Due to differences in resource endowments and industrial policies among different regions， the role of distributed production in improving the competitiveness of manufacturing enterprises is very important. How to use distributed production to enhance the flexibility of mass customization is an important problem to be solved to boost consumer confidence. Combined with the idea of minicells — small manufacturing cells， in the distributed mixed production scenario with the multi-market and multi-product characteristics， an integrated model of distributed factory construction and production scheduling was proposed with the objectives to minimize the operating costs （e.g.， labor and transportation costs） and minimize the makespan. By the proposed model， the minicell construction， worker and machine configuration， as well as production strategies for each batch of products were able to be solved. With the help of the proposed model， the enterprises were able to realize the quick release of production capacity and reasonable mixed flow production， so as to realize distributed manufacturing and sales that meet the multi-region， multi-product， and differentiated needs， and reduce the operating cost in the manufacturing process while guaranteeing the throughput. In addition， a Multi-Objective Particle Swarm Optimization （MOPSO） algorithm was designed to solve the proposed model， and was compared with Non-Dominated Sorting Genetic Algorithm Ⅱ （NSGA-Ⅱ） and Multi-Objective Simulated Annealing （MOSA） algorithm. The results of extensive numerical experiments show that MOPSO algorithm outperforms NSGA-Ⅱ and MOSA algorithm with the same running time in terms of three metrics： C-Metric （CM）， Mean Ideal Distance （MID） and Maximum Spread （MS）. The proposed algorithm can provide a high-quality decision-making scheme of production operation for the miniaturized distributed production system.

Software testing is an effective way to ensure the quality of Android applications. Understanding the functions of Android applications is the basis of the Android testing process. It aims to deeply explore the application’s business logic and reveal its functional defects， playing an important role in testing. User behavior patterns can assist testers in understanding an Android application’s functions， thereby improving test efficiency. Based on the idea “similar Android applications share user behavior patterns”， a user behavior pattern mining and reuse method was proposed to reduce the cost of Android application testing and improve the testing efficiency. Specifically， for the Android application under test， the user behavior patterns from a similar Android application were mined. Then， the semantic-based event fuzzy matching strategy was used to search the corresponding events for the application under test， and the Graphical User Interface （GUI） model based optimal path selection strategy was used to generate target event sequences for the application under test， thereby achieving user behavior pattern reuse across similar applications. The experiments were conducted on 32 user behavior patterns of three categories of Android applications. The results show that 87.4% of user behavior patterns can be completely reused on similar Android applications， and the reused user behavior patterns can effectively cover 90.2% of important states in applications under test. Thus， the proposed method provides effective support for the testing of Android applications.

With the development of smart power grid， Unmanned Aerial Vehicles （UAVs） are more and more widely used for inspection of transmission lines. In order to effectively implement fault location and type judgment of transmission lines， UAVs are required to transmit videos and images with high resolution. Under the condition of limited bandwidth， it is necessary to improve the spectral efficiency of UAV return communication link as much as possible to meet the transmission rate requirements of high-resolution videos and images. A video image transmission communication method based on mesh network was proposed. By deploying the wireless access nodes on tower and building Mesh network， the communication devices carried by UAVs could communicate with the built Mesh network as the network nodes at any time. After capturing a video of the failure on transmission lines， the video could be quickly transmitted to the data center by UAVs. For this purpose， the communication module of the patrol UAV was equipped with a large-scale antenna array， in millimeter wave frequency band， a heuristic point-to-point directional hybrid beamforming method was adopted to improve the spectral efficiency of receiving communication link. The simulation results show that the performance of the proposed method is better than that of the Orthogonal Matching Pursuit （OMP） method and is closer to that of the fully digital beamforming method.

Focusing on the unbalance issue between local optimization and global optimization and the inability to jump out of the local optimum of Artificial Fish Swarm Algorithm （AFSA）， an Adaptive AFSA utilizing Gene Exchange （AAFSA-GE） was proposed. Firstly， an adaptive mechanism of view and step was utilized to enhance the search speed and accuracy. Then， chaotic behavior and gene exchange behavior were employed to improve the ability of jumping out of the local optimum and the search efficiency. Ten classic test functions were selected to prove the feasibility and robustness of the proposed algorithm by comparing it with the other three modified AFSAs， which are Normative Fish Swarm Algorithm （NFSA）， FSA optimized by PSO algorithm with Extended Memory （PSOEM-FSA）， and Comprehensive Improvement of Artificial Fish Swarm Algorithm （CIAFSA）. Experimental results show that AAFSA-GE achieves better results in local and global search ability than those of PSOEM-FSA and CIAFSA，and better search efficiency and better global search ability than those of NSFA.

