The existing image super-resolution reconstruction algorithms can improve the overall visual effect of the image or promote the objective evaluation value of the reconstructed image， but have poor balanced improvement effect of image perception effect and objective evaluation value， and the reconstructed images lack high-frequency information， resulting in texture blur. Aiming at the above problems， an image super-resolution reconstruction algorithm based on parallel convolution and residual network was proposed. Firstly， taking the parallel structure as the overall framework， different convolution combinations were used on the parallel structure to enrich the feature information， and the jump connection was added to further enrich the feature information and fuse the output to extract more high-frequency information. Then， an adaptive residual network was introduced to supplement information and optimize network performance. Finally， perceptual loss was used to improve the overall quality of the restored image. Experimental results show that， compared with the algorithms such as Super-Resolution Convolutional Neural Network （SRCNN）， Very Deep convolutional network for Super-Resolution （VDSR） and Super-Resolution Generative Adversarial Network （SRGAN）， the proposed algorithm has better performance in image reconstruction and has clearer detail texture of the enlarged effect image. In the objective evaluation， the Peak Signal-To-Noise Ratio （PSNR） and Structural SIMilarity （SSIM） of the proposed algorithm in \times \mathrm{4} reconstruction are improved by 0.25 dB and 0.019 averagely and respectively compared with those of SRGAN.

In recent years, Software Supply Chain (SSC) security issues have frequently occurred, which has brought great challenges to software security research. Since there are millions of new software released every day, it is essential to detect the pollution of SSC automatically. The problem of SSC pollution was first analyzed and discussed. Then focusing on the requirements of pollution detection in the downstream of SSC, the automatic program reverse analysis methods and their applications in the SSC pollution detection was introduced. Finally, the shortcomings and challenges faced by the existing technologies in solving the problem of SSC pollution was summarized and analyzed, and some researches worth studying to overcome these challenges were pointed out.

Traditional relational data storage systems have been criticized by poor performance and lacking of fault tolerance, therefore it cannot satisfy the efficiency requirement of the massive unstructured data management. A non-relational storage management mechanism with high-performance and high-availability was proposed. First, a user-friendly application interface was designed, and data could be distributed to multiple storage nodes through efficient consistent hashing algorithm. Second, a configurable data replication mechanism was presented to enhance availability of the storage system. Finally, a query fault handling mechanism was proposed to improve the storage system's fault-tolerance and avoid service outages, which were caused by the node failure. The experimental results show that the concurrent access capacity of the proposed storage system increases by 30% and 50% respectively compared to traditional file system and relational database under different user workloads; meanwhile, the availability loss of the storage system under the fault state is less than 14% in a reasonable response time. Therefore, it is applicable for efficient storage management of massive unstructured data.

For precision control problem of multi-resolution fairing, specific impact of fairing precision caused by fairing scale was studied on the basis of the researches of multi-resolution fairing algorithm and software. Taking semicircular curve as a calibration object, this method revealed the internal relations between selection of fairing scale and fairing precision. The experimental results show that the smaller the fairing scale is, the larger the fairing error is. Secondly, multi-resolution fairing can reflect original curves with less control vertexes and own a strong ability of data compression. Finally, fairing error would be larger at the place of curves with larger curvature.

According to characters of marine ecology domain knowledge, a marine ecology knowledge organization model was proposed. Referring to engineering field literature and the device-function knowledge representation theory that the "function" concept was used to describe marine ecology functional process; a viewpoint of device-function was fixed, a domain upper ontology for marine ecosystem was presented, and then marine ecological conceptual model and marine ecology OWL ontology were constructed. By extending OWL-DL, OWL-Process model oriented function-process was proposed, and then marine ecology function-process ontology instance was constructed. Based on constructed marine ecology ontology repository, marine ecological knowledge management system was developed. The ontology application system provides marine ecology knowledge query and crisis early warning functions; and it also verifies the validity, rationality and feasibility of constructed marine ecology ontology.

