In order to solve the intelligence extraction of information in the era of remote sensing big data,it is important to build the model and method of intelligent information analysis fitting the intrinsic characteristics of remote sensing data. To meet the demand of universal remote sensing intelligent acquisition of large-scale water information,an intelligent extraction method of remote sensing water information based on visual selective attention mechanism and AdaBoost algorithm was proposed. Firstly,by the optimization design of RGB color scheme of remote sensing multi-feature index,the enhancement and visual representation of the water information image features were realized. Then,in HSV color space,the key node information of the chromatic aberration distance image was used to construct the classification feature set,and AdaBoost algorithm was used to construct the water recognition classifier. On this basis,the category that the water belongs to was automatically recognized from the image color clustering result,so as to realize the intelligent extraction of water information. Experimental results show that the proposed method has the water information extraction results improved on Leak Rate(LR)and Composite Classification Accuracy(CCA). At the same time,the proposed method not only effectively reduces the dependence on high quality training samples,but also has good performance on the recognition of temporary water areas such as water with high sediment concentration at wet season and submerged area caused by flooding.

To meet the demands of the public security department to search out specific target in complicated real environment, the target re-IDentification (re-ID) technique was introduced in vehicle identification field, and a vehicle re-ID algorithm based on the model of Bag of Visual Words (BoVW) was proposed. Firstly, in order to solve the probloms of occlusion pose change, target size and position difference in images, the improved scales and poses adaptive Part-based One-vs-One Feature (POOF) were extracted. Secondly, a set of visual words was clustered as a vocabulary by using k-means algorithm based on Euclidean distance, and the features of each image (or target) were expressed as the composition of visual vocabularies. Thirdly, the improved Keep It Simple and Straightforward Metric (KISSME) method followed with re-rank step was used to separate the between-classes and within-classes distances. Finally, the result was obtained by using K-Nearest Neighbor ( KNN) method. The experimental results show that the algorithm has 3.85 percentage points increasement of identification rate in feature representation step compared with Bubble Bank (BB) and 3.14 percentage points increase in metric learning step compared with Bayesian face revisited. Furthermore, it is proved that the proposed algorithm is economical in time-consuming and has strong adaptability to target pose change and small portion of occlusion, which further domonstrates that it can adapt to complicated environments.

Aiming at the uncertainty and dynamism in agile manufacturing and the features of Hybrid Flow Shop (HFS) scheduling problem, a multi-Agent based dynamic scheduling system for hybrid flow shop was developed, which consists of management Agent, strategy Agent, job Agent and machine Agent. First, a HFS aimed Interpolation Sorting (HIS) algorithm was proposed and integrated into the strategy Agent, which is suitable for static scheduling and dynamic scheduling under a variety of dynamic events. Then the coordination mechanism between the various Agents was designed. In the process of production, all Agents worked independently and coordinate with each other according to their behavioral logic. If a dynamic event occurred, the strategy Agent called HIS algorithm to generate the sequence of jobs according to the current workshop state, and then the Agents continued to coordinate according to the generated sequence until the production was finished. Finally, simulations of dynamic scheduling such as machine failure, rush order and online scheduling were carried out. The experimental results show that compared with a variety of scheduling rules, HIS algorithm has better schedule results than those by scheduling rules in these cases; especially in machine breakdown rescheduling, the consistency of makespan before and after rescheduling was up to 97.6%, which means that the HFS dynamic scheduling system is effective and flexible.

The construction of measurement matrix in compressive sensing varies widely and is on the development constantly. In order to sort out the research results and acquire the development trend of measurement matrix, the process of measurement matrix construction was introduced systematically. Firstly, compared with the traditional signal acquisition theory, the advantages of high resource utilization and small storage space were expounded. Secondly, on the basis of the framework of compressive sensing and focusing on four aspects:the construction principle, the generation method, the structure design of measurement matrix and the optimal method, the construction of measurement matrix in compressive sensing was summarized, and advantages of different principles, generations and structures were introduced in detail. Finally, based on the research results, the development directions of measurement matrix were prospected.

