The formation of carotid artery plaque is closely related to complex hemodynamic factors. The accurate simulation of complex flow conditions is of great significance for the clinical diagnosis of carotid artery plaque. In order to simulate the pulsating flow accurately， Large Eddy Simulation （LES） model was combined with Lattice Boltzmann Method （LBM） to construct a LBM-LES carotid artery simulation algorithm， and a real geometric model of carotid artery stenosis was established through medical image reconstruction software， thus the high-resolution numerical simulation of carotid artery stenosis flows was conducted. By calculating blood flow velocity and Wall Shear Stress （WSS）， some meaningful flow results were obtained， proving the effectiveness of LBM-LES in the study of blood flow in the carotid artery narrow posterior. Based on the OpenMP programming environment， the parallel computation of the grid of ten million magnitude was carried out on the fully interconnected fat node of high-performance cluster machine. The results show that the LBM-LES carotid artery simulation algorithm has good parallel performance.

Kernel Canonical Correlation Analysis (KCCA) is a recently addressed supervised machine learning methods, which is a powerful approach of extracting nonlinear features. However, the standard KCCA algorithm may suffer from computational problem as the training set increases. To overcome the drawback, an improved KCCA was proposed. Firstly, a scheme based on geometrical consideration was proposed to select a subset of samples that were projected to feature space (Reproducing Kernel Hilbert Space). And then, an efficient algorithm was proposed to enhance the efficiency of the feature extraction, which selected the most contributive eigenvectors for training and classification, and then calculated the corresponding eigenvectors for classification. Finally, the improved KCCA was combined with a multi-class classification method based on Support Vectors Data Description (SVDD) for classification and recognition, which put forward new ideas for pattern recognition based on kernels. The experimental results on ORL face database show that the proposed method reduces the training time and the system storage without deteriorating the recognition accuracy compared with standard KCCA.