A stochastic optimization method was proposed in order to solve the vehicle number allocation problem of the minimum autonomous vehicle fleet driven by spatio-temporal multi-tasks of terminal delivery. Firstly, the influence of service time and waiting time on the route planning of autonomous vehicle fleet was analyzed to build the shortest route model, and the service sequence network was constructed based on the two-dimensional spatio-temporal network. Then, the vehicle number allocation problem of the minimum autonomous vehicle fleet was converted into a network maximum flow problem through the network transformation, and a minimum fleet model was established with the goal of minimizing the vehicle number of the fleet. Finally, the Dijkstra-Dinic algorithm combining Dijkstra algorithm and Dinic algorithm was designed according to the model features in order to solve the vehicle number allocation problem of the minimum autonomous vehicle fleet. Simulation experiments were carried out in four different scales of service networks, the results show that:under different successful service rates, the minimum size of autonomous vehicle fleet is positively correlated with the scale of service network, and it decreases with the increase of waiting time and gradually tends to be stable, the One-stop operator introduced into the proposed algorithm greatly improves the search efficiency, and the proposed model and algorithm are suitable for the calculation of the minimum vehicle fleet in large-scale service network.

Focused on the issue that the traditional interest area based visualization method can not pay attention to the details in the process of analyzing pilot eye movement data, a visual analysis method of eye movement data based on user-defined interest area was proposed. Firstly, according to the specific analysis task, the self-divison and self-definition of the background image of the task were introduced. Then, multiple auxiliary views and interactive approaches were combined, and an eye movement data visual analysis system for pilot training was designed and implemented to help analysts analyze the difference of eye movement between different pilots. Finally, through case analysis, the effectiveness of the visual analysis method and the practicability of the analysis system were proved. The experimental results show that compared with the traditional method, in the proposed method, the analysts' initiative in the analysis process is increased. The analysts are allowed to explore the local details of the task background in both global and local aspects, making the analysts' analyze the data in multi-angle; the analysts are allowed find the flight students' cognitive difficulties in the training process as a whole, so as to develop more targeted and more effective training courses.

6LoWPAN (IPv6 over Low power Wireless Personal Area Network) is a technology to realize the IP connection of wireless sensor network based on IEEE802.15.4 standard. The network congestion and energy consumption problem exists around the single bounder router under the existing single DODAG (Destination Oriented Directed Acyclic Graph) protocol. An embedded 6LoWPAN multi-gateway protocol and system was designed. The embedded gateway node has dual-mode communication function, which can realize the physical connection between WSN (Wireless Sensor Network) and fixed IPv6 network. The dual-mode gateway implements uplink and downlink routes by establishing an IP tunnel between the 6LoWPAN root border router and it. By supplementing and optimizing the existing 6LoWPAN protocol standard, the dual-mode node has intra-network and inter-network routing capabilities, so as to achieve multi-gateway architecture and multi-path routing function. The optimized multi-point interworking topology and traffic sharing algorithm was used to realize the effective load balance between uplink and downlink links, and also to reduce the energy consumption of multi-hop routing of nodes. Experiments were carried out on multi-gateway platforms and single gateway systems. The results show that the proposed scheme can achieve 6LoWPAN multi-gateway Ethernet access, reduce network node transmission delay and packet loss rate, and improve the overall network throughput.

In the age of big data, HBase which can store massive data is widely used. HBase only can optimize index for the rowkey and donot create indexes to the columns of non-rowkey, which has a serious impact on the efficiency of complicated condition query. In order to solve the problem, a new scheme about secondary indexes in HBase based on memory was proposed. The indexes of mapping to rowkey for the columns which needed to be queried were established, and these indexes were stored in memory environment which was built by Spark. The rowkey was firstly got by index during query time, then the rowkey was used to find the corresponding record quickly in HBase. Due to the cardinality size of the column and whether or not the scope query determined the type of index, and different types of indexes were constructed to deal with three different situations. Meanwhile, the memory computation and parallelization were used in Spark to improve the query efficiency of indexes. The experimental results show that the proposed secondary indexes in HBase can gain better query performance, and the query time is less than the secondary indexes based on Solr. The proposed secondary indexes can solve the problem of low query efficiency, which is caused by the lack of indexes of non-rowkey columns in HBase, and improve the query efficiency for large data analysis based on HBase storage.

