wavelet_filter_construction_and_noise_elimination

EM algorithm for Gaussian mixture. Works on arbitray dimensions with high speed and precision

两步法求方程的根植，，仅供参考仅供参考仅供参考仅供参考仅供参考(Two-step method for the equation of rooted, for reference purposes only for reference purposes only for reference purposes only)

本程序是求解神经电另一chay方程的较为简单的例子。还有一个非线性物理中的Lorenz模型。可以帮助大家了解神经放电的问题()

作业的第二部分 数字信号处理的基本思想rls算法(Operation of the second part of digital signal processing algorithms the basic idea of rls)

基于DCT的数字水印基本代码框架，能够完整的嵌入水印信息、提取水印信息。可根据需要再此基础上修改算法。(DCT-based digital watermarking code framework, a complete embedded watermark information to extract the watermark information. According to the need for this based on the modified algorithm.)

说明：  FM的M文件,是基于MATALB环境下运行的(FM M document is based on enriching environment running)

利用二维有限差分法对单脊波导TM模进行计算。通过调整网格数目观察计算结果的精度，可以设定出最优的网格数目，然后改变波导中脊的尺寸，观察最低两个TM模对应的截止频率的变化规律，最后给出对应的横截面电场、磁场分布图(The two-dimensional finite difference method to calculate the single-ridge waveguide TM mode. The optimal number of grid can be set by the accuracy of the adjustment of the calculation result of the grid number observed, and then change the size of the waveguide ridge observed the lowest two corresponding to the TM mode of the cutoff frequency of the variation, and finally to give the corresponding cross cross-section of the electric field, magnetic field maps)

说明：  在二进制图像中利用lookup 表进行边沿操作(In binary images using lookup table edge operation)

pls toolbox for matlab

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.)