Aiming at problems such as high memory bandwidth demand, high computational complexity, long design and exploration cycle, and inter-layer computing delay of cascade convolution in two-dimensional Winograd convolution algorithm, a double-buffer 5×5 convolutional layer design method based on two-dimensional Winograd algorithm was proposed. Firstly, the column buffer structure was used to complete the data layout, so as to reuse the overlapping data between adjacent blocks and reduce the memory bandwidth demand. Then, the repeated intermediate calculation results in addition process of Winograd algorithm were precisely searched and reused to reduce the computational cost of addition, so that the energy consumption and the design area of the accelerator system were decreased. Finally, according to the calculation process of Winograd algorithm, the design of 6-stage pipeline structure was completed, and the efficient calculation for 5×5 convolution was realized. Experimental results show that, on the premise that the prediction accuracy of the Convolutional Neural Network (CNN) is basically not affected, this calculation method of 5×5 convolution reduces the multiplication computational cost by 83% compared to the traditional convolution, and has the acceleration ratio of 5.82; compared with the method of cascading 3×3 two-dimensional Winograd convolutions to generate 5×5 convolutions, the proposed method has the multiplication computational cost reduced by 12%, the memory bandwidth demand decreased by about 24.2%, and the computing time reduced by 20%.

In order to improve the exploration efficiency of the design space for floating-point expression, a design space exploration method based on heuristic search was proposed. The design space of non-dominated expression was explored firstly during each iteration. At the same time, the non-dominated expression and the dominated expression were added to the non-dominated list and the dominated list respectively. Then the expression in the dominated list was explored after the iteration, the non-dominated expression in the dominated list was selected, and the neighborhood of the non-dominated expression in the dominated list was explored. And the new non-dominated expression was added to the non-dominated list, effectively improving the diversity and randomness of the non-dominated expression. Finally, the non-dominated list was explored again to obtain the final equivalent expression and further improve the performance of optimal expression. Compared with the existing design space exploration methods for floating-point expression, the proposed method has the calculation accuracy increased by 2% to 9%, the calculation time reduced by 5% to 19% and the resource consumption reduced by 4% to 7%. Experimental results show that the proposed method can effectively improve the efficiency of design space exploration.

In order to solve the problems of the difficulty to extract features on the smooth regions of mesh models and the impossibility to recognize the feature vertices distributed only along one specific direction by the existing feature detection methods, a direction-perception method of feature recognition on mesh models was proposed. Firstly, the changes of the normal vectors of the mesh vertex adjacent surfaces were detected in x, y and z directions separately. With a suitable threshold set, if the change of a normal vector of the mesh vertex adjacent surfaces exceeded the threshold in any direction, the vertex would be recognized as a feature vertex. Then, concerning the problem that the existing mesh model feature detection algorithms cannot recognize the terraced field structure only distributed along the z-axis of three-dimensional medical model, the algorithm detected the change of normal vectors of the mesh vertex adjacent surfaces just along the z-axis direction, and recognized the vertex as a terraced field structure vertex once the change of the vertex exceeds the threshold. The abnormal terraced field structures were separated from the normal structures of the human body successfully. The experimental results show that, compared with the dihedral angle method, the proposed method can identify the features of the mesh model better under the same conditions. The proposed method solves the problem that the dihedral angle method cannot effectively identify the feature vertices on the smooth regions without obvious broken lines, and also solves the problem that the existing mesh model feature detection algorithms cannot distinguish the abnormal terraced field structures from the normal human body structures due to the lack of the direction detection ability, and establishes a base for the following digital geometry processing of the medical model.

A Quantum-inspired Migrating Birds Co-Optimization (QMBCO) algorithm was proposed for minimizing the makespan in Lot-streaming Flow shop Scheduling Problem (LFSP). Firstly, the quantum coding based on Bloch coordinates was applied to expand the solution space. Secondly, an initial solution improvement scheme based on Framinan-Leisten (FL) algorithm was used to makeup the shortage of traditional initial solution and construct the random initial population with high quality. Finally, Migrating Birds Optimization (MBO) and Variable Neighborhood Search (VNS) algorithm were applied for iteration to achieve the information exchange between the worse individuals and superior individuals in proposed algorithm to improve the global search ability. A set of instances with different scales were generated randomly, and QMBCO was compared with Discrete Particle Swarm Optimization (DPSO), MBO and Quantum-inspired Cuckoo Co-Search (QCCS) algorithms on them. Experimental results show that compared with DPSO, MBO and QCCS, QMBCO has the Average Relative Percentage Deviation (ARPD) averagely reduced by 65%, 34% and 24% respectively under two types of running time, verifying the effectiveness and efficiency of the proposed QMBCO algorithm.

