eigenvalue_p1_2D

功率谱密度计算程序，可以用于计算时域信号的功率谱密度。(The power spectral density calculation procedures can be used to calculate the power spectral density of the time-domain signal.)

计算信号的Kolmogorov熵,计算信号的Kolmogorov熵,计算信号的Kolmogorov熵,计算信号的Kolmogorov熵(computing Kolmogorov entropy of signal)

比特加载技术相关算法，包含Fischer算法，贪婪算法以及chow算法。(bit loading algorithm, include Hughes-Hartogs Algorithm, Fischer Algorithm and chow algorithm)

用matlab的simulink实现状态观测器设计及仿真，绘出state-space 模块的输出值的阶跃响应曲线以及观测器的输出值的阶跃响应曲线。(To achieve observer status with the design and simulation of matlab simulink, step response curve step response curve and observing the output value is plotted state-space module output value.)

ppt文件，讲述如何使用matlab调用zemax中数据的文档(ppt file explains how to use the data in matlab call zemax document)

电网同步跟踪技术，自己搭的锁相环,可以运行出来。有频率跟踪，还有正负序分离(Grid synchronization tracking technology, take their own phase-locked loop, you can run out. There is frequency tracking, there are positive and negative sequence separation)

用于进行小波时频分析的matlab源码,可进行测井小波时频划分!(Used in wavelet time-frequency analysis of the matlab source code, can be divided into time-frequency wavelet logging!)

给定一批的样本，当然这些样本间必须相互独立。如果Q > chi2(p, nu)，假设拒绝，否则通过。 ARGUMENTS: x Absolut numbers. p The prob ability value, calculated from Q. Q The resulting Q-value.(A given batch of samples, of course, among these samples must be independent of each other. If Q> chi2 (p, nu), the assumption that refuse, or else through. ARGUMENTS: x Absolut numbers. P The prob ability value, calculated from Q. Q The resulting Q-value.)

无刷双馈电机模型，其模型为S函数，另外还有矢量控制模型，但模型还不够完善，尚需改进(The brushless doubly fed motor and vector control simulation model)

张量分解提取生物学特征，NFEA: Tensor Toolbox for Feature Extraction and Applications( Data in modern applications such as BCI based on EEG signals often contain multi-modes due to mechanism of data recording, e.g. signals recorded by multiple-sensors (electrodes), in multiple trials, epochs, for multiple subjects and with different tasks, conditions. Moreover, during processing and analysis, dimensionality of the data could be augmented due to expression of the data into sparse domain (time-frequency representation) by different transforms such as STFT, wavelets. That means data itself is naturally a tensor, and has multilinear structures. Standard approaches which analyze such data by considering them as vectors or matrices might be not suitable due to risk of losing the covariance information among various modes. To discover hidden multilinear structures, features within the data, the analysis tools should reflect the multi-dimensional structure of the data)