post-stack seismic trace data. Firstly, the multi-granularity temporal structures for the single seismic trace data was extracted, and the independent feature representations were formed. Secondly, based on extracting multiple granularity temporal structure representations, feature fusion was carried out to form the fusion representation of seismic trace data. Finally, a cost-sensitive method was utilized for the joint training and judgement to the fused features, so as to obtain the results of oil reservoir prediction for these seismic data. Experiments and simulations of the proposed algorithm were performed on an actual seismic data of Jiangsu Oilfield. Experimental results show that the proposed algorithm is improved by 10% on Area Under Curve (AUC) compared to both of the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms.

In order to solve the problem that Scale Invariant Feature Transform (SIFT) algorithm has low matching accuracy and long time consuming in image matching, an improved image matching algorithm based on grid motion statistical feature, namely RANSAC-GMS, was proposed. Firstly, the image was pre-matched by Oriented FAST and Rotated BRIEF (ORB) algorithm and Grid-based Motion Statistics (GMS) was used to support the estimator to distinguish the correct matching points from the wrong matching points. Then, an improved RANdom SAmple Consensus (RANSAC) algorithm was used to filter the feature points according to the distance similarity between the matching points, and an evaluation function was used to reorganize the filtered new datasets to eliminate the mismatching points. The experiments were carried out on Oxford standard image library and images taken in reality. Experimental results show that the average matching accuracy of the proposed algorithm in image matching is over 91%. Compared with algorithms such as GMS, SIFT and ORB, the near-scene matching accuracy and the far-scene matching accuracy of the proposed algorithm are improved by 16.15 percentage points and 3.56 percentage points respectively. The proposed algorithm can effectively eliminate mismatching points and achieve further improvement of image matching accuracy.

Concerning the security problem of local and global attacks on the Integrity Report Protocol (IRP), the StatVerif syntax was extended by adding constructors and destructors associated with the integrity measurement. The security of the Platform Configuration Attestation (PCA) was analyzed and the local and global attacks were found, including tampering the platform configuration register or revising stored measurement log by running unauthorized commands. The abilities of attackers were modeled, and how attackers accumulated knowledge and tampered PCA protocol by using constructors and destructors was introduced. Finally, the existence of attacking sequence was proved theoretically when PCA does not satisfy the correspondence property; and several propositions that PCA can meet the local reliability and gloabal reliability were given, which were proved by the formal verification tool Proverif.

Concerning the parallelism maximization of hardware task partitioning in reconfigurable system, a task partitioning algorithm based on parallelism maximization for multi-objective optimization was proposed. Firstly, the operating nodes to be partitioned were discovered according to the breadth first search under the constraints of hardware area resource and reasonable dependency relation. Then, considering the effect of execution delay on system completion time, the parallelism of intra-block operations was maximized. Finally, the new nodes were accepted under the principle of reducing the fragment area without increasing the number of connections between blocks. Otherwise, a block partitioning was ended. The experimental results show that the proposed algorithm achieves the maximum intra-block parallelism and reduces the number of blocks and connecting edges compared with the existing Level Based Partitioning (LBP) and Cluster Based Partitioning (CBP) algorithms.

Due to NAND flash' inherent restrictions like erase-before-write and a large erase unit, flash-based Solid-State Drives (SSD) demand garbage collection operations to reclaim invalid physical pages. However, the high overhead caused by garbage collection significantly decrease the performance and lifetime of SSD. Garbage collection performance will be more serious, especially when the data fragments of SSD are frequently used. Existing Garbage Collection (GC) algorithms only focus on some steps of the garbage collection operation, and none of them provids a comprehensive solution that takes into consideration all the steps of the GC process. On the basis of detailed analysis of the GC process, a whole process optimized garbage collection algorithm named WPO-GC (Whole Process Optimized Garbage Collection) was proposed, which integrated optimizations on each step of the GC in order to reduce the negative impact on normal read/write requests and SSD' lifetime at the greatest extent. Moreover, the WPO-GC was implemented on SSDsim which is an open source SSD simulator to evaluate its efficiency. The experimental results show that the proposed algorithm can decreases read I/O response time by 20%-40% and write I/O response time by 17%-40% respectively, and balance wear nearly 30% to extend the lifetime, compared with typical GC algorithm.

Large amount of user rating data, involving plentiful users' opinion and preference, is produced in e-commerce applications. An construction and inference method for latent variable model (i.e., Bayesian Network with a latent variable) oriented to user preference discovery from rating data was proposed to accurately infer user preference. First, the unobserved values in the rating data were filled by Biased Matrix Factorization (BMF) model to address the sparseness problem of rating data. Second, latent variable was used to represent user preference, and the construction of latent variable model based on Mutual Information (MI), maximal semi-clique and Expectation Maximization (EM) was given. Finally, an Gibbs sampling based algorithm for probabilistic inference of the latent variable model and the user preference discovery was given. The experimental results demonstrate that, compared with collaborative filtering, the latent variable model is more efficient for describing the dependence relationships and the corresponding uncertainties of related attributes among rating data, which can more accurately infer the user preference.

