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yuan
计算孔口应力集中的计算复变函数方法。本人自己编的程序，可读性比较差。(Calculation of stress concentration calculation orifice complex variable function method. I own procedures, a relatively poor readability.)
- 2009-06-12 15:08:35下载
- 积分：1
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FANGZHENDAIMA-
是用matlabK联合FPGA一起实现波形仿真，不同于大家常见的别的方法。是平行于SILK技术的一种技术(DDS MATLAB 仿真)
- 2012-05-05 17:55:22下载
- 积分：1
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image-histogram-
统计图像直方图参考程序框架（matlab程序）(Procedural framework for statistical reference image histogram matlab program)
- 2011-09-29 19:52:29下载
- 积分：1
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ing
分别实现由MVDR和RLS实现了单波束形成（signal beamfroming using MVDR and RLS）(Were implemented by MVDR and RLS achieve a single beam forming (signal beamfroming using MVDR and RLS))
- 2013-11-24 19:18:28下载
- 积分：1
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Desktop
模糊 ， PID在储能充电放电中的应用。(Application of fuzzy PID in energy storage charge and discharge)
- 2019-05-29 19:17:38下载
- 积分：1
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A_Study_og_Face_Recognition_Methods_Baced_on_Wavel
针对灰度图像，提出一种基于知识的人脸检测方法。
提出了一种给予支持向量机的人脸检测方法。
提出了一种基于小波分解的LDA人脸识别方法。
提出了一种基于小波和DCT的人脸识别方法。
提出了一种机遇CEDT和支持向量机的人脸分类和识别方法。
(For gray-scale images, a knowledge-based face detection methods. A support vector machine method of face detection. A wavelet decomposition of the LDA-based face recognition methods. A wavelet and DCT-based face recognition methods. A CEDT opportunities and support vector machine classification and face recognition.)
- 2009-05-01 10:56:58下载
- 积分：1
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matlab
经典matlab信号处理学习介绍了信号处理里的各种函数 (Signal processing matlab classic study describes the various functions in signal processing)
- 2010-12-04 13:58:00下载
- 积分：1
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Signal-processing-simulation
运用MATLAB对自适应滤波器的应用、非平稳信号、非高斯信号进行分析处理。(Use of MATLAB in adaptive filter, non-stationary signals, the non-Gaussian signal analysis and processing.)
- 2012-05-15 14:58:10下载
- 积分：1
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基于music算法，圆阵列天线多基线干涉仪测向
基于music算法，圆阵列天线多基线干涉仪测向(Music algorithm based on two-dimensional, circular array antenna multi-baseline interferometer DF)
- 2013-09-27 11:12:23下载
- 积分：1
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基于PCA的SVM分类
说明： 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
