搜索资源列表
Wavelet_OMP
- 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
Wavelet_SP
- 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
Wavelet_ROMP
- 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 matr
Wavelet_SL0
- 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
Particle-filter-resampling-methods
- 粒子滤波是基于递推的MonteCarlo仿真方法的总称, 原则上可用于任意非线性、非高斯随机系统的状态估计。-Particle filter is based on the the MonteCarlo simulation method of recursive general principle can be used for any nonlinear, non-Gaussian random system state estimation.
Particle-filter-algorithm-
- 粒子滤波是基于递推的蒙特卡罗模拟方法的总称,可用于任意非线性,非高斯随机系统的状态估计。-The particle filter is based on recursive Monte Carlo simulation method general, can be used for any non-linear, non-Gaussian random system state estimation.
Gauss
- 生成三维的高斯随机曲面,用matlab语言编写的源程序。-Generate three-dimensional Gaussian random surfaces, using matlab language of the source.
mmtry
- 基于dwt的音频水印技术,在音频中加入高斯随机水印,达到防伪的效果。-Based on dwt audio watermarking technology, adding Gaussian random audio watermark, achieve security results.
communication
- 产生高斯随机信号,参数为标准差 0.2 2 10 均值 0 10 样本点 100 1000 共3×2×2=12组 -Gaussian random signal
[kimi]Noise
- 实现了在图像中生成均匀随机噪声、高斯随机噪声和椒盐随机噪声的算法。-The code for creating uniform noise, Gaussian noise and Salt-pepper noise in image processing.
DCT_Gaosi_fenkuai
- 对256*256大小的8bit灰度lena图像进行仿真 将图像分为16*16的分块进行计算 稀疏矩阵采用DCT矩阵,观测矩阵采用高斯随机矩阵,重构采用OMP算法- 256* 256 size lena image simulation 8bit grayscale image is divided into 16 * 16 calculate block sparse matrix using DCT matrix, observation matrix using Ga
Wavelet_OMP
- 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
Gaussian_sum
- 用中心极限定理得原理产生高斯随机分布的序列。-Principle with the central limit theorem is a sequence of Gaussian random distribution.
zuoye3youxianyuan
- 悬臂梁基础固支端水平方向受到振动强度RMS值为1g的高斯随机激励,有效激振频带为200Hz。要求设计合适的振动控制方案,使得在控制器的作用下,该悬臂梁自由端的随机振动响应具有良好的抑制效果。-Gaussian basis cantilever clamped horizontally by the end of the vibration intensity 1g RMS value of random excitation, the effective excitation band of 2
Sequential_Detection
- 已知方差的高斯随机信号的序贯检测:给出了H0和H1假设下理论推导得到的序贯检测平均样本数的曲线,以及仿真得到的序贯检测平均样本数的曲线,理论结果和仿真结果较为吻合。-Sequential detection of known variance Gaussian random signals: H0 and H1 are given under the assumption that the theoretical derivation curve sequential detect the av
roughness
- 用于轴承球与滚道之间,模拟生成非高斯随机粗擦表面-Analog generate non-Gaussian random rough surfaces rub
myomp
- 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. ca
QuartercarJJJ
- 通过具有随机结构参数的四分之一车辆模型研究了具有不确定性结构参数的车辆在受到来自道路的随 机激励作用下的振动响应问题。将簧上质量、簧下质量、悬挂阻尼、悬挂刚度以及轮胎刚度均认为是随机变量。将路面的不平整引起的对车辆的激励看作高斯随机过程并通过简单指数功率谱密度来建立力学模型。-By a quarter vehicle model with random parameters studied vehicle structure uncertain structural parameters of
GPstuff_matlab-4.6
- 这是关于高斯随机回归模型的经典代码!有很有价值的参考意义!-This is the classic Gauss random regression model code! Has a very valuable reference value!
demo
- 将一副图像进行压缩并重现,使用dct测量矩阵,高斯随机矩阵,omp恢复算法-A pair of images were compressed and reproduce, using DCT measurement matrix, Gauss random matrix, OMP recovery algorithm