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.
標簽： implementation distribution restricted Gaussian
上傳時間： 2016-12-30
上傳用戶：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
標簽： ABC_FDTD_Die Implements simulation Gaussian
上傳時間： 2013-12-22
上傳用戶：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
標簽： ABC_FDTD_Die Implements simulation Gaussian
上傳時間： 2014-07-27
上傳用戶：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.
標簽： Description algorithm estimates proposed
上傳時間： 2013-12-08
上傳用戶：hgy9473
Q: 我應該怎樣處理內存泄漏？ A: 很簡單，只要寫“不漏”的代碼就完事了啊。顯然，如果你的代碼到處是new、delete、指針運算，那你想讓它“不漏”都難。不管你有多么小心謹慎，君為人，非神也，錯誤在所難免。最終你會被自己越來越復雜的代碼逼瘋的——你將投身于與內存泄漏的奮斗之中，對bug們不離不棄，直至山峰沒有棱角，地球不再轉動。而能讓你避免這樣困境的技巧也不復雜：你只要倚重隱含在幕后的分配機制——構造和析構，讓C++的強大的類系統來助你一臂之力就OK了。標準庫中的那些容器就是很好的實例。它們讓你不必化費大量的時間精力也能輕松愜意地管理內存。我們來看看下面的示例代碼——設想一下，如果沒有了string和vector，世界將會怎樣？如果不用它們，你能第一次就寫出毫無內存錯誤的同樣功能代碼嗎？
標簽： 內存泄漏
上傳時間： 2017-01-25
上傳用戶：alan-ee
粒子效果演示(附代碼) 利用C++所提供的一些標準容器很容易實現粒子效果. 簡單的說就是,將粒子數據寫在一個類里面,有一個粒子源,不停地生成粒子,然后放入一個stl::list中(push_back()方法), 然后在一個循環中遍例粒子鏈表,并渲染粒子,粒子的參數并不斷更新,如果粒子的ALPHA值小于0,即不可見了,即代表粒子已經死亡, 即從list中刪去(erase()方法), 銷毀死去的粒子很重要, 如果搞忘了這一步, 你的內存很快就會被吃掉干凈了. 第一個版本是用交錯平面畫的粒子. 第二個版本是用Point Spirit(點精靈)畫的粒子, 可以看看效果上面的區別 SPACE - 暫停粒子源發射粒子 LINK中忘記去掉cg.lib cgGL.lib 了, 此程序不需要CG, 如果要編譯請去掉.
上傳時間： 2013-12-15
上傳用戶：jing911003
本文檔介紹了如何使用各種內嵌工具，函數和其他一些小技巧來加強使用matlab的速度和效率，是廣大愛好者必讀的文檔。具體內容請參閱文檔。 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
上傳時間： 2013-12-11
上傳用戶：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
標簽： engineering code-what chances reverse
上傳時間： 2014-01-17
上傳用戶：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.
標簽： salesperson principle branchand the
上傳時間： 2017-02-19
上傳用戶：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
標簽： documentation mathematical for filtering
上傳時間： 2014-01-20
上傳用戶：changeboy