Focused on the issue that the existing password evaluation models cannot be used universally, and there is no evaluation model applicable from simple passwords to very complex passwords. A password evaluation model was designed based on multi-model ensemble learning. Firstly, an actual password training set was used to train multiple existing password evaluation models as the sub-models. Secondly, a multiple trained evaluation sub-models were used as the base learners for ensemble learning, and the ensemble learning strategy which designed to be partial to weakness, was used to get all advantages of sub-models. Finally, a common password evaluation model with high accuracy was obtained. Actual user password set that leaked on the network was used as the experimental data set. The experimental results show that the multi-model ensemble learning model used to evaluate the password strength of different complexity passwords, has a high accuracy and is universal. The proposed model has good applicability in the evaluation of passwords.

The SQL (Structured Query Language) injection attack is a threat to Web applications. Aiming at SQL injection behaviors in PHP (Hypertext Preprocessor) applications, a model of detecting SQL injection behaviors based on tainting technology was proposed. Firstly, an SQL statement was obtained when an SQL function was executed, and the identity information of the attacker was recorded through PHP extension technology. Based on the above information, the request log was generated and used as the analysis source. Secondly, the SQL parsing process with taint marking was achieved based on SQL grammar analysis and abstract syntax tree. By using tainting technology, multiple features which reflected SQL injection behaviors were extracted. Finally, the random forest algorithm was used to identify malicious SQL requests. The experimental results indicate that the proposed model gets a high accuracy of 96.9%, which is 7.2 percentage points higher than that of regular matching detection technology. The information acquisition module of the proposed model can be loaded in an extended form in any PHP application; therefore, it is transplantable and applicable in security audit and attack traceability.

In order to solve the waiting problem of data transmission in the process of orthogonal communication, a non-orthogonal network coding strategy for complex field distributed detection based on super node Multiple Access Channel (MAC) relay was proposed. Firstly, the classical orthogonal channel distribution detection technology was introduced, and aiming at its existing problems, the relay MAC was used in wireless sensor networks, and the complex network coding technology was also used in wireless sensor networks, which contributed to achieve cooperative diversity to reduce the adverse effects of channel fading. Secondly, according to the relay MAC complex field network coding based orthogonal channel distribution detection technology, a Maximum Likelihood (ML) optimal sensor label selection algorithm based on network symbol error probability was proposed to reduce error probability, which considered the false alarm rate and detection probability of sensor. At the same time, the fair distribution of relay power and total transmit power was obtained by the super node approximation. The simulation results show that, in the detection of non-orthogonal network coding, the detection rate of the proposed algorithm can achieve 91.3%, and the error rate is only 25.1%. The proposed algorithm can effectively improve the detection performance of non-orthogonal network coding algorithm in practical applications.

Hadoop has been widely used in large data parallel processing. The existing tasks assignment strategies are almost oriented to a homogenous environment, but ignore the global cluster state, or not take into account the efficiency of the implementation and the complexity of the algorithm in a heterogeneous environment. To solve these problems, a new tasks assignment algorithm named λ-Flow which was oriented to a heterogeneous environment was proposed. In λ-Flow, the tasks assignment was divided into several rounds. In each round, λ-Flow collected the cluster states and the execution result of the last round dynamically, and assigned tasks in accordance with these states and the result. The comparative experimental result shows that the λ-Flow algorithm performs better in a dynamic changing cluster than the existing algorithms, and reduces the execution time of a job effectively.

A new spatial adaptive wavelet threshold denoising method was presented, which was based on a non-Gaussian bivariate distribution and context model for image denoising inspired by image coding. The dependency between coefficients and their parents was carefully studied and a new distribution model composed of two variables and a free parameter was proposed. Context model is the core method in image coding and is applied in this project to choose the spatial adaptive threshold derived in a Bayesian framework. Experiment results show that this new method outperforms the best of the recently published methods, such as SureShrink, Wiener2, and BayesShrink.