Nowadays, with the development of semiconductor technology and the requirement of the diversification of applications, heterogeneous multi-core processors have been widely used in high-performance embedded systems. How to manage and distribute the available resources (such as processing cores) during running in order to meet the requirements in performance and power consumption of the system and the applications that the system runs is a main challenge that the system focuses. However, although some mainstream resource management techniques have achieved good results in terms of performance and/or power consumption optimization, they lack the strict reliability guarantee for the resource management component. Therefore, a method based on Discrete Controller Synthesis (DCS) was proposed to automatically and reliably design the online resource management scheme for heterogeneous multi-core systems, which applies DCS (which is formal and can construct management control components automatically) to the design of online resource management components for heterogeneous multi-core systems. In this method, the heterogeneous system’s running behaviors (such as how to distribute the processing cores to the applications) were described by using the formal models, and the online resource management problem was transformed to a DCS problem aiming at one system management objective (such as maximizing system performance). On this basis, the existing DCS tools were used to illustrate and validate the proposed method, and the scalability of the DCS method was evaluated.

Focusing on the low efficiency and poor effect of image style transfer, a feedforward residual image style transfer algorithm based on pre-trained network and combined with image texture feature analysis was proposed. In the algorithm, the pre-trained deep network was applied to extract the deep features of the style image, and the residual network was used to perform deep training and realize image transfer. Meanwhile, by analyzing the influence of input style image and content image texture on transfer effect, the corresponding measures were adopted for different input images to improve the transfer effect. Experimental results show that the algorithm can achieve better output visual effect, lower normalized style loss and less time consumption. Besides, according to the information entropy and moment invariant calculation of the input image to guide the setting and adjustment of the network parameters, the network was optimized pertinently, and good effect was obtained.

Concerning the problem that traditional intrusion detection method cannot detect the specific attacks aiming at Software Defined Network (SDN) architecture, an intrusion detection model based on Convolutional Neural Network (CNN) was proposed. Firstly, an feature extraction method was designed based on SDN flow table entry. The SDN specific attack samples were collected to form the attack flow table dataset. Then, the CNN was used for training and detection. And focusing on the low recognition rate caused by small sample size of SDN attacks, a reinforcement learning method based on probability was proposed. The experimental results show that the proposed intrusion detection model can effectively detect the specific attacks aiming at SDN architecture with high accuracy, and the proposed reinforcement learning method can effectively improve the recognition rate of small probability attacks.

Concerning at the high energy consumption of heterogeneous multi-core platforms, an algorithm for solving the optimal energy allocation scheme of periodic tasks by using optimization theory was proposed. The optimal energy consumption problem of periodic tasks was modeled and added constraints to the model. According to the optimization theory, the binary integer programming problem was relaxed to obtain the convex optimization problem. The interior point method was used to solve the optimization problem and the relaxed distribution matrix was obtained. The allocation scheme for partial tasks was obtained after the judgement processing of the decision matrix. On this basis, the iterative method was used to find the allocation scheme for the remaining tasks. Experimental results show that the energy consumption of this distribution scheme is reduced by about 1.4% compared with the similar optimization theory algorithm, and compared with the optimization theory algorithm with the similar energy consumption, the execution time of this scheme is reduced by 86%. At the same time, the energy consumption of the scheme is only 2.6% higher than the theoretically optimal energy consumption.

For the existing steganography, it is hard to extract secret information without original cover-image and easy to be detected by steganalysts when the hiding capacity is high. To solve this problem, a new scheme of steganography based on fractal graph was proposed. In this scheme, firstly, Black-and-White Fractal Graph (BWFG) was created by utilizing affine transformation and fractal iterated function system. Then the BWFG was transformed to Black-and-White Pixel Image (BWPI) based on the idea of coordinate transformation. At last, the BWPI was divided into several non-overlapping blocks and the positions of black and white pixels in each block were altered to hide the secret information, generating stego-image. The receiver could create the cover-image by utilizing the parameters of affine transformation and times of iteration, and extract secret information by comparing the difference of pixels in corresponding blocks. Theoretical analysis and simulation experiments show that, compared with the information hiding algorithm in frequency domain, the proposed scheme has good imperceptibility and high hiding capacity, and can resist steganalysis based on image features and transform domain coefficient change.

