It is difficult to identify video images with low contrast in a low-light environment, so an adaptive contrast compensation enhancement algorithm was proposed. First, the average gray of video image feature parameters in the low-light environment was extracted, then a mathematical model of human visual contrast resolution compensation based on the grayscale difference of original images was established, and the true color three-primary colors were compensated respectively by proportional integration. Second, the compensation threshold was set to linearly compensate photopic vision until full bandwidth when the compensation degree was lower than the just noticeable difference of photopic vision. Finally, the automatic optimization model of compensation proportion coefficient was established based on the subjective image quality evaluation and image feature parameters, which was embedded in DirectShow video processing system for video image adaptive enhancement. Experimental results show that the video enhancement system has good real-time performance, which can mine scotopic vision information effectively and be widely applied in different scenes.

Energy efficiency of Heterogeneous cellular Network(HetNet)has attracted wide attention in recent years. However,researches on the energy efficiency of macro base stations using non-Poisson process are not enough. To solve this problem,the energy efficiency of the two-tier HetNet was investigated,in which the deployment of macro base stations was modeled by β-Ginibre Point Process( β-GPP). Firstly,a simple approximation method was used to analyze the Signal to Interference Ratio(SIR)distribution in two-tier HetNet,then the coverage probability,the average achievable throughput and the energy efficiency of the system were derived. Finally,an energy efficiency optimization algorithm was proposed to find the optimal transmitting power of pico base station,maximizing the energy efficiency. The simulation results show that when β=1,the distribution density of macro base station is 2×10 ^-4 m ^-2,and the distribution density of pico base station is 2 times of that of macro base station,the proposed energy efficiency optimization scheme can improve the system energy efficiency by about 20%. The experimental results verify the accuracy of the theoretical analysis and the effectiveness of the proposed energy efficiency optimization algorithm.

To detect surface object information quickly and accurately by using remote sensing images is a current research hot spot. In order to solve the problems of inefficiency of the traditional manual visual interpretation segmentation method as well as the low accuracy and a lot of background noise of the existing remote sensing image segmentation based on deep learning in complex scenes, an image segmentation algorithm based on improved U-net network architecture and fully connected conditional random field was proposed. Firstly, a new network model was constructed by integrating VGG16 and U-net to effectively extract the features of remote sensing images with highly complex background. Then, by selecting the appropriate activation function and convolution method, the image segmentation accuracy was improved while the model prediction time was significantly reduced. Finally, on the basis of guaranteeing the segmentation accuracy, the segmentation result was further improved by using fully connected conditional random field. The simulation test on the standard dataset Potsdam provided by ISPRS showed that the accuracy, recall and the Mean Intersection over Union (MIoU) of the proposed algorithm were increased by 15.06 percentage points, 29.11 percentage points and 0.3662 respectively, and the Mean Absolute Error (MAE) of the algorithm was reduced by 0.02892 compared with those of U-net. Experimental results verify that the proposed algorithm is an effective and robust algorithm for extracting surface objects from remote sensing images.

To solve the problem of low Energy Efficiency (EE) of relay assisted cellular networks where the Macro Base Station (MBS) was equipped with a single-antenna, the downlink transmission of multi-antenna relay-assisted cellular networks were considered, meanwhile, a strategic sleep scheme was proposed. Firstly, according to whether the number of users serviced by the relay exceeds a given threshold, the relay's working mode was dynamically adjusted. And then the coverage probabilities and mean achievable rates of MBS to user (UE), MBS to Relay Station (RS), RS to UE links were deduced. Finally, the energy efficiency of the system was derived based on the power consumption per unit area and the reachable rate per unit area. The simulation results show that when the density of MBS is 2×10 ^-5m ^-2, the energy efficiency of the multi-antenna network with strategic sleep scheme is about 5.6% higher than that of the cellular network with non-sleeping strategy; the system energy efficiency of MBS equipped with multiple antennas is 30% high than without sleep strategy scheme with single antenna. Experimental results indicate that the multi-antenna relay-assisted cellular network with sleep strategy scheme has higher energy efficiency than the single-antenna relay-assisted cellular network.

