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 Internet of Vehicles(IoV)environment,a single vehicle cannot meet all the time-sensitive driving safety requirements because of limited capability on information acquiring and processing. Cooperation among vehicles to enhance information sharing and channel access ability is inevitable. In order to solve these problems,a cooperative feedback control algorithm based dynamic reinforcement model for driving safety was proposed. Firstly,a virtual fleet cooperation model was proposed to improve the precision and expand the range of global traffic sensing,and a stable cooperation relationship was constructed among vehicles to form cooperative virtual fleet while avoiding channel congestion. Then,a joint optimization model focusing on message transmission and driving control was implemented,and the deep fusion of heterogeneous traffic data was used to maximize the safety utility of IoV. Finally,an adaptive feedback control model was proposed according to the prediction on spatial-temporal change of traffic flow,and the driving safety strategy was able to be adjusted in real-time. Simulation results demonstrate that the proposed model can obtain good performance indexes under different traffic flow distribution models, can effectively support driving assisted control system, and reduce channel congestion while maintaining driving safety.

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.

The distribution of color image pixel value is difficult to describe in hierarchical Markov Random Field (MRF) segmentation algorithm, therefore, a hierarchical MRF segmentation algorithm based on RGB color statistic distribution was proposed to solve this problem. The key parameters of the MRF model were set up, and the related formulas were deduced. With the RGB color statistic distribution model, the hierarchical MRF energy function was rewritten, and the k-means algorithm was used as presegmentation method to realize unsupervised segmentation. The proposed algorithm has fewer color distribution parameters and lower computational cost in comparison with traditional MRF segmentation model, which describes color distribution more accurately; and it can describe different targets and background very well without being restricted by target and background color distribution and target spatial distribution. Experimental results prove the effectiveness of the proposed algorithm, which is superior to the MRF algorithm and Fuzzy C-Means (FCM) algorithm in computing speed and segmentation accuracy.

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.

In order to solve the problem of low accuracy and big error in hill climb searching method caused by the unimodal focal value function, a new autofocus method based on blur difference qualitative analysis was presented. First, the spatial-domain convolution/deconvolution transform was used to compute the blur difference at every point of two probed images corresponding to two different focus positions. Second, blur difference qualitative measurement of two images was made by voting policy. Then, the searching direction was determined by blur difference qualitative measurement of two probed images. Finally, using the variable step scheme, the searching range was gradually narrowed down and the searching steps was reduced until the best focus position was found. Three image sequences of difference focus positions were collected by an 18X zoom surveillance camera. The experimental result indicates that, compared with two typical methods based on focal value function, the proposed method keeps the advantages of the hill climb searching method with increasing the accuracy and reducing the error, and resolves the influence of local minima.

Considering that the Scale Invariant Feature Transform (SIFT) algorithm extracts a great number of feature points, consumes a lot of matching time but with low matching accuracy, a fast image registration algorithm based on local significant edge features was proposed. Then SIFT algorithm was used to extract feature points, while wavelet edge detection was also used to extract image edge to establish feature points around the edge of the neighborhood characteristics, which filtered out points with a significant edge feature characteristic as significant feature points. A feature vector was formed by the shape-context operator and edge features. Euclidean distance was used as the match metric function to preliminarily match the feature points extracted from different images. Afterwards, RANdom SAmple Consensus (RANSAC) algorithm was applied to eliminate the mismatching points. The experimental results show that the algorithm effectively controlled the number of feature points, improved qulity of the feature points, reduced the feature search space and enhanced the efficiency of the feature matching.

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.

The performance of some current area-based image registration algorithms declines when image transformation parameters are of both wide range and high precision. Concerning this problem, a parameter-adaptive registration algorithm was proposed based on the natures of image transformation in frequency/space domain, and the estimation steps and fusion method for rotation parameter and shift parameter were designed. A set of simulation experiments were implemented to compare the performance of the proposed algorithm with the Vandewalle's and improved Keren's. Mean square error and standard deviation of square error were used as evaluation indicators for registration precision and parameters adaptation. The two indicators of the proposed algorithm are lower than those of the other two methods, which means the proposed algorithm has adaptive ability in wide range parameters estimation and high accuracy of registration.

To eliminate the mosaic and "bell" effects due to cumulative errors during large object image inpainting, the Markov Random Fields (MRF) exemplar inpainting based on dual-tree complex wavelet domain was proposed. The image was converted to complex-frequency domain by Dual-Tree Complex Wavelet Transform (DTCWT) and the exemplar inpainting order was computed by rational confidence and data item, the unknown region was inpainted based on multiscale and multiband. The inpainted images were reconstructed by dual-tree complex wavelet inverse transform. The experimental results show that compared with classical discrete wavelet methods, the mosaic and "bell" effects can be avoided and the more favorable textural and structural information can be preserved.

Concerning the problem that the impact of illegal nodes (including the node unable to locate) on the localization process in DV-Hop localization algorithm has not been taken into consideration, this paper proposed a secure localization mechanism based on DV-Hop. In other words, the character of message exchange between the nodes was introduced to detect the wormhole attacks in this paper. Time property and space property were used to define the valid beacon nodes, along with encryption and authentication mechanisms to resist against the node-tampering attack in the communication process. Finally, the nodes were located securely. The simulation results show that, in hostile environment, the proposed mechanism has a high probability to detect the wormhole attacks, and the relative localization error can be reduced by 63% or so.

A new localization algorithm based on reputation mechanism was proposed to improve the robustness of the node positioning system in Wireless Sensor Network (WSN). This algorithm introduced a monitoring mechanism and reputation model to filter out malicious beacon nodes giving the false location information, used Beta distribution to update and integrate the reputation of the beacon nodes. Through the cluster head node, the proposed algorithm collected and judged which beacon nodes were reliable, increased the malicious beacon nodes detection rates while the positioning error was reduced. Finally, the simulation and detailed analysis prove its efficiency and robustness. The algorithm is efficient in self-positioning of sensor nodes in distributed WSN, and the localization accuracy and security are greatly improved.

The Wireless Transport Layer Security (WTLS) handshake protocol was built based on digital certificate mechanism. However, there exist several flaws in WTLS. For example, both the communication and computation overload are high. Moreover, it does not verify the server certificate on-line. In order to solve these issues, an improved WTLS handshake protocol based on Identity-based Cryptosystem (IBC) was proposed. It is constructed based on ID, and IDs are exchanged between server and client instead of certificates. Identity-based Encryption (IBE), Identity-based Signature (IBS) and Identity-based Authenticated Key Agreement (IBAKA) were adopted to implement security functions of encryption, signature and key agreement respectively. Sender's ID information was embedded into encryption key computation, which can be used to authenticate the source of message. The analysis on security and efficiency shows that the efficiency of wireless communication is improved without security loss.

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.