Description: 提出一种基于视觉特性的图像摘要算法，增大人眼敏感的频域系数在计算图像Hash时的权重，使得图像Hash更好地体现视觉特征，并提高鲁棒性。将原始图像的分块DCT系数乘以若干由密钥控制生成的伪随机矩阵，再对计算的结果进行基于分块的Watson人眼视觉特性处理，最后进行量化判决产生固定长度的图像Hash序列。本算法比未采用视觉特性的算法相比，提高了对JPEG压缩和高斯滤波的鲁棒性。图像摘要序列由密钥控制生成，具有安全性。-Based on the visual characteristics of the image digest algorithm, increasing the human eye-sensitive frequency-domain coefficients in the calculation of the image when the weight of Hash, Hash makes images better reflect the visual characteristics, and improve robustness. Will block the original image multiplied by the number of DCT coefficients generated by the key control of pseudo-random matrix, then the results of calculation based on the sub-block of Watson HVS treatment, and finally quantify the judgments arising from fixed-length sequence of images Hash . Than the algorithm did not use the visual characteristics of the algorithm, improve the JPEG compression and Gaussian filtering robustness. Abstract image sequence generated by the key control, with security. Platform: |
Size: 167936 |
Author:kurt |
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Description: 程序的主要功能是做了一定范围的信噪比下，对每个信噪比：随机信号QPSK调制；
根据Alamouti方案的矩阵进行编码；发送信号经过瑞利信道和加入高斯白噪声；
接收信号采用最大比合并的方法；最后对合并信号进行最大似然判决并求误符号率。
结果表明10^-3对应大约12->13dB-Procedure main function is to do a certain range of SNR for each signal to noise ratio: random signal QPSK modulation program in accordance with Alamouti coding matrix send signals through Rayleigh channel and adding Gaussian white noise received signal using maximal-ratio combining method Finally the combined signal and the maximum likelihood decision for symbol error rate. The results showed that about 10 ^-3 corresponds to 12-> 13dB Platform: |
Size: 1024 |
Author: |
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Description: LDPC校验矩阵生成、高斯消去法编码、BP译码程序，可以作为自己编写LDPC编译码程序的参考。因为matlab代码效率不高，所以这个程序仿真起来比较慢.-LDPC check matrix generation, Gaussian elimination coding, BP decoding procedure can be used as its own procedures for the preparation of LDPC codec reference. Because matlab code efficiency is not high, so this process is relatively slow simulation together. Platform: |
Size: 8192 |
Author:李枫 |
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Description: 本文提出了一种新的跨国家的障碍
检测技术为基础的立体视觉系统。
原始图像的预处理的高斯
过滤器和对比度限制的自适应直方图
均衡（ CLAHE ）方法来削弱作用 噪音，光线和对比度。哈里斯的角落位于与子像素精确。
-Cross-country intelligent vehicles always work in
complicated environments with varying illuminations.
The paper presents a new cross-country obstacle
detection technology based on stereo vision system.
The original images are preprocessed by Gaussian
filter and contrast-limited adaptive histogram
equalization (CLAHE) method to weaken the effect of
noise, light and contrast. Harris corners are located
with sub-pixel accurate. To guarantee the overall
system real-time performance, feature-based matching
techniques are studied and fundamental matrix is
calculated based on random sample consensus
(RANSAC). Also restrains are studied to eliminate
pseudo matching pairs. Then data interpolation is
introduced to build elevation maps. Edge extraction
and morphological processing are concerned to
accomplish obstacle detection. Experiment results for
different conditions are presented in support of the
obstacle detection technology. Platform: |
Size: 971776 |
Author:晓翠 |
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Description: 第二篇 文件操作
实例24 文件加密（1）
实例25 文件加密（2）
实例26 批处理程序的加密
实例27 给自己的程序加上行号
实例28 文件分割程序
实例29 删除目录树
实例30 显示系统文件表
实例31 显示一个目录的存储内容
实例32 递归读取磁盘文件
实例33 C语言直接读取FoxPro的.DBFYYWR
实例34 用索引文件读取数据项
实例35 加密数据库
第三篇 系统调用
实例36 用C语言内嵌汇编语言实现一个字符的显示
实例37 C语言中汇编语言子程序的调用
实例38 用栈实现两个数相加
实例39 用汇编子程序进行冒泡排序
实例40 复制前的空间测试程序
实例41 自定义文本模式
实例42 设计立体投影窗口
实例43 编写汉字彩色弹出式菜单
实例44 口令程序设计
实例45 程序自我保护技术——“程序自杀”
实例46 获取国家信息
实例47 C语言可变参数函数设计
实例48 对环境变量的读取和修改
实例49 给硬盘加软锁
实例50 挽救磁盘数据
实例51 硬盘分区表的保存与恢复
实例52 IDE硬盘参数的测定
实例53 CMOS信息保存到文件
实例54 将CMOS信息保存到文件
实例55 获取BIOS设备列表-Title VI of Scientific Computing
74 instances of polynomial multiplication
Instances of 75 to achieve a high degree of random sequence of random
76 instances of four operations with the stack calculation expression
Recursive implementation of 77 instances of integer four operations Calculator
78 instances of complex data mapping
Instances of 79 paintings parabolic interpolation method
Instances of 80 normal distribution curve generation
81 of solving an instance of the dichotomy of the real roots of nonlinear equations
82 instances of real matrix multiplication
83 instances of Gaussian elimination solution of linear equations
Instances of 84 Gauss- Jordan method inverse matrix
85 instances of complex matrix multiplication
Simpson, 86 instances of the value of the definite integral of the Method of
Title VII Graphics