Aiming at the problems of limited hiding capacity and obvious increasing bit rate in the existing hiding solutions, an intra-frame video information hiding algorithm based on diamond coding was proposed. Firstly, based on High Efficiency Video Coding (HEVC), two prediction models of adjacent 4×4 blocks were combined into a pattern pair, then the improved diamond coding algorithm was used to guide pattern modulation and information embedding. Next, the embedding coding for hidden informtion was done for second time with keeping the optimal coding division, thus ensuring the embedding quantity and eliminating intra frame distortion drift. The experimental results show that the Peak Signal-to-Noise Ratio (PSNR) is reduced by less than 0.03dB and the bit rate is increased by less than 0.53% by using the proposed algorithm, while the embedding capacity is greatly improved, and both the subjective and objective qualities of the video are well guaranteed.

In the dynamic spectrum access network, how to utilize the spectrum resource efficiently is a vital problem to be solved. Presently, the main study focuses on the scenario that spectrum providers lease spectrum to users for spectrum access. Considering the issue that the user's actual payment capacity and Quality of Service (QoS) demands were different, a joint budget and power control scheme was proposed, the interactions among spectrum providers and users were investigated as a two-stage Stackelberg game model. Based on the budget and QoS demands, providers dynamically adjusted spectrum leasing prices, and users adjusted their demands according to the prices. Finally, the existence and uniqueness of Nash Equilibrium (NE) of maximum profit of users and providers was proved. Simulation results show that the sum of budget of users is the upper bound of the whole network payoff, users and providers can get the optimal profit and achieve the Nash Equilibrium (NE).

The analysis of sentiment-aspect for product or service is useful for finding the information of sentiment-aspect from the mess of comment set. This paper proposed a new method of sentiment-aspect based on seed words of aspect. Firstly, seed words of aspect and documents of aspect automatically could be achieved by this method. Secondly, Sentiment-Aspect Analysis model Supervised by Seed Words (SAA_SSW) was employed by this method to find aspect and related sentiment. The experimental results show that, compared with traditional Joint Sentiment/Topic Model (JST) and Aspect and Sentiment Unification Model (ASUM), SAA_SSW can find the sentiment labels for same word under different topics and achieve higher relevance between sentiment word and topic. In addition, SAA_SSW model, compared with traditional JST and ASUM model, can improve the classification accuracy by at least 7.5%. So, SAA_SSW model can achieve the extraction of sentiment-aspect well and improve the classification accuracy.

Author Topic (AT) model is widely used to find the author's interests in scientific literature, but AT model cannot take advantage of the correlation between category labels and topics. Through integrating the inherent category labels of documents into AT model, Labeled Author Topic (LAT) model was proposed. LAT model realized the predicate of multi-labels by optimizing the mapping relation between labels and topics and improved the clustering results. The experimental results suggest that, compared with Latent Dirichlet Allocation (LDA) model and AT model, LAT model can improve the decision accuracy of multi-labels, and optimize the generalization ability and operating efficiency.

Train tracking problem under moving block condition was studied according to the characteristics of CTCS-4 train operation. A Multi-Agent System (MAS) model of multi-train tracking under moving block condition was built based on multi-Agent theory and the interaction mechanism between the train and the Radio Block Center (RBC) was proposed. The model realized the real-time vehicle ground communication and safe distance control of multi-train tracking. The influences of the speed change in train tracking process and different line initialization density on line traffic were simulated, and corresponding quantitative analysis conclusions were obtained. The simulation results show that the proposed MAS model can realize Complex System (CS) formal description of the train control system in the static and dynamic environment, and accurately describe CTCS-4 train tracking mechanism. The example evaluation results prove the feasibility and effectiveness of the proposed method.

To effectively capture the dynamic information of the gait and accelerate the authentication and identification, a novel gait recognition algorithm was presented in this paper, which employed the row mass vector of the Frame Difference Energy Image (FDEI) as the gait features. The gait contour images were extracted through the object detection, binarization, morphological process and connectivity analysis of the original images. Using the width of the contour images sequence, the quasi-periodicity analysis and the row mass vector of the frame difference image were obtained, then the Continuous Hidden Markov Model (CHMM) was employed to train and recognize the parameters of model. The proposed algorithm was applied to Central Asia Student International Academic (CASIA) gait database. The experimental results show that it can easily extract the features of the gait with low dimension, achieving fast recognition speed and high recognition rate, so it can be used for real-time gait recognition.