Coding cryptography is widespread concerned because it has the advantages of resisting quantum algorithm. To protect the anonymous message, a kind of blind signature scheme based on coding was proposed. By Hash technique and blind factor, message owner sent irreversible and blinding message to the signer, signer completed blind signature by using Courtois-Finiasz-Sendrier (CFS) signature scheme and sent it back to the message owner, message owner obtained signature by blind removing operation. Analysis show that the new scheme not only has the basic nature of general blind signature, but also inherit the advantages of CFS signature scheme such as high security and short signature length, in addition, it can effectively resist attacks of quantum algorithm.

Aiming at the problems of automatic control of camera load parameters and real-time tracking of the flight path in the remote sensing photography of Unmanned Aerial Vehicle (UAV), this paper presented a design scheme which could complete camera load control and aerial control automatically. First, the information of real-time geographic location and environment forecasting could be acquired in the system according to experimental requirements, and the parameter encoding was completed based on the table of camera control parameters; second, the custom protocol instruction set was sent to hardware control circuits through the communication port to complete the set of camera load parameters, and photography could be completed. Meanwhile, the geographic coordinate information of real-time flight path was recorded by the route planning software. The system can combine hardware control platform with software data processing, to achieve collaborative control. The UAV experiment results show that compared with the mode of single parameter aerial control, the proposed system in this paper can automatically control camera parameters and track real-time flight path according to different photography conditions and photography scenes.

An improved method based on Local Binary Pattern (LBP) was proposed to solve the problem that the representing ability of LBP is bad because only the relationship between neighbors and the central pixels are considered while the floating relationship of the gray values in the neighbor region is ignored. Firstly, each neighbor was compared clockwise with its next adjacent neighbor before threshold and an LBP-like code was generated. Secondly, the code was encoded to a decimal number named as Float-LBP (F-LBP). Thirdly, the features extracted by the F-LBP and the basic LBP operators were combined together. The experimental results show that the combination of the F-LBP and the basic LBP operators can improve the retrieval accuracy by extracting more discriminative information while reserving the local micro-texture.

The concept and architecture of the Internet of Things (IoT) were introduced. Key characteristics and technologies, which included the ubiquity of the IoT, intelligent identification and sensing technologies, uncertainty of data, representation methods of data, information propagation towards massive data, security and privacy were discussed in detail, and related open issues were presented. Based on the future ubiquitous applications, the research directions of the IoT were put forward.

To solve the problems in Internet of Things (IOT), such as high coupling between levels, low reusability and massive data processing difficulty, this paper proposed a Web service-based middleware model of IOT. It used XML file to configure and implement logical separation, achieved function modularization by the application of "object" in IOT, distributedly processed massive data by load balance, and ensured information security by role permissions setting. The experimental results show that, the middleware model reduces reliance on the type of gateway, improves the reusability of the program and achieves ten-thousand data processing. According to the application of middleware system developed by the model in the vehicle monitoring, the cost of development is reduced, the efficiency of development is improved and the system has a flexible configuration.

In order to solve the system integration problems of high-speed railway integrated dispatching system in highly-distributed, highly heterogeneous environment, system integration framework based on Service Oriented Architecture (SOA) was proposed. The high-speed railway integrated dispatching system structure and its distributed SOA application were constructed based on Service Component Architecture (SCA) and Service Data Object (SDO) technology. The integration of power dispatching subsystem and other scheduling subsystems was achieved based on SCA and SDO technology on Java EE platform. The method fully embodies the openness and cross-platform features of SOA, and it is easy to implement.

To study the effect of meteorological conditions on Unmanned Aerial Vehicle (UAV), an algorithm of multi-level fuzzy comprehensive evaluation method based on threat value was proposed. This algorithm improved a two-level weight value determination and the comprehensive evaluation model, which can get the comprehensive threat index after being calculated. The simulation results show that this algorithm can assess the degree of weather threat accurately and have faster operation speed, smaller error and lower complexity. The efficiency and validity have also been improved.

To solve the problems of distributed control and lack of geographic information technical assistance in the process of integrated automation development for Supervisory Control and Data Acquisition (SCADA) systems, system integration technology was applied to achieve comprehensive design of the Geographic Information System (GIS) and SCADA system in terms of data, interface and function. The comprehensive monitoring system was developed based on the multilayer structure of C/S. ActiveX technology was used in the developmental process to complete the system integration, and the SCADA system was used as the basic means of monitoring and control, while the GIS was used to provide the necessary geographic information analysis and processing functions. With the establishment of integrated database, the seamless connection of GIS with SCADA and sharing were realized, and the data consistency and real-time performance of the integrated system were ensured. The communication between the devices and remote clients helped the system to realize on-site video monitoring. Safety measures were also integrated in the system. The system is now successfully applied in actual working environment, and all functions are verified with stable operation and high scalability.

