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图像类型的相互转换，灰度图像的二值化方法，图像类型的相互转换，灰度图像的二值化方法 (Image type conversion, gray image binarization method, image type conversion, gray image binarization method)

有关移动通信中莱斯(Rician)信道衰减的仿真，对信道的理解有一定的帮助(About mobile communications (Rician) Rice fading channel simulation, the channel has some help understanding)

基于MATLAB心音信号的特征提取的平均的算法(Average extracted based on the characteristics of heart sound signals in MATLAB algorithm)

Tulloch M. - Training Guide Installing and Configuring Windows Server 2012

D-S证据理论数据融合，包括压缩比、运行时间和计算复原图像的峰值信噪比,合成孔径雷达（SAR)目标成像仿真。( D-S evidence theory data fusion, Including compression ratio, image restoration computing uptime and peak signal to noise ratio, Synthetic Aperture Radar (SAR) imaging simulation target.)

BIRCH (balanced iterative reducing and clustering using hierarchies) is an unsupervised data mining algorithm used to perform hierarchical clustering over particularly large data-sets. An advantage of Birch is its ability to incrementally and dynamically cluster incoming, multi-dimensional metric data points in an attempt to produce the best quality clustering for a given set of resources (memory and time constraints). In most cases, Birch only requires a single scan of the database. In addition, Birch is recognized[1] as the, "first clustering algorithm proposed in the database area to handle noise (data points that are not part of the underlying pattern) effectively".

胡广书（数字信号处理的算法及实现）光盘，非常好的数字信号处理教程，内含matlab算法(HU Guang-book (digital signal processing algorithms and implementation) CD-ROM, a very good digital signal processing tutorial, containing matlab algorithm)

数组运算示例，演示二维数组的存储方式（不同于其它语言）以及数组的运算和输出方法。(example of array operations. Demostration of storage (unlike other languages) , operation and output method of two dimensional array.)

多用户检测，解相关、MMSE以及传统多用户检测比较。(Multi-user detection, decorrelating, MMSE, as well as traditional multi-user detection comparison.)

包括语音信号处理（胡航版），matlab扩展编程，HMM学习最佳范例(Including voice signal processing (Hu Hang Edition), matlab extension programming, HMM learning best practices )