Instrument Landing System （ILS） is a critical navigation device for ensuring flight safety， whose signal quality affects the accuracy and safety of aircraft landing accuracy and safety directly. However， the increasingly complex electromagnetic environment around airports leads to more and more multipath effects， thereby impacting the reliability and precision of ILS signals significantly. Therefore， taking LOCalizer （LOC） as the research object， a signal propagation path analysis method based on backward ray-tracing method was proposed. In the method， by establishing an electromagnetic propagation model of the airport environment and incorporating ray-tracing and path validity determination rules， the propagation characteristics of ILS signals in multipath environments were investigated systematically. And the influence of signal reflection and diffraction effects caused by obstacles on the Difference in Depth of Modulation （DDM） of the airborne LOC receiving signals were particularly analyzed. Simulation results demonstrate that when obstacles are near the runway centerline， DDM experiences significant fluctuations， with the maximum jitter reached 283.4% of the value specified by the International Civil Aviation Organization （ICAO） approximately. And after adjusting the obstacle positions appropriately， occlusions between obstacles and the increased distance between obstacles and the runway centerline lead to a reduction in multipath interference. As a result， the fluctuation of DDM decreases significantly and meets the ICAO’s prescribed limit， with the maximum jitter reached 99.0% of the specified value approximately. The above verifies that this method can assess the impact of multipath propagation on ILS performance in complex airport environments effectively.

In the non-contact respiratory state monitoring based on WiFi Channel State Information （CSI）， respiratory state data are easily affected by environmental interference during acquisition， resulting in degraded signal quality. Meanwhile， due to constrained acquisition conditions and privacy protection， the number of samples is often insufficient， leading to a few-shot problem， and the generated samples usually lack consistency with real samples in key discriminative features， thereby reducing the effectiveness of data augmentation. In addition， the small differences among different respiratory states further increase the difficulty of classification and limit the depth of research on multi-state respiratory recognition. To address these challenges， an SiamGAN-CNNBiLSTM-based respiratory state recognition system was proposed. First， a multi-state respiratory dataset consisting of normal breathing， apnea， and sneezing was constructed， and a systematic CSI signal preprocessing pipeline was designed to improve signal quality. Second， an SiamGAN module integrating Generative Adversarial Network （GAN） and Siamese network was developed， so that through the adversarial mechanism of generator and discriminator， samples with higher realism were generated. At the same time， a Siamese encoder was introduced to extract 128-dimensional intrinsic features of the signals， and a Mean Squared Error （MSE） constraint was imposed in the feature space to enhance the consistency between generated and real samples in key discriminative features. By this strategy， the discriminability and generalization of generated samples were strengthened， and the physical properties of the original signals were preserved， so as to avoid the introduction of classification-irrelevant high-frequency noise. Finally， a deep classification network combining Convolutional Neural Network （CNN） and Bidirectional Long Short-Term Memory （BiLSTM） network was constructed to fully mine the spatio-temporal features of respiratory signals， and achieve high-precision recognition of multiple respiratory states. Experimental results show that in the office scenario， SiamGAN-CNNBiLSTM achieves excellent performance with an accuracy improvement of 2.2 percentage points， compared with CNN-BiLSTM method， and an accuracy improvement of 8.3 percentage points， compared with the Transformer method； in corridor scenario， SiamGAN-CNNBiLSTM achieves an accuracy of 96.7%， representing a decrease of only 1.6 percentage points compared with that in office scenario， indicating the robustness of the proposed method across different environments.

Facing the higher demands for communication and sensing in upcoming Dual-Function Radar Communication （DFRC） systems， a DFRC system model was proposed that combines multi-cluster Non-Orthogonal Multiple Access （NOMA） technology and Reconfigurable Intelligent Surface （RIS）. In the proposed model， the superimposed multi-cluster NOMA signals were utilized by the DFRC base stations to achieve target perception and the virtual line-of-sight links established by RIS reflection were used to enhance the communication performance of users in multi-cluster NOMA. Based on the proposed model， with the goal of maximizing weighted sum of the system sum rate and the sensing power， a non-convex objective function with multiple constraints and coupled variables was constructed. To solve this objective function， an optimization scheme for joint beamforming and power allocation was proposed. In the proposed scheme， firstly， the original optimization problem was decomposed into three subproblems. Subsequently， methods such as Successive Convex Approximation （SCA） and SemiDefinite Relaxation （SDR） were employed to transform the original non-convex optimization subproblems into convex optimization subproblems. Finally， the Alternating Optimization （AO） method was applied to solve the subproblems， thereby achieving joint beamforming （including active and passive beamforming） and intra-cluster power allocation coefficient optimization. Simulation results indicate that the proposed scheme has good performance of communication and sensing， and compared with the Orthogonal Multiple Access （OMA） scheme， it has the system sum rate improved by about 1 bit/（s·Hz） with high target perception performance， achieving a good compromise between communication performance and perception performance.

To address the challenges caused by the rapid development of the Fifth-Generation （5G） mobile Internet of Things （IoT）， including spectrum shortages， coverage blind zones， and power supply deficiencies， by integrating Intelligent Reflecting Surfaces （IRS）， Simultaneous Wireless Information and Power Transfer （SWIPT）， and Non-Orthogonal Multiple Access （NOMA） technologies， a multi-IRS-assisted NOMA-SWIPT cooperative transmission model was proposed. Based on this model， a non-convex optimization problem was formulated with the goal of maximizing system sum rate， and a two-stage joint beamforming （including active beamforming of base stations and passive beamforming of IRS） and power allocation optimization algorithm was then proposed to solve this problem. During the solution process， to decouple the variables to be optimized， a popular optimization method was first used to solve for the passive beamforming in the cooperative transmission phase. Then， the Block Coordinate Descent （BCD） algorithm was applied to decompose the non-convex problem into four sub-problems through alternating iteration， which were later solved using methods such as quadratic transformation and Successive Convex Approximation （SCA）. Simulation results show that compared with schemes of no SWIPT and Orthogonal Multiple Access （OMA）， the proposed cooperative transmission model and its corresponding optimization algorithm improves the system sum rate by approximately 0.5 bit·s^-1·Hz^-1 and 1.5 bit·s^-1·Hz^-1， respectively， and has more robust convergence characteristics.

The phenomenon of data imbalance is very common in real life. In order to improve the overall classification accuracy， classifiers often misclassify minority class at the cost. But in real life， the consequences of misclassifying minority class may be very serious. Considering that the traditional resampling algorithm ignores the relationship between the spatial distribution of data and the sample features of minority class， a new sampling algorithm SABRF （Sampling Algorithm Based on Relationship between Features） was proposed to generate a new sample set. The key distinguishing features of imbalanced dataset were preserved through Pareto-based multi-objective feature selection， and the relationships among key features of minority class samples were captured through XGBoost （eXtreme Gradient Boosting） regression model. In addition， considering the quality of newly generated samples， a new sample selection strategy was proposed to retain better samples. Experiments were conducted on six publicly available UCI datasets and one real post-orthopedic thrombus dataset. Experimental results show that the proposed algorithm has good performance on Area Under receiver operating characteristic Curve （AUC）， F1 score （F1_score） and Geometric Mean （G_mean）. In addition， when using the new samples selected by the sample selection strategy based on multi-index evaluation for classification， the classification result of imbalanced data is also the best， which verifies the effectiveness of the sample selection strategy.

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.