The initial clustering centers of K-means algorithm are randomly selected, which may result in low accuracy and unstable clustering. To solve these problems, a K-means initial clustering center selection algorithm was proposed. The locations of data points were determined by analyzing Difference of K-dist (DK) graph. One point with the least k-dist value on the main density curves was selected as an initial clustering center. The experimental results demonstrate that the improved algorithm can select unique initial clustering center, gain stable clustering result, get higher accuracy and reduce times of iteration.

In view of the difficult in management of airborne multi-sensor, a fuzzy decision tree algorithm is applied to multi-sensor intelligent management, combined with operational processes, and combat stage will be merging with the target type, different target attributes is built reasonable. The fuzzy decision trees model are established based on target type, then the airborne multi-sensor management model is established. Finally, the simulation results show that the method can compose battlefield objectives and tactical effectively, complete airborne multi-sensor management rapidly, and the method is reasonable and effective.

Concerning the unbalanced energy and "hotspot" energy hole problems caused by uneven distributed node in Wireless Sensor Network （WSN） clustering routing protocols, an energybalanced uneven distributed node routing protocol was proposed. In the routing protocol, the three parameters, i.e., the "degree" of the node, the distance of nodes to Sink, and the ratio between the average residual energy of clusters nodes and the node itself residual energy, were considered when primary(deputy)cluster head was chosen，and each cluster communicated with Sink through routing tree. The simulation results show that the new protocol can reduce the cluster head rotation frequency when cluster is in "hotspot" or clusters destiny is high, delay the first nodes death time, and make the energy consumption more balanced and the network life cycle longer.

In order to improve the searching accuracy of motion estimation, a new algorithm called Directional Adaptive Sampling Search (DASS) algorithm was proposed. According to the information of matching error in search patterns, the algorithm adopts two sorts of triangle search patterns adaptively. The strategy of local sampling search was employed for the sake of big-motion vector. Early termination strategy was adopted in the procedure of DASS so as to further speed up the search. The experimental results show that the proposed algorithm not only ensures the speed but also improves the accuracy of search with Peak Signal-to-Noise Ratio (PSNR) degradation of only 0.28dB on average.

Poor adaptability and slow convergence rate are the two main disadvantages of the existing precision test methods. To solve this problem, an elementary functions precision test method based on continued-fraction approximation was proposed by analyzing Unit in the Last Place (ULP) error and comparing several different functions true values calculations. The different calculations were analyzed and tested in the following two ways: time complexity and convergence degree. The experimental results show that the precision test method based on continued-fraction approximation is more effective, less complex and achieves faster convergence.

A testing design decision analysis algorithm based on test importance was proposed for software test design strategy optimization. According to Analytic Hierarchy Process (AHP) theory, the algorithm was used to solve the problem of test importance comparison and analysis through combining quantitative and qualitative methods. Then the targets including optimization of test design strategies, reasonable distribution of test resources, effectively finding software defects can be achieved.

In view of the demand for multi-signature and fine-grained signature in workflow systems, the authors proposed the "Signature on Signature" mechanism and put forward an application model of eXtensible Markup Language (XML) signature in workflow systems. In this model, the document to be signed was converted to XML format, and this facilitated the handling of the document for the system. In the processing of the XML document, every processing node signed and verified on the basis of the former processing node. In the end, a purchase approval workflow system was developed. Taking a typical purchase approval scenario for example, the validity and effectiveness of the presented model were verified, and a feasible solution was provided for the application of XML digital signature in workflow systems.

A method based on a 3D model converter to simulate the development of virtual plant was presented. The converter was mainly used to import the plant organs which have fine details into the virtual plant developmental system, then communicating with the L-system to implement the simulation of the development of plants controlled by the physiology of plant. It improves the virtual effect produced by the former systems which only take account of the geometric model.

 JavaSpaces is a Java-based implementation of tuplespace and it provides a conceptually simple but powerful solution to design and build distributed systems that are reliable, flexible and extendable. This paper detailed the program model and application patterns of JavaSpaces. And a distributed parallel computing framework based on JavaSpaces was also introduced and tested.