This software is a Matlab implementation of restricted sampling from Gaussian distribution, and sample x (column vector) from N(x_mu, x_var), restricted in x_min<=x<=x_max.
标签： implementation distribution restricted Gaussian
上传时间： 2016-12-30
上传用户：6546544
ABC_FDTD_Die(T) Implements simulation of a Gaussian Pulse over T time steps. ABC are for free space. If boundaries are in the Dielectric medium then the ABC fail. Dielectric medium begin and end can be specified with the code
标签： ABC_FDTD_Die Implements simulation Gaussian
上传时间： 2013-12-22
上传用户：caiiicc
ABC_FDTD_Die(T) Implements simulation of a Gaussian Pulse over T time steps. ABC are for free space. If boundaries are in the Dielectric medium then the ABC fail. Dielectric medium begin and end can be specified with the code
标签： ABC_FDTD_Die Implements simulation Gaussian
上传时间： 2014-07-27
上传用户：LIKE
Description The MUSIC algorithm, proposed by Schmidt, first estimates a basis for the noise subspace and then determines the peaks the associated angles provide the DOA estimates. The MATLAB code for the MUSIC algorithm is sampled by creating an array of steering vectors corresponding to the angles in the vector angles.
标签： Description algorithm estimates proposed
上传时间： 2013-12-08
上传用户：hgy9473
Q: 我应该怎样处理内存泄漏？ A: 很简单，只要写“不漏”的代码就完事了啊。显然，如果你的代码到处是new、delete、指针运算，那你想让它“不漏”都难。不管你有多么小心谨慎，君为人，非神也，错误在所难免。最终你会被自己越来越复杂的代码逼疯的——你将投身于与内存泄漏的奋斗之中，对bug们不离不弃，直至山峰没有棱角，地球不再转动。而能让你避免这样困境的技巧也不复杂：你只要倚重隐含在幕后的分配机制——构造和析构，让C++的强大的类系统来助你一臂之力就OK了。标准库中的那些容器就是很好的实例。它们让你不必化费大量的时间精力也能轻松惬意地管理内存。我们来看看下面的示例代码——设想一下，如果没有了string和vector，世界将会怎样？如果不用它们，你能第一次就写出毫无内存错误的同样功能代码吗？
标签： 内存泄漏
上传时间： 2017-01-25
上传用户：alan-ee
粒子效果演示(附代码) 利用C++所提供的一些标准容器很容易实现粒子效果. 简单的说就是,将粒子数据写在一个类里面,有一个粒子源,不停地生成粒子,然后放入一个stl::list中(push_back()方法), 然后在一个循环中遍例粒子链表,并渲染粒子,粒子的参数并不断更新,如果粒子的ALPHA值小于0,即不可见了,即代表粒子已经死亡, 即从list中删去(erase()方法), 销毁死去的粒子很重要, 如果搞忘了这一步, 你的内存很快就会被吃掉干净了. 第一个版本是用交错平面画的粒子. 第二个版本是用Point Spirit(点精灵)画的粒子, 可以看看效果上面的区别 SPACE - 暂停粒子源发射粒子 LINK中忘记去掉cg.lib cgGL.lib 了, 此程序不需要CG, 如果要编译请去掉.
上传时间： 2013-12-15
上传用户：jing911003
本文档介绍了如何使用各种内嵌工具，函数和其他一些小技巧来加强使用matlab的速度和效率，是广大爱好者必读的文档。具体内容请参阅文档。 Learn how to use the Profiler tool, vectorized functions, and other tricks to writing efficient MATLAB code. This article includes how to convert any array into a column vector, bounding a value without if statements, and repeating/tiling a vector without repmat. Contents: * The Profiler * Array Preallocation * JIT Acceleration * Vectorization * Inlining Simple Functions * Referencing Operations * Numerical Integration * Signal Processing * Miscellaneous Tricks
上传时间： 2013-12-11
上传用户：cuiyashuo
A "code-what"? Unless you have spent some time working in the area of reverse engineering, chances are you have not heard of the term "codecave" before. If you have heard of it, you might not have read a clear definition of it or quite understand what it is or why it is useful. I have even asked seasoned assembly programmers about the term before and most of them had not heard of it. If it is new to you, do not worry, you are not the only one. It is a term that is scarcely used and is only useful in a reverse engineering context. Furthermore, is it "codecave" or "code cave"? I am not quite sure, but I will try my best to refer to it consistently as a "codecave". A space may sneak in there from time to time
标签： engineering code-what chances reverse
上传时间： 2014-01-17
上传用户：hn891122
The basic principle using the branchand- bound strategy to solve the traveling salesperson optimization problem (TSP) consists of two parts. There is a way to split the solution space. There is a way to predict a lower bound for a class of solutions. There is also a way to find an upper bound of an optimal solution. If the lower bound of a solution exceeds this upper bound, this solution cannot be optimal. Thus, we should terminate the branching associated with this solution.
标签： salesperson principle branchand the
上传时间： 2017-02-19
上传用户：comua
documentation for optimal filtering toolbox for mathematical software package Matlab. The methods in the toolbox include Kalman filter, extended Kalman filter and unscented Kalman filter for discrete time state space models. Also included in the toolbox are the Rauch-Tung-Striebel and Forward-Backward smoother counter-parts for each filter, which can be used to smooth the previous state estimates, after obtaining new measurements. The usage and function of each method are illustrated with five demonstrations problems. 1
标签： documentation mathematical for filtering
上传时间： 2014-01-20
上传用户：changeboy
