For the flexible job shop rescheduling problem with machine breakdown, an improved Imperialist Competition Algorithm (ICA) was proposed. Firstly, a flexible job shop dynamic rescheduling model was established with the maximum completion time, machine energy consumption and total delay time as the objective functions, and linear weighting method was applied to three objectives. Then, the improved ICA was proposed to retain the excellent information for the next generation. A roulette selection mechanism was added after the assimilation and revolutionary steps of the general ICA, so that the excellent genes in the initial empire were able to be retained, and the updated empire quality was better and closer to the optimal solution. Finally, after the machine breakdown, the event-driven rescheduling strategy was adopted to reschedule the unprocessed job procedures after the breakdown point. Through production examples, simulation experiments were carried out on three hypothetical machine breakdown scenarios, and the proposed algorithm was compared with improved Genetic Algorithm (GA) and Genetic and Simulated Annealing Algorithm (GASA). Experimental results show that the proposed improved ICA is effective and feasible.

Traffic mode recognition is an important branch of user behavior recognition, the purpose of which is to identify the user's current traffic mode. Aiming at the demand of the modern intelligent urban transportation system to accurately perceive the user's traffic mode in the mobile device environment, a traffic mode recognition algorithm based on the residual temporal attention neural network was proposed. Firstly, the local features in the sensor time sequence were extracted through the residual network with strong local feature extraction ability. Then, the channel-based attention mechanism was used to recalibrate the different sensor features, and the attention recalibration was performed by focusing on the data heterogeneity of different sensors. Finally, the Temporal Convolutional Network (TCN) with a wider receptive field was used to extract the global features in the sensor time sequence. The data-rich High Technology Computer (HTC) traffic mode recognition dataset was used to evaluate the existing traffic mode recognition algorithms and the residual temporal attention model. Experimental results show that the proposed residual temporal attention model has the accuracy as high as 96.07% with friendly computational overhead for mobile devices, and has the precision and recall for any single class reached or exceeded 90%, which verify the accuracy and robustness of the proposed model. The proposed model can be applied to intelligent transportation, smart city and other domains as a kind of traffic mode detection for supporting mobile intelligent terminal operation.

Concerning the the size and morphological diversity of thyroid tissue and the complexity of surrounding tissue in thyroid ultrasound images, an ultrasound thyroid segmentation network based on feature fusion and dynamic multi-scale dilated convolution was proposed. Firstly, the dilated convolutions with different dilation rates and dynamic filters were used to fuse the global semantic information of different receptive domains and the semantic information in the context details with different ranges, so as to improve the adaptability and accuracy of the network to multi-scale targets. Then, the hybrid upsampling method was used to enhance the spatial information of high-dimensional semantic features and the context information of low-dimensional spatial features during feature dimensionality reduction. Finally, the spatial attention mechanism was introduced to optimize the low-dimensional features of the image, and the method of fusing high- and low-dimensional features was applied to retain the useful features of high- and low-dimensional feature information with the elimination of the redundant information and improve the network's ability to distinguish the background and foreground of the image. Experimental results show that the proposed method has an accuracy rate of 0.963±0.026, a recall rate of 0.84±0.03 and a dice coefficient of 0.79±0.03 in the public dataset of thyroid ultrasound images. It can be seen that the proposed method can solve the problems of large difference of tissue morphology and complex surrounding tissues.

Recently, botnets, domain name hijacking, phishing websites and other typosquatting attacks are more and more frequent, seriously threatening the security of society and individuals. Therefore, the typosquatting detection is an important part of network protection. The current typosquatting detections mainly focus on public domain names, and the detection methods are mainly based on edit distance which is difficult to fully reflect the visual characteristics of domain names. In addition, using the related information of the given domains for determination can help to increase the detection efficiency, but it also introduces a large additional cost. Based on this, a lightweight detection strategy only based on domain name strings was adopted for typosquatting detection. By comprehensively considering the influence of character locations, character similarities and operation types on the vision of domain names, the edit distance algorithm based on visual characteristics was proposed. According to the characteristics of typosquatting, firstly the domain names were preprocessed, then different weights were given to the characters according to their positions, character similarities and operation types, and finally, the typosquatting determination was performed by calculating the edit distance value. Experimental results show that compared with the detection method based on edit distance, the typosquatting lightweight detection method based on visual features has the F1 value increased by 5.98% and 13.56% respectively when the threshold value is 1 and 2, which proves that the proposed method has a good detection effect.