Gene data has the characteristics of small sample, high dimensionality and high redundancy, which easily lead to "curse of dimensionality" and "over-fitting" in feature gene selection. To overcome these obstacles, a feature gene selection algorithm, named Mutual Information Maximum Value Filter Criteria-Inertia-Weight Particle Swarm Optimization (MIMVFC-IWPSO), was proposed. Firstly, interaction between genes was calculated by newly defined feature entropies of gene-category and gene-gene, and Feature Gene Candidates Subset (FGCS) was obtained by MIMVFC (Mutual Information Maximum Value Filter Criteria) which reduced the scope of classification operations and improved the probability of feature genes being covered. Secondly, the Particle Swarm Optimization (PSO) algorithm was reconstructed to IWPSO (Inertia Weight Particle Swarm Optimization) by introduction of self-adjusted inertia weight which enabled the algorithm to have strong global optimization ability in the early stage of iteration and strong local search ability in the later stage. Lastly, Core Feature Gene Subset (CFGS) was extracted from FGCS by IWPSO which was exploited in the classification of samples into tumor and normal classes. The experiments were carried out based on three public tumor gene databases. Compared with four popular filter methods, MIMVFC achieved higher correct classification rate than the methods based on Signal-to-Noise Ratio (SNR), t-statistic and Information Gain (IG), and ranked nearly the same as Chi-Square method, but the proposed method still had the optimized step to enhance the results furthermore. For the same FGCS, compared with BPSO-CGA (Binary Partical Swarm Optimization and Combat Genetic Algorithm), an algorithm with good performance, IWPSO gained a smaller CFGS with slightly increased time consumption and a higher accuracy; compared with classic PSO, IWPSO gained a smaller FGCS with less time consumption and a higher accuracy. The simulation results show that MIMVFC-IWPSO has comprehensive classification performance in both the aspects of accuracy and efficiency which proves to be feasible and effective in feature gene selection of multiple types of tumors, and it can be employed in assisting instruction in molecular biology experiment design and validation.

Resources in cloud computing environment are dynamic and heterogeneous. The goal of resource allocation in large-scale tasks is to minimize the completion time and resource occupation while having the best load balancing, which is a Non-deterministic Polynomial (NP) problem. Drawing on the advantages of intelligent swarm optimization, a hybrid swarm intelligence scheduling strategy named Dynamic Random Distribution PSO (DRDPSO) was proposed based on an improved PSO algorithm. Firstly, the inertia weight constant of PSO was modified to be a variable to control the convergence speed of solution process reasonably. Secondly, the search scope of each iteration was shrinked so as to reduce invalid search on the premise of retaining candidate optimal set. Then, selection operation was introduced to select high-quality individuals and pass them on to the next generation. Finally, random disturbance was designed to improve the diversity of candidate solutions and avoid the local optimal trap to some extent. Two kinds of simulation tests were carried out on the CloudSim platform. The experimental results show that, the proposed DRDPSO is better than Simulated Annealing Genetic Algorithm (SAGA) and Genetic Algorithm (GA)+PSO in most cases when dealing with isomorphic tasks. The total execution time of the proposed algorithm is less than SAGA by 13.7%-37.0% and less than GA+PSO by 13.6%-31.6%, the resource consumption of the proposed algorithm is less than SAGA by 9.8%-17.1% and less than GA+PSO by 0.6%-31.1%, the number of iterations of the proposed algorithm is less than SAGA by 15.7%-60.2% and less than GA+PSO by 1.4%-54.7%, the load balance degree of the proposed algorithm is less than SAGA by 8.1%-18.5% and less than GA+PSO by 2.7%-15.3% with the smallest fluctuation amplitude. When dealing with heterogeneous tasks, three algorithms has the similar properties:in aspect of the total execution time consumption, CPU tasks are the most, the mixed tasks take the second place, and IO tasks are the least. The comprehensive performance of DRDPSO is the best, which is the most suitable for dealing with multiple types of heterogeneous tasks. GA+PSO algorithm is suitable for solving hybrid tasks and SAGA algorithm is suitable for solving IO tasks quickly. When dealing with large-scale isomorphic and heterogeneous tasks, the proposed DRDPSO can significantly shorten the total task execution time and improve the utilization of resources in varying degrees with proper load balancing of computing nodes.