87 instances of art with the C language implementation clear screen
Instances of 88 graphics Circle Algorithm
Filled with examples Platform: |
Size: 7130112 |
Author:qunniao |
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Description: 用来产生多变量高斯过程的MATLAB源程序。-MULTI_GP generates a multivariate Gaussian random process with mean vector m (column vector) and covariance matrix C。 Platform: |
Size: 1024 |
Author:selen32 |
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Description: 对lena.map先分块处理，然后做cs变换，观测矩阵用随机高斯矩阵，重构算法用l1算法-On lena.map first block processed, and then do cs transform, random Gaussian matrix with the observation matrix, reconstruction algorithm algorithm using l1 Platform: |
Size: 163840 |
Author:liuyaxin |
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Description: randgen(mu,mu1,mu2,cov1,cov2,cov3) = Random generation of Gaussian Samples
in d-dimensions where d = 2
mu, mu1, mu2 = (x,y) coordinates(means) that the gaussian samples are centered around
cov1, cov2, cov3 are the covariance matrices and will vary changing the
shape of the distribution, example: cov = sigma^2*Identity Matrix, where sigma^2 = a scalar
N = the number of gaussian samples used are provided as user input,
A test set of N/2 and a training set of N/2 gaussian samples is also generated
Output is directed to the command window and a plot of the distributions are generated Platform: |
Size: 1024 |
Author:resident e |
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Description: 压缩感知CS——采用小波变换进行稀疏表示，高斯随机矩阵为观测矩阵，重构算法为SP算法，对256*256的lena图处理，比较原图和SP算法在不同采样比例(0.74、0.5、0.3)下的重构效果，并各运行50次，比较算法性能PSNR和每次的运行时间-Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix and SP algorithm as the reconstruction algorithm. Compare lena figure and the reconstruction results using SP algorithm at different sampling ratio (0.74,0.50.3),then each runs 50 times, compare the performance of PSNR and each running time Platform: |
Size: 46080 |
Author:沈芳 |
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Description: 压缩感知CS——采用小波变换进行稀疏表示，高斯随机矩阵为观测矩阵，重构算法为SL0算法，对256*256的lena图处理，比较原图和SL0算法在不同采样比例(0.74、0.5、0.3)下的重构效果，并各运行50次，比较算法性能PSNR和每次的运行时间
-Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix and SL0 algorithm as the reconstruction algorithm. Compare lena figure and the reconstruction results using SL0 algorithm at different sampling ratio (0.74,0.50.3),then each runs 50 times, compare the performance of PSNR and each running time Platform: |
Size: 50176 |
Author:沈芳 |
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Description: 压缩感知CS——采用小波变换进行稀疏表示，高斯随机矩阵为观测矩阵，重构算法为ROMP算法，对256*256的lena图处理，比较原图和ROMP算法在不同采样比例(0.74、0.5、0.3)下的重构效果，并各运行50次，比较算法性能PSNR和每次的运行时间
-Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix and ROMP algorithm as the reconstruction algorithm. Compare lena figure and the reconstruction results using ROMP algorithm at different sampling ratio (0.74,0.50.3),then each runs 50 times, compare the performance of PSNR and each running time Platform: |
Size: 47104 |
Author:沈芳 |
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Description: 压缩感知CS——采用小波变换进行稀疏表示，高斯随机矩阵为观测矩阵，重构算法为OMP算法，对256*256的lena图处理，比较原图和OMP算法在不同采样比例(0.74、0.5、0.3)下的重构效果，并各运行50次，比较算法性能PSNR和每次的运行时间
-Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix and OMP algorithm as the reconstruction algorithm. Compare lena figure and the reconstruction results using OMP algorithm at different sampling ratio (0.74,0.50.3),then each runs 50 times, compare the performance of PSNR and each running time Platform: |
Size: 46080 |
Author:沈芳 |
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Description: 压缩感知CS——采用小波变换进行稀疏表示，高斯随机矩阵为观测矩阵，重构算法为ILRS算法，对256*256的lena图处理，比较原图和IRLS算法在不同采样比例(0.74、0.5、0.3)下的重构效果，并各运行50次，比较算法性能PSNR和每次的运行时间-Compressed sensing CS- using wavelet transform as sparse representation, Gaussian random matrix as the observation matrix and IRLS algorithm as the reconstruction algorithm. Compare lena figure and the reconstruction results using IRLS algorithm at different sampling ratio (0.74,0.50.3),then each runs 50 times, compare the performance of PSNR and each running time Platform: |
Size: 46080 |
Author:沈芳 |
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Description: 包含压缩传感的随机矩阵程序，如小波变换和高斯随机矩阵和omp重构算法-Random matrix containing the compressed sensing programs, such as wavelet transform and Gaussian random matrices and omp reconstruction algorithm Platform: |
Size: 165888 |
Author:方刚 |
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Description: 应用正交匹配追踪求解等式y=Ax,要求：
待求x是稀疏向量，A为高斯随机矩阵
调用形式：x = myomp(A,y,err)
A -线性投影矩阵；
y -投影向量
err -所需精度-apply Orthogonal matching pursuit to solve the equation y = Ax,
requirements:
the unknown x is sparse vector,
A is a Gaussian random.
