In order to reduce the dependence on the number of labeled images, a novel semi-supervised learning method was proposed for automatic segmentation of nuclei. Firstly, a novel Convolutional Neural Network (CNN) was used to extract the cell region from the background. Then, a confidence map for the input image was generated by the discriminator network via applying a full convolutional network. At the same time, the adversarial loss and the standard cross-entropy loss were coupled to improve the performance of the segmentation network. Finally, the labeled images and unlabeled images were combined with the confidence maps to train the segmentation network, so that the segmentation network was able to identify the nuclei in the extracted cell regions. Experimental results on 84 images (1/8 of the total images in the training set were labeled, and the rest were unlabeled) showed that the SEGmentation accuracy measurement (SEG) score of the proposed nuclei segmentation method achieved 77.9% and F1 score of the method was 76.0%, which were better than those of the method when using 670 images (all images in the training set were labeled).

Autonomous Underwater Vehicle (AUV) task planning is the key technology that affects the level of cluster intelligence. In the existing task planning models, only the problem of homogeneous AUV cluster and single dive task planning are considered. Therefore, a multi-dive task planning model for AUV heterogeneous clusters was proposed. Firstly the model considered the energy constraints of AUV, the engineering cost of AUV multiple round-trip charging in mother ship, the efficiency difference between heterogeneous cluster individuals, and the diversity of tasks. Then in order to improve the efficiency of solving the problem model, an optimization algorithm based on discrete particle swarm was proposed. The algorithm introduced matrix coding for describing particle velocity and position and the task loss model for evaluating particle quality to improve the particle updating process, achieving efficient target optimization. Simulation experiments show that the algorithm not only solves the multi-dive task planning problem of heterogeneous AUV clusters, but also reduces the task loss by 11% compared with the task planning model using genetic algorithm.

To solve the problem that the antenna resources in heterogeneous network are limited which leads to the unrealizable Interference Alignment (IA), a partial IA scheme for maximizing the utilization of antenna resources was proposed based on the characteristics of heterogeneous network. Firstly, a system model based on partial connectivity in heterogeneous network was built and the feasibility conditions for entire system to achieve IA were analyzed. Then, based on the heterogeneity of network (the difference between transmitted power and user stability), the users were assigned to different priorities and were distributed with different antenna resources according to their different priorities. Finally, with the goal of maximizing total rate of system and the utilization of antenna resources, a partial IA scheme was proposed, in which the high-priority users had full alignment and low-priority users had the maximum interference removed. In the Matlab simulation experiment where antenna resources are limited, the proposed scheme can increase total system rate by 10% compared with traditional IA algorithm, and the received rate of the high-priority users is 40% higher than that of the low-priority users. The experimental results show that the proposed algorithm can make full use of the limited antenna resources and achieve the maximum total system rate while satisfying the different requirements of users.

Since a large number of Trojans are easily tracable on the Internet, a new Trojan attack scheme based on multimedia document was proposed. Firstly, the Trojan program was embedded into a carrier image as secret data by steganography. After the Trojan program was successfully injected, the encrypted user information was also hidden into the carrier image by steganography. Then the host automatically uploaded pictures to a social network. Finally, the attacker downloaded images from the social network and extracted secret data from images. The theoretical analysis and simulation results show that the proposed JPEG image steganography algorithm has good performance, and the Trojan scheme based on it outperfoms some existing algorithms in concealment, anti-forensics, anti-tracking and penetrating auditing. Such Trojans in social networks can cause user privacy leaks, so some precautions are given at last.

In order to solve the problem that observational image quality and profits are low in satellite scheduling by adopting lagged weather forecast cloud information, a mathematic model capturing real-time cloud distribution was proposed. The Agile Earth Observation Satellite (AEOS) scheduling model was also built based on the real-time cloud information. Considering the local optimization of Greedy Search Algorithm (GSA) and it can give full consideration for constraints such as cloud of satellite observation and limited storage resources, the applications of GSA for the satellite scheduling problem were researched. Firstly, the cloud coverage of observation task was considered in priority order by GSA. The image quality value of observation task was calculated according to the size of cloud coverage and the observation task was selected by the sort of the image quality value. Secondly, the task with the maximize profit was selected according to task size, deadline and satellite storage resource. Finally, satellite observation and task transmission were completed according to their ability of improving profit. The simulation experiments show that, on the case of 100 tasks, the task profit of satellite schedule adopting GSA was improved by 14.82% and 10.32% compared with the Dynamic Programming Algorithm (DPA) and Local Search Algorithm (LSA) respectively. Besides, the image quality of applying GSA is higher than taking DPA and LSA in the same circumstance. The experimental results show that the GSA can effectively improve the image observation quality and task observation profit of satellite scheduling.

