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ADPCM的matlab算法，很简单，一般人都能看懂。(Matlab the ADPCM algorithm, is very simple, ordinary people can understand.)

Practical FIR filter design in matlab

通过恢复低秩矩阵得到显著性图，其中详细的运用了mean-shift算法，可以很好的了解mean-shift算法的使用，同时还包含了RPCA算法。(Significantly through the recovery of low rank matrix diagram, which detailed the use of mean-shift algorithm, can be a very good understanding of the use of the mean-shift algorithm, also contains RPCA algorithm.)

说明：  启发因子，信息素的重要程度 期望因子，城市间距离的重要程度 信息素增强系数 蚂蚁数量 表示每条边的能见度 参数可修改(heuristic factor, the importance of pheromone Expectation factor, the importance of distance between cities Pheromone enhancement coefficient Ant number Indicates the visibility of each side Parameters can be modified)

Element Remove Attribute N S extends D O M Test Case Source Code for Andriod.

在matlab上画出三个脑电信号的图像，并进行对比(Draw three EEG images on matlab, and compare)

灰狼优化算法的核心算法描述，并且配有中文说明(Gray Wolf optimization algorithm)

说明：  era函数，求频率，阵型，阻尼比,用于模态分析的数据处理，等等(era,frequency,damping ratio)

基于MATLAB M文件的IIR数字滤波器的设计(The design of IIR Digital Filter Based on MATLAB M file)

说明：  EKF仅仅利用了非线性函数Taylor展开式的一阶偏导部分（忽略高阶项），常常导致在状态的后验分布的估计上产生较大的误差，影响滤波算法的性能，从而影响整个跟踪系统的性能。最近，在自适应滤波领域又出现了新的算法——无味变换Kalman滤波器（Unscented Kalman Filter-UKF）。UKF的思想不同于EKF滤波，它通过设计少量的σ点，由σ点经由非线性函数的传播，计算出随机向量一、二阶统计特性的传播。因此它比EKF滤波能更好地迫近状态方程的非线性特性，从而比EKF滤波具有更高的估计精度。 (EKF only uses non-linear function of the first-order Taylor expansion of some partial derivatives (ignoring higher order terms), often leading to the posterior distribution of the state estimates to generate large errors affect the performance of filtering algorithms, which affect the whole tracking system performance. Recently, the field of adaptive filtering algorithms and the emergence of new- and tasteless transform Kalman filter (Unscented Kalman Filter-UKF). EKF UKF filter is different from the idea that it points through the design of a small amount of σ by σ point spread through the nonlinear function to calculate the random vector first and second order statistical properties of the transmission. Therefore it is better than the EKF filter nonlinear characteristics equation of state approach, which is more than the EKF filter estimation accuracy.)