case33ref

说明：  基于压缩感知思想的图像分块压缩与重构方法 考虑到大多数图像信号信息分布有差异, 编码端, 在对图像分块的基础上, 融合熵估计 和边缘检测方法计算各图像块的信息含量, 再从两个不同的角度进行分类采样: 依据信息量多少将图像块分为平滑、过渡和纹理3 类, 使用不同的采样率采样 依据信息量的分布特征, 采用不同的采样率分配策略进行采样. 在解码端, 根据不同类型的图像块构造不同的线性算子进行重构, 再运用改 进的迭代阈值算法去除块效应和噪声. 实验证明, 算法在提升图像重构质量的同时缩短了重构时间,并且对纹理边缘多的图像的重构效果较其他方法理想.(Considering that most information of the image signal difference distribution, the encoding side, in the right image sub-blocks based on the integration entropy estimation And edge detection method for calculating the information content of each image block, and two different angles classification sampling: The number of image blocks based on the amount of information into a smooth, transition and texture three classes, using different sampling rates based on the amount of information distribution characteristics, using different sampling sampling rate allocation strategy. in the decoding side, depending on the type of image blocks to construct different linear operator reconstructed, re-use change Iterative thresholding algorithm into blockiness and noise removal experiments show image reconstruction algorithm to enhance the quality while reducing reconfiguration time, and much of the texture edge image reconstruction results are satisfactory compared to other methods.)

RBF预测，用RBF预测蒙脱土纳米复合材料的双指标性能(RBF prediction, prediction with RBF nanocomposites performance of two indicators of)

detect face of people with matlab

说明：  地球物理勘探过程中对地震记录数据添加道头(Geophysical data in the process of seismic records to add Wharf)

matlab与vc的混合编程说明，主要是如何配置环境，有详细的截图说明(the help for together using of matlab and VC6.0，including collocate environment and giving detailed illustrate )

基于瞬时无功功率的谐波检测，pq法。已验证(Harmonic detection based on instantaneous reactive power)

耦合矩阵的优化 滤波器方面的应用 无源单通带的滤波器(Optimization filter coupling matrix aspects of the application of passive single pass band filter)

EEG signal classification by SVM

将压缩感知用于谱估计中，根据论文谱压缩感知的一些程序(Compressive sensing (CS) is a new approach to simultaneous sensing and compression of sparse and compressible signals based on randomized dimensionality reduction. To recover a signal from its compressive measurements, standard CS algorithms seek the sparsest signal in some discrete basis or frame that agrees with the measurements. A great many applications feature smooth or modulated signals that are frequency sparse and can be modeled as a superposition of a small number of sinusoids. Unfortunately, such signals are only sparse in the discrete Fourier transform (DFT) domain when the sinusoid frequencies live precisely at the center of the DFT bins. When this is not the case, CS recovery performance degrades significantly. In this paper, we introduce a suite of spectral CS (SCS) recovery algorithms for arbitrary frequency sparse signals. The key ingredients are an over-sampled DFT frame, a signal model that inhibits closely spaced sinusoids, and classical sinusoid parameter e)

code for alamouti scheme using bpsk modulation