资源列表
19263409
- 在计算机内存中绘图,实现了全图的漫游功能,可以学习,推荐给大家(Drawing in the computer memory, has realized the full roaming function, can learn, recommend to everyone)
图像处理基本算法源码
- c++平台编写的图像处理基本算法源码，所有的处理函数都有。(C++ platform prepared by the basic algorithm for image processing source code, all the processing functions are.)
opencv学习资料
- opencv的学习资料，函数api直接查询，是初学者或opencv从业者必备书籍(Opencv learning materials, function API direct query, is essential for beginners or practitioners opencv books)
FATFS sample code
- Petit FatFs Module Sample Projects DIRECTORIES <generic> Generic microcontrollers with MMC <avr> ATMEL AVR (ATtiny85) with MMC <avr_boot> ATMEL AVR generic MMC boot loader <pic> Microchip PIC
6503076
- 在这个示例中,你将学习到如何制作网络浏览器,很好的(In this case, you will learn how to create a web browser, good)
vision-3.4
- matlab图像处理算法包，加载到matlab，可以直接调用里面的函数，详细请参考配套书籍(MATLAB image processing algorithm package, loaded to Matlab, you can call directly inside the function, in detail, please refer to supporting books)
robot-9.10
- matlab开发的机器人算法包，加载到matlab，可直接调用，具体请参考相关书籍(Matlab developed robot algorithm package, loaded to Matlab, can be called directly, please refer to specific books)
cekeralreadyready
- 在采用多道程序设计的系统中,往往有若干个进程同时处于就绪状态(In a multiprogramming system, often have several process at the same time in the ready state)
painning__method
- 在这个程序中,给出了怎样自绘位图的一种方法,这个源代码是vc开发大全书中的例子(In this program, this paper gives a method of how to custom painting bitmap, this source code is example of vc development big encyclopedia)
Arch Model
- 金融时间序列分析 1. 采用Pandas从Yahoo网上下载上市公司的5到10年的日收盘数据，上证指数的日收盘数据。 2. 计算上市公司和上证指数的收益率， 3. 针对上市公司收益率进行ARMA建模，确定P和q，并对残差进行分析，最后向前预测多期，显示预测图。 4. 针对上市公司收益率进行ARCH建模，确定阶数，并对残差进行分析，最后进行预测。 5. 针对上市公司收益率进行GARCH建模，确定阶数，并对残差进行分析，最后进行预测。(use Arch Model to ananlyse
noje
- 埃特金不等距逐步插值,给定n个不等距节点的函数值及精度要求,不错(Aitken not equidistant interpolation step by step, for a given n not equidistant node function value and the accuracy requirement, good)
visualization
- 用Python实现数据可视化的一个小案例，数据来自金融(Using Python to achieve data visualization of a small case, data from the financial)