miaobiao

MSER 算法的matlab实现，保证可以运行(MSER algorithm matlab implementation, to ensure you can run)

高阶谱双谱程序，非常实用的双谱例程供你参考(Order spectrum bispectral procedures, very practical double spectral routines for your reference)

MATLAB M文件调用SIMULINK完成LDPC误码率仿真(MATLAB M SIMULINK LDPC BER SIMULATION)

通过KNN算法，实现WIFI室内定位在matlab环境下的仿真(Simulation of WIFI indoor location in Matlab environment through KNN algorithm)

说明：  包含多个进行数值积分的MATLAB函数，如NewtonCotes , Roberg , SmartSimpson, (numerical integration, NewtonCotes , Roberg , SmartSimpson, CombineTraprl , DDSimpson , IntGaussLobato)

改程序可以实现任意阶的伪随机序列的产生，并且以文本格式保存起来，方便查看和调用(Reform program can generate pseudo-random sequence of arbitrary order, and saved in text format for easy viewing and calling)

基于光伏模块直流物理模型 在 matlab 仿真环境下 开发了光伏阵列通用仿真模型 利用该 模型 可以模拟任意太阳辐射强度 环境温度 光伏模块参数 光伏阵列串并联方式组合下的光伏阵列 I-V 特性 此外 该模型还融合了光伏阵列的最大功率跟踪 MPPT 功能 可以用于光伏发电系统和风光复合发电系统的动态仿真(PV module DC physical model in matlab simulation environment developed photovoltaic array generic simulation model using the model can simulate the IV characteristics of the PV array at any ambient temperature solar radiation intensity photovoltaic array photovoltaic module parameters of series and parallel combinations based on the model also incorporates the addition The maximum power of the PV array can be used to track the MPPT function dynamic simulation of photovoltaic systems and wind hybrid power system)

四模块永磁轮毂电机的容错控制策略的仿真，使用的是矢量控制的策略。(Permanent magnet synchronous motor using two-phase four-leg vector control simulation, on the basis of the platform can simulate the four-module permanent magnet wheel motor fault tolerance, including speed, torque ripple.)

实现目标形状的物体检测，该程序是从一对简单形状图形中提取出矩形(Goals shape object detection, the program is a pair of simple rectangular shape extracted graph)

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.