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minsum

于 2015-07-01 发布文件大小:12KB

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下载积分: 1 下载次数: 34

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代码说明：

二元ldpc译码器 min-sum算法迭代次数固定输入信号长度一帧512bits(binary ldpc decoder. min-sum algorithm。fixed iter number.512 bits/frame)

文件列表：

minsum
......\b2.mat,1848,2015-05-05
......\CNU_ROM.txt,5117,2015-05-24
......\CNU_ROM.txt.bak,8189,2015-05-24
......\matrix.asv,780,2015-05-27
......\matrix.m,784,2015-05-27
......\Min_Sum.asv,3798,2015-05-24
......\Min_Sum.m,3864,2015-05-24
......\newG.mat,2522,2015-05-05
......\VNU_ROM.txt,6142,2015-05-24
......\VNU_ROM.txt.bak,14334,2015-05-24

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image_compress
本程序利用奇异值分解对3通道彩色图像进行压缩分解，具体步骤如下：压缩过程： 1. 选取子图像大小K值，把图像分解成M×M个子图像，IMG(s),s=1,2,…, M2，其中M=N/K，原始图像IMG大小为N×N。 2. 计算这M2个子图像的平均值average，对每幅子图像减去均值图像得到新图像。 3. 计算相关矩阵R，其元素定义为。 4. 计算R的特征值与特征向量，计算每幅子图像与最大特征向量的内积，便得到编码，即压缩后的图像。 (This procedure using singular value decomposition of 3-channel color image compression decomposition, concrete steps are as follows: compression process: 1. Select the sub-image size K value, the image is decomposed into M × M sub-image, IMG (s), s = 1, 2, ..., M2, in which M = N/K, the original image IMG size N × N. 2. M2 calculate the average of sub-image average, for each sub-image minus the new images mean images. 3. Calculation of correlation matrix R, defined as its elements. 4. Calculation of the characteristics of R value and eigenvector calculated each sub-image with the largest eigenvector of the inner product, they will have to code, that is, the compressed images.)

2008-01-03 23:00:33下载

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说明：针对粘土剑桥模型，开发了显式应力积分算法，用于模拟应力应变关系(For clay Cambridge model, an explicit stress integration algorithm is developed to simulate the stress-strain relationship)

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此代码是在MATLAB下进行清浊音短时频谱分析，用到汉明窗。(This code is carried Voicing short time spectral analysis in MATLAB, use the Hamming window.)

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2016-07-13 10:40:49下载

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评判标准
说明：数字图像修复算法的评价标准也是从两个方面进行评价的，一个是修复算法的效率，一个是修复后图像质量。修复算法的效率主要就是看修复图像所用的时间，可以用时间就行量化。而图像质量很难从量上唯一的进行评价，目前图像修复质量还没有统一的评价标准，从实际应用出发，修复后图像质量的好坏大多数是从以下两个方面就行评价，修复后图像给人主观上的“满意度”和客观上图像的cnr、enl、epi、psnr、snr来评判。(The evaluation criteria of digital image restoration algorithms are also evaluated from two aspects, one is repair. One of the efficiency of complex algorithm is the image quality after restoration. The efficiency of the repair algorithm depends mainly on the repair. The time spent on the image can be quantified in terms of time. But image quality is difficult to improve quantitatively only. At present, there is no uniform evaluation criterion for image restoration quality. Starting from practical application, repair The quality of restored images is mostly evaluated from the following two aspects, and the restored images are given to people. Subjective "satisfaction" and objective image cnr, enl, epi, psnr, SNR to judge.)

2021-04-18 23:48:57下载

积分：1

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