Aiming at the problem that the traditional Laplacian score for feature selection cannot be directly applied to multi-label tasks, a multi-label feature selection algorithm based on Laplacian score was proposed. Firstly, the sample similarity matrix was reconstructed by the correlation of the common and non-correlated correlations of the samples in the overall label space. Then, the correlation and redundancy between features were introduced into Laplacian score, and a forward greedy search strategy was designed to evaluate the co-operation ability between candidate features and selected features, which was used to evaluate the importance of candidate features. Finally, extensive experiments were conducted on six multi-label data sets with five different evaluation criteria. The experimental results show that compared with Multi-label Dimensionality reduction via Dependence Maximization (MDDM), Feature selection for Multi-Label Naive Bayes classification (MLNB) and feature selection for multi-label classification using multivariate mutual information (PMU), the proposed algorithm not only has the best classification performance, but also has a remarkable performance of up to 65%.

Focusing on the issue that current real-time map matching algorithms are difficult to keep high efficiency and high accuracy simultaneously, an improved dynamic weighted real-time map matching algorithm was proposed. Firstly, considering the temporal, speed, heading and direction constraints of Global Positioning System (GPS) points and the topological structures of road network, a weighted model was constructed in the algorithm based on spatio-temporal analysis, which consisted of proximity weight, heading weight, direction weight and connectivity weight. Then according to the properties of GPS points, a dynamic weighted coefficient model was created. Lastly, the best matching road segment was selected according to the confidence level of current GPS point. The experiments were conducted on three city bus trajectories with length of 36 km in Chongqing. The average matching accuracy of the algorithm was 97.31% and the average matching delay of each GPS point was 17.9 ms. The experimental results show that compared with the contrast algorithms, the proposed algorithm has higher accuracy and efficiency, and has better performance in matching Y-junctions and parallel roads.

Focusing on the issue that the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm may introduce some false targets in moving target detection with the presence of multipath reflections, a descending dimension method for designed matrix based on LASSO was proposed. Firstly, the multipath propagation increases the spatial diversity and provides different Doppler shifts over different paths. In addition, the application of broadband OFDM signal provides frequency diversity. The introduction of spatial diversity and frequency diversity to the system causes target space sparseness. Sparseness of multiple paths and environment knowledge were applied to estimate paths along the receiving target responses. Simulation results show that the improved LASSO algorithm based on the descending dimension method for designed matrix has better detection performance than the traditional algorithms such as Basis Pursuit (BP), Dantzig Selector (DS) and LASSO at the Signal-to-Noise Ratio (SNR) of -5 dB, and the target detection probability of the improved LASSO algorithm was 30% higher than that of LASSO at the false alarm rate of 0.1. The proposed algorithm can effectively filter the false targets and improve the radar target detection probability.

In order to alleviate the problem of the imbalance energy consumption and hotspot due to the uneven distribution of nodes and the different amount of perception data in the Wireless Sensor Network (WSN), a Path Planning Algorithm of Mobile Sink named MSPPA was proposed to optimize network lifetime and shortest path in WSN. Firstly, by defining the grids in the network area, several candidate sites of mobile sink were distributed in each grid, and then sink node selected a site for sojourning and collecting data of nodes in each grid. Secondly, based on the relationship between network lifetime and the selection of sink sites, an optimization model was established to weigh network lifetime and mobile journey of sink. Finally, the double-stranded genetic algorithm was proposed to plan the order of mobile sink traversing grids and selecting site of the mobile sink in each grid, then the optimal path of mobile sink was obtained. The simulation results show that, compared with Low-Energy Adaptive Clustering Hierarchy (LEACH) algorithm and optimizing LEACH clustering algorithm with Mobile Sink and Rendezvous Nodes (MS-LEACH-RN), the network lifetime of MSPPA was increased by 60%. The proposed MSPPA has a good balance of energy consumption as well. The experimental results indicate that the proposed MSPPA can effectively alleviate the imbalance of energy consumption and the hotspot problems, prolonging the network lifetime.

