GPSchengxu

SMITH算法，计算归一化阻抗，电流，电压，离负载的距离等等(SMITH algorithm, calculation of normalized impedance, current, voltage, distance from the load and so on)

数值分析中，用弦截方法求解方程的根 (Numerical analysis, the strings cut method for solving equations with roots)

将矩阵内的数据排序，并输出相应原位置坐标 (The matrix of data sorting, and output the corresponding original position coordinates)

在重构相空间的基础上计算吸引子的分形维时,合适的嵌入维m和时间滞后τ的选取至关重要.在虚假邻域概念基础上,本文给出了确定m和τ的方法.较之以前的方法,此方法更准确、简单、容易理解、易实施,对较小样本数据集也可靠.最后的应用实例验证了理论的正确性.(In the reconstructed phase space based on the calculation of the fractal dimension of attractor, the appropriate embedding dimension m and time lag τ selection is essential. In a mock neighborhood based on the concept, this paper presents the method to determine m and τ. Compared with the previous methods, this method is more accurate, simple, easy to understand, easy to implement on a smaller set of sample data is also reliable. The last application example to verify the theoretical correctness.)

应用MATLAB编写的关于lbp与hog特征的提取代码，可直接应用(Applications written in MATLAB and hog on lbp feature extraction code, can be applied directly)

孤立波的 matlab 的数值计算和模拟(Solitary waves and numerical simulation matlab)

改进的Cholesky分解的过程演示，本人原创，测试无误，内含详细注释。 (Cholesky decomposition process improvement presentation, my original test and correct, containing detailed notes.)

一款工作于2.4 GHz 频段的带有源负载的高性能双平衡有源混频器0479-8023(2012)04-0538-07(A High Performance Double-Balanced Gilbert Mixer with Active Loads for 2.4-GHz ISM Band Application)

说明：  支持向量机的C++源代码包，用机器学习的方法处理优化问题，可以作为matlab的工具箱。(C++ code for proceeding SVM problems！)

Edge detection result should be enhanced using linear method like Median filter to remove the garbage around the pupil to gain clear pupil to determine perfect centre. Get the centre of the pupil by counting the number of black pixels (zero value) of each column and row. Then get each row and column that has the maximum number of these black pixels. Then determine the center by simple calculation according to the image coordinate to set it correct on the image, consequently we can determine the radius of the pupil. Thus we can find the pupillary boundary (inner). A similar procedure is extended by using a coarse scale to locate the outer boundary (limbus) which can be apparent by using the mid-point algorithms of circle and ellipse.