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Description: 将潮流计算公式矢量化处理,利用Matlab编写潮流程序,并将Matlab的m文件编译成COM组件,可简单实现
Matlab和其它语言的混合编程。通过简单的函数调用即可完成如稀疏、解方程等一系列数学运算,并可轻易实现相关图形的
绘制。潮流计算中采用AMD算法对修正方程系数矩阵进行节点优化,并采用LDLT算法进行求解,有效地减少了注入元,计
算速度成倍提高。在混合编程的模式下,提高了程序开发速度、程序可靠性、计算效率和保密性。
-Formula for calculating the trend vector processing, the use of Matlab procedures for the preparation of the tide, and Matlab m-file compiled into a COM component, can be simply the realization of Matlab and other programming languages mixed. Through a simple function call to complete, such as sparse, solve a series of mathematical equations, and easily achieve the related graphics rendering. Power flow calculation algorithm used in AMD coefficient matrix of the correction equation for node optimization, and the use of LDLT algorithm, effectively reduces injection yuan, doubling to improve computing speed. Programming in mixed mode, increased the speed of program development, program reliability, computational efficiency and confidentiality.
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Size: 222208 |
Author: wuzhicheng |
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Description: LDPC codes BER simulation under AWGN channel. MacKay-Neal based LDPC matrix. Message encoding uses sparse LU decomposition. There are 4 choices of decoder: hard-decision/bit-flip decoder, probability-domain SPA decoder, log-domain SPA decoder, and simplified log-domain SPA decoder. Documentation and update can be found at: http://sites.google.com/site/bsnugroho/ldpc
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Size: 9216 |
Author: Józef |
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Description: 最热门的稀疏表示的算法，马毅等人在PAMI上发表的文章代码-Sparse representation of the most popular algorithms, Yi Ma, and others published an article in the PAMI code
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Size: 4096 |
Author: zhoujingbo |
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Description: 此文的目的有三个：第一，当地连续均值量化变换特征是提出照明和传感器敏感操作在目标识别上。其次，注册稀疏Winnows网络分割，提出了加快原分类。最后，特点和分类相结合对于正面人脸检测任务。检测结果列
为MIT + CMU系统和BioID数据库。关于这人脸检测器，接收器操作特征曲线BioID数据库产生最好的结果公布。对于结果麻省理工学院的中央结算系统+数据库相当于国家的最先进的脸探测器。一个人脸检测算法的MATLAB版本可以从http://www.mathworks.com/matlabcentral/fileexchange/
loadFile.do？的ObjectID = 13701＆的objectType =FILE下载。
-The purpose of this paper is threefold: firstly, the local Successive
Mean Quantization Transform features are proposed for illumination
and sensor insensitive operation in object recognition. Secondly, a
split up Sparse Network of Winnows is presented to speed up the
original classifier. Finally, the features and classifier are combined
for the task of frontal face detection. Detection results are presented
for the MIT+CMU and the BioID databases. With regard to this
face detector, the Receiver Operation Characteristics curve for the
BioID database yields the best published result. The result for the
CMU+MIT database is comparable to state-of-the-art face detectors.
A Matlab version of the face detection algorithm can be downloaded
from http://www.mathworks.com/matlabcentral/fileexchange/
loadFile.do?objectId=13701&objectType=FILE.
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Size: 1397760 |
Author: 霞 |
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