syntraces

matlab灰度二值化经典教程Classic Gray binary matlab tutorial(matlab binary Gray Classic Classic Gray binary matlab tutorial tutorial)

jpeg image compression

The basic snake segmentation (2d and 3d). A snake is an active (moving) contour, in which the points are attracted by edges and other boundaries. To keep the contour smooth, an membrame and thin plate energy is used as regularization.

This file contains the e-book of Radar systems analysis using matlab

详细且一目了然的RS编译码matlab仿真程序。 包括GF(Q)域的各种运算（每种运算一个函数功能文件），RS编码模块及译码模块。 期中译码模块又分为伴随式计算模块，错误位置多项式计算模块，错误位置计算模块，错误数值计算模块。 还包含了专门用于测试每个模块是否正确的testbache。完整，详细，正确(Detailed and clear RS codec matlab simulation program. Including the GF (Q) domain of a variety of computing (computing a function of each functional document), RS coding and decoding module module. During the decoding module is divided into modules with computing, error location polynomial calculation module, the error location calculation module, module numerical error. Also includes a special module for testing the validity of each testbache. Complete, detailed and correct)

在802.16e的系统中，仿真分析AWGN，瑞利衰落信道中的LDPC码的性能。程序中包括编译码的程序。(In 802.16e systems, simulation analysis AWGN, Rayleigh fading channel the performance of LDPC codes. Program, including encoding and decoding process.)

CMOS集成电路基础知识，CMOS集成电路的接口电路，CMOS集成电路使用注意事项 (Basic knowledge of CMOS CMOS integrated circuit, integrated circuit interface circuit, CMOS integrated circuit use matters needing attention )

AP聚类方法，是08年发表在“科学”上的聚类方法，本人从图像图像方面研究近十年，这是我所见过最好的聚类算法，本程序提供聚类演示，还把它用图像分割试验（matlab7.0环境调试）。(AP clustering method, is 08 published in " Science" on the clustering method, the image from the image in my research in recent years, this is the best I have ever seen clustering algorithm, Clustering presentation of this program provides, but also test it with the image segmentation (matlab7.0 debugging environment).)

基于MATLAB的kmeans聚类分析，包含数据和源代码，(MATLAB-based kmeans cluster analysis, including data and source code,)

matlab中的主成分分析法，有效的计算了以后预测问题，精度高。(matlab in the principal component analysis, effective after the prediction calculated with high precision.)