Focusing on the problems of low feature extraction speed and low matching efficiency of the traditional image copy detection algorithm based on Scale Invariant Feature Transform (SIFT) feature, a fast image copy detection algorithm based on location distribution and orientation distribution features of SIFT feature points was proposed. Firstly, the two-dimensional location information of SIFT feature points was extracted. The number of feature points in each interval was counted with block statistics by calculating the distance and angle between each feature point and image center point. The binary hash sequence was generated to construct the first order robust feature according to the quantitative relationship. Then, the numbers of sub-interval feature points in all directions were counted with block statistics according to the one-dimensional direction distribution feature of feature points, and the secondary image feature was constructed according to the quantitative relationship. Finally, a cascade filter framework was used in the copy detection to make a judgement about whether the copy or not. The simulation experimental results show that, compared with the traditional copy detection algorithm which constructs the hash sequence based on the SIFT feature with 128-dimensional descriptor, the feature extraction time of the proposed algorithm is shortened to the original 1/20, and the matching time is also reduced by more than 1/2. Therefore the proposed algorithm meet the requirement of online copy detection.

In order to deal with the cache pollution attacks in Content Centric Networking (CCN), a defense scheme based on cache diversification was proposed. To reduce the attack scope, the in-network content services were divided into three categories and different cache strategies were used for different services. For private and real-time services, contents were directly delivered without being cached; for streaming media services, contents were pushed to be cached in the edge of network according to probablity; for document services, the priority was caching contents in the upstream, then pushing them to the downstream. Then different defense methods were configured on different nodes. For the edge nodes, attacks were detected by observing the request probability variation of different contents; for the upstream nodes, contents with low request rate were ruled out from the cache space by setting filter rules. The simulation results show that the network average hit ratio under service diversification mechanism is 17.3% higher than that under CCN with traditional caching strategies.The proposed scheme can effectively improve the defense capability of the network for the cache pollution attack.

Aiming at the capacity P-median problem of continuous domains under the dense demand, the Centroidal Capacity Constrained Power Diagram (CCCPD) theory was proposed to approximately model the continuous P-median problem and accelerate the solving process. The Power diagram was constructed by extended Balzer's method, centroid restriction was imposed to satisfy the requirements of P-median, and capacity constraint was imposed to meet the capacity requirements of certain demand densities. The experimental results show that the proposed algorithm can quickly obtain an approximate feasible solution, having the advantages of better computing efficiency and capacity accuracy compared to Alper Murata's method and Centroidal Capacity Constrained Voronoi Tessellation (CCCVT) respectively. Additionally, the proposed method has excellent adaptability to complex density functions.

To overcome the shortcoming that the Non-negativity And Support constraint Recursive Inverse Filtering (NAS-RIF) algorithm is noise-sensitive and time-consuming, an improved NAS-RIF algorithm for blind restoration was proposed. Firstly, a new cost function of the NAS-RIF algorithm was introduced, and then the noise resistance ability and the restoration effect were both improved. Secondly, in order to enhance computational efficiency of the algorithm, after decomposed by Haar wavelet transform, only degraded image in low frequency sub-bands was restored with the NAS-RIF algorithm, while information in high frequency sub-bands was predicted from the restored image of low frequency sub-bands by interband prediction. Finally, an interband prediction based on Minimum Mean Square Error (MMSE) was presented to guarantee the accuracy of the predicted information in high frequency sub-bands. The experiments on synthetic degraded images and real images were performed, and the Signal-to-Noise Ratio (SNR) gain by proposed algorithm were 5.2216 dB and 8.1039 dB respectively. The experimental results demonstrate that the proposed algorithm not only preserves image edges, but also has good performance in noise suppression. In addition, the computational efficiency of the proposed algorithm is greatly enhanced.

