matlab_dsp_pp

C语言编写P-Q分解法潮流计算程序，基于BB方案，且该程序采用基于稀疏技术的因子表法进行迭代求解，以降低存储量，提高运算速度。附有IEEE5,14,30,39,118节点系统数据及潮流结果，已验证程序的可行性。(C language PQ decomposition method flow calculation procedures, based on BB plan and carry out the program uses an iterative solution technique based on sparse factor table method to reduce the storage capacity, improve processing speed. IEEE5,14,30,39,118 node system with data and trend results verified the feasibility of the program.)

Dijkstra最短路算法通用Matlab程序(Dijkstra Matlab )

Matlab 官方工具箱使用说明 symbolic_tb符号运算工具箱.pdf(Matlab toolbox for use symbolic_tb of official symbols computing toolbox. Pdf)

利用MATLAB语言实现生命规则在元胞自动机模型下的模拟(The use of MATLAB language implementation rules of life under the Cellular Automata Simulation)

汉明编码汉明编码汉明编码汉明编码汉明编码汉明编码汉明编码汉明编码汉明编码汉明编码(Hamming code Hamming code Hamming code Hamming code Hamming code Hamming code Hamming code Hamming code Hamming code Hamming code)

实现Turbo码的编码以及译码的代码，其中调用了一些子代码。(Turbo codes to achieve the encoding and decoding of the code, which calls some of the sub-code.)

outage in cognitive radio

GDP32可控源数据编辑软件，可对原始数据进行选择和编辑，频点选取等，为后续反演提供高质量数据体(GDP32 CSAMT data edit)

编写PCA算法的程序，对想要学习如何利用PCA算法进行故障诊断的同学很有帮助！( a PCA algorithm program, for the person who want to learn how to use PCA algorithm for fault diagnosis of classmates the progran is very helpful!)

本文针对SLAM数据关联中使用最为广泛的最近邻方法作了改进，利用特征估计位置与载体预测位置之间的欧氏距离计算代替了全部特征与每个量测之间的马氏距离计算，避免了大量的矩阵乘法计算。该算法简单易行，降低了算法的计算复杂度，有利于SLAM算法的实时执行，且关联效果与全局最近邻法相同(In this paper, SLAM data association in the most widely used methods of improving the nearest neighbor, using the characteristics of the estimated location and the location of vector prediction Euclidean distance between the calculation instead of all the characteristics of each measurement between the Mahalanobis distance calculation, to avoid a large number of matrix multiplication calculation. The algorithm is simple, the algorithm to reduce computational complexity and is conducive to the implementation of real-time SLAM algorithm, and the effects associated with the same overall nearest neighbor method)