The quality of low-light images is poor and Low-Light Image Enhancement （LLIE） aims to improve the visual quality. Most of LLIE algorithms focus on enhancing luminance and contrast， while neglecting details. To solve this issue， a Progressive Enhancement algorithm for low-light images based on Layer Guidance （PELG） was proposed， which enhanced algorithm images to a suitable illumination level and reconstructed clear details. First， to reduce the task complexity and improve the efficiency， the image was decomposed into several frequency components by Laplace Pyramid （LP） decomposition. Secondly， since different frequency components exhibit correlation， a Transformer-based fusion model and a lightweight fusion model were respectively proposed for layer guidance. The Transformer-based model was applied between the low-frequency and the lowest high-frequency components. The lightweight model was applied between two neighbouring high-frequency components. By doing so， components were enhanced in a coarse-to-fine manner. Finally， the LP was used to reconstruct the image with uniform brightness and clear details. The experimental results show that， the proposed algorithm achieves the Peak Signal-to-Noise Ratio （PSNR） 2.3 dB higher than DSLR （Deep Stacked Laplacian Restorer） on LOL（LOw-Light dataset）-v1 and 0.55 dB higher than UNIE （Unsupervised Night Image Enhancement） on LOL-v2. Compared with other state-of-the-art LLIE algorithms， the proposed algorithm has shorter runtime and achieves significant improvement in objective and subjective quality， which is more suitable for real scenes.

Cognitive Radio Network (CRN) plays a critical role in achieving better wireless bandwidth utilization and improving the quality of wireless applications. Multicasting in CRN is a challenging problem due to the dynamic nature of spectrum opportunities available to the secondary users. Researchers have proposed a variety of schemes for efficient multicast in cognitive radio networks, including schemes based on optimization theory, network coding, machine learning, and game theory. The algorithms and techniques for solving the multicast problem effectively were summarized, and a comprehensive survey of protocols was given. Finally, future research directions were identified.

To overcome the disadvantages of the unclear results and time consuming in the sparse representation of image super-resolution reconstruction with single redundant dictionary, a single image super-resolution reconstruction method based on multi-dictionary learning and image patches mapping was proposed. In the framework of the traditional sparse representation, firstly the gradient structure information of local image patches was explored, and a large number of training image patches were clustered into several groups by their gradient angles, from those clustered patches the corresponding dictionary pairs were learned. And then the mapping function was computed from low resolution patch to high resolution patch in each clustered group via learned dictionary pairs with the idea of neighbor embedding. Finally the reconstruction process was reduced to a projection of each input patch into the high resolution space by multiplying with the corresponding precomputed mapping function, which improved the images quality with less running time. The experimental results show that the proposed method improves the visual quality significantly, and increases the PSNR (Peak Signal-to-Noise Ratio) at least 0.4 dB compared with the anchored neighborhood regression algorithm.

Focusing on the issue that different scale noise exists in denoising and smoothing of 3D point cloud data model, a bilateral filtering denoising algorithm for 3D point cloud based on noise classification was proposed. Firstly, the noise points were subdivided into the large-scale and the small-scale noise, and the large-scale noise was removed with statistical filtering and radius filtering. Secondly, the curvature of the 3D point cloud data was estimated, and the bilateral filter was improved to enhance the robustness and security. Finally, the small-scale noise was smoothed with the improved bilateral filter to achieve the smoothing and denoising of 3D point clouds. Compared with the algorithms simply based on bilateral filtering or Fleishman bilateral filtering, the smoothing average error index of 3D point cloud data model obtained by the proposed method respectively decreased by 50.53% and 21.67%. The experimental results show that the proposed algorithm increases the efficiency of calculation by scale subdivion of noise points, and avoids excessive smoothing and detail distortion, which can better maintain the geometric characteristics of the model.

Focusing on the issue that remote sensing fusion image based on Contourlet transform has low spatial resolution, a remote sensing image fusion algorithm based on Modified Contourlet Transform (MCT) was proposed. Firstly, the multi-spectral image was decomposed into intensity component, hue component and saturation component by Intensity-Hue-Saturation (IHS) transform; secondly, Modified Contourlet decomposition was done between the intensity component and the panchromatic image after histogram matching to get low-pass subband coefficients and high-pass subbands coefficients; and then, the low-pass subband coefficients were fused by the averaging method, and the high-pass subbands coefficients were merged by Novel Sum-Modified-Laplacian (NSML). Finally, the fusion result was regarded as the intensity component of multi-spectral image, and remote sensing fusion image was obtained by inverse IHS transform. Compared with the algorithms based on Principal Components Analysis (PCA) and Shearlet, based on PCA and wavelet, based on NonSubsampled Contourlet Transform (NSCT), the average gradient that was used for evaluating image sharpness of the proposed method respectively increased by 7.3%, 6.9% and 3.9%. The experimental results show that, the proposed method enhances the frequency localization of Contourlet transform and the utilization of decomposition coefficients, and on the basis of keeping multi-spectral information, it improves the spatial resolution of remote sensing fusion image effectively.

To deal with the under-resourced labeled pronunciation data in mispronunciation detection, some other data were used to improve the discriminability of feature in the framework of Tandem system. Taking Chinese learning of English as object, unlabeled data, native Mandarin data and native English data which can be relatively easily accessed were selected as the assisted data. The experiments show that these types of data can effectively improve the performance of system, and the unlabeled data performs the best. And the effect to system performance was discussed with different length of frame context, the shallow and deep neural network typically represented by Multi-Layer Perception (MLP) and Deep Neural Network (DNN), and different structure of Tandem feature. Finally the strategy of merging multiple data streams was used to further improve the system performance, and the best system performance was achieved by combining the DNN based unlabeled data stream and native English stream. Compared with the baseline system, the recognition accuracy is increased by 7.96%, and the diagnostic accuracy of mispronunciation type is increased by 14.71%.

〖JP2〗With different way of link established, the high frequency IP wide-area network shows a different topological feature during the network operation process. Modeling study and simulation analysis were made with a modified harmonious unifying hybrid preferential model on high frequency IP wide-area network. And the results were found that the degree distribution, shortest path and clustering coefficient would be impacted by the different node selection method for new connection. Mean-while, the average shortest path of the network would be affected by the different edge deletion method. What's more the topological characteristics of the network were determined by the proportion of different node selection method.〖JP〗