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SIMULINK仿真教程，介绍了SIMULINK的模块库、功能模块的处理、仿真的运行、s-function的设计、SIMULINK仿真举例(SIMULINK simulation tutorials introduce the SIMULINK module library, function module processing, the simulation is running, s-function design, SIMULINK simulation example)

filters signals EKG to detect qrs complex

lmmse file lest mean square algoritham materal

MatLab+CDMA+Reference+Blockset+for+Use+with+Simulink matlab仿真(MatLab CDMA Reference Blockset for Use wit h Matlab Simulink simulation)

阵列结构为面阵，在面阵的基础上进行求解music谱。(Array array structure, based on a planar array to solve music )

用上跨零点法对波浪数据进行统计分析 有效波高 波周期 (Spend zero-crossing method of wave data for statistical analysis of the significant wave height wave period)

MA（q)模型逆相关函数估计方法MATLAB例程(MA (q) model of inverse correlation function estimation routines MATLAB)

kalman跟踪滤波程序目标运动状况kalman-filterl滤波result输出结果(alman filtering programs target movement condition filterl kalman filter- much output)

用于计算二维光子晶体（用于计算二维光子晶体）(Used to calculate the two-dimensional photonic crystals (used to calculate the two-dimensional photonic crystal))

Principal component analysis (PCA) is a popular tool for linear dimensionality reduction and feature extraction. Kernel PCA is the nonlinear form of PCA, which is promising in exposing the more complicated correlation between original high-dimensional features. In this paper, we first talk about the basic ideas of PCA and kernel PCA, and then focus on the reconstruction of pre-images for kernel PCA. We also give an introduction on how PCA is used in active shape models (ASMs), and discuss how kernel PCA can be applied to improve traditional ASMs. Then we show some experiment results to compare the performance of kernel PCA and traditional PCA for pattern classification. We also implement the kernel PCA-based ASMs, and use it to construct human face models.