Concerning the incomplete extraction of river information easily affected by synonyms spectrum phenomenon from the high resolution remote sensing image, considering the specific frequency texture information of the river region, a river information extraction method from the high resolution remote sensing image based on homomorphic system filtering was proposed. The spectral analysis of remote sensing image was used by the proposed algorithm to define the feature identification of river information in the frequency domain. The one-dimensional profile line adding-window short-time analysis river location technology was adopted to realize river location and width estimation of the remote sensing image automatically and quickly. Finally, the low-pass filter under the homomorphic system was designed to extract low frequency river information. The GF-1 image was used as experimental data. The extraction accuracy of the proposed algorithm was higher than the traditional method based on spectral information. The Kappa coefficient reached 0.85, and the extraction process was automatic. The experimental results show that the proposed algorithm can extract river information from complex geomorphology region quickly and effectively.

To deal with the lung segmentation problem under complex conditions, Random Walk algorithm was applied to automatic lung segmentation. Firstly, according to the anatomical and imaging characteristics of the chest Computed Tomography (CT) images, foreground and background seeds were selected respectively. Then, CT image was segmented roughly by using the Random Walk algorithm and the approximate mask of lung area was extracted. Next, through implementing mathematical morphology operations to the mask, foreground and background seeds were further adjusted to adapt to the actually complicated situations. Finally, the fine segmentation of lung parenchyma for chest CT image was implemented by using the Random Walk algorithm again. The experimental results demonstrate that, compared with the gold standard, the Mean Absolute Distance (MAD) is 0.44±0.13 mm, the Dice Coefficient (DC) is 99.21%±0.38%. Compared with the other lung segmentation methods, the proposed method are significantly improved in accuracy of segmentation. The experimental results show that the proposed method can solve the difficult cases of the lung segmentation, and ensure the integrity, accuracy, real-time and robustness of the segmentation. Meanwhile, the results and time of the proposed method can meet the clinical needs.

In order to effectively master protocol interactive behavior, a method to automatically build protocol interactive process based on message sequence chart was proposed. Firstly, according to the characteristics of the protocol interactive process, the dependency graph was defined to represent the partial order of events in message sequence, and the network flows were converted to dependency graphs. Secondly, the basic message sequences were used to describe protocol interactive behavior fragments, and the basic message sequences were mined by defining event maximum suffix. Finally, the maximum dependency graphs that were found out were connected and merged to build a message sequence chart. The experimental results show that the proposed method has a high accuracy and the built message sequence chart can visually represent the protocol interactive process.

For too many similar words and lots of irregular writing ways of the same words in the handwritten character recognition, a modified Affinity Propagation (AP) clustering algorithm was proposed to add to the recognition process. Clustering judging function Silhouette was combined with original AP algorithm in the proposed algorithm. Class number was updated by changing preference parameter adaptively through iterative process of AP algorithm. And then the optimal clustering result was obtained by assessing clustering quality of every iteration. The experiment of handwritten Chinese character recognition indicates that the recognition rate of recognition process added original AP algorithm is 1.52% higher than the rate of traditional recognition process. And the recognition rate of recognition process added modified AP algorithm is 1.28% higher than the rate of recognition process added original AP algorithm. The experimental results verify that it is effective to add clustering algorithm to the handwritten character recognition process. And compared with original AP algorithm, convergence and clustering quality of modified AP algorithm are also improved.

For the problem of minimizing the energy consumption under performance constraint of cloud center, an optimal power consumption allocation method among multiple heterogeneous servers was proposed. First, an optimal energy consumption mathematical model of cloud center was built. Second, a Minimizing Power Consumption (MPC) algorithm for calculating the minimum energy was developed by using Lagrange multiplier method to obtain the optimal solution of the model. Finally, the MPC algorithm was verified by plenty of numerical experiments and compared with the Equal-Power (EP) baseline method. The experimental results indicate that MPC algorithm can save approximately 30% energy than the EP baseline method under the same load and the same response time conditions, and the proportion of energy saving will increase with load increasing. The MPC algorithm can effectively avoid energy configuration overload and it will provide ideas and reference data for optimal resource allocation of cloud center.

