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CodeLinesCounter

于 2013-12-19 发布 文件大小:13532KB
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下载积分: 1 下载次数: 2

代码说明:

  C++代码统计器,统计代码行、注释行、函数个数(C++ code statistics, statistical lines, comment lines, the number of functions)

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  • robotxy
    very excellent 2 degree of freedom two link robot inverse kinematic. its graphically good.
    2009-04-04 17:07:32下载
    积分:1
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    2013-11-21 11:05:48下载
    积分:1
  • x
    说明:  Steganography techniques in matlab
    2015-02-24 19:09:34下载
    积分:1
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    本段代码主要是为了实现讲多光谱图像进行光谱分离然后再合并成一种可以在matlab中显示的格式.tif的图像,同时保证了原始的图像信息不丢失(This code is mainly to achieve multispectral images speak for spectral separation and then combined into a format that can be displayed in matlab. Tif image, while maintaining the original image information is not lost)
    2014-02-21 20:26:19下载
    积分:1
  • led
    xs128 SPI 控制数码管 串行通信(the xs128 SPI control digital tube serial communication)
    2013-05-14 09:16:19下载
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  • case9
    基于matlab的电力系统配电网IEEE9节点潮流计算,希望对大家有帮助(Matlab-based power system IEEE54 node flow calculation, we hope to help)
    2020-07-03 21:40:02下载
    积分:1
  • 采用总变分法(TV)进行图像修复的matlab源代码
    采用总变分法(TV)进行图像修复的matlab源代码。包括热扩散修复和多尺度方法。(Using variational method (TV) for image restoration matlab source code. Repair and multi-scale methods include thermal diffusion.)
    2014-03-28 11:08:07下载
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    说明:  MIMO-OFDM技术,提供的关于MIMO_OFDM技术仿真(MIMO-OFDM technology to provide technical simulation on MIMO_OFDM)
    2008-11-12 10:09:42下载
    积分:1
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    buck current source on simulink
    2013-03-09 00:06:17下载
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  • bbbb
    摘 要:提出一种新的基于Pareto多目标进化免疫算法(PMEIA)。算法在每一代进化群体中选取最优非支配抗体保存到记忆细胞文档中 同时引入Parzen窗估计法计算记忆细胞的熵值,根据熵值对记忆细胞文档进行动更新,使算法向着理想Pareto最优边界搜索。此外,算法基于点在目标空间分况进行克隆选择,有利于得到分布较广的Pareto最优边界,且加快了收敛速度。与已有算法相比, PMEIA在收敛性、多样性,以及解的分布性方面都得到很好的提高。(Abstract:This paperproposed a new pareto-based multi-object evolutionary mi mune algorithm(PMEIA). PMEIA selected optmi alnon-dominated antibodieswhichwere then reserved inmemory cellarchive, and introducedParzenwindow to calculate entropy ofmemory cells. Updated thememory cell archive according to entropy ofmemory cells. This guarantees the conver- gence to the true Pareto fron.t Moreover, the performance of clone selection was dependent on distribution in the objective space, whichwas favorable forgetting awidely spreadPareto frontand mi proving convergence speed. Comparedwith the exis- ted algorithms, the obtained solutions ofPMEIA havemuch betterperformance in the convergence, diversity and distribution.)
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