omp

输入信号五个基于瞬时信息的特征：零中心归一化瞬时幅度功率谱密度的最大值,零中心归一化瞬时幅度绝对值的标准偏差，零中心非弱信号段瞬时相位非线性分量绝对值标准偏差，零中心非弱信号段瞬时相位非线性分量标准偏差，零中心归一化的非弱信号段瞬时频率绝对值的标准偏差，得到识别出的信号类别，本例为2PSK, 4PSK, GMSK, OQPSK, pi/4DQSK中的一个，还可以看Art网络的扩展性。(input signal instantaneous information on five characteristics : In the center is one of the instantaneous rate of the power spectral density of the maximum, is a center to the absolute value of the instantaneous rate of the standard deviation, Non-zero weak signal instantaneous phase of the nonlinear component absolute standard deviation, Non-zero weak signal instantaneous phase of the nonlinear component standard deviation, In the center is one of the non-weak signal of the absolute value of the instantaneous frequency standard deviation, the identification of signal type, The cases of two PSK, 4PSK, GMSK, OQPSK, pi/4DQSK of one, Art can be viewed network scalability.)

simulation presure proses rig with NN backpropagation

根据路线查找规律，利用Matlab编程实现公交路线的查询(Route search in accordance with the law, use of Matlab programming implementation of the query bus route )

认知无线电技术和频谱感知相关matlab代码，循环检测，周期性检测等(cognitive radio，spectrum sensing， iterative detection)

图像标注算法 MildML 的 matlab 代码, 多示例学习标注算法(image annotation algorithm of MildML in matlab code of <Multiple Instance Logistic Discriminant-based Metric Learning (MildML) and Logistic Discriminant-based Metric Learning (LDML)>)

he files in this package comprise the Matlab implementation of a foreground segmentation algorithm based upon graph cuts, described in: Better Foreground Segmentation Through Graph Cuts, N. Howe & A. Deschamps. Tech report, http://arxiv.org/abs/cs.CV/0401017. The file extractForeground.m contains a sample function that will perform a complete foreground segmentation for static camera video. It uses a number of parameters, which are described in the documentation. An attempt was made to choose sensible default values, but they may need to be adjusted for some videos. More generally, the implementation in extractForeground.m is only a suggestion of how the graph-based segmentation might be used. The same approach can be applied with other (perhaps time-adaptive) background models. The key step is at line 113 of the code, once the deviation array has been created. The graph-cut segmentation can be applied similarly to a deviation matrix computed in any other way.

It is one of the files which will help in designing the biped simulation model by controlling it through regression model.

my papers for LDA PCA for reference purpose

说明：  自己搭建的matlab电机仿真中的SVPWM模块，其中U_alpha,U_beta为电压输入，T为开关周期，U_dc为直流母线电压，PWM为六路脉冲信号，绝对好用(Matlab built their own motor simulation SVPWM module, which U_alpha, U_beta the voltage input, T is the switching cycle, U_dc for the DC bus voltage, PWM pulse signal for the six-way, absolutely easy to use )

语音增强系统，包括信噪比估计和传递函数，分段函数和重建函数(speech enhancement project, including Prior SNR estimate, noise PSD estimate, gain function, framing and over-lap add)