Feature point matching is of central importance in feature-based image registration algorithms such as Scale-Invariant Feature Transform (SIFT) algorithm. Since most of the existed feature matching algorithms are not so powerful and efficient in mismatch removing, in this paper, a mismatch removal algorithm was proposed which adopted the depth information in an image to improve the performance. In the proposed approach, the depth map of an acquired image was produced using the clues of defocusing blurring effect, and machine learning algorithm, followed by SIFT feature point extraction. Then, the correct feature correspondences and the transformation between two feature sets were iteratively estimated using the RANdom SAmple Consensus (RANSAC) algorithm and exploiting the rule of local depth continuity. The experimental results demonstrate that the proposed algorithm outperforms conventional ones in mismatch removing.

Multi-Symbol Detection (MSD) and Turbo Product Code (TPC) can greatly improve the performance of PCM/FM (Pulse Code Modulation/Frequency Modulation) telemetry system. To solve the high computational complexity issues in MSD algorithm, an improved algorithm which reduced the computational complexity of MSD was proposed. Chase decoding algorithm for TPC also reduced the system memories by simplifying the calculation of the soft input information. The simulation results show that despite of 1.7dB loss, the improved algorithm still obtains about 8dB performance gain. Because of low-complexity and low system memories, it is more suitable for hardware implementation.

Vehicular network has the following characteristics such as nodes moving fast, topology changing rapidly. The direct use of Global Positioning System (GPS) devices causes large positioning error and low routing connectivity rate. Therefore, the packet delivery rate of the existing location-based routing algorithm is not high enough to provide reliable routing. A geographic routing algorithm based on anchor node in vehicle networks named Geographic Routing based on Anchor Nodes (GRAN) was proposed. Using street lamps as anchor nodes, a vehicle could locate itself through the anchor nodes. Combined with the road gateway and the central data, GRAN established a hierarchical routing structure, thus removing the steps of route discovery and the whole network broadcast. Thus, the routing overhead was reduced and the routing efficiency and the packet delivery rate were improved. By using the NS-2 software and selecting a realistic urban scene, a simulation was conducted on Greedy Perimeter Stateless Routing (GPSR), Graphic Source Routing (GSR) and GRAN. The experimental results show that GRAN can provide a lower average delay, higher packet delivery ratio and throughput at a lower load, compared with several typical location-based routing protocols.

Hardware/Software (HW/SW) partitioning is one of the crucial steps in the co-design of embedded system, and it has been proven to be a NP problem. Considering that the latest work has slow convergence speed and poor solution quality, the authors proposed a HW/SW partitioning method based on greedy algorithm and Simulated Annealing (SA) algorithm. This method reduced the HW/SW partitioning problem to the extended 0-1 knapsack problem, and used the greedy algorithm to do the initial rapid partition; then divided the solution space reasonably and designed a new cost function and used the improved SA algorithm to search for the global optimal solution. Compared to the existing improved algorithms, the experimental results show that the new algorithm is more effective and practical in terms of the quality of partitioning and the running time, and the promotion proportions are 8% and 17% respectively.

By investigating the main physical factors that may affect the high-speed Electric Multiple Unit (EMU) generalized comfort, a portable generalized comfort test system was designed, which integrated all the sensors, signal adjusting modules, embedded data detection and computer system into a simulation human model, and this model was capable to test the generalized comfort in real-time without the requirement of car wiring and would not affect the operation of the train. The characteristics and requirements of the test system were analyzed, its components and operating principles were introduced, and the feasibility of this system was verified as well. The results show that the testing system is highly stable and precise.

As the correlation between color components in color image is not considered in the traditional edge detection algorithms, and the detection results will be affected by the threshold value, a new kind of edge detection method for color image was proposed combining quaternion with Self-Organizing Map (SOM). Based on the quaternionic Cauchy integral formula and vector product, the edge characteristic vector was constructed. Then the SOM was trained by the vector and used for edge detection. The experimental results show that the proposed method has stronger retention capacity of the details.

Fast and accurate Point Spread Function (PSF) estimation method is the premise to obtain good results on the blur image restoration. To solve the deficiency of the discrete PSF of defocus-blurred and motion-blurred images, a discretization method was proposed based on the combination of geometric property of degradation model and sub-pixel estimation. Specifically, the principle of weight allocation was defined, which was related to the distance with the neighboring pixels. Thus, the discretization of PSF was realized. Finally, the experimental results illustrate that the proposed method improves result precision and outperforms the traditional one on visual quality, sharpness evaluation function, Peak Signal-to-Noise Ratio (PSNR) and Improved Signal-to-Noise Ratio (ISNR).

