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kmltoolbox_v1.4
基于这个类的工具箱允许你创建在“Google地球”,许多不同的地块,由自动创建所需的基于XML的KML文件,而无需用户交互。
有了它,您可以创建:
- 线图,散点图
- 二维和三维轮廓
- 2D和3D多边形
- 颤动地块
- 写在一个特定点的文本
- 将三维模型
- 覆盖图像
- 为图像传输更复杂的数字
- 文件夹,子文件夹,... 总相似图
- 动画三维模型到一个预定义的轨迹
安装到您的MATLAB路径工具箱,工具箱目录,并运行:
>> kml.install
现在,每个功能和可用的参数文件。要查看帮助,在MATLAB中运行:
>> kmldoc
或
>> kmldoc kml.plot%或任何其他可用的地块
如果你喜欢它,只是给我发电子邮件在说什么你使用它:) kml@rafael.aero。
灵感来自于原来的Google地球工具箱,但使用XML对象实现的,这使得它更容易使用。我只是检查在MATLAB 2011a释放,但它应该在旧版本的工作太... (让我知道如果不!)(Based on this class allows you to create a" toolbox in Google earth", many different plots, created automatically by the XML based on the KML file, without the need for user interaction.
With it, you can create:
- line graphs, scatter diagram
-2D and 3D contour
- 2D and3D polygon
- fibrillation plots
- written at a particular point in the text
- the three-dimensional model
- cover image
For more complex digital image transmission
Folder, subfolders, total similarity graph ...
- Animated3D model into a predefined trajectory
Install to your path to the MATLAB toolbox, toolbox directory, and run:
> > kml.install
Now, each function and the available parameter file. To view the help, running in the MATLAB:
> > kmldoc
Or
> > kmldoc kml.plot or any other available plots
If you like it, just email me say in what you use it ): kml@rafael.aero.
Inspiration from the original Google earth toolbox, but using the XML object is achieved, which makes it easier to )
- 2012-05-02 20:26:22下载
- 积分:1
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有明确的程序和结果(Mc integration method used to calculate the value of pi has clear procedures and results)
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基于BP神经网络的供应链绩效评价方法 论文 及其 MATLAB 实现(Based on BP neural network of supply chain performance evaluation method of the papers and MATLAB to achieve)
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simulink使用详细介绍;smulink参数设置教程( )
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Digital-Signal-Processing
book matlab Digital Signal Processing it good and help
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matlab
Matlab in image processing ebook
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ccdrekgx
LCMV优化设计阵列处理信号,本科毕设要求参见标准测试模型,在matlab R2009b调试通过,包含特征值与特征向量的提取、训练样本以及最后的识别,BP神经网络用于函数拟合与模式识别。( LCMV optimization design array signal processing, Undergraduate complete set requirements refer to the standard test models, In matlab R2009b debugging through, Contains the eigenvalue and eigenvector extraction, the training sample, and the final recognition, BP neural network function fitting and pattern recognition.)
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