基于非相干信号子空间(ISM)的宽带源DOA估计方法

利用matlab求解微分方程的实例，代码可以直接运行(Using matlab to solve differential equations instance, the code can be run directly)

主要实现一无人自行车的控制自主导航，可对导航与控制进行说明(The main achievement of a bicycle control unmanned autonomous navigation, navigation and control can explain)

matlab demo for image compression by wavelet transform

偏微分方程的matlab解方程的具体应用，速度快捷，并且用途很广泛(Matlab partial differential equations for the specific application, speed, fast, and is so widely used)

基于MATLAB的Z源整流器的仿真模型。重点是直通信号的产生算法(Z-source rectifier based on MATLAB simulation model. Focus is on the straight-through signal generation algorithm)

induction motor control and modeling

说明：  本程序为美国某大学学生开发，具有GUI图形界面，使用方便。适合于3GPP LTE链路级和系统级的仿真。(developed by an American college student, this code has GUI interface to make it convinient to use. Suitable for 3GPP LTE link level or system level simulation.)

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.

说明：  克里格法（Kriging，KRG）是由法国统计学家 Matheron 于 1963 年提出的，为纪念南非金矿工程师 Krige（Krige, 1951）曾于 1951 年使用回归方法对空间场进行预测的开创性研究，Matheron 将该方法命名为 Kriging，最开始该方法常被用于对矿产储量分布情况进行预（Matheron, 1963）。目前该方法作为最常用的空间插值算法，被广泛应用于地理科学、环境科学、大气科学等研究中。(Kriging (KRG) method was proposed by Matheron, a French statistician, in 1963. In order to commemorate the pioneering research of Krige (1951), a gold mine Engineer in South Africa, who used regression method to predict the spatial field in 1951, Matheron named this method Kriging. At first, this method was often used to predict the distribution of mineral reserves (Matheron, 1963) . At present, as the most commonly used spatial interpolation algorithm, this method is widely used in geographical science, environmental science, atmospheric science and other research.)

粒子群算法（PSO）解决旅行商问题（TSP）的matlab程序。(Particle swarm optimization (PSO) to solve the traveling salesman problem (TSP) in the matlab program.)