To solve the problem of k nearest neighbor query in obstacle space, a k nearest neighbor Query method based on improved Parallel Ant colony algorithm (PAQ) was proposed. Firstly, ant colonies with different kinds of pheromones were utilized to search k nearest neighbors in parallel. Secondly, a time factor was added as a condition of judging path length to directly show the searching time of ants. Thirdly, the concentration of initial pheromone was redefined to avoid the blind searching of ants. Finally, visible points were introduced to divide the obstacle path into multiple Euclidean paths, meawhile the heuristic function was improved and the visible points were selected by ants to conduct probability transfer making ants search in more proper direction and prevent the algorithm from falling into local optimum early. Compared to WithGrids method, with number of data points less than 300, the running time for line segment obstacle is averagely reduced by about 91.5%, and the running time for polygonal obstacle is averagely reduced by about 78.5%. The experimental results show that the running time of the proposed method has obvious advantage on small-scale data, and the method can process polygonal obstacles.

In order to solve the problems such as low transmission efficiency and high network resource overhead in Internet of Vehicles (IoV) environment, a new routing algorithm based on node cognitive interaction, which is suitable for urban traffic environment, was proposed. Firstly, based on trust theory, a concept of cognitive interaction degree was proposed. Then, based on this, the vehicle nodes in IoV were classified and given with different initial values of cognitive interaction degree. Meanwhile, the influence factors such as interaction time, interaction frequency, physical distance, hops between nodes and the Time-To-Live of message were introduced, and a cognitive interaction evaluation model of vehicle nodes was constructed. The cognitive interaction degrees of vehicle nodes were calculated and updated by using the proposed model, and a neighbor node with higher cognitive interaction degree than others could be selected as relay node to forward the messages after the comparison between the nodes. Simulation results show that compared with Epidemic and Prophet routing algorithms, the proposed algorithm effectively increases the message delivery rate and reduces the message delivery delay, while significantly reducing the overhead of network resources and helping to improve the quality of message transmission in IoV environment

Aiming at the problem that the indoor mobile fire extinguishing robot can not obtain comprehensive environmental information in time by means of its inner sensors and the absence of remote network control function, a system architecture based on Wireless Sensor Network (WSN) with function of remote network control was proposed. Firstly, a WSN with mesh topology was built to collect indoor environmental information. Secondly, after analyzing the logic of parts of the system, a database and a Web server were completed to achieve the browsing function for remote clients. Finally, the function of remote network control of robot was achieved by developing the software with Socket communication function for network clients. The test results show that the rate of data packet loss for mesh topology without covering the gateway node is 2% at 1.5s sending interval, which is 67% lower than the tree topology in the same situation. By adopting the proposed system architecture, both more comprehensive indoor environment information and reduction of data packet loss rate for WSN are achieved, and the function of remote network control is also realized.

Concerning the problems that Two-Dimensional Multiple-Input Multiple-Output (2D MIMO) systems only consider the effects of horizontal radiation pattern, ignoring the effects of vertical radiation pattern, and the closed-form on the sum rate of 2D MIMO system over K (Rayleigh/Gamma) fading channels involves special functions, two closed-form upper bounds on achievable sum rate of Three Dimensional Distributed Multiple-Input Multiple-Output (3D D-MIMO) systems with Zero-Forcing (ZF) receivers over K composite fading channels were proposed. The upper bounds considered Rayleigh multipath fading, Gamma shadow fading, geometric path-loss, 3D antenna radiation loss, and user distribution. The experimental results show that the obtained expressions accurately match with the Monte Carlo simulation conclusions.

With the rapid development of the domestic high-speed railway construction, customer demand for mobile office and entertainment on high-speed railway is growing rapidly. While both of the existing cellular mobile communication and proprietary communication network for Global System for Mobile communication-Railway (GSM-R) cannot satisfy customer demand for Quality of Service (QoS) of broadband wireless communication. High-speed railway will experience all kinds of complex scenarios during the actual driving, and U-shaped groove scene is a common one. However, there is not a full research on time-varying channel modeling of the U-shaped groove scenario under high-speed mobile environment. Therefore, a U-shaped groove time-varying channel modeling method under high-speed mobile environment was proposed and simulated. Firstly, the geometric random distribution theory was used to established geometric distribution model for high-speed railway scenario under U-shaped groove, and the change law of scatterers was analyzed. Besides, the parameters' closed mathematical expressions such as line-of-sight distribution, time-varying angle spread, time-varying Doppler spread were deduced, and the closed solution of the channel impulse response was given. Secondly, the time-variant space-time cross-correlation function, time-variant auto-correlation function and time-variant space-Doppler power spectrum density were analyzed. Finally, the simulations of statistical performance were carried out to verify the proposed model. The simulation results show that the proposed model has the properties of time-varying and high correlation, which verifies the non-stationary of high-speed wireless channel and satisfies the characteristics of high-speed wireless channel.

