svmcwtest1

通信方面的基础算法，关于相空间分析，小波标架，时频分析等的一些matlab源代码(Communications based on algorithms, on the phase-space analysis, wavelet frame, time-frequency analysis, such as some matlab source code)

说明：  一阶500s模型的模糊控制，用matlab 实现(First-order fuzzy control model 500s with matlab realize)

It is the huffman codage on matlab

说明：  matlab预处理程序，具体是杨氏干涉下条纹图像的预处理和算法的程序(matlab pretreatment procedures, specifically under the Young' s interference fringes image pre-processing procedures and algorithms)

线性方程组的列主元Gauss消去法，可以直接调用的，呵呵(Linear equations of Gauss elimination pivot column)

说明：  image与dat文件转换，可供DSP使用的dat文件。是用matlab写的。。。。。。。。。(dat files with image conversion for DSP using the dat file. Is written in matlab. . . . . . . . .)

这是一本matlab的入门教材，对matlab介绍很精简，电子版画面非常清楚，适合初学者学习(This is a Matlab introductory textbook description is very concise, very clear picture of the electronic version, suitable for beginners to learn matlab)

ultimate co pvp.. souce c#

通过建立运动模糊数学模型, 进行了消除运动模糊的仿真实验, 维纳滤波恢复运动模糊图像效果较 好。在图像恢复技术中, 点扩展函数( PSF) 是影响图像恢复结果的关键因素, 所以常常利用先验知识和后验判 断方法估计PSF函数来恢复图像。实验表明在实际恢复过程中如果运动模糊图像混入了噪声, 必须考虑到信噪 比、噪声的自相关函数和原始图像的自相关函数对恢复后图像的影响。(Through the establishment of mathematical model of motion blur was conducted simulation experiments to eliminate motion blur, Wiener filter to restore motion-blurred image effect is good. In the image restoration technology, the point spread function (PSF) that affect the results of image restoration a key factor, so often used to determine a priori knowledge and a posteriori estimation PSF function to restore the image. Experimental results show that the actual recovery process if the motion-blurred images mixed with the noise, we must take into account the signal to noise ratio, the noise autocorrelation function and the autocorrelation function of the original image of the restored image effects.)

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.)