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Textembedkey
Embedding text into random pixels of an image
- 2013-11-19 15:27:54下载
- 积分：1
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bidirection1
二维弹塑性本构程序，可用于混凝土或钢梁二维弹塑性分析(Two dimensional elastic plastic programme)
- 2021-04-15 20:18:54下载
- 积分：1
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GeneticAlgorithms
Multicycle Scheduling under Local Timing Constraints using Genetic Algorithms
- 2010-12-11 03:46:49下载
- 积分：1
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watermark
说明： 变换域的数字图像水印技术及图像质量评价研究(Study of digital image watermarking in frequency
domain and image quality evaluation
)
- 2010-04-06 18:00:08下载
- 积分：1
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facedetection
Abstract—In this paper, a new lossy technique based on wavelet
transform for compression of breast ultrasound images is
presented. The experiments are performed on 16 different
wavelet functions and the quality of reconstructed images is
evaluated by using Compression Ratio (CR), Normalized Mean
Square Error (NMSE), Normalized Absolute Error (NAE), and
Peak Signal to Noise Ratio (PSNR) criterion. An
ultrasonographer performed the subjective assessment. This
study shows that among the utilized wavelets,
- 2014-01-07 11:24:59下载
- 积分：1
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ssb
说明： 基于MATLAB的SSB调制课程设计论文与源程序(MATLAB-based curriculum design of the SSB modulation papers and source code)
- 2008-10-22 17:26:18下载
- 积分：1
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PID_5
普通PID控制及其扩展算法控制的MATALB仿真程序，内容包含面广．(ordinary PID control algorithm and its expansion of enriching control simulation program, which includes many sectors.)
- 2007-03-11 14:01:25下载
- 积分：1
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ex_6
this code is calculating the fundamental spectral responsivities functions.
- 2011-11-18 05:28:31下载
- 积分：1
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MyKmeans
实现聚类K均值算法： K均值算法：给定类的个数K，将n个对象分到K个类中去，使得类内对象之间的相似性最大，而类之间的相似性最小。 缺点：产生类的大小相差不会很大，对于脏数据很敏感。 改进的算法：k—medoids 方法。这儿选取一个对象叫做mediod来代替上面的中心 的作用，这样的一个medoid就标识了这个类。步骤： 1，任意选取K个对象作为medoids（O1,O2,…Oi…Ok）。 以下是循环的： 2，将余下的对象分到各个类中去（根据与medoid最相近的原则）； 3，对于每个类（Oi）中，顺序选取一个Or，计算用Or代替Oi后的消耗—E（Or）。选择E最小的那个Or来代替Oi。这样K个medoids就改变了，下面就再转到2。 4，这样循环直到K个medoids固定下来。 这种算法对于脏数据和异常数据不敏感，但计算量显然要比K均值要大，一般只适合小数据量。(achieving K-mean clustering algorithms : K-means algorithm : given the number of Class K, n will be assigned to target K to 000 category, making target category of the similarity between the largest category of the similarity between the smallest. Disadvantages : class size have no great difference for dirty data is very sensitive. Improved algorithms : k-medoids methods. Here a selection of objects called mediod to replace the center of the above, the logo on a medoid this category. Steps : 1, arbitrary selection of objects as K medoids (O1, O2, Ok ... ... Oi). Following is a cycle : 2, the remaining targets assigned to each category (in accordance with the closest medoid principle); 3, for each category (Oi), the order of selection of a Or, calculated Oi Or replace the consumption-E (Or))
- 2005-07-26 01:32:58下载
- 积分：1
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LUGS
在matlab中实现矩阵的lu分解和Gauss-Seidel求特征值(Implemented in the matlab lu matrix decomposition and Gauss-Seidel eigenvalue)
- 2010-11-20 19:48:41下载
- 积分：1
