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bpsk_awgn
高斯信道下的BPSK信号 对其进行归一化 并求出均值 方差 包络等(Under the Gaussian channel BPSK signals normalized them and obtained such as mean-variance envelope)
- 2009-04-07 16:10:58下载
- 积分：1
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graz_data
BCI 2003 脑机接口竞赛运动想象脑电数据data3(dataset of BCI 2003)
- 2011-05-06 11:34:51下载
- 积分：1
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MIMO_Equlaization
MIMO系统的典型zf均衡器的算法C＋＋实现。brief ZF Equalizer, i.e. Matrix inversion and multiplication
block, i.e. the solution of a linear equation system.(Zf typical MIMO systems equalizer algorithm C to achieve. brief ZF Equalizer, ie Matrix inversion and multiplicationblock, ie the solution of a linear equation system.)
- 2008-02-19 12:48:51下载
- 积分：1
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ISO-8859-1__camera_tracking_1245
to search centroid from image
- 2011-01-18 19:08:14下载
- 积分：1
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lwt2
小波的源代码。有关MATLAB的共同学习吧(wavelet source. The MATLAB common learning it)
- 2007-03-11 21:06:27下载
- 积分：1
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example
matlab中gui界面编程的一个小例子。(matlab gui interface programming in a small example.)
- 2011-10-23 18:43:21下载
- 积分：1
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sync
synchronisation passive satellite
- 2013-10-23 16:29:52下载
- 积分：1
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doa-estimation
各种doa估计代码，包括均匀线阵，均匀圆阵等的估计。包含music，esprit，空间平滑等(Various doa estimation code, including ULA, uniform circular array of estimates. Including music, esprit, spatial smoothing)
- 2013-10-19 18:23:07下载
- 积分：1
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PV
说明： 相对于传统的逆变器，光伏逆变器并网，使得并网过程更佳(Compared with the traditional inverter, the grid connected photovoltaic inverter makes the grid connected process better)
- 2017-10-16 15:43:44下载
- 积分：1
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MyKmeans
实现聚类K均值算法： K均值算法：给定类的个数K，将n个对象分到K个类中去，使得类内对象之间的相似性最大，而类之间的相似性最小。 缺点：产生类的大小相差不会很大，对于脏数据很敏感。 改进的算法：k—medoids 方法。这儿选取一个对象叫做mediod来代替上面的中心 的作用，这样的一个medoid就标识了这个类。步骤： 1，任意选取K个对象作为medoids（O1,O2,…Oi…Ok）。 以下是循环的： 2，将余下的对象分到各个类中去（根据与medoid最相近的原则）； 3，对于每个类（Oi）中，顺序选取一个Or，计算用Or代替Oi后的消耗—E（Or）。选择E最小的那个Or来代替Oi。这样K个medoids就改变了，下面就再转到2。 4，这样循环直到K个medoids固定下来。 这种算法对于脏数据和异常数据不敏感，但计算量显然要比K均值要大，一般只适合小数据量。(achieving K-mean clustering algorithms : K-means algorithm : given the number of Class K, n will be assigned to target K to 000 category, making target category of the similarity between the largest category of the similarity between the smallest. Disadvantages : class size have no great difference for dirty data is very sensitive. Improved algorithms : k-medoids methods. Here a selection of objects called mediod to replace the center of the above, the logo on a medoid this category. Steps : 1, arbitrary selection of objects as K medoids (O1, O2, Ok ... ... Oi). Following is a cycle : 2, the remaining targets assigned to each category (in accordance with the closest medoid principle); 3, for each category (Oi), the order of selection of a Or, calculated Oi Or replace the consumption-E (Or))
- 2005-07-26 01:32:58下载
- 积分：1
