资源列表
SeismicDepthImaging
- 目前，有许多勘探目标无法使用常规地震成像方法进行识别，而利用叠前深度成像技术，作业公司可以对包括最复杂构造在内的地质特征进行清晰成像。这种准确的结果可以降低风险并帮助确定储量-Currently, there are not many exploration targets using conventional seismic imaging methods for the identification, and the use of pre-stack depth imaging techno
dll
- 用VC++编写的动态连接库。木林要村 顶戴 -VC prepared by the DLL. Forest to the village Spirit
c2000
- 用于在CCS环境下的烧写插件，最新Flash烧写插件v1.12.0 for CCS C2000 v2.21-for CCS in the environment of burning plug-ins, The latest Flash plug-burning v1.12.0 for CCS C2000 v2.21
setup
- 定时专家v1.0 功能介绍： 定时启动程序、定时关机。 闹钟：支持自定义播放音乐。 支持导出导入事件。 支持多事件。-regular expert v1.0 functional presentations : Timing commencement of the proceedings, regular shutdown. Clock : support from the definition of broadcast music.
guanli
- The human resources management system management system operatingprocedure, welcome everybody to evaluate after testing with the use-The human resources management system man agement system operatingprocedure. welcome everybody to evaluate after test
PCasm
- PC汇编资料大全-PC compilation of the data volumes !!!!!!!!!!!!!!!!!!!!!!!!!!!!
MLS1DShape
- 求解无网格法的近似函数，（伽辽金无网格法移动最小二乘近似程序）-for non-grid method similar function (Galerkin grid Mobile least squares approximation procedures)
Weight
- 无网格法权函数求解子程序，需要和以上载的第一个程序和用！-without grid power function for subroutines, and the need to contain the above procedures and the use of one!
EFG1D
- 无网格伽辽金法，主程序，结合以上传的前两个程序来使用，算杆的应力，和位移！-element-free Galerkin method, main program, a combination of the first two upload procedures to use, operator of stress and displacement.
MLPG1D
- 无网格局部彼得洛程序,此方法不需要背景网格 的积分!-mesh pattern of Bideiluo procedures, this method does not need background mesh with the score!
JCSP
- 这是英国Kent大学开发的进程代数的JAVA库,对付多线程和并发系统变得很容易,有大量的范例,我们导师很看好的东西-developed at the University of Kent in the process algebra JAVA basement, deal with multithreading and concurrency system is very easy, we have a lot of examples, we have very good instructors
11
- 本文介绍了医学信号分析的常用方法，着重介绍了神经网络模式识别，包括他的优点及不足，最后介绍了神经网络的模型选择。-medical signal analysis methods commonly used to highlight the neural network pattern recognition, including his strengths and weaknesses, and the final presentation to the neural network model