For the problem that the Rivest Cipher 4 (RC4) algorithm has invariant weak key, the randomness of the key stream sequence is not high and the initial state of the algorithm can be cracked, an improved RC4 algorithm based on elliptic curve was proposed. In the algorithm, the initial key was generated by using elliptic curve, Hash function and pseudo-random number generator, and a nonlinear transformation was performed under the action of the S-box and the pointer to finally generate a key stream sequence with high randomness. The randomness test carried out by National Institute of Standards and Technology (NIST) shows that the frequency test, run test and Maurer are 0.13893, 0.13081, and 0.232050 respectively higher than those of the original RC4 algorithm, which can effectively prevent the generation of invariant weak keys and resist the "sentence" attack. The initial key is a uniformly distributed random number without deviation, which can effectively resist the distinguishing attack. The elliptic curve and Hash function have one-way irreversibility, the pseudo-random number generator has high password strength, the initial key guess is difficult to assign and is not easy to crack, which can resist the state guessing attack. Theoretical and experimental results show that the improved RC4 algorithm is more random and safe than the original RC4 algorithm.

Aiming at the problems that it is difficult to improve performance and reduce power consumption in the process of High Level Synthesis (HLS), an automatic custom instructions identification method for high level synthesis was proposed. The enumeration and selection of custom instructions were implemented before high level synthesis, so as to provide a universal automatic custom instructions identification method for high level synthesis. Firstly, the high level source code was transformed into a Control Data Flow Graph (CDFG), and the source code was preprocessed. Secondly, a subgraph enumeration algorithm was used to enumerate all the connected convex subgraphs in a bottom-up manner from the Data Flow Graph (DFG) based on control data flow graph, which effectively improved the user's ability to flexibly modify the constraints. Then, considering the area, performance and code size, the subgraph selection algorithms were used to select partial optimal subgraphs as the final custom instructions. Finally, a new code was regenerated by incorporating the selected custom instructions as the input of high level synthesis. Compared with the traditional high level synthesis, the pattern selection based on frequency of occurrence reduced the area by an average of 19.1%. Meanwhile, the subgraph selection based on critical paths reduced the latency by an average of 22.3%. In addition, compared with Transitive Digraph (TD) algorithm, the enumeration efficiency of the proposed algorithm was increased by an average of 70.8%. The experimental results show that the automatic custom instructions identification method can significantly improve performance and reduce area and code size for high level synthesis in circuit design.

As the traditional Scale Invariant Feature Transform (SIFT) image matching algorithm has high false matching rate and eliminating the condition of mismatching points is unitary, an adaptive image matching method based on SIFT operator fused with maximum dissimilarity coefficient was proposed. Firstly, On the basis of Euclidean distance measurement, the optimal maximum dissimilarity coefficients values of the 128-dimensional feature vectors in SIFT algorithm were obtained. Then, the matching points were selected according to the obtained optimal values. Random Sample Consensus (RANSAC) was used to calculate the correct rate of matching. Finally, the stereo matching images of Daniel Scharstein and Richard Szeliski were used to verify the algorithm. The experimental results show that the correct matching rate of the improved algorithm is about 10 percentage points higher than that of the traditional SIFT algorithm. The improved algorithm effectively reduces the mismatches and is more suitable for image matching applications with similar regions. In terms of runtime, the proposed method has an average time of 1.236 s, which can be applied to the systems with low real-time requirements.

Concerning data sparseness, low accuracy and poor real-time performance of traditional collaborative filtering recommendation algorithm in e-commerce system under the background of big data, a new collaborative filtering recommendation algorithm based on improved clustering and matrix decomposition was proposed. Firstly, the dimensionality reduction and data filling of the original data were reliazed by matrix decomposition. Then the time decay function was introduced to deal with user score. The attribute vector of a project was used to characterize the project and the interest vector of user was used to characterize the user, then the projects and users were clustered by k-means clustering algorithm. By using the improved similarity measure method, the nearest neighbors and the project recommendation candidate set in the cluster were searched, thus the recommendation was made. Experimental results show that the proposed algorithm can not only solve the problem of sparse data and cold start caused by new projects, but also can reflect the change of user's interest in multi-dimension, and the accuracy of recommendation algorithm is obviously improved.