calling form: x = myomp (A, y, err)
A - linear projection matrix
y - projection vector
err - desired accuracy. Platform: |
Size: 1024 |
Author:聂志鹏 |
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Description: 应用傅立叶变换矩阵对信号进行稀疏,经高斯随机观测矩阵观测,经正交匹配追踪算法重构.压缩感知入门程序-The Fourier transform matrix is used to spill the signal. Observed by Gaussian random observation matrix and reconstructed by orthogonal matching tracing algorithm. Compression Sensing Getting Started Platform: |
Size: 1024 |
Author:誠 |
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Description: （a）产生两个都具有200个二维向量的数据集和（注意：在生成数据集之前最好使用命令randn(‘seed’,0)初始化高斯随机生成器为0（或任意给定数值），这对结果的可重复性很重要）。向量的前半部分来自均值向量的正态分布，并且协方差矩阵。向量的后半部分来自均值向量的正态分布，并且协方差矩阵。其中是一个2*2的单位矩阵。
（b）在上述数据集上运用感知器算法，并且使用不同的初始向量初始化参数向量。
（c）测试每一次算法在和上的性能。
（d）画出数据集和，以及分类面。((a) Generate the sum of two datasets with 200 two-dimensional vectors (Note: before generating the dataset, it is better to initialize the Gaussian random generator to 0 (or any given value) with the command randn ("seed", 0), which is important for the repeatability of the results). The first half of the vector comes from the normal distribution of the mean vector and the covariance matrix. The second half of the vector comes from the normal distribution of the mean vector and the covariance matrix. Where is a 2 * 2 identity matrix.
(b) The perceptron algorithm is used on the above data set, and different initial vectors are used to initialize the parameter vector.
(c) Test the performance of each algorithm on and.
(d) Draw the data set and, as well as the classification surface.) Platform: |
Size: 1024 |
Author:zilong1999 |
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Description: a）产生两个都具有200个二维向量的数据集和（注意：在生成数据集之前最好使用命令randn(‘seed’,0)初始化高斯随机生成器为0（或任意给定数值），这对结果的可重复性很重要）。向量的前半部分来自均值向量的正态分布，并且协方差矩阵。向量的后半部分来自均值向量的正态分布，并且协方差矩阵。其中是一个2*2的单位矩阵。
（b）在上述数据集上和分别属于+1类和-1类，请在上述数据集的两类中各随机抽取150个样本作为训练集，运用Logistic regression算法得到的分类面，然后对余下的各50个样本进行分类，画出测试样本及其分类面，统计错误率，给出每个样本属于该类别的概率值。(a) Generate the sum of two datasets with 200 two-dimensional vectors (Note: before generating the dataset, it is better to initialize the Gaussian random generator to 0 (or any given value) with the command randn ("seed", 0), which is important for the repeatability of the results). The first half of the vector comes from the normal distribution of the mean vector and the covariance matrix. The second half of the vector comes from the normal distribution of the mean vector and the covariance matrix. Where is a 2 * 2 identity matrix.
(b) On the above datasets, and belong to + 1 and - 1 classes respectively. Please randomly select 150 samples from each of the above data sets as the training set, use the logistic regression algorithm to get the classification surface, and then classify the remaining 50 samples, draw the test samples and their classification surface, count the error rate, and give the probability value of each sample belonging to this category.) Platform: |
Size: 1024 |
Author:zilong1999 |
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Search - gaussian random matrix