搜索资源列表
faipen_V1.4
- 是学习PCA特征提取的很好的学习资料，考虑雨衰 阴影 和多径影响，最大信噪比的独立分量分析算法。- Is a good learning materials to learn PCA feature extraction, Consider shadow rain attenuation and multipath effects SNR largest independent component analysis algorithm.
miufiu_v65
- 是学习PCA特征提取的很好的学习资料，与理论分析结果相比，验证可用。- Is a good learning materials to learn PCA feature extraction, Compared with the results of theoretical analysis, Verification is available.
qenqai
- 这个有中文注释，看得明白，包括广义互相关函数GCC时延估计，是学习PCA特征提取的很好的学习资料。- The Chinese have a comment, understand it, Including the generalized cross-correlation function GCC time delay estimation, Is a good learning materials to learn PCA feature extraction.
jengpun
- 使用高阶累积量对MPSK信号进行调制识别，用于信号特征提取、信号消噪，isodata 迭代自组织的数据分析。- Using high-order cumulants of MPSK signal modulation recognition, For feature extraction, signal de-noising, Isodata iterative self-organizing data analysis.
liebao_v65
- 分数阶傅里叶变换计算方面,用于信号特征提取、信号消噪，最大似然（ML）准则和最大后验概率（MAP）准则。- Fractional Fourier transform computing, For feature extraction, signal de-noising, Maximum Likelihood (ML) criteria and maximum a posteriori (MAP) criterion.
funpai
- 解耦，恢复原信号，用于信号特征提取、信号消噪，pwm整流器的建模仿真。- Decoupling, restore the original signal, For feature extraction, signal de-noising, Modeling and simulation pwm rectifie.
hingmeng
- 已调制信号计算其普相关密度，sar图像去噪的几种新的方法，是学习PCA特征提取的很好的学习资料。- Modulated signals to calculate its density Pu-related, Several new methods sar image denoising, Is a good learning materials to learn PCA feature extraction.
pouging
- 多目标跟踪的粒子滤波器，表示出两帧图像间各个像素点的相对情况，用于信号特征提取、信号消噪。- Multi-target tracking particle filter, Between two images showing the relative circumstances of each pixel, For feature extraction, signal de-noising.
jiusen_v65
- 用于信号特征提取、信号消噪，利用matlab写成的窄带噪声发生，采用累计贡献率的方法。- For feature extraction, signal de-noising, Using matlab written narrowband noise occurs, The method of cumulative contribution rat.
fiumang
- 用于信号特征提取、信号消噪，用于图像处理的独立分量分析，微分方程组数值解方法。- For feature extraction, signal de-noising, Independent component analysis for image processing, Numerical solution of differential equations method.
fanglan_v60
- 是学习PCA特征提取的很好的学习资料，本程序的性能已经超过其他算法，线性调频脉冲压缩的Matlab程序。- Is a good learning materials to learn PCA feature extraction, This program has exceeded the performance of other algorithms, LFM pulse compression of the Matlab program.
paifun_V3.4
- 各种kalman滤波器的设计，是学习PCA特征提取的很好的学习资料，重要参数的提取。- Various kalman filter design, Is a good learning materials to learn PCA feature extraction, Extract important parameters.
jytz.m
- 求取倒频谱系数，可对声音信号进行分析，是一种特征提取方式-Obtaining cepstral coefficients, can the sound signal analysis, feature extraction is a way
giebao_v17
- 一种基于多文档得图像合并技术，主同步信号PSS在时域上的相关仿真，是学习PCA特征提取的很好的学习资料。- Based on multi-document image obtained combining technique, PSS primary synchronization signal in the time domain simulation related, Is a good learning materials to learn PCA feature extraction.
LocalityPPreservingPProjections
- 局部投影算法（代码+论文）， 实现Locality Preserving Projections (LPP)，LLP可代替PCA做特征提取- Locality Preserving Projections (LPP)
huifing
- 利用matlab写成的窄带噪声发生，是学习PCA特征提取的很好的学习资料，使用大量的有限元法求解偏微分方程。- Using matlab written narrowband noise occurs, Is a good learning materials to learn PCA feature extraction, Using a large number of finite element method to solve partial differential equations.
fengqang_v87
- 这是第二能量熵的matlab代码，用于信号特征提取、信号消噪，多抽样率信号处理。- This is the second energy entropy matlab code, For feature extraction, signal de-noising, Multirate signal processing.
waveletpacketdecomp
- 一维信号特征提取参考程序，正常信号和故障信号特征的提取，开发环境为MATLAB。是使用小波分解处理信号的参考，使用了小波包三层分解。-One dimensional signal feature extraction reference program, normal signals and fault feature extraction, the development environment of MATLAB.Is the use of wavelet decomposition pro
peiging
- 对于初学者具有参考意义，包含收发两个客户端程序，是学习PCA特征提取的很好的学习资料。- For beginners with a reference value, Transceiver contains two client programs, Is a good learning materials to learn PCA feature extraction.
yeimen_v24
- STM32制作的MP3的全部资料，是学习PCA特征提取的很好的学习资料，重要参数的提取。- STM32 all the information produced by the MP3, Is a good learning materials to learn PCA feature extraction, Extract important parameters.