C_CMethod

求对称矩阵全部特征值的Jacobi方法(Symmetric matrix for all the eigenvalues of Jacobi method)

自己收集的粒子滤波程序，包括基本的程序，卡尔曼滤波以及改进的算法。(Their own procedures for the collection of the particle filter.)

SPIHT图像压缩编码的Matlab程序。(SPIHT image compression encoding procedure of Matlab.)

利用LDA与SVM实现两类之间的分类程序(classification program between the two classes using LDA and SVM)

This is a source code for qam modulation without using MATLAB built-in function

功能列表= mmwrite（文件名，选择... ...） mmwrite是能写的AVI，WMV，的WMA，ASF文件。对于AVI文件，您可以选择从现有的编解码器压缩的音频和视频流。 对于WMV，WMA和ASF的编码默认为16位的Windows Media 9率为44100Hz立体声为98％质量的音频和Windows媒体视频9 98 质量。质量可以指定音频和视频。 环绕声，似乎只与AVI和多通道编码不支持。编写任何其他文件类型不支持。本使用Windows的DirectX基础设施，以便其他操作系统是出于运气。 输入：     文件名：这一定是第一个参数，并指定文件名写。     影片结构：视频结构相匹配的mmread输出。在最低限度，必须有4个领域的“框架”，“时代”，“高度”和“宽度”。该“框架”字段必须是一个结构与一个字段数组“的CDATA”的原始框架，它包含由彩色编码的数据宽度高度（3）UINT8s。钍(function list = mmwrite(filename,...options...) mmwrite is able to write AVI,WMV,WMA,ASF files. For AVI files you can choose from the available codecs to compress the audio and video streams. For WMV,WMA and ASF the encoding defaults to Windows Media 9 44100Hz 16bit stereo 98 quality for the audio and Windows Media 9 Video with 98 quality. The quality can be specified for both audio and video. Surround sound only seems to work with AVI and multi-pass encoding is not supported. Writing any other file type is not supported. This uses Windows DirectX infrastructure, so other OSs are out of luck. INPUT: filename: This must be the first parameter and specifies the filename to write. video structure: The video structure matches the output of mmread. At a minimum it must have 4 fields "frames", "times", "height" and "width". The "frames" field must be a struct array with a field "cdata" that contains the raw frame data encoded as height by width by color(3) as UINT8s. Th)

PCA-L1算法程序，主要用于人脸识别，人工智能(PCA-L1 method)

这是加权L1的算法代码，用于压缩感知信号处理，很不错的(This is the weighted L1 algorithm code for compressed sensing, signal processing, it is good)

mimo系统性能测试的matlab源程序(mimo system performance test matlab source)

Regularized Logistic Regression