To deal with the problems that the routing algorithm based on Collaborative communication of Cluster Parent (CRPL) for Low Power and Lossy Network (LLN) can't balance the energy consumption of the node and maximize the extension of the lifetime for network efficiently due to take no account of the residual energy of the node, a high-efficient routing algorithm based on collaborative communications of cluster parent set HE-CRPL was proposed. The proposed algorithm chiefly carried out three optimization schemes. Firstly, the wireless link quality and the residual energy of node could be considered during the cluster parent selection. Secondly, the wireless link quality and the Expected LifeTime (ELT) of cluster parent node were combined while estimating the priority of the cluster parent node and selecting the optimal cluster parent set. Thirdly, the cluster parent nodes were notified the priority list by Destination Advertisement Object (DAO) message during the initialization of the network topology. The simulation results show that, compared with the CRPL algorithm, the performance of the HE-CRPL algorithm is improved obviously in prolonging the network lifetime, increasing the packet delivery success rate and reducing the number of packet retransmissions, and that the lifetime of network prolonging by more than 18.7% and the number of retransmissions decrease by more than 15.9%.

The transmission energy consumption of nodes does not be considered in the exiting Network Coding Aware Cooperative MAC (NCAC-MAC) protocol for Ad Hoc Network, and the control message sent by the candidate cooperative relay node can not make the other candidate nodes which are not in the communication range give up competition, thus causing collision. To deal with those problems, a high efficiency Medium Access Control (MAC) protocol based on cooperative network coding High Efficiency MAC protocol based on Cooperative Network Coding (HECNC-MAC) was proposed. Three optimization schemes were carried out by the protocol. Firstly, candidate cooperative relay node need to prejudge whether the destionation can decode the packet, so as to reduce the number of competitive relay nodes and ensure that the destination node could be successfully decoded. Secondly, the transmission energy consumption of nodes should be synthetically considered when selecting the cooperative relay node. Finally, the Eager To Help (ETH) message is canceled, and the destination node sents conformation message through pseudo-broadcast. Theoretical analysis and simulation results show that in the comparison experiments with Carrier Sense Multiple Access (CSMA), Phoenix and NCAC-MAC protocols, the transmission energy consumption of nodes and the end-to-end delay of date packages can be effectively reduced, and the network throughput can be improved by HECNC-MAC.

In order to improve the positioning accuracy of mobile robots, a kind of positioning system for wheeled mobile robots based on orthogonal encoder and gyroscope was proposed, and the positioning model and kinematics model of robot were established. With the purpose of obtaining better robustness, Support Vector Regression (SVR) algorithm was studied, the error square of objective function was weighted, and the effect of different parameter optimization algorithms on the accuracy of SVR were analyzed. The experimental platform was established by homemade mobile robot, the Least Squares Support Vector Regression (LSSVR) algorithm and the Weighted Least Squares Support Vector Regression (WLSSVR) algorithm were compared with the improved algorithm. The positioning errors of the improved algorithm when the robot worked on ceramic and wood floor were compared, and the orthogonal encoder plus gyroscope positioning system was compared with the double encoder positioning system and the single encoder plus gyroscope positioning system. The experimental results show that the robot positioning accuracy of the improved algorithm is higher than comparison algorithms, and the proposed positioning system has a better location performance.

Aiming at the noise data problem in collaborative filtering recommendation, a user entropy model was put forward. The user entropy model combined the concept of entropy in the information theory and used the information entropy to measure the content of user information, which filtered the noise data by calculating the entropy of users and getting rid of the users with low entropy. Meanwhile, combining the user entropy model with the item timeliness model, the item timeliness model got the timeliness of item by using the contextual information of the rating data, which alleviated the data sparsity problem in collaborative filtering algorithm. The experimental results show that the proposed algorithm can effectively filter out noise data and improve the recommendation accuracy, its recommendation precision is increased by about 1.1% compared with the basic algorithm.

