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sound1
sound very useful e-book
- 2009-06-07 15:11:52下载
- 积分：1
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MATLAB-coding-(second-ver)
本文为Stephen J.Chapman《MATLAB 编程（第二版）》英文影印版的中文译本，这本
书对初学者很好的入门教材。(The the Stephen J.Chapman MATLAB Programming (second edition) " The Chinese translation of the English photocopy edition, this book is good for beginners introductory textbook.)
- 2013-05-13 16:33:08下载
- 积分：1
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Titere
A tiny app that controls a puppet with a wiimote controller. Uses the wiimotelib and a serial port to communicate the arduino board. The code for the arduino board is in other file.
- 2015-01-11 02:07:07下载
- 积分：1
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cyc4cESPRIT2ext
此算法实现了对多径信号的优化，仿真结果显示具有很好的效果(multipath estimation)
- 2009-10-23 09:29:44下载
- 积分：1
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lle
局部线性降维方法LLE 用于高维空间数据的降维处理(Local linear dimension reduction methods for high dimensional data LLE dimensionality reduction process)
- 2011-08-22 10:58:01下载
- 积分：1
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Mie
均匀球形粒子的Mie理论散射光强计算程序：
1．RB1.m 计算第一类Ricatti-Bessel函数的函数
2．RB2.m 计算第二类Ricatti-Bessel函数的函数
3. Alegendr.m 计算角函数 和 的函数
4. MieCoeff.m 计算米尔系数的函数
5. Amp.m 计算散射光复振幅的函数
6. MieIntensity.m 画均匀粒子散射角-光强图
(Uniform spherical particle Mie theory scattering intensity calculation procedures: 1. RB1.m calculate the first class Ricatti-Bessel function of the function 2. RB2.m calculate the second category Ricatti-Bessel function of the function 3. Alegendr.m computing angle functions and function 4. MieCoeff.m Mill coefficient calculation function 5. Amp.m calculate the scattering amplitude recovery function 6. MieIntensity.m Painting uniform particle scattering angle- light intensity diagram)
- 2013-10-05 14:26:10下载
- 积分：1
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circle-diffraction-plane-wave
matlab模拟的圆孔衍射 平面波 衍射间距可调(Matlab simulation using circular aperture diffraction images, the incident light for the plane wave)
- 2012-04-27 15:05:30下载
- 积分：1
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matlabCommunication
一些有关matlab在通信仿真中用到的源码,例如各种编码方法。很有实用价值，(some communications in the Matlab simulation to use the source code, such as the various coding method. Useful,)
- 2007-06-08 01:24:37下载
- 积分：1
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ELM
极限学习机的matlab源程序,很有用，请需要的下载参考(matlab source program of elm)
- 2013-03-03 22:08:35下载
- 积分：1
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基于PCA的SVM分类
选择“BreastCancer”数据集，使用支持向量机(SVM)对其进行分类。作为对比，第一次对特征集直接进行支持向量机分类，第二次对特征集进行主成分分析法的特征提取后，再对特征提取后的特征集进行支持向量机分类。并且对比和分析了两次分类的结果。(The BreastCancer data set is selected and classified by Support Vector Machine (SVM). For comparison, the first time the feature set is classified directly by support vector machine, the second time the feature set is extracted by principal component analysis, and then the feature set is classified by support vector machine. The results of the two classifications are compared and analyzed.)
- 2020-06-20 10:20:02下载
- 积分：1