Aiming at the problem of pulmonary small nodules was difficult to identify, a method using fuzzy C-means clustering algorithm to analyse the lung Region Of Interest (ROI) was presented. An improved Fuzzy C-Means clustering algorithm based on Plurality of Weight (PWFCM) was presented to enhance the accurate rate and speed of small nodules recognition. To improve the convergence, each sample and its features were weighted and a new membership constraint was introduced. The low sensitivity from the uneven ROI data was decreased by using a double clustering strategy. The experimental results tested on the real CT image data show that PWFCM algorithm can detect lung nodules with a higher sensitivity and lower false positive rate.

Since it is necessary to evaluate and analyze the service performance of cloud computing center to guarantee Quality of Service (QoS) and avoid violation of Service Layer Agreement (SLA), a approximated analysis model based on M/M/n/n+r queue theory was proposed for cloud computing center. By solving this model the probability distribution function of response time and other QoS indicators were acquired, meanwhile the relationship among the number of servers, size of queue buffers, response time, blocking probability and instance service probability were revealed and verified by simulation.The experimental results indicate that improving server service rate is better than increasing the number of servers for improving service performance.

In data layer, the course model and resource model were built based on Markov chain and vector space model, and the teacher model was built based on teachers' personal registration information and nodes of course model. In off-line layer, the content features of course model and resource model were extracted via Term Frequency-Inverse Document Frequency (TF-IDF) algorithm, and the course model and resource model of data layer were initialized and optimized. Then relations between any two resources or recourse and course were calculated using association rules mining and similarity measure, and intermediate recommendation results were given using teacher model and course model. A weighted hybrid recommendation algorithm was proposed to generate recommendation list in on-line layer. The proposed system has been successfully applied in a real education resources sharing platform which consists of 600 thousand teaching resources.

In order to improve the packet loss resilience of Scalable Video Coding (SVC) over communication networks, an efficient Unequal Error Protection (UEP) algorithm for SVC using adaptive genetic algorithm was proposed. A method to encapsulate network abstract layer units according to the feature of the head information of a packet was introduced. Then the problems of pair codes assignment were transformed into the problems of multi-constraint optimization, which could be transformed into unconstrained objective by exploiting penalty function. Therefore, the adaptive genetic algorithm was employed to obtain globally optimal solution. The simulation results reveal that compared with the typical unequal error protection algorithms, the Peak Signal-to-Noise Ratio (PSNR) is improved by 0.8dB-1.95dB, and the proposed algorithm provides substantial improvement for the decoding speed and received video quality over best effort packet networks.

A new fast algorithm was proposed to solve the heavy computational load of coherent wideband signal DOA (Direction Of Arrival) estimation. Firstly, through Toeplitz technique of matrix, incoherent wideband signals were obtained without any division for the array. Secondly, according to the character of Hermitian matrix of received data, utilized the unitary transformation matrix, the real matrix could be acquired and the computation of eigendecomposition could be decreased. Finally, through the orthogonality technique of projected subspaces, new space spectrum was acquired with noise subspace and conjugate noise subspace, and the DOA could be obtained just through the angle search from zero to ninety degree in semi-spectrum according to the symmetrical spectrum, while the computational complexity for the search of spectrum peak was reduced by half. The theoretical analysis and simulation results show that the new algorithm has higher precision and smaller computational load, and it is effective for wideband coherent signals.

Currently, to develop context-aware applications that are flexible and adaptable is complex and laborious. There are many unexpected cases to handle. As one of the important approaches to achieve context-awareness, predicate detection can represent context effectively. However, how predicate detection supports the development of context-aware applications on a real device is still largely unknown. In order to cope with these issues, a simple scenario was created. Predicate detection was practically applied to control the car running in a designated environment. The original context was formally modeled and contextual properties were specified into snapshot predicates and sequence predicates. By detecting these specified predicates in the case study, predicate detection was applied to the robot car. The performance analysis shows that predicate detection can effectively detect the car's contextual properties and successfully help the car finish the running task.