Focusing on the issue that the noise influences transmission of symbol in the digital communication system, to improve system reliability and reduce Bit Error Rate (BER) of the received signal, a new Stochastic Resonance (SR) system based on the optimal match and parallel array theory was proposed. Firstly, the parallel array theory was used to improve the stochastic resonance effect of single bistable system. Secondly, the method of optimal match for SR was also used in array systems for weak signal detection. Finally, an analytical expression of the Signal-to-Noise Ratio (SNR) gain in the optimal matching stochastic resonance bistable system was derived; furthermore, the influence of array number on the BER was analyzed. In the comparison experiments with single stochastic resonance system, the performance of the optimal matching array stochastic resonance system on the detection of weak signal in strong noise background was improved, the SNR gain of the output signal of the system was significantly higher than 1, and the BER was also significantly reduced; in addition, SR performed better while the number of units of stochastic resonance system increased. The theoretical analysis and simulation results show that the optimal matching array stochastic resonance system can effectively improve the reliability of the digital communication system in practical engineering.

Signcryption is a cryptography primitive which can provide message confidentiality and sender authentication in a single logic step. In order to improve computational efficiency of certificateless aggregate signcryption scheme based on bilinear pairings, a new CertificateLess Aggregate SignCryption (CLASC) scheme based on bilinear pairings was proposed. In the proposed scheme, any user could be allowed to be an aggregator to initiate the signcryption protocol. After signcryption ciphertexts were generated by users, the ciphertexts were sent to the aggregator for aggregating to be one single ciphertext. The scheme was proved to be existentially unforgeable and confidential under the random oracle model through security analysis. The comparison results show that the proposed scheme only requires pairings operation only once for each signcryption user, which can improve the computational efficiency and can be applied to applications with high real-time requirements.

In practical application, Modified Gain Extended Kalman Filter (MGEKF) algorithm generally uses erroneous measured values instead of the real values for calculation, so the modified results also contain errors. To solve this problem, an improved MGEKF algorithm based on Back Propagation Neural Network (BPNN), termed BPNN-MGEKF algorithm, was proposed in this paper. At BPNN training time, measured values were used as the input, and modified results by true values as the output. BPNN-MGEKF was applied to single moving station bearing-only position experiment. The experimental results shows that, BPNN-MGEKF improves the positioning accuracy of more than 10% compared to extended Kalman filter, MGEKF and smoothing modified gain extended Kalman filter algorithm, and it is more stable.

To solve the problems that the data stream outliers can not be disposed well, the efficiency of clustering data stream is low and the dynamic changes of data stream can not be real-time detected, an evolutionary data stream clustering algorithm based on integration of affinity propagation and density (I-APDenStream)was proposed. The traditional two-stage processing model was used in this algorithm, namely online and offline clustering. Not only the decay density of micro-cluster which could represent the dynamic changes of data stream and deletion mechanism for online dynamic maintenance of micro-cluster were introduced, but also the outliers' detection and simplification mechanism for model reconstruction by using the extended Weight Affinity Propagation (WAP) cluster was introduced. The experimental results on two types of data sets demonstrate that the cluster accuracy of the proposed algorithm remains at above 95%, and also achieves considerable improvements with respect to the purity compared to other algorithms. The proposed algorithm can cluster the data stream with high real-time, high quality and high efficiency.

Concerning that the existing visibility estimation methods based on region growing method has shortcomings of low precision and high computational complexity, a new algorithm was proposed to measure the visibility based on Inflection Point Line (IPL). Firstly, the three characteristics including anisotropy, continuity and level of inflection point line were analyzed. Secondly, a new 2-D filter to detect the IPL based on the three characteristics was proposed to improve the accuracy and speed of the inflection point detection. Finally, the visibility of fog weather could be calculated through combing the visibility model and detection results of the proposed filter. Compared with the visibility estimation algorithm based on region growing, the proposed algorithm decreased the time cost by 80% and detection error by 12.2%, respectively. The experimental results demonstrate that the proposed algorithm can effectively improve the detection accuracy, meanwhile reducing the computational complexity of positioning inflection points.

The emergence of RAMCloud has improved user experience of Online Data-Intensive (OLDI) applications. However, its energy consumption is higher than traditional cloud data centers. An energy-efficient strategy for disks under this architecture was put forward to solve this problem. Firstly, the fitness function and roulette wheel selection which belong to genetic algorithm were introduced to choose those energy-saving disks to implement persistent data backup; secondly, reasonable buffer size was needed to extend average continuous idle time of disks, so that some of them could be put into standby during their idle time. The simulation experimental results show that the proposed strategy can effectively save energy by about 12.69% in a given RAMCloud system with 50 servers. The buffer size has double impacts on energy-saving effect and data availability, which must be weighed.