Since the exsiting dictionary learning methods can not effectively construct discriminant structured dictionary, a discriminant dictionary learning method with discriminant and representative ability was proposed and applied in software defect detection. Firstly, sparse representation model was redesigned to train structured dictionary by adding the discriminant constraint term into the object function, which made the class-dictionary have strong representation ability for the corresponding class-samples but poor representation ability for the irrelevant class-samples. Secondly, the Fisher criterion discriminant term was added to make the representative coefficients have discriminant ability in different classes. Finally, the optimization of the designed dictionary learning model was solved to obtain strongly structured and sparsely representative dictionary. The NASA defect dataset was selected as the experiment data, and compared with Principal Component Analysis (PCA), Logistics Regression (LR), decision tree, Support Vector Machine (SVM) and the typical dictionary learning method, the accuracy and F-measure value of the proposed method were both increased. Experimental results indicate that the proposed method can increase detection accuracy with improving the classifier performance.

Conventional watermarking algorithms usually pay more attention to the visual quality of embedded carrier while ignore the security of watermarking. Although some methods provided watermarking encryption procedures, they usually embed watermarks in fixed positions which are prone to be attacked. The sensitivity of watermarking algorithm based on parameterized wavelet transform is difficult to be applied in practice. To address these problems, a public sensitive watermarking algorithm with weighted multilevel wavelet coefficient mean and quantization was proposed. In the proposed algorithm, firstly the Message Digest Algorithm 5 (MD5) value of cover image, user keys and initial parameters were bound with Logistic map which were used to encrypt watermarks and select wavelet coefficients in different decomposition levels; secondly weights of wavelet coefficients in different levels were estimated by absolute variation means of wavelet coefficients before and after Joint Photographic Experts Group (JPEG) compression, and then weighted multilevel wavelet coefficient mean was adjusted to embed watermark; finally an isolated black point filtering strategy was adopted to enhance the quality of fetched watermark. The experiments show the proposed method has better sensitivities of plaintext image and user keys and still is robust for common image attacks such as image clipping, white noise, JPEG compression, covering and graffiti. The Peak Signal-to-Noise Ratio (PSNR) of image after embedding watermarks can reach 45 dB. The embedded watermark is difficult to be tampered or extracted even if all watermarks embedding procedures are published.

The analysis about Argo buoy profile is not comprehensive for taking the single Argo buoy as a processing object, and the calculation time of uniprocessing method is long. In order to solve the problems, a new algorithm using latitude and longitude cell as analysis object and combining MapReduce with principal curve analysis was proposed. In Map processing, the effective information of Argo buoy was extracted from the big data files and the extracted Argo profiles were classified according to the latitude and longitude. In Reduce processing, the principal Argo profile of each region was generated. Firstly, the information was normalized, and then the principal Argo profile of regional profile characteristics which consisted of a small amount of profile points and lines was obtained through the Kegl's principal curve theory, the information fusion of massive Argo buoy was realized. The proposed algorithm was verified through the global Argo buoy sample data, the new algorithm achieved the mean of residual errors was within 0.1 in the condition of 0.03-0.10 squared distance, the data storage space was saved by 99.4%, and the computation speed was increased by 36.4%, compared with the traditional method only based on uniprocessing. The experimental results show that the proposed algorithm can generate principal profiles accurately, meanwhile reduce the data storage space and effectively improve the computation speed.

Aiming at how to effectively monitor the network abnormal events, find the bottleneck of network performance and potential point of failure in large-scale data center network, based on the deep analysis of the characteristics of InfiniBand (IB) network and introducing the feature selection strategy and incremental learning strategy, an incremental learning method of fault diagnosis for large-scale IB network (IL_Bayes) which based on the Bayes classification and added incremental learning mechanism was proposed. It could effectively improve the accuracy of fault classification. Through testing and verifying the diagnostic accuracy and the rate of misdiagnosis of this method in the Tianhe-2's real network environment, the result shows that the IL_Bayes method has higher classification accuracy and lower misdiagnosis rate.

In order to deal with the channel fading in Underwater Wireless Sensor Networks (UWSN) changing randomly in time-space-frequency domain, underwater cooperative communication model with relays was proposed in this paper to improve reliability and obtain diversity gain of the communication system. Based on the new model, a relay selection algorithm for UWSN was proposed. The new relay selection algorithm used new evaluation criteria to select the best relay node by considering two indicators: channel gain and long delay. With the selected relay node, source node and relay nodes could adjust their sending power by the power allocation algorithm which was based on the principle of minimizing the bit error rate. In a typical scenario, by comparing with the traditional relay selecting algorithm and equal power allocation algorithm, the new algorithm reduces the delay by 16.7% and lowers bit error rate by 1.81dB.

