eryuan1

dtw的matlab程序的实现，已经编译通过的。(dtw the realization of Matlab procedures already adopted by the compiler.)

EMD方法的matlab实现，并配有实例。(EMD method matlab realize, and with examples.)

说明：  Matlab的kmeans算法实现，有很详细得说明(Matlab)

Support Vector Machines solution

Compressive sensing (CS) has been proposed for signals with sparsity in a linear transform domain. We explore a signal dependent unknown linear transform, namely the impulse response matrix operating on a sparse excitation, as in the linear model of speech production, for recovering compressive sensed speech. Since the linear transform is signal dependent and unknown, unlike the standard CS formulation, a codebook of transfer functions is proposed in a matching pursuit (MP) framework for CS recovery. It is found that MP is efficient and effective to recover CS encoded speech as well as jointly estimate the linear model. Moderate number of CS measurements and low order sparsity estimate will result in MP converge to the same linear transform as direct VQ of the LP vector derived the original signal. There is also high positive correlation between signal domain approximation and CS measurement domain approximation for a large variety of speech spectra.

此函数中详细介绍了如何现代功率谱估计法中的AR模型来妒忌信号的功率谱；(This function is described in detail how modern power spectrum estimation of AR model to envy the power spectrum of the signal )

近红外光谱建模 进行定量分析 偏最小二乘法(very good)

两用户NOMA功率分配问题，并给出相应的仿真图形(Two user NOMA power allocation problem, and gives the corresponding simulation graphics.)

函数优化，随机输入一个函数，优化产生最优值。(Function optimization, random input a function, optimization produces optimal value.)

能求出关联矩阵、所有的树，并可选定每一条树，求的的其相应的回路矩阵Qf，及割集矩阵Bf(To find the correlation matrix, all the trees, each tree can be selected, find the circuit of its corresponding matrix Qf, and cut-set matrix Bf)