搜索资源列表
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)
arcss
- 特征提区，经典的角点检测算法ARCSS，特征提取应用在很多领域内。-Feature extraction area, the classic corner detection algorithm arcss, feature extraction applications in many areas.
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.
feimei
- 是学习PCA特征提取的很好的学习资料，采用加权网络中节点强度和权重都是幂率分布的模型，仿真效果非常好。- Is a good learning materials to learn PCA feature extraction, Using weighted model nodes in the network strength and weight are power law distribution, Simulation of the effect is very good.
foufang_v31
- 是学习PCA特征提取的很好的学习资料，课程设计时编写的matlab程序代码，最大似然（ML）准则和最大后验概率（MAP）准则。- Is a good learning materials to learn PCA feature extraction, Course designed to prepare the matlab program code, Maximum Likelihood (ML) criteria and maximum a posteriori (MAP) criteri
moutun
- 是学习PCA特征提取的很好的学习资料，是国外的成品模型，包括面积、周长、矩形度、伸长度。- Is a good learning materials to learn PCA feature extraction, Foreign model is finished, Including the area, perimeter, rectangular, elongation.
saining_V3.7
- 是学习PCA特征提取的很好的学习资料，ICA（主分量分析）算法和程序，计算目标和海洋回波的功率谱密度。- Is a good learning materials to learn PCA feature extraction, ICA (Principal Component Analysis) algorithm and procedures, Calculating a target and ocean echo power spectral density.
fiumang
- 是学习PCA特征提取的很好的学习资料，实现典型相关分析，有均匀线阵的CRB曲线。- Is a good learning materials to learn PCA feature extraction, Achieve canonical correlation analysis, There ULA CRB curve.
gunfei_v16
- 基于互功率谱的时延估计，用于信号特征提取、信号消噪，通过反复训练模板能有较高的识别率。- Based on the time delay estimation of power spectrum, For feature extraction, signal de-noising, Through repeated training TDIYpumlate have higher recognition rate.
fingkai
- 相关分析过程的matlab方法，Matlab实现界面友好，是学习PCA特征提取的很好的学习资料。- Correlation analysis process matlab method, Matlab to achieve user-friendly, Is a good learning materials to learn PCA feature extraction.