RGB2YUV222

In this paper, we show how support vector machine (SVM) can be employed as a powerful tool for $k$-nearest neighbor (kNN) classifier. A novel multi-class dimensionality reduction approach, Discriminant Analysis via Support Vectors (SVDA), is introduced by using the SVM. The kernel mapping idea is used to derive the non-linear version, Kernel Discriminant via Support Vectors (SVKD). In SVDA, only support vectors are involved to obtain the transformation matrix. Thus, the computational complexity can be greatly reduced for kernel based feature extraction. Experiments carried out on several standard databases show a clear improvement on LDA-based recognition

隐马尔科夫过程预测编码，对于使用或研究隐马尔科夫模型的人会有帮助。(Hidden Markov process predictive coding, for the use of hidden Markov models or research who will help.)

卡尔曼滤波，卡尔曼滤波，卡尔曼滤波，卡尔曼滤波，卡尔曼滤波，(Kalman filtering, Kalman filtering, Kalman filtering, Kalman filtering, Kalman filtering,)

OFDM with BER calculation using bpsk

基础fxlms算法在有源噪声控制中的应用(Application of basic FXLMS algorithm in active noise control)

通过画出概率密度的的分布曲线，得到节点位置。(Through the draw probability density distribution curve, to be the node location.)

matlab实现的非线性模型预测控制算法(matlab implementation of nonlinear model predictive control algorithm)

在matlab中实现基于IHS的多光谱图像和全色高分辨率图像的融合，效果十分良好。(The integration of the IHS-based multi-spectral image and panchromatic high-resolution images in matlab, the effect is very good.)

变换编码中相关系数的求解，特别适用于KLT变换，得到对角矩阵(Correlation coefficients in transform coding solution, especially suitable for KLT transform, the diagonal matrix)

控制excle 生成控制cpn的编码； (Control excle generate control cpn encoding )