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Principal Components Analysis (PCA) is used to compress data in such a way that the least information is lost. It does so by truncating data and thereby leaving out the data which is of the least importance to the information stored in the data. This PCA process is called dimensionality reduction,

一个navigation vieew的xcode手机程序可直接使用。 (A navigation vieew the xcode phone program can be used directly.)

Krivenko internal tool for glucometer accuracy control

信号特征提取16个时域、频域指标 均值、标准差、方差、 偏斜度指标、峭度指标、 峰-峰值、峰值、均方幅值、平均幅值、方根幅值、波形指标、峰值指标(Signal feature extraction 16 time domain, frequency domain mean, standard deviation, variance, skewness index, kurtosis index, peak- peak, mean square amplitude, average amplitude, square root amplitude, waveform indicators Peak Indicators)

findEnergy.m 计算出图像的梯度图 findSeamImg.m 计算出所有可能的竖直缝 findSeam.m找出能量最小的一条竖直缝 removalMap.m将需要删除的N条竖直缝存储起来并按照索引逐条删除 SeamCut.m将选中的缝一条条的删除 SeamPlot.m在图中画出要删除的缝 Testseam.m调用以上函数的主程序(FINDENERGY creates an gradient img from a given RGB or grayscale image. FINDSEAMIMG finds the seam map from which the optimal (vertical running) seam can be calculated. FINDSEAM returns a column vector of coordinates for the pixels to be removed (the seam). SEAMPLOT takes as input an image and the SeamVector array and produces an image with the seam line superimposed upon the input image, x, for display purposes. SEAMCUT takes as input a RGB or grayscale image and SeamVector array to find the pixels contained in the seam, and to remove them from the image. REMOVALMAP takes a given image and finds the ordered set of (vertical) seams that are removed from an image and returns them in an array, where the Nth column in the array corresponds to the Nth seam to be removed.)

用于信号特征提取、信号消噪，用MATLAB编写的遗传算法路径规划，采用加权网络中节点强度和权重都是幂率分布的模型。( For feature extraction, signal de-noising, Genetic algorithms using MATLAB path planning, Using weighted model nodes in the network strength and weight are power law distribution.)

Tutorial on Sift master matlab

用米尔恩（Milne）公式求解常微分方程初值问题的数值解(By Milne (Milne) for solving ordinary differential equations numerical solution of initial value problems)

Simulation of an AR-GJR process

说明：  vmd是2014提出的模态分解算法，对非平稳信号的分析产生了重大的影响。(VMD is a modal decomposition algorithm proposed in 2014, which has a significant impact on the analysis of non-stationary signals.)