create_segmentations_from_detections

瑞利信道matlab仿真源代码。简单，描述如何进行信道仿真(Rayleigh channel simulation matlab source code. Easy to describe how the channel simulation)

暗示大家说的教案设计的就撒娇的就撒娇大家(js)

adaboost代码，比较简单。但方便了解Adaboost算法的基本原理(The aim of the project is to provide a source of the meta-learning algorithm known as AdaBoost to improve the performance of the user-defined classifiers. )

说明：  该程序能够成功地基于EPANET进行压力驱动状态模拟。(this program can perform pressure driven simulation based on EPANET.)

文中介绍了传统的PID控制原理，PID分为位置式和增量式，计算机控制是数字PID控制。一些 改进的PID控制算法是针对实际应用中的不足提出的，如积分分离式PID控制算法，抗 积分饱和算法，变速积分算法等，实际应用中，控制又分为简单PID控制和串级PID控 制，对一些典型的控制算法，选用典型控制对象用MArLAB软件进行了对应仿真。对仿 真结果进行了对比分析，说明了改进算法的作用。(This paper introduces the principle of the traditional PID control, PID into position and incremental, computer-controlled digital PID control. Some improvements in the PID control algorithm is less than the practical application of the proposed, such as discrete integral PID control algorithm, anti-integral saturation algorithm, changing integration algorithm and so on, practical application, the control is divided into a simple cascade PID control and PID control , for some typical control algorithm, selection of a typical control object MArLAB software with the corresponding simulation. The simulation results of comparative analysis carried out to illustrate the role of the improved algorithm.)

说明：  生成EA随机网络 节点任意设置 参数可交互式设置(EA random networks)

动态聚类的k均值算法,采用matlab编程,内有多个子程序，和一个主程序(dynamic clustering algorithm k mean using matlab)

平移运动阵列，每次快拍平移一个阵元间距，然后将各个时刻阵元上接收到的信号相加，从而使得原来相干的信号变成部分相关信号。该程序观察阵元上信号形成的干涉条纹，以此判断信号的相干程度(Translational motion array, shifting each time a snapshot array element spacing, array element and then each time the signal received on the sum, so that the original signal into a coherent part of the relevant signals. Array element of the program observe the interference fringes formed on the signal in order to determine the degree of coherent signals)

2011年最新维纳滤波器的设计，课程设计验收结果为优(In 2011, the latest wiener filter design, course design acceptance for optimal results)

ConvolveByRiemannSum assumes that x(t) and h(t) have finite support, and that the user provides samples of these functions in two vectors, x and h. These vectors contain samples of x, sampled at time points given in vector tx = tx_min:Delta_t:tx_max and samples of h sampled at time points in vector th = th_min:Delta_t:th_max, respectively. The functions x and h are assumed to be zero outside tx_min and tx_max and outside th_min and th_max, respectively. Note that while tx_min, tx_max, th_min, and th_max may differ, the time sample vectors must share the same time-interval, Delta_t.