Stanford地震数据处理反演解释工具QSItools

费诺码在MATLAB条件下的实现，可按概率降序排列的码字，求出码长及编码时间等功能 ((Fenaux code in MATLAB to achieve under the conditions, in descending order according to the probability of the code word, find time to code length and coding functions ()

实现DQPSK信号调制解调功能，并给出了详细的matlab仿真结果(The realization of DQPSK signal modulation and demodulation functions, and gives a detailed matlab simulation results)

MSK在高斯白噪声信道的误码率仿真.......................................................................(BER of MSK modulation)

说明：  信道仿真模型 可以方便快捷的进行相关的仿真操作(Channel simulation models can be convenient and efficient operation of the associated simulation)

采用matlab编写的DPCM程序，实现了DCT编码的功能(DPCM matlab prepared using procedures to achieve the DCT encoding function)

matlab开发环境，．编程实现二维DFT的变换，并与标准函数结果fft2进行比较，基本傅里叶、离散余弦变换，空域（完成两个程序） 增强噪声后再进行处理，利用标准函数，求图像阈值分割、边缘检测的基本使用（lena256.bmp，方法：graythresh、edge），求分水岭，利用标准函数dwt2、idwt2分解图像man_pic.jpg，基本 BP-BPbase1 (matlab development environment. Programming dimensional DFT transform, and with the standard functions compare results fft2 basic Fourier, discrete cosine transform, airspace (to complete the two programs) to enhance the noise and then processed using standard functions for image thresholding, edge Detection of basic use (lena256.bmp, method: graythresh, edge), seeking watershed, using a standard function dwt2, idwt2 decomposed image man_pic.jpg, basic BP-BPbase1)

一个图像去噪的Matlab程序，可用于图像的边缘提取等工作。(Image denoising)

傅里叶变换小程序。利用matlab编写的傅里叶变换小程序(Fourier transform applet. Fourier transform using matlab write small programs)

小波域内去噪滤波器matlab代码(MATLAB 图像处理与计算机视觉 )(Wavelet domain denoising filter matlab code (MATLAB image processing and computer vision))

支持向量机的研究现已成为机器学习领域中的研究热点，其理论基础是Vapnik[3]等提出的统计学习理论。统计学习理论采用结构风险最小化准则，在最小化样本点误差的同时，缩小模型泛化误差的上界，即最小化模型的结构风险，从而提高了模型的泛化能力，这一优点在小样本学习中更为突出。SVM理论正是在这一基础上发展而来的，经过十几年的研究和发展，已开始逐步应用于一些领域。在解决小样本、非线性及高维模式识别问题中表现出许多特有的优势，已经在模式识别、函数逼近和概率密度估计等方面取得了良好的效果。( Support Vector Machine (SVM) is a new machine learning technique in recent years developed based on statistical learning theory (SLT). It wins popularity due to many attractive features and emphatically performance in the fields of nonlinear and high dimensional pattern recognition. The theory and algorithm of SVC is studied at first, then, simulation is to recognize handwritten numeral with the Lib-SVM toolbox. At last, we study the result, which shows that the SVC can do the classification problem with good performance, shorter operation time and is more suitable for real-time implementation.)