595643723dRecon

流形学习matlab实现源码，好东西大家一起分享(Manifold learning matlab source code to achieve good things to share with everyone)

这个程序实现了一个mini的MAC系统，唯一的缺点是并没有考虑到功率限制的问题。(This program realise a system of minipac,the only shortcoming is that the lack consideration of power constraint.)

牛顿拉夫逊法 的matlab 程序,已经验证 可以使用(Newton Raphson method)

recursive least square (RLS) algorithm using matlab with examples

投影寻踪等级评级模型求解和遗传算法的实现(Projection Pursuit Model and Genetic Algorithm for Solving)

说明：  提取图像的NMI特征（归一化转动惯量特征）(NMI feature extraction of image（normalized moment of inertia）)

说明：  这个事matlab作图的一个ppt演示范本，希望对大家有用(The matter of a ppt presentation matlab mapping model, we want to be useful)

：该文把局部三值模式(Local Ternary Patterns, LTP)纹理特征引入Mean Shift 跟踪算法，提出了基于多 特征的Mean Shift 人脸跟踪算法以解决Mean shift 跟踪算法的鲁棒性问题。通过对LTP 纹理特征的分析、研究， 提出了一个LTP 关键纹理模型，既增强了目标的关键纹理信息，又简化了LTP 纹理模型。在此基础上，提出一 种基于LTP 关键纹理特征和肤色特征的Mean Shift 人脸跟踪算法，有效地解决了Mean Shift 算法的鲁棒性问题。 为进一步提高对快速运动目标的跟踪速度和跟踪性能，该文引入了卡尔曼滤波器对目标进行预测。实验结果表明， 该文的算法在目标定位的准确性和跟踪性能上比Mean Shift 算法均有明显的提高。(: In this paper, the texture characteristics of the local ternary patterns Local Ternary Patterns (LTP) Mean Shift tracking algorithm proposed Mean Shift face tracking algorithm based on multiple features in order to solve the Mean shift tracking algorithm robustness. Texture characteristics of LTP analysis, research, a key LTP texture model, not only enhanced the key goal of texture information, but also simplifies the the LTP texture model. On this basis, based on the LTP key texture features and color characteristics Mean Shift face tracking algorithm, effectively solved the robustness of the Mean Shift algorithm. To further enhance the fast-moving target tracking speed and tracking performance, this paper introduces the Kalman filter to predict the target. The experimental results show that the algorithm of the text in the target positioning accuracy and tracking performance than the Mean Shift algorithm to significantly improve.)

结合实例，使用matlab语言，完成递归神经网络程序的编写（LSTM）(Combined with an example and matlab language, completed the compilation of recursive neural network program (LSTM))

关于SAR和极化SAR信息处理的好程序。Cloude.m——经典的Cloude极化分解方法中H/alpha参数的求解方法；classes.m——Cloude极化分解方法；myCFAR.m——三种SAR图像目标检测的恒虚警处理方法。全局CFAR，局部CFAR，指数CFAR(SAR and POLSAR information processing codes. Cloude.m-- H/alpha parameter ofclassic Cloude polarization decomposition classes.m--Cloude decomposition myCFAR.m-- the three CFAR methods of target detection in SAR images)