meshgen

费聂耳衍射成像的模拟，采用透镜成像原理，有效地利用fft(simulation of the diffraction，based on the optical lens and the theory of fft)

用列梅兹算法确定函数的最佳一致逼近多项式(With Lie Meizi algorithm to determine the best uniform approximation polynomial function)

PCA故障检测MATLAB 基于PCA的故障检测MATLAB程序，有完整数据获取程序(PCA PCA-based fault detection fault detection MATLAB MATLAB program, a complete data acquisition program)

对信号进行短时傅立叶变换，输出的序列在进行循环平稳处理，最后用mesh函数进行3维画图。()

采用小波相邻系数降噪，然后用改进的局域均值分解对滚动轴承进行特征提取，得到特征向量(Using adjacent wavelet coefficients of noise reduction, and then use the improved local mean decomposition characteristics of rolling bearing are extracted, get the feature vector)

Medical image processing is the most challenging and emerging field now a days. Processing of MRI images is one of the part of this field. This paper describes the proposed strategy to detect & extraction of brain tumour patient’s MRI scan images of the brain. This method incorporates with some noise removal functions, segmentation and morphological operations which are the basic concepts of image processing. Detection and extraction of tumour MRI scan images of the brain is done by using MATLAB software.

这个是关于随机数产生的matlab仿真程序(This is a random number generator on the simulation program)

线性缓冲区FIR滤波器设计，可利用MATLAB产生系数，利用CCS开发环境，调试成功！(FIR filter design for linear buffer can be generated using MATLAB coefficient, the use of CCS development environment, debugging success!)

说明：  单极性码间干扰与信道均衡，眼图观察均衡效果(Unipolar inter-symbol interference and channel equalization, eye observation balanced results)

这里主要对LMS算法及一些改进的LMS算法（NLMS算法、变步长LMS算法、变换域LMS算法）之间的不同点进行了比较，在传统的LMS算法的基础上发展了LMS算法的应用。另一方面又从RLS算法的分析中对其与LMS算法的不同特性进行了比较，很值得研究！(The key players here and some of the LMS algorithm is an improved LMS algorithm (NLMS algorithm, variable step size LMS algorithm, transform domain LMS algorithm) the differences between the points of comparison, in the traditional LMS algorithm is developed on the basis of the LMS algorithm applications. On the other hand and from the analysis of RLS algorithm and LMS algorithm for its different characteristics were compared, it is worth studying!)