jseasyui

说明：  OFDM通信系统仿真设计的MATLAB实现(OFDM Communication System Simulation and Design of MATLAB Implementation)

四轮转向两自由度模型，测质心侧偏角和横摆角速度(Four-wheel steering two degree of freedom model of center of mass of side-slip Angle and yawing angular velocity measurement)

实现 NL_means 算法，用来去噪。并赋带使用例子和实验结果。(Achieve NL_means algorithm to de-noising. And endowed with the use of examples and experimental results.)

antenna design using matlab patch antenna.

数据包传送源码程序，连续相位调制信号(CPM)产生，是信号处理的基础，进行逐步线性回归，LDPC码的完整的编译码。( Data packet transfer source program, Continuous phase modulation signal (CPM) to produce, Is the basis of the signal processing, Stepwise linear regression, Complete codec LDPC code.)

混沌图像加密，不错的界面程序，可以添加图片，非常实用(Chaotic image encryption, good interface program, you can add pictures, very practical)

信号稀疏表示是一种新兴的信号分析和综合方法，其目的就是在过完备字典中用尽可能少的原子来表示信号。采用时频原子字典的信号稀疏表示能够有效地揭示非平稳信号的时变特征。信号稀疏表示吸引了研究者的大量关注，这种方法已经被应用到信号处理的许多方面，例如非平稳信号分析，信号编码、识别与信号去噪等。(Signal sparse representation is a new method of signal analysis and synthesis, and its purpose is in over-complete dictionary of atoms using as little as possible to represent the signal. When using frequency atom dictionary signal sparse representation can effectively reveal the time-varying characteristics of non-stationary signals. Signal sparse representation attracted a lot of attention of researchers, this method has been applied to many aspects of signal processing, such as non-stationary signal analysis, signal encoding, recognition and signal denoising.)

implementation of notch filter

MYW - ARMA 算法的MATLAB代码, 是频谱分析(通常是在高级DSP这门课中会用到的)的常用算法(MYW- ARMA algorithm MATLAB code, Analysis of the spectrum (usually at the senior DSP This class will be used) to the commonly used algorithm)

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.