To evaluate the operational efficiency and emergency strategies of the trail transit under different passenger flow conditions, also simulate and analyze the emergency strategies quantitatively, a simulation platform for urban rail transit was proposed. This system modeled four main objects that consisted of the kinetic model of train, the Automatic Train Control (ATC), the trackside equipment and the moving block system. On this basis, the whole simulation system was designed and implemented based on VC〖KG-*3〗+〖KG-*3〗+ development platform combined with computer network and database technology. Finally, the operation of the train was able to be automatically implemented on this simulation platform driven by the train timetable. The system was assessed by using the data of the rail transit of Shanghai 8th line and the simulation results show good consistency with the real timetable.

With the rapid development of Chinese railway, requirements for running safety of trains are more high. This paper used the Event-B formal modeling approach to research on the high-speed train safety distance control. With the support of simulation tool Rodin, combined with the multi-Agent theory, the safety distance control model of multi-train operation was constructed. The simulation researched the modeling and verification on the minimum interval tracking control for high speed train. The simulation results show that, the binding of formal verification methods of Event-B and Multi-Agent System (MAS) is meaningful. So the method has some practical significance for the modeling and verification of complex system.

Since the isotropic diffusion will easily blur edge features,and coherence-enhancing diffusion will produce pseudo striations in the background regions during the denoising process, a weighted diffusion algorithm was proposed to reduce the Rician noise of Magnetic Resonance Imaging (MRI) image according to the distribution of noise. A threshold value was calculated by the Rician noise variance in the background region of MRI image, which might be used to distinguish the image background and the edge of Region-Of-Interest (ROI). A weighting function combining the isotropic diffusion and the coherence-enhancing diffusion based on the calculated value was constructed. The constructed function could adaptively adjust the weight values of two kinds of diffusion in different structural regions in order to give full play to the advantages while overcoming the disadvantages of the above two kinds of diffusion.The experimental results show that it is better than some classical diffusion algorithms in Peak Signal-to-Noise Ratio (PSNR) and Mean Structural Similarity(MSSIM).Thus, it has better performance on noise reduction and edge preservation or enhancement.

In order to solve the problems that the semi-supervised classification method based on Laplacian Support Vector Machines (LapSVM) requires that all unlabeled sample should be added to the training set to train a classifier, the algorithm demands high time and space and cannot effectively deal with large-scale image classification, Fuzzy C-Mean (FCM) pre-selected sample of LapSVM image classification method was proposed. The method used FCM algorithm for clustering the unlabeled samples. According to the clustering results, unlabeled samples of near optimal separating hyper-plane were selected to add to the training sample set, and these samples may be support vector carrying useful information for classification. The quantity was only a small part of the unlabeled samples, so the training sample set was reduced. The simulation results show that this method takes advantage of the inherent discrimination information of the unlabeled samples, effectively improves the accuracy of classifiers, and reduces the algorithm's time and space complexity.

In order to deal with the problems that defensive measures are lagging behind the attack and that the payoffs of attacker and defender are unequal, an active strategy selection method based on non-zero-sum game was proposed. Firstly, a network security game graph was presented combined with the actual situation of network security and the relationship between the attacker and the defender. Secondly, a network attack-defense game model was proposed based on non-zero-sum game. The attack-defense cost of single security attribute was calculated combined with the host important degree and success rate of defense measures, and according to attack-defense intention, the total attack-defense cost was quantified. Finally, the best strategy for defender was obtained by analyzing the Nash equilibrium of the game model. A representative example was given to illustrate the efficacy and feasibility of the method on attack prediction and active defense strategy selection.

Based on the cellular automata theory, for the safe evacuation of the inside passengers of the high-speed rail, the authors proposed a method, which considered the personnel individual differences and adjusted their own behavior dynamically to model the process of evacuation in the case of multi-speed, and researched on computer simulation. The experimental results show that, according to individual differences in the distribution, the strong-ordinary-weak distribution in evacuation has two faster time steps than weak-ordinary-strong. Meanwhile, the disaster points appear at different locations inside, and the evacuation time is quite different. The simulation in the case of different personnel distribution and disaster points has more realistic simulation of emergency evacuation and evacuation situation, and this provides the theoretical guidance for the safe evacuation problem.

