To address the limitations of existing multiscale information diffusion prediction models， which ignore the dynamic characteristic of cascade propagation and exhibit limited performance in independent microscopic information prediction， a Multiscale Information Diffusion prediction model based on HyperGraph Neural Network （MIDHGNN）was proposed. Firstly， Graph Convolutional Network （GCN） was used to extract user social relationship features from the social network graphs， while HyperGraph Neural Network （HGNN）was used to extract global user preference features from propagation cascade graphs. These two types of features were fused to enable microscopic information diffusion prediction. Secondly， Gated Recurrent Unit （GRU） was employed to sequentially predict potential spreaders until reaching virtual users. The cumulative number of predicted users at each step was regarded as the determined cascade size for macroscopic propagation forecasting. Finally， a Reinforcement Learning （RL） framework using policy gradient to optimize parameters significantly enhanced macroscopic information diffusion prediction performance. For microscopic information diffusion prediction， compared to the suboptimal model， MIDHGNN achieves average improvements of 12.01%， 11.64%， and 9.74% in Hits@k on Twitter， Douban， and Android datasets， respectively， and average improvements of 31.31%， 14.85%， and 13.24% in mAP@k. For macroscopic prediction， MIDHGNN reduces the Mean Squared Logarithmic Error （MSLE） by at least 8.10%， 12.61%， and 3.24% on these three datasets， respectively， with all metrics significantly outperforming the comparison models， validating its effectiveness.

An important strategy for lightweighting a 3D model is to use the mesh simplification algorithm to reduce the number of triangular meshes on the model surface. The widely used edge collapse algorithm is more efficient and has better simplification effect than other mesh simplification algorithms， but some detailed geometric features may be damaged or lost during the simplification process of this algorithm. Therefore， the approximate curvature of curve and the average area of the first-order neighborhood triangle of the edge to be collapsed were added as penalty factors to optimize the edge collapse cost of the original algorithm. First， according to the definition of curve curvature in geometry， the calculation formula of the approximate curvature of curve was proposed. Then， in the calculation process of vertex normal vector， two stages - area weighting and interior angle weighting were used to modify the initial normal vector， thereby considering more abundant geometric information of the model. The performance of the optimized algorithm was verified by experiments. Compared with the classical Quadratic Error Metric （QEM） algorithm and the mesh simplification algorithm considering the angle error， the optimized algorithm has the maximum error reduced by 73.96% and 49.77% at least and respectively. Compared with the QEM algorithm， the optimized algorithm has the Hausdorff distance reduced by 17.69% at least. It can be seen that in the process of model lightweighting， the optimized algorithm can reduce the deformation of the model and better maintain its own detailed geometric features.

To remove the stripe noise from the line scan images, a transform domain destriping method which combined Fourier transform and wavelet decomposition was proposed. Firstly, the image was decomposed using multi-resolution wavelet decomposition to separate the subband which contained the stripe noise from other subbands. Then the subband that contained stripe noise was transformed into Fourier coefficients. The Fourier coefficients were processed by a band-stop filter to remove the stripe noise. The live collected cotton foreign fiber images with stripe noise were used in the simulation experiment. The experimental results indicate that the proposed approach which combined Fourier transform with wavelet decomposition can effectively remove the stripe noise from the image while preserving the characteristics of the original image. It gets better destriping effect than just using Fourier transform or wavelet decomposition separately.

The energy frequency distribution of non-stationary signal could not be got correctly with short-time Fourier transform. A new method was proposed to separate the audio sources from a single mixture based on Hilbert-Huang transform. Hilbert transform combined with Intrinsic Mode Functions (IMFs) constituted Hilbert Spectrum (HS) of mixture, which was a time-frequency representation of a non-stationary signal. The HS of mixture was used to derive the independent source subspaces. The time domain source signals were reconstructed by applying the inverse transformation. The simulated results show that the proposed method is efficient and improves the separation performance. It was observed that HS-based TF representation performed better than using STFT.