Feature selection can improve the performance of data classification effectively. In order to further improve the solving ability of Ant Colony Optimization (ACO) on feature selection, a hybrid Ant colony optimization with Brain storm Optimization (ABO) algorithm was proposed. In the algorithm, the information communication archive was used to maintain the historical better solutions, and a longest time first method based on relaxation factor was adopted to update archive dynamically. When the global optimal solution of ACO was not updated for several times, a route-idea transformation operator based on Fuch chaotic map was used to transform the route solutions in the archive to the idea solutions. With the obtained solutions as initial population, the Brain Storm Optimization (BSO) was adopted to search for better solutions in wider space. On six typical binary datasets, experiments were conducted to analyze the sensibility of parameters of the proposed algorithm, and the algorithm was compared to three typical evolutionary algorithms:Hybrid Firefly and Particle Swarm Optimization (HFPSO) algorithm, Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA) and Genetic Algorithm (GA). Experimental results show that compared with the comparison algorithms, the proposed algorithm can improve the classification accuracy by at least 2.88% to 5.35%, and the F1-measure by at least 0.02 to 0.05, which verify the effectiveness and superiority of the proposed algorithm.

Aiming at the problem that the scale of integrated circuits and the number of on-chip registers are increasing, which makes the verification more difficult, a lightweight register model was proposed. Firstly, a concise underlying structure was designed, and parameterized settings were combined to reduce the memory consumption of the register model at runtime. Then the register verification requirements at different levels such as module level and system level were analyzed, and SystemVerilog language was used to implement various functions required for verification. Finally, the built-in test cases and register model automatic generation tools were developed to reduce the setup time of the verification environment in which the register model was located. The experimental results show that the proposed register model is 21.65% of the Universal Verification Methodology (UVM) register model in term of memory consumption at runtime; in term of function, the proposed register model can be applied to traditional UVM verification environments and non-UVM verification environments, and the functions such as read-write property, reset value and backdoor access path of 25 types of registers are checked. This lightweight register model has good universality and flexibility in engineering practice, meets the needs of register verification, and can effectively improve the efficiency of register verification.

To inpaint the image with large damaged region and complex structure texture, a new method based on neighborhood reference priority which can not only maintain image character but also improve inpainting speed was proposed, by which the problem of image inpainting was translated into the best sample searching process. Firstly,the structure information of target image was extracted, and the sample region was divided into several sub-regions to reduce the sample size and the search scope. Secondly, in order to solve the problem that Sum of Squares of Deviations (SSD) method ignores the matching of structure information, structure symmetry matching constraint was introduced into matching method, which effectively avoided wrong matches and improves sample matching precision and searching efficiency. Then, priority formulas which highlights the effect of structure was obtained by introducing structure weight and confidence and combining the traditional priority calculation. Finally,the priority of four-neighborhood was got by computing overlapping information between target block and neighborhood blocks patches, according to the reliable reference information provided by four-neighborhood and the improved block matching method, the samples were pruned and the optimal sample was retrieved. The inpainting was completed until all the the optimal samples for all the target blocks were retrieved. The experimental results demonstrate that the proposed method can overcome the problems like texture blurring and structure dislocations and so on, the Peak Signal-to-Noise Ratio (PSNR) of the improved algorithm is increased by 0.5 dB to 1 dB compared with the contrast methods with speeding up inpainting process, the recovered image is much continuous for human vision. Meanwhile, it can effectively recover common damaged images and is more pervasive.

When inpainting the image of large damaged region with complex geometric structure and rich texture, the PatchMatch-based image inpainting algorithm has disadvantages like texture extension and some incorrect sample patches being selected as candidate patches. To solve these problems, a new image inpainting algorithm was proposed for improving accuracy and efficiency. In terms of exact matching of sample patches, an image was preprocessed to obtain priori information of the image, which was used to initialize the constraint of the offset map, while PathMatch algorithm used global random initialization. In the process of pixel patch matching, to improve the matching accuracy of the sample, mean method and angle method were introduced to compute the similarity of different categories of pixel patches. In terms of efficiency, according to the statistical characteristics of similar patches of an image, histogram statistical method was introduced to reduce the labels for inpainting. The proposed algorithm was verified by some instances. The simulation results show that compared with the original PatchMatch algorithm, the Peak Signal-to-Noise Ratio (PSNR) of the proposed algorithm was improved by 0.5dB to 1dB, and the running time was reduced by 5s to 10s, which indicates that the proposed algorithm can effectively improve the accuracy and efficiency of image inpainting.