To solve permutation flowshop scheduling problem, an improved particle swarm optimization was proposed. Improved algorithm introduced a method to judge the premature state of the particle swarm, and used reversion strategy to mutate the best particle after the particle swarm being trapped in premature convergence, and used simulated annealing method to accept the new particle. The mutation for best particle can guide the particle swarm to escape from the local best values limit and overcome the particles premature stagnation. The simulation results based on Car and Rec benchmarks of permutation flowshop scheduling problem prove the effectiveness of the proposed algorithm.

Transportation energy consumption has aroused high attention of the decision-makers. In this case, an energy consumption of Bus Rapid Transit (BRT) Cellular Automata (CA) model was designed, which was based on NaSch traffic model and Kinetic energy theorem. Taking Lanzhou city bus rapid transit traffic system as an example, the effects of BRT vehicle random slowness in the different traffic density, different road condition and drivers' behavior under periodic boundary conditions was studied. And corresponding quantitative analysis conclusion was get. The simulation results show that the rapid transit vehicle stops longer, the greater the range of congestion and the smaller the energy loss of the road traffic flow. Besides, the corresponding flow rate is also smaller, thus the system is plugged into congestion phase earlier.

Based on the characteristics of high-speed maglev train line structure and operation control system, this paper set up a model for the high-speed maglev train diagram of double-track lines, and put forward the knitting algorithm to resolve the model. The algorithm drew the up and down maglev train running lines in chronological order of trains’ departure time from stations, which tended to do some knitting. When meeting the conflict, the algorithm resolved it by changing the station tracks or increasing the departure time intervals, at last it gained global optimization maglev train diagram step by step. A numerical example shows that the method can draw the maglev train diagram quickly and effectively.

In view of the characteristics of cable channel, a joint design of adaptive equalizer and carrier recovery loop for high order QAM signals was proposed, which was suitable for ITU-T J.83 standard. The equalizer initially operated in Constant Modulus Algorithm (CMA) mode to compensate the channel distortion. When the eye diagram was opened, the equalizer switched to Least Mean Square (LMS) mode to decrease the Mean Square Error (MSE). For high order QAM signals, the carrier recovery loop initially operated in Polarity Decision Algorithm (PDA) under fast and slow modes which were set in Loop Filter (LPF) to capture the frequency offset. When the frequency offset was captured, the loop switched to Decision Direct Algorithm (DDA) to decrease the phase jitter. The simulation studies show the good performances of this design. The whole design was synthesized under Altera Stratix Ⅱ EP2S130F1020C5 FPGA. Highest clock frequency after being routed reached as high as 90.47MHz.

The paper analyzed the difference of operation control system between railway train and maglev train. Based on the construction of the train working diagram of railways, the restriction model and algorithm of maglev train working diagram were put forward. The paper used the "station-time" matrix to check the train operation conflict and applied the iterative repair method to remove the conflicts; at last, the system got the feasible train working diagram. The computational example indicates that the proposed algorithm is able to work out the working diagram schemes of maglev effectively, and it has higher effectiveness and practicability.

 Aimed at image watermark robustness, an evaluative method based on synergetic order parameter and its usage was first advanced. Many kinds of robust experiment of Image carrier were done. After procession, the synergetic original order parameter of patterns in the set were calculated and compared. Through validation by the order parameter evolvement curves, their robust conclusions were gotten. This method is more convenient and applied than the correlative coefficient method, although the two methods have same conclusion.

Personal Digital Assistants and Personal Computers often utilize synchronization protocols in order to maintain data consistency. These protocols operate in environments where network resources such as bandwidth, memory, and processing power are limited. In this paper a novel PDA synchronization scheme(CPISync) predicated upon recent information-theoretic research was introduced. The characteristic of this scheme was that its communication complexity depended on the number of differences between the PDA and PC. Moreover, experiments showed that the computational complexity and energy consumption of CPISync were practical and that the overall latency was typically much smaller, which played an important role in their scalability. Thus, CPISync had a potential of significantly improving synchronization efficiency for PDAs on large, heterogeneous, wireless networks.