BPfengdianyuce

使用了JIT的高效的k均值算法，因为使用了JIT加速，所以在统计工具箱下，比一般的k均值要快许多(Efficient K-Means using JIT (Matlab code) This code uses the JIT acceleration in Matlab, so it is much faster than k-means in the Statistics Toolbox. It is very simple and easy to read. )

（一）时域采样及其频谱 一．实验目的:1.熟悉Matable的实验环境,并学会 如何利用其进行对信号处理进行图形分析. 2.编程计算于模拟信号的连续频谱和离散采样信号的连续频谱,实现时域采样及其频谱分析,充分理解时域采样的特点及定理,并在实验过程中体会奈奎斯定理. 二．实验环境: MATABE软件平台 ((1) domain and a spectrum. Experimental purposes : 1. Matable familiar with the experimental environment, and learn how to use their right for signal processing graphics for analysis. 2. Programming from the calculation of continuous analog signal spectrum and from the San continuous sampling signal spectrum to achieve sampling and time-domain analysis of the spectrum, the time-domain fully understand the characteristics and sampling theorem. and the experimental process Experience Naikuishi theorem. 2. Experimental environment : MATABE software platform)

实现了对10个数字音的识别，部分实现了追踪测速迭代松弛算法，FIR 底通和带通滤波器和IIR 底通和带通滤波器。( To achieve the recognition of 10 digital sound, Partially achieved tracking speed iterative relaxation algorithm, Bottom-pass and band-pass FIR and IIR filter bottom pass and band-pass filter.)

介绍卡尔曼滤波的，我看过，真的很不错的。希望可以给控制，电子，信号的朋友分享 (Introduced the Kalman filter, and I have seen, really good. Wish to give control, electronics, signal friends Share)

实现了线性回归和非线性回归，代码简单，容易上手(regression-Optimization)

用蒙特卡罗方法求半径为1的圆的面积,matlab实现(Monte Carlo)

有关信道编码的word文档，内容很详细，(Channel coding the word about the document, the content is very detailed,)

circular plate mesh matlab

Our toolbox currently contains implementations of robust methods for location and scale estimation, covariance estimation (FAST-MCD), regression (FAST- LTS, MCD-regression), principal component analysis (RAPCA, ROBPCA), princi- pal component regression (RPCR), partial least squares (RSIMPLS) and classi¯ cation (RDA). Only a few of these methods will be highlighted in this paper. The toolbox also provides many graphical tools to detect and classify the outliers. The use of these features will be explained and demonstrated through the analysis of some real data sets.

台湾林智仁教授开发的svm开发包 内含java matlab c++等多种语言的开发模块(SVM no-linear)