With the existing deep learning algorithms， it is difficult to restore the highly blurred solar speckle images taken by Yunnan Observatories， and it is difficult to reconstruct the high-frequency information of images. In order to solve the problems， a deblurring method for restoring the solar speckle images and recovering the high-frequency information of images based on Generative Adversarial Network （GAN） and gradient information was proposed. The proposed method was consisted of one generator and two discriminators. Firstly， the image multi-scale features were obtained by the generator with the Feature Pyramid Network （FPN） framework， and these features were input into the gradient branch hierarchically to capture the smaller details in the form of gradient map， and the solar speckle image with high-frequency information was reconstructed by combining the gradient branch results and the FPN results. Then， based on the conventional adversarial discriminator， another discriminator was added to ensure the gradient map generated by the gradient branch more realistic. Finally， a joint training loss including pixel content loss， perceptual loss and adversarial loss was introduced to guide the model to perform high-resolution reconstruction of solar speckle images. Experimental results show that， compared with the existing deep learning deblurring method， the proposed method with image preprocessing has stronger ability to recover the high-frequency information， and significantly improves the Peak Signal-to-Noise Ratio （PSNR） and Structural SIMilarity （SSIM） indicators， reaching 27.801 0 dB and 0.851 0 respectively. The proposed method can meet the needs for high-resolution reconstruction of solar observation images.

Aiming at the problem that the existing signal control logic cannot respond to the bus cumulative number and the sensitivity of control parameters is poor, a bus priority strategy trigger probability model was constructed to detect and analyze the methods for improving trigger accuracy. Based on the Siemens 2070 signal controller, the triggering theory of Transit Signal Priority (TSP) was analyzed, and the trigger probability models were constructed for green-extension strategy and early-green strategy. Taking the actual intersection as an example, the trigger probability results of different signal timing plans were calculated and compared by simulation, the trigger characteristic of TSP strategies was studied and the improvement was discussed. The research shows that the trigger probability of green-extension strategy is so far below the early-green strategy; the trigger probability of green-extension is inversely proportional to the mini-green and the max-green time, while the trigger probability of early-green strategy is mainly related to the number of buses which applying for priority in the non-favored signal phase; the trigger probability of green-extension strategy can be improved by optimizing the mini-green, max-green time and increasing the bus number applying for priority; the trigger probability of early-green strategy can be improved by optimizing the original signal timing scheme then adding TSP subsequently.

For the complex behaviors of vehicle-bicycle conflict at the conflict zones of channelized islands, the capacity of right-turn lane which is calculated by the traditional analytical method is different with the practical condition. In order to solve the problem, a cellular automaton model based on VISSIM microscopic traffic simulation software for vehicle-bicycle conflict at channelized islands was proposed. According to the proposed rules of cellular automaton, the component object model of VISSIM was used for programming to control the velocity variation of right-turn vehicles by setting a series of detectors which were used to simulate cellular. Therefore, the closure effect of right-turn vehicles when they were in conflict with non-motorized vehicles or pedestrians could be simulated by these settings. Meanwhile, the crossing behaviors of non-motorized vehicles or pedestrians were controlled by using the priority rules of VISSIM. The simulation results show that the average relative error between the capacity of right-turn lane by the proposed model and the practical observation value was 5.45%. The experimental results show that the proposed model is better than the traditional analytical methods and can reflect the practical condition of conflict zones of of channelized islands, thus it can provide theoretical basis for the planning, design, traffic management and organization of channelized islands under the condition of mixed traffic flow.

Concerning the fixed direction and monotony of lossless image compression template of Gradient Adjusted Predictor (GAP), according to the characteristic of the actual edge with the same linear increments, this paper proposed Multidirectional Linear Gradient Adjusted Predictor (MLGAP) template. Firstly the image was cut into four sub-images from the center to the periphery. With the application of Graphics Processor Unit (GPU) parallel technology operation, the predictive value was calculated by MLGAP template in each sub-image, and then error feedback was used to construct prediction error image. The threshold was calculated by OSTU algorithm, and error image edge was classified. At last the edge was thinned by Hilditch algorithm. The simulation results show that using the proposed method can get clear, complete and precise edges. In addition, the GPU parallel technology accelerates the image processing.

By using Gradient Adjusted Predictor (GAP) and Gradient Edge Detection (GED) predictors of lossless image encoding for reference, with an improved one, a new image edge detection algorithm of the dynamic threshold control based on Multidirectional Gradient Edge Detection Predictor (MGEDP) template was proposed. The image was cut into four equal parts, and these parts would be executed simultaneously by MGEDP template in different direction of four opposite ways to calculate the error values by the parallel technology. From these feedback values, the algorithm created error image, calculated the threshold values by Otsu algorithm, classified the edges of error image, thinned the edges, and composed the last edge image. The experimental results show that the algorithm using parallel technology not only decreases the time complexity, but also gets the clearer edges, more details, and better visual image.

To improve the bandwidth utilization of a FlexRay network, it is necessary to gain a deep insight into this performance parameter. Based upon current model for time parameter optimization in a FlexRay network, through reducing a nonlinear operator to a linear one, analytical expressions of the optimal payload length of static frame and the maximum bandwidth utilization in the static segment of a FlexRay network were derived. The numerical experiments verify the validity of the analytical expressions, and demonstrate that the obtained analytical formulae are capable of providing an exact calculation for the bandwidth utilization in a FlexRay network.

Simultaneous Algebraic Reconstruction Technique (SART) is able to generate Computed Tomography (CT) images with higher quality compared to Filtered Back-Projection (FBP) method when the projection data is incomplete or noisy. However, it is very time-consuming; and parallel computation is one of those efficient approaches to manage the problem. In this study, a new parallel implementation of SART based on the platform of Compute Unified Device Architecture (CUDA) was proposed. The experimental results show that there are no differences between the images reconstructed by this new method and those by serial implementation, but the reconstruction time is greatly decreased, more applicable to clinical application.