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MATLABC

于 2010-03-24 发布 文件大小:1133KB
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说明:  MATLAB的C++数学使用手册(详尽)(MATLAB to C++ Math User' s Manual (detailed))

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  • final-code
    This paper presents a new approach to image segmentation using Pillar K-means algorithm. This segmentation method includes a new mechanism for grouping the elements of high resolution images in order to improve accuracy and reduce the computation time. The system uses K-means for image segmentation optimized by the algorithm after Pillar. The Pillar algorithm considers the placement of pillars should be located as far from each other to resist the pressure distribution of a roof, as same as the number of centroids between the data distribution. This algorithm is able to optimize the K-means clustering for image segmentation in the aspects of accuracy and computation time. This algorithm distributes all initial centroids according to the maximum cumulative distance metric. This paper evaluates the proposed approach for image segmentation by comparing with K-means clustering algorithm and Gaussian mixture model and the participation of RGB, HSV, HSL and CIELAB color spaces.
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  • kaam_ki_cheez
    The channel estimation algorithm is based on a subspace approach. The method works only for identifiable STBCs (excluding >=1-rate STBCs). See publication [1] for more detail about the algorithm and the identifiability conditions. The zip file contains three m files. - space_time_coding.m (perform space time coding) - subspace_channel_estimation_STBC.m (channel estimation) - one_shot.m (show an example) To use these files, extract the three files in the same folder. Then, call the script one_shot in the matlab command window. Reference: [1] Ammar, N. Ding, Z. "Blind Channel Identifiability for Generic Linear Space-Time Block Codes" IEEE Transactions on Signal Processing Volume 55, Issue 1, Jan. 2007 Page(s):202 - 217(The channel estimation algorithm is based on a subspace approach. The method works only for identifiable STBCs (excluding >=1-rate STBCs). See publication [1] for more detail about the algorithm and the identifiability conditions. The zip file contains three m files. - space_time_coding.m (perform space time coding) - subspace_channel_estimation_STBC.m (channel estimation) - one_shot.m (show an example) To use these files, extract the three files in the same folder. Then, call the script one_shot in the matlab command window. Reference: [1] Ammar, N. Ding, Z. "Blind Channel Identifiability for Generic Linear Space-Time Block Codes" IEEE Transactions on Signal Processing Volume 55, Issue 1, Jan. 2007 Page(s):202- 217)
    2009-12-05 17:14:58下载
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