Aiming at the problems that the traditional Support Vector Machine (SVM) classifier is sensitive to outliers and has the large number of Support Vectors (SV) and the parameter of its separating hyperplane is not sparse, the Truncated hinge loss SVM with Smoothly Clipped Absolute Deviation (SCAD) penalty (SCAD-TSVM) was put forward and was used for constructing the financial early-warning model. At the same time, an iterative updating algorithm was proposed to solve the SCAD-TSVM model. Experiments were implemented on the financial data of A-share manufacturing listed companies of the Shanghai and Shenzhen stock markets. Compared to the T-2 and T-3 models constructed by SVM with L1 norm penalty (L1-SVM), SVM with SCAD penalty (SCAD-SVM) and Truncated hinge loss SVM (TSVM), the T-2 and T-3 model constructed by the SCAD-TSVM had the best sparseness and the highest accuracy of prediction, and its average accuracies of prediction with different number of training samples were higher than those of the L1-SVM, SCAD-SVM and TSVM algorithms.

RAMCloud stores data using log segment structure. When large amount of small files store in RAMCloud, each small file occupies a whole segment, so it may leads to much fragments inside the segments and low memory utilization. In order to solve the small file problem, a strategy based on file classification was proposed to optimize the storage of small files. Firstly, small files were classified into three categories including structural related, logical related and independent files. Before uploading, merging algorithm and grouping algorithm were used to deal with these files respectively. The experiment demonstrates that compared with non-optimized RAMCloud, the proposed strategy can improve memory utilization.

Formal approaches are construction methods with accurate mathematical semantics, which are based on strict mathematical proofs. Generally, Petri nets are considered as a class of computation models to model the concurrent behavior. Also, formal specifications and analysis of a system can be conveniently developed by Petri nets. However, it is difficult to model a train control system with prototype Petri nets. The difficulties can be solved by extended Petri nets with inhibitor arcs. Hence, some key problems of train control systems were modeled and analyzed by the computation models of extended Petri nets in this paper. Two control sub-systems, station management sub-system and interval operation sub-system. were proposed. The former performed the entering and leaving of trains from stations by cooperative control. The later executed the safety control of block regions in stations, the safety recovery of emergency situations such as lightning stroke and the loss of signals, and the management of railway crossings. Finally, the activity, reachability, and boundedness of the proposed models were analyzed by S-invariants.

The Firefly Algorithm (FA) has a few disadvantages in the global searching, including slow convergence speed, low solving precision and high possibility of being trapped in local optimum. A FA based on chaotic dynamic population was proposed. Firstly, chaotic sequence generated by cube map was used to initiate individual position, which strengthened the diversity of global searching; secondly, through dynamic monitoring of population, whenever the algorithm meets the preset condition, the new population individuals were generated using chaotic sequences, thus effectively improving convergence speed; thirdly, a Gaussian disturbance would be given on the global optimum of each generation, thus the algorithm could effectively jump out of local minima. Based on six complex test functions, the test results show that chaos-based dynamic population FA improves the capacity of global searching optimal solution, convergence speed and computational precision of solution.

Delay Tolerant Network (DTN) can modify frequent network disruption and segmentation in mobile Internet access. Its core technology includes routing resource allocation. However, the existed knowledge oracles of DTN routing algorithms are time probabilistic uncertainty in public transport means mobile network or logistics, which reduces the efficiency of resource allocation. So it was proposed that general k-anycast allocates bandwidth resources to k eligible access routers in access period, which diversify the time deviation degrees and decrease the uncertainty. And access router information matrixes decide general k-anycast router aggregation. Therefore packets can be transmitted simultaneously to multiple destinations. The probabilistic uncertain utility model was further proposed for routing resource allocation based on DTN custody transfer. Simulations show that its transmission performance and robustness are better than Multicast DTN routing algorithm.

The navigation accuracy can not be tested through real experiments during the process of underwater robot design. How to forecast the Circular Error Probability (CEP) becomes an urgent problem. Therefore, linear track was used to test navigation accuracy. The sources of navigation error in linear track were presented. The expressions of errors in x-axis and y-axis were deduced. The deduced result shows that the CEP is smaller than the larger error in x-axis and y-axis if the ratio of errors is below 0.7.The CEP iterative equation was established. The results of real experiments indicate that the forecasted CEP is in accordance with actual experimental results. And the proposed method can effectively forecast the navigation accuracy of underwater robot.

