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这工程序的的功能主要是机遇MATLAB的通信数字调制系统(This work program is an opportunity the main function of digital modulation system of communication MATLAB)

盲信号分离中算法中的ELSA算法，是自然梯度发的改进(Blind signal separation algorithm ELSA algorithm to improve the natural gradient hair)

差集天线阵列分析\方向图、二维图、三维图(difference sets)

求一个系统的幅频响应相频响应单位冲击响应以及其它(Find the frequency response of a system unit impulse response of the phase-frequency response and other)

说明：  matlab实用程序300例，非常好的学习资料，300个经典的实用例子，可以使你对matlab有更深入的了解。(matlab utility 300 cases, very good learning materials, 300 classical and practical examples, matlab can make you a better understanding.)

说明：  扩展卡尔曼滤波程序，经过MATLAB测试过，万全好用。(Extended Kalman filter procedure, after MATLAB tested, foolproof to use.)

Tracking of visual phenomena is hard. Very, very hard. And frustrating. You should try not to get discouraged by poor tracking results, but rather concentrate on the specific reasons why your tracker may not be performing well. Is the bad performance predictable from the theoretical properties of the tracker? If so, that s a valuable observation that will serve you well in the future

摘要:本算法是在经典 Sobel算子基础上 ,结合汽车本身的实际情况 ,增加了 6个方向模板 ,同时利用基于 L范数的各同性扩散去噪模型消除噪声 ,最终得出汽车轮廓图。仿真结果表明:该算法对汽车图像的噪声干扰 有较强的抑制能力 ,汽车轮廓提取定位更准 ,精度更高 ,图像更清晰。 关键词:边缘检测算法 车辆轮廓 噪声 Sobel算子

generacion de figuras en matlab

matlab源代码，是非负矩阵分解工具箱，包含乘法更新，最小二乘约束，投影梯度法等方法(Usage: [W,H] = nmf(X,K,alg,maxiter,speak) W: output matrix H: output matrix X: input matrix K: number of components alg: algorithm to use maxiter: maximum number of iterations speak: print to screen Algorithms: mm: Multiplicative updates method using euclidean distance measure. cjlin: Projected gradient method prob: Probabilistic non-negative matrix factorization. als: Alternating least squares. alsobs: Alternating least squares with optimal brain surgeon. Demonstrations: PET: NMF on a PET dataset Text: NMF used on a three different datasets Email, medical, and CNN. Algorithms mm: Multiplicative update method using euclidean distance measure. Described in Lee and Seung, 2001, Algorithms for Non-negative Matrix Factorization, Advances in Neural Information Processing Systems 13, 556-562. This algorithm is the most commonly used algorithm to solve NMF. cjlin: Alternative non-negative least squares using projected gradients. Author: Chih-Je)