DE_8PSK

code for BPRS input generation

读取mit-bih心电信号数据的m文件，rddata.m,英文(Mit-bih read ECG data m file, rddata.m, English)

对图像进行逐点计算，并且取最大值和最小值的差值作为该点新的灰度值。 在区别边缘点和非边缘点时，首先确定一个阈值T，若图像中像素点的新 值大于该门限值，则可以初步确定为边缘点，小于T的新值被判断为非边缘点.(Image point by point basis, and taking the maximum and minimum points of difference as the new gray value. Edge in the difference between point and non-edge point, first determine a threshold T, if the image of the new pixel value is greater than the threshold, you can initially identified as the edge point, the new value is less than T have been judged as non-edge point.)

串行，并行MODEM 的实例 串行，并行MODEM 的实例(serial, parallel, serial modem example, parallel examples MODEM)

说明：  数学建模竞赛：露天矿的开采问题，运用lingo与matlab编程，内附代码(Mathematical Contest in Modeling: Open-pit mining problem, the use of lingo with the matlab programming, containing code)

利用MATLAB实时处理GPS数据,：利用MATLAB软件环境，现场接收并实时处理海上地球物理调查过程中的GPS定位数据，剔除原始数据中的跳点，处理定位误差.(Real-time GPS data processing, using MATLAB: using the MATLAB software environment, the site receives and real-time processing GPS positioning in the process of Marine geophysical survey data, eliminating hops in the raw data, processing positioning error.)

说明：  二维阵列波束形成，波束指向图MATLAB程序(phase beamforming )

阵列方向图的matlab仿真，方向图的画法是阵列信号处理学习的最基本的能力，(Array pattern of matlab simulation, pattern of the array signal processing, painting is the most basic learning ability,)

matlab file for cbir using progect..useful to all

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.