The precise near field model of sound transmission was deduced. Generalized Cross Correlation (GCC) algorithm based on Phase Transform (PHAT) was adopted to estimate time-delay, and a time-delay selection algorithm based on data relation was also put forward. Then the time difference of arrival algorithm was used for sound source localization, by which the development platform was built based on the DSP auditory localization hardware system. The results show that the error of the angle estimate is less than 3° in near field model of sound transmission．

Concerning the accurate positioning of Optical Mark Recognition (OMR) images without any position information, an image segmentation approach of mutation signal detection based on wavelet transformation was proposed. Firstly, the horizontal and vertical projective operations were processed, and then these functions were transformed by wavelet to detect mutation points, which can better reflect the boundary of OMR information. This algorithms adaptability is based on limited times of wavelet transform and mutation signal detection. The experimental results demonstrate that the method possesses high accuracy of segmentation and stability, and the mean square error of segmentation accuracy can be 0.4167 pixels. The processing of this method is efficient because the segmentation only used the horizontal and vertical information. This algorithm is not sensitive to noise because of the statistic characteristic of projection functions and multi-resolution characteristic of wavelet tranformation.

For solving combinational optimization problems, a Binary Harmony Search Algorithm (BHSA) based on three discrete operators of Harmony Search Algorithm （HSA）was proposed. Then, BHSA was used to solve the famous k-SAT problem and 0-1 knapsack problem. The numeral results of BHSA, Binary Particle Swarm Optimization （BPSO） and Genetic Algorithm (GA) show that the BHSA is feasible and highly efficient.

Passing-by passenger flow is the basis of rail transit passenger analysis. Based on the bus Origin-Destination (OD) matrix estimation model, attraction weight coefficient method was introduced into passenger flow research in rail transit no-barrier transfer mode. Using passenger entry and exit data from Automatic Fare Collection (AFC) system as input, a new passenger flow analysis model was presented. Passengers transfer times and passing-by stations (distance factor) were considered in this passenger distribution method. The case indicates that it can increase the accuracy to consider the range of passenger flow peak.

Concerning the copyright protection of binary document images, a zero watermark algorithm was proposed. This algorithm constructed the texture image based on Local Binary Pattern (LBP), and then zero watermark information was constructed from the texture spectral histograms of the texture image. This method had a better invisibility compared to other text image watermarking, and the original image information would not be changed. Watermark attacks including image cropping, adding noise and rotation operators were tested. The experimental results show that the proposed zero watermark algorithm has a good performance in robustness. And these attack operators have little impact on zero watermark information, and the algorithm is of stability with the lowest standard correlation above 0.85.

The application-layer multicast in end-system has overwhelming advantages compared with network-layer multicast. For improving the efficiency and performance of application-layer multicast, a multicast algorithm based on maximum interference network coding was presented. After adopting network coding, the new algorithm selected the maximum interference paths from source to every destination to improve coding efficiency and save network bandwidth. The simulations show that compared with non-coding multicast and simple network coding multicast algorithm, the new algorithm performs better in network throughout put and resource utilization.

The new method proposed by Lee et al. (LEE C F, CHEN H L. A novel data hiding scheme based on modulus function. The Journal of Systems and Software, 2010, 83(5): 832-843) was based on modulus function. In this method, each pixel can carry a maximum of 4 bits with an acceptable visual quality. When each pixel is embedded with 4 bits, the quality of stegoimages is much worse; as a result, this may catch the attention of attackers. Consequently, this paper improved the method, which narrowed the range that pixels changed. The theoretical analysis and simulation results show that the new method not only keeps the various advantages of original method, but also makes the Peak Signal-toNoise Ratio (PSNR) value increase 1.5~3.5 dB. Thus, it can raise imperceptibility and the ability resisting RS attack of stego-images.

The reduction and storage of enormous point cloud data is a crucial link in reverse model reconstruction. Considering the features of point cloud data by single station laser scanning, a new method — grid sector method was put forward for its reduction and storage. Point cloud data could be filtered and stored only by traversal. This method was realized on VC++ 6.0. Multi-station scanning of point cloud registration and stitching would be more quickly and efficiently, if the site goes through the fan in a single grid after treatment. Based on the contrast with traditional compressing methods, this paper analyzed its characteristics and proved its applicability in battlefield terrain digitization.

Multiprocessor scheduling algorithm is the key in the embedded real-time systems. According to the multiprocessor features, a new dynamic parallel scheduling algorithm of real-time multiprocessor, named Split-Parallel (SPara), was proposed. The algorithm solved the problem that the previous algorithms, such as Myopic, EDPF, only judge by the deadline to schedule the tasks, and it was also developed by adding the restriction of the urgency and an effective method as the task with long execution time and tight deadline. Furthermore, the multiprocessor scheduling algorithm which combined the theory of high-level coloured time Petri net was analyzed by modeling, and according to the model, an example of SPara algothrim was simulated and tested. The experimental results show that SPara performances are much better than the other algorithms like Myopic in processor utilization and scheduling success ratio.