Aiming at the problems of limited monitoring area and difficult target locating in video security surveillance system, a radar-guided video linkage monitoring model was established with the characteristics of wide radar monitoring range and freedom from optical conditions. On this basis, a target location algorithm and a multi-target selection algorithmm were proposed. Firstly, according to the target information detected by radar, the corresponding camera azimuth and pitch angle of a moving target in the system linkage model were automatically calculated so that the target could be accurately locked, monitored and tracked by camera in real-time. Then, with multiple targets appearing in the surveillance scene, the multi-target selecting algorithm was used for data weighted fusion of discrete degree of target, radial velocity of target and the distance between target and camera to select the target with the highest priority for intensive monitoring. The experimental results show that, the locating accuracy of the proposed target location algorithm for pedestrians and vehicles can reach 0.94 and 0.84 respectively, which can achieve accurate target location. Moreover, the proposed multi-target selection algorithm can effectively select the best monitoring target in complex environment, and has good robustness and real-time performance.

To eliminate the zigzagging and blocky effects in homogeneous areas in an interpolated image, a variable exponent variational method was proposed for image interpolation. An exponent function with diffusion characteristic of image interpolution was introduced by analyzing the diffusion characteristic of variable exponent variational model. Two parameters in the exponent function act on interpolation: the one controlled the intensity of diffusion which eliminated the width of image edges while the other controlled the intensity of smoothness which retained the fine textures in the image. The new variable exponent variatonal model made the Total Variation (TV) variational diffuse along image contours and the heat diffusion on smooth areas. The numerical experiment results on real images show that image interpolated by the proposed method has better interpolated edges, especially for fine textures. Compared to the method proposed by Chen et al. (CHEN Y M, LEVINE S, RAO M. Variable exponent, linear growth functionals in image restoration. SIAM Journal on Applied Mathematics, 2006, 66(4): 1383-1406) and robust soft-decision interpolation method, the visual improvement is prominent for retaining fine textures, and the Mean Structural SIMilarity (MSSIM) is increased by 0.03 in average. The proposed model is helpful to further study variable exponent variational model for specifical image processing and worthy to practical applications such as image network communication and print.

A Robust Feature Selection and Classification algorithm based on Partial Least Squares Regression (RFSC-PLSR) was proposed to solve the problem of redundancy and multi-collinearity between features in feature selection. Firstly, the consistency coefficient of sample class based on neighborhood estimation was defined. Then, the k Nearest Neighbor ( kNN) operation was used to select the conservative samples with local class structure stability, and the partial least squares regression model was used to construct the robust feature selection. Finally, a partial least squares classification model was constructed using the class consistency coefficient and the preferred feature subset for all samples from a global structure perspective. Five data sets of different dimensions were selected from the UCI database for numerical experiments. The experimental results show that compared with four typical classifiers-Support Vector Machine (SVM), Naive Bayes (NB), Back-Propagation Neural Network (BPNN) and Logistic Regression (LR), RFSC-PLSR is more efficient in low-dimensional, medium-dimension, high-dimensional and other different cases, and shows stronger competitiveness in classification accuracy, robustness and computational efficiency.