To solve the problems existing in BLP (Bell-LaPadula) model, such as information aggregation leakage, excessive privileges of trusted subject and the deficiency of integrity, with reference to the application requirement of hierarchical file protection, an information aggregation leakage proof model named IALP (Information Aggregation Leakage Proof) was proposed based on assignment partition. First of all, the cause of information aggregation leakage and the current research situation were discussed. Secondly, on the basis of assignments partition, the knowledgeable degree of subject and the information weight of object were quantized, and the relatively trusted subject was proposed. Security axioms and state transition rules were given. Finally, the theoretical proof, application examples and analysis indicate that IALP can control the knowable degree of the subject towards the object set with the aggregation leakage relation, and limits the privilege of trusted subject and enhances the integrity to some extent.

It is essential for software development and maintenance to predict which modules are change-prone in an Object-Oriented (OO) software system. In this paper, a light-weight approach was developed to compute the change probability metric by leveraging the dependence relationships between classes in a system. Then, based on Logistic regression model, an experimental analysis was conducted using Eclipse 2.0. The experimental results indicate that, on one hand, the proposed change probability metric captures different information from traditional OO metrics. On the other hand, when being used with traditional OO metrics together, the proposed change probability metric can significantly improve the accuracy for predicting the change-prone classes.

In order to improve the efficiency of smartphone performance test, the methodology of automatic test was introduced. According to the method, an Android smartphone performance test tool called FLEX-ANDROID was developed. The components of the test tool and the test scripts were described in detail. In addition, it analyzed how to calculate and generate test results. Then, the FLEX-ANDROID test tool was used for automatic test. The time costs of automatic test and manual test were compared. The result shows that automatic test rate is about three times as fast as that of manual test rate. It indicates that the test tool can effectively improve the performance test efficiency, and greatly reduce test time and duplicate test.

Robot soccer moves in dynamic environment which exists unknown outside interference. In the process of competition, the soccer often collides with obstacles such as robots, thus causing change of position and direction. Aiming at the above, an algorithm based on calculation of strong tracking filter(STF) and H∞ filter is proposed to predict the position of football. By introducing a time-varying fading factor, it can both improve the tracking ability of state mutation and avoid making assumptions on the interference signals. In the middle size league of robot soccer competition platform, experiments were conducted to verify the effectiveness of the proposed algorithm.

Concerning the security issues in the current road transport logistics, an early-warning model of road transport logistics was proposed. First, five influence aspects of road transport logistics were discussed, including people, vehicles, roads, environment and carried goods. Then the early-warning indexes and the weight of each index were determined using the Analytic Hierarchy Process (AHP) and the expert scoring method. Finally, based on the multi-level Fuzzy Comprehensive Evaluation (FCE) method, the early-warning model of road transport logistics was constructed by selecting proper fuzzy operators. With the model, the early-warning information of road transport logistics could be obtained by calculating the early-warning level. The experiment shows that constructing the early-warning model of logistics transport by FCE is feasible. It realizes the early-warning in the transport process.

In order to meet the requirement of developing a full-customized pattern designing system for the full-electronic flat machine, after introducing the basic principles of the full-electronic flat machine and the role of the computer aided pattern designing system in the flat machine system, the software framework and main modules were analyzed. And by using the object-oriented method, the key data structure and program flows involved in the designing system were focused on, the full-electronic flat machine was realized finally. In the application with the relative flat machine, it is confirmed that relative designing jobs can be finished by utilizing this designing system.

A kind of discrete Particle Swarm Optimization (PSO) algorithm named NDPSO was proposed for extending the classic PSO model to solve the discrete optimization problems with high effectiveness and stability. The concept of comparison with probability was also introduced based on NDPSO, and then the stochastic repairing operator was constructed for heuristic search to solve the Winner Determination Problem (WDP) in combinatory auction. The experimental results show that NDPSO has great advantages in both success rate and convergence speed compared with other discrete PSO algorithms and genetic algorithm.

In FIRA MiroSot robot-soccer game,vision system was a unique way throught which the whole system obtained the global information. The speed and precision of the recognition of the vision system directly affected the victory or defeat of the game. According to the disadvantage of the traditional vision system in the robot-soccer game, a new design of robot-soccer vision system was put forward based on multi-resolution analyse and FCM algorithm. Experiment results show that the design can improve the speed and precise of recognition in the game,and has well adaptability.