In order to reduce the number of triangular patches in surface rendering, save space and improve reconstruction results, a contour connecting method based on interzone line was proposed in this paper. This connecting method needed to resample with angle and generate secondary resampling points with geometric methods. And resampling points corresponded with each other. Tissue surfaces were reconstructed with quadrilateral patches and 3D reconstruction was realized. In experimental simulation, a series of head CT images were researched. The simulation analysis shows that to resample the contour line and use quadrilateral patches instead of triangular ones, the number of patches is reduced and the storage space is saved. The reconstruction time is short and the reconstruction efficiency is high.

Concerning the specific application environment of embedded microprocessor, the branch prediction technology was researched in this paper, and a new scheme of branch prediction was proposed. Compatible with cache design, jump direction and destination address of branch prediction happened on extended instruction bus. The unexecuted instruction and address pointer were saved for possible recovery after misprediction, which reduced misprediction penalty, simultaneously guaranteed the instruction flow to execute correctly. The study shows this scheme is of little hardware spending, high prediction efficiency and low misprediction penalty.

Hyperspectral image has high spectral dimension, vast data and altitudinal interband redundancy, which brings problems to image classification. To effectively reduce dimensionality and improve classification precision, a new extraction method of nonlinear manifold learning feature based on Supervised Local Linear Embedding (SLLE) for classification of hyperspectral image was proposed in this paper. A data point's k Nearest Neighbours (NN) were found by using new distance function which was proposed according to prior class-label information. Because the intra-class distance is smaller than inter-class distance, classification is easy for SLLE algorithm. The experimental results on hyperspectral datasets and UCI data set demonstrate the effectiveness of the presented method.

Aiming at the problem that the stack space size of μC/OS-II task is not easily confirmed by existing ways, this paper presented a new way by which the stack space size of μC/OS-II task can be computed based on tree structure. Firstly, the stack space structure of μC/OS-II task was analyzed. Secondly, some tree structures that can show the extreme usage of the stack space were defined. Lastly, the formula that can calculate the stack space which is maximal was presented based on tree structure and the stack space of a real system was calculated by the formula. The stack space size which is calculated by this way can reflect the extreme usage of a stack space factually and can reduce the requirement for RAM.

With the advancement of Graphics Processing Unit (GPU) and the creation of its new feature of programmability, it has come possible to transfer some of the processing stages in general numerical algorithms from CPU to GPU in order to accelerate the computation. In this paper, starting from a brief introduction to Alternative Direction Implicit Finite Difference Time Domain (ADI-FDTD) algorithm, detailed introduction and analysis were given to the fundamentals and the key technique of GPU for accelerating ADI-FDTD computation, in combination with the implementation frame of the conjugate gradient method for solving linear equations system on GPU. Finally, some computed examples were presented, and various comparisons were made to prove the efficiency and accuracy of this acceleration approach.

In a multi-system heterogeneous LAN, the lack of interoperability for the MPI program in the different operating environments makes it difficult to make full use of the LAN computing resources in the parallel Finite Difference Time Domain(FDTD) algorithm. To resolve this problem, a new method of using Serverce Message Block（SMB） protocol to realize heterogeneous FDTD computing was presented, and by some means such as memoryfile access, memorymapped array and redundancy computing, the impact of SMB communication latency upon the parallel performance was released. Some computed examples were given to prove the feasibility and accuracy of this method. The numerical results show that performance indexes, such as speedup, parallel efficiency, etc. are close to the isomorphism FDTD computing using MPI.

Relevant subscriptions are stored in the routing tables in a Publish/Subscription (P/S) system. The already existing optimizations focus more on single router and didn't exploit from this. In this paper, the XPath subscription aggregation techniques were used and a 2-phase encapsulation strategy was proposed to generate a header of the XML to speed up the processing and matching of the downstream router. Traditional immediate forwarding protocol can improve the dissemination by skipping the remaining processing after first match, but it can't get entire header to be used by the downstream router. An adaptive forwarding strategy was proposed to combine the immediate forwarding and delay forwarding together to give a full exploitation on the subscriptions and matching, and the whole dissemination efficiency of the P/S was consequently improved. Our experimental results demonstrate the correctness.