xiaobobianhuan

基于小波变换的心电信号预处理MATLAB源代码(The heart preprocessind based on wavelet transform)

An Efficient Randomized Algorithm for Detecting Circles

Wavelet Neural networks

离散小波变换，再用OMP算法重构出原始信号(DWT transmission，OMP )

数字全息和计算程式 计算全息图的制作和再现过程主要分为以下几个步骤： 1抽样。得到物体或波面在离散样点上的值。 2.计算。计算物光波在全息平面上的光场分布。 3.编码。把全息平面上光波的复振幅分布编码成全息图的透过率变化。 4.成图。在计算机控制下，将全息图的透过率变化在成图设备上成图。如果成图设备分辨率不够，再经光学缩版得到实用的全息图。 5.再现。这—步骤在本质上与光学全息图的再现没有区别。 (Digital Holography and CGH for calculating the production and reproduction process is divided into the following steps: 1 sampling. Be objects, or wavefront-like point in the discrete value. 2. Calculated. Calculation of complex light plane in the holographic optical field distribution. 3. Coding. The hologram plane the complex amplitude distribution of light into a hologram coding changes in the transmittance. 4. As Fig. Under the control of the computer, the hologram transmittance change in the mapping device mapping. Mapping equipment, if not resolution, then reduced by the optical version of a hologram to be practical. 5. Reproduction. This is- these in essence a hologram and the optical reproduction is no different.)

基于小波变换的通信信号码元速率估计.pdf(Estimated rate based on the communication signal element wavelet transform. Pdf)

单小波变换应用于图像压缩,及其应用于图像融合的MATLAB代码(Single-wavelet transform to image compression, image fusion and its application to the MATLAB code)

说明：  提出了一种结合SVD的小波变换方法，对其在外弹道测量数据中的野值剔除进行了研究。对观测数据进行小波分解，将小波分解后的近似分量和细节分量组合实现相空间重构，作为SVD方法的输入观测矩阵，根据奇异 熵增量准则，对奇异值进行筛选，根据SVD逆变换重构原信号。这一方法克服了Hankel矩阵相空间构建方法数据 端点失真问题。以小波分解后分量重构的相空间可以满足正交性，进一步提高了SVD进行数据降噪和野值检测的精度。仿真数据和试验数据处理结果证明了这一方法的有效性。(Proposed a method of combining the SVD wavelet transform its outer ballistic measurement data in excluding outliers were studied. Observational data on wavelet decomposition, wavelet decomposition of the approximate combined component and detail component to achieve the phase space reconstruction, as the input observation matrix SVD method, based on singular entropy increment standards, singular value filter, according to the inverse transform heavy SVD structure of the original signal. This approach overcomes the Hankel matrix phase space construction method data endpoint distortion. Wavelet decomposition component of reconstruction phase space satisfy orthogonality, to further improve the SVD for data noise reduction and outlier detection accuracy. Simulation data and experimental data processing results prove the effectiveness of this approach.)

多波束、波束形成、DSP实现、多波束分析(Multibeam、Multi-beam forming、Multi-beam analysis、DSP、)

利用CVX进行优化，得到期望加权矢量，从而的得到需要的波束形成(CVX using optimized to give the desired beamforming)