In big data environment, the research on mobile crowdsourcing system has become a research hotspot in Mobile Social Network (MSN). However, the selfishness of individuals in networks may cause the distrust problem of mobile crowdsourcing system. In order to inspire individuals to select trustful strategy, an online incentive mechanism based on reputation for mobile crowdsourcing system named RMI was proposed. Combining evolutionary game theory and Wright-Fisher model in biology, the evolution trend of mobile crowdsourcing system was studied. To solve free-riding and false-reporting problems, the reputation updating methods were established. Based on the above researches, an online incentive mechanism was built, which can inspire workers and requesters to select trustful strategies. The simulation results verify the effectiveness and adaptability of the proposed incentive mechanism. Compared with the traditional social norm-based reputation updating method, RMI can improve the trust degree of mobile crowdsourcing system effectively.

Focusing on the issue that most of existing approaches need sentiment lexicon annotated manually to extract sentiment features, a sentiment analysis method of product reviews based on Contrastive Divergence-Restricted Boltzmann Machine (CD-RBM) deep learning was proposed. Firstly, product reviews were preprocessed and represented as vectors using the bag-of-words. Secondly, CD-RBM was used to extract the sentiment features from product review vectors. Finally, the sentiment features were classified with Support Vector Machine (SVM) as the sentiment analysis result. Without any manually pre-defined sentiment lexicon, CD-RBM can automatically obtain the sentiment features of higher semantic relevance; combining with SVM, the correctness of the sentiment analysis result is guaranteed. The optimum training period of RBM was experimentally determined as 10. In the comparison experiments with methods including RBM, SVM, PCA+SVM and RBM+SVM, the combination method of CD-RBM feature extraction and SVM classification shows the best precision and best F-measure, as well as better recall.

Concerning the problem that uncertainty information is difficult to be merged during the decision-making process of multi-source irrigation information, a decision fusion method based on fuzzy rough set and Dempster-Shafer (D-S) evidence theory was proposed. Using the fuzzy rough set theory,the basic probability distribution function was established, the interdependence between irrigation factors and irrigation decision was calculated, and the identification framework of irrigation decision on the multiple fusion irrigation factors was built. Using the improved D-S evidence theory, the multi-source irrigation information was fused at the decision-making level, the expression and synthesis problems of uncertain information were solved. The information of winter wheat such as soil moisture, photosynthetic rate and stomatal conductance in north China was fused in irrigation decision by the application of the methods mentioned above. The results show that the uncertainty of the irrigation decision decreases from 38.0% before fusion to 9.84%. The method can effectively improve the accuracy of irrigation decision and reduce the uncertainty of the irrigation decision.

To improve the convergence rate of digital repeater echo cancellation, firstly, the echo cancellation technique based on adaptive filter was studyed; secondly, the recursion algorithm of adaptive filter was improved by the technical schemes that two adaptive filters compute in parallel and update the weights jointly and recursively. Since the error signal to adjust weights of the two adaptive filters was generated in different ways, the schemes were divided into two categories: scheme one is that the weights of two filters were adjusted by error signal of echo cancellation (simultaneously); scheme two is that the weights of the first filter were adjusted by error signal as the difference value of received signal from antenna and output signal of the first filter; and the weights of the second filter were adjusted by error signal as the difference value of the above-mentioned error signal and the output signal of the second filter (separately). The simulation results show that the convergence rate of the echo cancellation is increased by 11.11%~17.78% in the improved technique scheme, so as to improve the condition effectively with the slow convergence rate of digital repeater echo cancellation.

To solve the problems that the index value of cryptographic modules is not fixed, the index system is hardly built, and the security assurance ability can not be quantitatively assessed, a security assurance capability assessment for cryptographic module was proposed. The description on indexes by interval number was applied to illustrate the security attribute of cryptographic modules. This paper determined the weight vector of each period point by entropy weight coefficient method combined with expert decision weight method. According to the interval multi-attribute decision methodology, a feasible methodology was adopted to solve the interval Information Assurance (IA) capability evaluation problem of cryptographic modules. Finally, through analyzing two kinds of cryptographic modules, the experimental results show that the proposed method is feasible.

Combined Particle Swarm Optimization (PSO) and Solver add-in, this paper proposed a hybrid PSO-Solver algorithm to solve the optimization problems. As a global search algorithm, PSO looks for the global feasible solution, and Solver is a local search tool based on gradient information, which refines the solution obtained by PSO. The hybrid algorithm could speed up the global search, as well as avoid getting into local minima. VBA was used to code, which is simple and easily conducted. Results of solving some unconstrained and constrained examples, compared to the standard PSO and other heuristic algorithms, show that this hybrid PSO-Solver algorithm can improve the speed of convergence and the accuracy of solutions significantly.

The macro handover has caused high packet loss and long handover latency in Hierarchical Mobile IPv6 (HMIPv6) protocol. To solve these problems, this paper proposed a protocol named Tunnel-based Fast Macro-Handover (TBFMH), which introduced the mechanism of tunnel, acquired care-of addresses on the grounds of handover information, conducted duplication address detection in advance and completed local binding update while building the tunnels. The simulation results show that TBFMH can decrease the handover latency by 50% at least and reduce the packet loss rate compared to HMIPv6, which effectively improves the performance in the macro handover.