In order to solve the problem of inpainting missing information in the large damaged region with rich texture information and complex structure information, an image inpainting algorithm for partitioning feature subregions was proposed. Firstly, according to the different features contained in the image, the feature formula was used to extract the features, and the feature subregions were divided by the statistical eigenvalues to improve the speed of image inpainting. Secondly, on the basis of the original Criminisi algorithm, the calculation of priority was improved, and the structural fracture was avoided by increasing the influence of the structural term. Then, the optimal sample patch set was determined by using the target patch and its optimal neighborhood similar patches to constrain the selection of sample patch. Finally, the optimal sample patch was synthesized by using weight assignment method. The experimental results show that, compared with the original Criminisi algorithm, the Peak Signal-to-Noise Ratio (PSNR) of the proposed algorithm is improved by 2-3 dB; compared with the patch priority weight computation algorithm based on sparse representation, the inpainting efficiency of the proposed algorithm is also obviously improved. Therefore, the proposed algorithm is not only suitable for the inpainting of small-scale damaged images, but also has better inpainting effect for large damaged images with rich texture information and complex structure information, and the restored images are more in line with people's visual connectivity.

It is hard to obtain a satisfactory reconstructive quality while compressing time-varying signals monitored by Wireless Sensor Network (WSN) using Compressive Sensing (CS), therefore a novel dynamic sampling method based on data prediction and sampling rate feedback control was proposed. Firstly, the sink node acquired the changing trend by analyzing the liner degree differences between current reconstructed data and last reconstructed data. Then the sink node calculated the suitable sampling rate according to the changing trend and fed back the result to sensors to dynamically adjust their sampling process. The experimental results show that the proposed dynamic sampling method can acquire higher reconstructed data accuracy than the CS data gathering method based on static sampling rate for WSN.

Aiming at the problem of small gradient, low learning rate, slow convergence of error during the process of using Deep Belief Network (DBN) algorithm to correct connection weight and bias of network by the method of back propagation, a new algorithm called Multi-Innovation DBN (MI-DBN) was proposed based on combination of standard DBN algorithm with multi-innovation theory. The back propagation process in standard DBN algorithm was remodeled to make full use of multiple innovations in previous cycles, while the original algorithm can only use single innovation. Thus, the convergence rate of error was significantly increased. MI-DBN algorithm and other representative classifiers were compared through experiments of datasets classification. Experimental results show that MI-DBN algorithm has a faster convergence rate than other sorting algorithms; especially when identifying MNIST and Caltech101 dataset, MI-DBN algorithm has the fewest inaccuracies among all the algorithms.

Concerning the lack of refined and quantized real-time evaluation of rainstorm disaster risk level, this paper applied meteorological data, historical disaster data, height and distance from the sea of Guangxi, and confirmed the identification technique and data sequence building method of hazard-formative factors of rainstorm of Guangxi based on hazard-bearing body, the hazard-formative environment, hazard-formative factors, and anti-disaster capability. Real-time evaluation model and grade index of rainstorm disaster risk level based on risk, subsequently environment fragile degree, vulnerability and anti-disaster capability were constructed for different hazard-bearing body, such as agriculture and social economy. And then the rainstorm risk level of real-time evaluation system was developed, with real-time evaluation model as the core. By using the Geographic Information System (GIS) secondary development techniques, the operating process of rainstorm risk level real-time evaluation was simplified and standardized. By using the proposed system to evaluate the violent typhoon named Neuchatel on September 29, 2011, the experimental results show that it is consistent with disaster condition.

To detect text from images with different backgrounds, a text detection method with ant colony optimization algorithm was proposed. Before text detection, an ant colony optimization algorithm was adopted to detect image edges, and then features were extracted from the edge image. Afterwards, a coarsetofine strategy was applied to detect the text lines from image. Finally, the experimental results show that the proposed method achieves more precise detection than Soblebased, Cannybased and othor two detection methods.

Many researches have shown that TCP performance in ad hoc networks is extremely poor, because the congestion control mechanism of TCP cannot effectively deal with the problem of packet drops caused by the shared channel contention. In this paper, a cross-layer method was presented,which adaptively adjusted the TCP maximum window size according to the number of RTS (Request To Send) retries of the MAC layer at the TCP sender, to control the number of TCP packets in the network and thus decrease the channel contention. The simulation results show that this method can remarkably improve the throughput and stability of TCP.