语音信号的调制在 MATLAB 环境中

program from calling VC++ programs from Matlab

用FAMALE.TXT和MALE.TXT的数据作为本次作业使用的样本集，利用K-L变换对该样本集进行变换，与过去用Fisher线性判别方法或其它方法得到的分类面进行比较.(Use FAMALE. TXT and MALE. TXT data as the assignment is to use the data sets, by K-L of the sample set of change, and the past with Fisher transformation linear discriminative methods or other methods available for the classification comparison. )

基于matlab识别手势，可以识别石头，剪刀，布，进行猜拳判断输赢(Identify gestures, guessing and win)

运用极限学习机实现数据的预测，通过验证发现效果比普通神经网络好(Using Extreme Learning Machine to Achieve Data Forecast)

far field modeling if active multicore fibre

说明：  对多机电力系统进行小干扰稳定分析 可以计算特征值，特征向量(Of multi-machine power system small signal stability analysis can calculate the eigenvalue, eigenvector)

说明：  优化常用的共轭梯度法和投影梯度法求解目标函数最优解问题(Conjugate gradient optimization of common law and the projected gradient method problems the optimal solution objective function)

leach algorithm for wireless sensor networks

OFDM(正交频率调制)实现多载波调制基本思想是将带宽信道分解成许多并行的窄子信道，是每个子信道的想干带宽，从而实现平稳衰落。搭建了完整的OFDM信道链路系统，运用三种不同的信道估计方法(LS信道估计、DFT信道估计、Improved-DFT信道估计)，并对AWGN理想信道和衰落信道误码情况进行了仿真和比较。(OFDM (Orthogonal Frequency Modulation) multicarrier modulation basic idea bandwidth channel is decomposed into a number of parallel narrow subchannels, each subchannel wanted bandwidth, in order to achieve a smooth fading. Build a complete OFDM channel link system, the use of three kinds of different channel estimated method (LS channel estimated, to the DFT letter Tao estimates, Improved-the DFT letter Road estimated), and AWGN ideal letter Road and fading channel bit error situation conducted a simulation and comparison .)

斯坦福大学的姚邦鹏开发的一个图像分类算法，使用random forest实现了图像的精细区域描述，赢得了PASCAL VOC2011图像分类竞赛中的winner prize.经测试，程序完全可运行，且提供了MAC和Windows下的两种程序。(Image Classification: An Integration of Randomization and Discrimination in A Dense Feature Representation The goal of our method is to identify the discriminative fine-grained image region that distinguishes different classes. To achieve this goal we sample image regions from dense sampling space and use a random forest algorithm with discriminative classifier. Each node of the tree of random forest is trained and tested with fine-grained image patches combining the information from upstream nodes together. We implemented each node of the tree with a discriminative SVM classifier, which makes the node as a strong classifier. )