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Fig563
直流-直流变换器的matlab仿真模块，实现了直流降压的功能。(Matlab simulation of the DC- DC converter module, the function of DC buck.)
- 2012-04-22 11:21:41下载
- 积分：1
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Slam
基于卡尔曼滤波的机器人定位及地图创建（salm），对1D、2D、3D等情况分别进行仿真(Kalman filter-based robot localization and map building (salm), on the 1D, 2D, 3D, etc. were simulated)
- 2010-06-03 22:00:47下载
- 积分：1
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exam5_1_post
后处理程序，主要是绘制单向拉伸杆中沿横截面的应力分布图(failed to translate)
- 2009-03-19 15:58:55下载
- 积分：1
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CANNY_EDGE
说明： CANNY边缘检测,matlab,很容易看懂(Canny edge detection, matlab, very easy to understand)
- 2008-11-30 21:12:21下载
- 积分：1
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Advanced-Mathematics-and-Mechanics-Applications-U
Advanced Mathematics and Mechanics Applications Using MATLAB - Howard B. Wilson
mat lab ö ğ renenler iç in gerekli kaynak
- 2012-04-03 05:09:41下载
- 积分：1
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K_Means_image_compression
- K means algorithm is performed with different initial centroids in order to get the best clustering.
- The total cost is calculated by summing the distance of each point to its cluster centre and then summing over all the clusters.Based on the minimum overall cost achieved during each iteration of iterKMeans the pixel assignment to their respective clusters are made and final compressed image is obtained. This algorithm will run slower as the number of clusters , size of the image and number of iterations increase.
- 2013-07-26 15:49:00下载
- 积分：1
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22
说明： 给仿真信号加入噪声信号，比如白噪声、有色噪声等等(Added to the simulated signal noise signal, such as white noise, colored noise, etc.)
- 2014-08-05 16:10:04下载
- 积分：1
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dct
extract file
and the working is to get dct matrix of 256*256 image which is divide into each 8*8 image block
- 2010-03-14 15:04:43下载
- 积分：1
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MLE
极大似然估计是概率论中的重要思想，阅读此文献可以帮助我们了解极大似然估计的含义(Maximum likelihood estimation is important in probability theory thinking, reading this literature can help us understand the meaning of maximum likelihood estimation)
- 2010-05-14 09:54:34下载
- 积分：1
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PCA
PCA算法。PCA的目的是找到能够分离出最大方差的方向，所以首先求原来所有数据三个维度上的协方差，然后求这个协方差的特征值，最大特征值为第一个方向，从此以此类推。(PCA algorithm. The purpose of PCA is to find able to isolate the direction of maximum variance, so first find all the data in three dimensions on the original covariance, and then find the eigenvalues of the covariance, the biggest feature is the first in one direction, from and so on.)
- 2011-05-15 00:25:49下载
- 积分：1