In the non-overlapping filed of multi-camera system, the single-shot person identification methods cannot well deal with appearance and viewpoint changes. Based on the multiple frames acquired from surveillance cameras, a new technique which combined Hidden Markov Model (HMM) with appearance-based feature was proposed. First, considering the structural constraint of human body, the whole-body appearance of each individual was equally vertically divided into sub-images. Then multi-level threshold method was used to extract Segment Representative Color (SRC) and Segment Standard Variation (SSV) feature. The feature dataset acquired from multiple frames was applied to train continuous density HMM,and the final recognition was realized by these well-trained model. Extensive experiments on two public datasets show that the proposed method achieves high recognition rate, improves robustness against viewpoint changes and low resolution, and it is simple and easy to realize.

The virtual machines in cloud computing platform exchange data in the shared memory of physical machine. In view of the problem that the traffic cannot be captured and detected in firewall or other security components, the OpenFlow technology was analyzed, and a traffic redirection method based on OpenFlow was presented. To control traffic forwarding process and redirect it to security components, the method provided network connection for virtual machines with OpenFlow controller and virtual switches instead of physical switches, and built a traffic detection system composed of four modules including virtual switch, control unit, intrusion detection and system configuration management. The experimental results show that the proposed scheme can realize traffic redirection and the subsequent detection processing, and the system can provide switch-level and host-level control granularity. It also solves traffic detection problem under cloud computing environment in traditional scene by traffic redirection, and provides great expansion of the traffic processing based on OpenFlow.

In order to improve the remote authentication efficiency for integrity measurement of computing platforms, this paper proposed a platform remote authentication mechanism based on unbalanced-Hash trees. Hash values of platform's trusted entities were stored in the structure of leaf nodes of unbalanced-Hash trees. Effectiveness of the metrics was verified through seeking corresponding leaf nodes of measured entities, recording the validation paths from leaf nodes to root nodes, passing from root nodes to the prover and finally recalculating the root nodes according to validation paths. The experimental results show that the proposed mechanism can effectively reduce time and space overhead of storing Hash values and the time complexity of integrity measurement authentication is O(lb N).

In view of the problem of environmental sensing in Underwater Cognitive Acoustic Networks (UCAN), a Passive Localization algorithm targeting Marine Mammals (PLM) and Speed Measurement algorithm based on Doppler effect (SMD) were proposed. PLM uses the method of retrieval and screening with received signal power to localize marine mammals based on the source level range of their signals. SMD calculates speed using Doppler effect of the received signals on the basis of PLM localization. The experimental results show that PLM and SMD can achieve high accuracy. The average error of PLM increases with the increase of dolpines speed, and its mean value is about 10m. Success rate of localization using PLM can be 90%. The combination of PLM and SMD can help to estimate the movement area of marine mammals accurately.

An index of network evolution speed and a network evolution model were put forward to analyze the effects of network evolution speed on propagation. The definition of temporal correlation coefficient was modified to characterize the speed of the network evolution; meanwhile, a non-Markov model of temporal networks was proposed. For every active node at a time step, a random node from network was selected with probability r, while a random node from former neighbors of the active node was selected with probability 1-r. Edges were created between the active node and its corresponding selected nodes. The simulation results confirm that there is a monotone increasing relationship between the network model parameter r and the network evolution speed; meanwhile, the greater the value of r, the greater the scope of the spread on network becomes. These mean that the temporal networks with high evolution speed are conducive to the spread on networks. More specifically, the rapidly changing network topology is conducive to the rapid spread of information, but not conducive to the suppression of virus propagation.

Software tamper resistance is an important method for software protection. Concerning the control-flow tampering invoked by buffer overflow as well as some other software attacks, a software tamper-proofing method based on Function-Level Control-Flow (FLCF) monitoring was proposed. This method described the software's normal behaviors by FLCF and instrumented one guard at every entrance of functions by binary rewriting technology. The monitoring module decided whether the software was tampered or not by comparing the running status received from the guards' reports with the expected condition. A prototype system was realized and its performance was analyzed. The experimental results show that this method can effectively detect the control-flow tampering with less overhead and no false positives. It can be easily deployed and transplanted as its implementation does not need source code or any modifications of underlying devices, and system security is strengthened by isolating the monitoring module with the software being protected.