Although the normal cloud model is universally used, it faces some difficulties when describing some monotonic rise/fall conceptions. This model also has big subjective influence under multiple conditions and large computation consumption. To overcome these shortcomings, a new kind of exponential cloud model was provided along with a weight based cloud reasoning algorithm. By splitting the multi-condition generator to several single-condition generators, the algorithm firstly used Analytic Hierarchy Process (AHP) method to get weight of each property, and then used them to calculate weighted average of single-condition generator output to quantitfy value. The validation and effectiveness of this method is checked through a comparison between fuzzy reasoning and stimulation of torpedo avoid system.

The diversity of population, the searching capability and the robustness are three key points to the multi-objective optimization problem, which directly affect the convergence of algorithm and the spread of solutions set. To better deal with above problems, a Scatter Search hybrid Multi-Objective Evolutionary optimization Algorithm (SSMOEA) was proposed. The SSMOEA followed the scatter search structure but designed a new selection strategy of diversity and integrated the method of co-evolution in the process of subset generation. Additionally, a novel adaptive multi-crossover operation was employed to improve the self-adaptability and robustness of the algorithm. The experimental results on twelve standard benchmark problems show that, compared with three state-of-the-art multi-objective optimizers, SPEA2, NSGA-Ⅱ and AbYSS, SSMOEA outperforms the other three algorithms as regards the coverage, uniformity and approximation. Meanwhile, its robustness is also significantly improved.

Recently, the research on clothing simulation is becoming hotter. But the flexibility, sense of reality, real-time and integrity are always difficult to be unified. Therefore, a new dressing simulation system was designed concerning the automatic fitting of any human body and clothes. At first, the surface of Non-Uniform Rational B-Spline (NURBS) was used to complete deformable body modeling. Then, particles were reconstructed from the 3DMAX model and multi-type springs were created to complete arbitrary cloth modeling. Finally, Verlet integrator was adopted to complete dressing simulation, while a new simplification algorithm for cloth models and a new method for judging interior point with a triangle were implemented. The results show that the proposed modeling approach for body and clothes guarantees the diversity of dressing effect, and the model simplification and interior point judgment can increase the simulation performance by 30% or so, which ensures the real-time quality.

Since there are more and more security issues in cloud storage, this paper designed a new secure and distributed cloud storage model based on the threshold Attribute-Based Encryption (ABE). Three phases in the model included: the encryption phase, the data storage phase and the decryption phase, and all messages in these phases were distributed through the whole process. It not only enhanced the security of the storage data by using the ABE but also supported the threshold decryption and allowed to add or remove the arbitrary attribute authorities, with the use of the multi-attribute authorities method in the model. In the data storage phase, this paper used the distributed erasure code to improve the robustness of our model and this model could resist against collusion attack. It can be applied in some special cloud situations and provides secure cloud storage service for users.

In this paper, a new efficient algorithm for ellipse detection was proposed, which was based on edge grouping, different from standard Hough transform. Firstly, It separated edge boundary into different arcs at the intersections, divided those arcs into two categories: the long and the short and sorted the two categories at non-increasing sequence, then estimated the parameters of the ellipses using least square fitting method with arcs which may belong to the same ellipse; at last testified whether ellipses coming from the front steps are real ones. The method has been tested on synthetic and real-world images containing both complete and incomplete ellipses. The outcome demonstrates that the algorithm is robust, accurate and effective.

In this paper, an adaptive color image enhancement method was proposed: Firstly, color image was transformed from RGB to HSV color space and the H component was kept invariable, while the illumination component of brightness image could be estimated through Adaptive Bidimensional Empirical Mode Decomposition (ABEMD); Secondly, reflection component was figured out by the method of center/surround Retinex algorithm, and the illumination and reflection components were controlled through Gamma emendation and Weber's law and processed with weighted average method; Thirdly, the S component was adjusted adaptively based on characteristics of the whole image, and then image was transformed back to RGB color space. The method could be evaluated by subjective effects and objective image quality assessment, and the experiment results show that the proposed algorithm is better in mean value, square variation, entropy and resolution than MSR algorithm and Meylan's algorithm.

The security of videoconferencing system is becoming more and more important. Authentication is the most important part of the system’s secure mechanism. The paper summarizes the ways of authentication in present videoconferencing sytem and points out its weakness. A new solution which based smart IC card and improved Kerberos is put forward and its security is analysed.