voice-recognition

simulation modulate & demod PM

对图像求拉普拉斯变换，分别使用中心为-4、-8其他值均为1的拉普拉斯滤波器(Laplace transform of image, Laplace filter respectively using the center for- 4、-8，other value for 1)

Meanshift运动目标跟踪matlab代码，实现运动目标的跟踪，代码适合新手学习(Meanshift moving target tracking matlab code to achieve the tracking of moving objects, the code for novices to learn!)

MATLAB 下的数字信号处理实现示例 附录一 信号、系统和系统响应 1、理想采样信号序列 （1）首先产生信号 x(n),0<=n<=50 (MATLAB under the realization of digital signal processing examples in Appendix 1 signal, systems and systems to respond to one, the ideal sampling signal sequence (1) First of all, have a signal x (n), 0 )

包含位置式PID算法、积分分离式PID，基于分段非线性权重值的Pso算法，基于matlab平台实现。( It contains positional PID algorithm, integral separate PID, Based on piecewise nonlinear weight value Pso algorithm, Based on matlab platform.)

经典的DOA估计算法，矩阵束的ESPRIT算法(Classical DOA estimation algorithm, ESPRIT algorithm of matrix pencil)

IDE接口的相当棒的资料 不看后悔 加油兄弟们 谢谢~(IDE interface rather stick information Bukanhouhui refueling brothers Thank you ~)

matlab在土木工程领域运用，有限元程序，求解振型和频率，给出了阵型图。程序已模块化(matlab use in the field of civil engineering, finite element program, to solve the vibration modes and frequencies are given in Figure formation. Program has a modular)

应用条件随机场知识对图像进行分割，场景理解。(Application CRFs knowledge of image segmentation, scene understanding.)

测试可以跑，根据自己情况修改下函数即可. NSGA-III 首先定义一组参考点。然后随机生成含有 N 个（原文献说最好与参考点个数相同）个体的初始种群，其中 N 是种群大小。接下来，算法进行迭代直至终止条件满足。在第 t 代，算法在当前种群 Pt的基础上，通过随机选择，模拟两点交叉（Simulated Binary Crossover，SBX）和多项式变异 产生子代种群 Qt。Pt和 Qt的大小均为 N。因此，两个种群 Pt和 Qt合并会形成种群大小为 2N 的新的种群 Rt=Pt∪Qt。 为了从种群 Rt中选择最好的 N 个解进入下一代，首先利用基于Pareto支配的非支配排序将 Rt分为若干不同的非支配层（F1，F2等等）。然后，算法构建一个新的种群St，构建方法是从 F1开始，逐次将各非支配层的解加入到 St，直至 St的大小等于 N，或首次大于 N。假设最后可以接受的非支配层是 L层，那么在 L+ 1 层以及之后的那些解就被丢弃掉了，且 St FL中的解已经确定被选择作为 Pt+1中的解。Pt+1中余下的个体需要从 FL中选取，选择的依据是要使种群在目标空间中具有理想的多样性。(The test can run and modify the function according to its own situation. NSGA-III first defines a set of reference points. Then the initial population containing N individuals (preferably the same number of reference points as the original literature) was randomly generated, where N was the size of the population. Next, the algorithm is iterated until the termination condition is satisfied. On the basis of current population Pt, the algorithm simulates two-point crossover (SBX) and polynomial mutation to produce offspring population Qt by random selection.)