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Debye级数展开模型，计算均匀球形粒子散射角——散射光强曲线： 1. RB1.m 计算第一类Ricatti-Bessel函数的函数 2．RB2.m 计算第二类Ricatti-Bessel函数的函数 3. Alegendr.m 计算角函数 和 的函数 4. MieCoeff.m 计算米尔系数的函数 5. Amp.m 计算散射光复振幅的函数 6. DebyeIntensity.m Debye级数展开模型画均匀球形粒子散射角-光强图 (Debye series expansion model to calculate the uniform spherical particles scattering angle- scattering intensity curve: 1. RB1.m calculated first class Ricatti-Bessel function of the function 2. RB2.m calculate the second category Ricatti-Bessel function of the function 3. Alegendr.m computing angle functions and function 4. MieCoeff.m Mill coefficient calculation function 5. Amp.m calculate the scattering amplitude recovery function 6. DebyeIntensity.m Debye series expansion model painted uniform spherical particles scattering angle- light intensity diagram)

最优化算法中，单纯形表法+两步法的matlab实现，通过两个实例调用两个函数实现(Optimization algorithm, matlab tableau method+ two-step method of implementation, call the two functions by means of two examples of the realization)

two pole voltage switched PWM voltage control circuit in matlab&simulink

无线传感器高效能量路由算法——heed算法(Energy efficient routing algorithm for wireless sensor- heed algorithm)

语音提取和分析，运用LPC的方法对语音信号进行分析以及最终的合成。(Voice extraction and analysis using a method of LPC analysis of the voice signal as well as the final synthesis.)

Harris的角点检测算法的MATLAB程序实现(Harris corner detection algorithm of the MATLAB program)

一本很好的matlab学习代码，内有详尽的方法，英文版的(Matlab code for a good, detailed methods in English version)

在matlab与SystemGenerator的平台下实现零阶时延数字正切锁相环。此模块最终能达到与输入信号同频率。(In the matlab, and SystemGenerator the platform, zero-order delay tangent digital phase-locked loop. This module is ultimately to reach input signal with the same frequency.)

基于matlab的迈克尔逊干涉仿真，帮助大家学习光的干涉(Based on the matlab simulation of Michelson interferometers, help you learn the interference of light )

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)