Tri-class classification method based on Support Vector Machine (SVM) is a kind of method for solving multi-class classification problems. Least Square Support Vector Classification-Regression (LSSVCR) was proposed, which considered the effects of all the sample points by using least squares objective function. Even if there were wrongly marked sample points in the training set, the result would not be affected largely by them. LSSVCR was more accurate and faster, and it was efficient for the problems that there are large differences among the number of sample points in different classes. The numerical experiments show that the proposed method raises the accuracy by 2.57% on average compared to the existing tri-classification methods.

Based on the access logs of network users, Feature Weighting Naive Bayes Classification(FWNBC) algorithm is used to identify users. Firstly, the data acquisition system based on WinPcap framework was used to collect the access logs of network users, characteristics are counted from five aspects by analyzing these access logs, and then selected after filtering, at last the FWNBC algorithm is used to identify the 3300 samples, and the recognition rate reached 85.73%.The experiment results show that this algorithm is effective to identify the identity of network users.

Concerning that the lesion areas of leukoaraiosis in Magnetic Resonance (MR) image present hyper intense signal on T ₂ flair sequence, a level set segmentation method based on C-V model was proposed. First, the C-V model was improved to avoid the re-initialization; second, the Otsu threshold method was used for image's pre-segmentation, and then the image's pre-segmentation result was directly used as the initial contour for the improved C-V model; finally, the segmentation result was obtained by curve evolution. The results show that the proposed segmentation method can get better separation effects, and realize fast auto-segmentation. It has certain application value for clinical diagnosis and prognosis on leukoaraiosis.

Concerning the drawbacks of traditional PCI frame grabber, using Altera's DE2 development platform, image acquisition and VGA display system of programmable logic chip based on Field-Programmable Gate Array (FPGA) were designed. This system used the programmable logic chip FPGA which was in-built into soft-core NiosⅡ as the controller. The FPGA has image sensor, digital memory, video D/A converter and VGA display interface as its accessories. System used System On a Programmable Chip (SOPC) technology to obtain control and coding over FPGA and its accessories and eventually to acquire, process and display the real-time images. The design results prove that, the electronic system based on SOPC technique is flexible and efficient in terms of designing, ported strong, easy to achieve high-speed data acquisition, and it has high compatibility.

Aiming at the lack of the function of 3D Modeling of ComGIS,the idea of 3D Modeling based on ComGIS and CAD’ Integration was put forward,and an integration method of 3D Modeling was introduced.The idea was that GIS data’ import,3D Modeling and raster picture of 3D perspective drawing were realized by Visual LISP programming of AutoCAD,the command of WIN32 API ran AutoCAD in background,and Command Scripts realized loading and running of Visual LISP,and at last,GIS displayed 3D Model.

 Automatic interpretation of synthetic aperture radar (SAR) images was one of the most important application fields in image processing. Focusing on the medium resolution SAR images and combining with the previous algorithms, a novel technique to detecting ship targets from coastal regions based on fuzzy theory was proposed. In this new method, the input image was first processed with improved fuzzy enhancement algorithm so as to alter the characteristics of the gray-level distribution; then, with the threshold method, the sea and land regions could be separated; following that, the maximum entropic algorithm was employed to further process the image and regions of interest which contained candidate ship targets could be extracted; Then ROIs were segmented and the features of ship targets were extracted. Finally the ship targets could be detected based on fuzzy reasoning technique.

Combined with fast convergence and high accuracy, a hybrid intelligent algorithm was proprosed to solve the parameter calibration of the nonlinear system. The results that computed by the improved least-square algorithm became the the original values of the genetic algorithm where gene range could be dynamically changed. The experiment shows that the hybrid algorithm is valid in practical application.

Aimed at the inter-block dependency, an image classification algorithm based on a two hidden Markov model(2DHMM) extension from the one dimensional HMM was developed. The 2DHMM has transition probabilities conditioned on the states of neighboring blocks from both directions. Thus, the dependency in two dimensions can be reflected simultaneously. The HMM parameters were estimated by the EM algorithm. A two dimensional version of the Viterbi algorithm was also developed to classify optimally an image based on the trained HMM. Application of the HMM algorithm to document image shows that the algorithm performs better than CART.

Fuzzy K-Prototypes(FKP) algorithm integrating K-Means and K-Modes algorithm is suited for clustering mixed numeric and categorical valued data. The use of fuzzy techniques makes it robust against noise and missing values in the databases. But, it is an open problem how to select an appropriate weighting exponent α when run FCM(Fuzzy C-Means algorithm) or FKP. Some researchers have suggested that the best choice for α in FCM be probably in the interval \ based on their experimental results. In this paper, the algorithm for searching suitable α in FKP was presented. The experimental results on several real datasets show that the valid clustering can be achieved when α is under 1.5.