LINEAR

进行图像的角点提取，方法是先通过Harris方法选初始点，然后对初始点进行Forstner算法进行筛选，最后得到图像角点。(The image of the corner detection, the method is to first select the initial point through the Harris method, and then point Forstner initial screening algorithm, the resulting image corner.)

求第一个城市到其它城市的最短路径.用矩阵（为顶点个数）存放各边权的邻接矩阵，行向量、、、分别用来存放标号信息、标号顶点顺序、标号顶点索引、最短通路的值(The first city to find the shortest path to other cities. With a matrix (for the number of vertices) records of the right side of the adjacency matrix, row vector, were used to store label information and label the vertex order, label the vertex indexes, the shortest pathway value)

通过matlab程序实现，基于光流法的运动估计，可以对图像进行预测(By matlab program, optical flow motion estimation method based on the image can be predicted)

利用KNN最近邻算法，对心音信号进行分割并判断，给出第一心音、第二心音和收缩期、舒张期。(Use KNN nearest neighbor algorithm, the heart sound signal is divided and judgment is given first, second heart sound and systolic, diastolic.)

说明：  利用BP神经网络作为分类器，对公司财务进行分类，构造分类器，结果表明能够很好的将数据分类，有兴趣可以看看。(BP neural network is used as a classifier to classify corporate finance and construct a classifier. The results show that the data can be well classified and you can have a look if you are interested.)

matlab code for rake receiver

边缘检测代码。多种检测方法，包括梯度算法（多种窗），LOG算法，sobel算子等等(Edge detection code. A variety of detection methods, including gradient algorithm (multiple windows), LOG algorithm, sobel operator, etc.)

求解电力网络机电暂态DAE方程，得到系统随时间变化的状态量轨迹(solve the mechanical/electrical transient DAE function of power system, in order to get the trace of the state parameter when time varies)

FIR数字滤波器的设计和实现. matlab 7.0实现(FIR digital filter design and realization. Matlab 7.0 to achieve)

Particle swarm optimization has been used to solve many optimization problems since it was proposed by Kennedy and Eberhart in 1995 [4]. After that, they published one book [9] and several papers on this topic [5][7][13][15], one of which did a study on its performance using four nonlinear functions adopted as a benchmark by many researchers in this area [14]. In PSO, each particle moves in the search space with a velocity according to its own previous best solution and its group’s previous best solution. The dimension of the search space can be any positive integer.