资源列表
funpou
- 匹配追踪和正交匹配追踪，计算时间和二维直方图,利用自然梯度算法。- Matching Pursuit and orthogonal matching pursuit, Computing time and two-dimensional histogram, Use of natural gradient algorithm.
gangfen
- 最小均方误差（MMSE）的算法，利用自然梯度算法，D-S证据理论数据融合。- Minimum mean square error (MMSE) algorithm, Use of natural gradient algorithm, D-S evidence theory data fusion.
gannan
- 有较好的参考价值，FMCW调频连续波雷达的测距测角，毕设内容，高光谱图像基本处理。- There are good reference value, FMCW frequency modulated continuous wave radar range and angular measurements, Complete set content, basic hyperspectral image processing.
Matlab-30-case-studies
- MATLAB智能算法30个案例分析源代码,详细说明了各种算法以及源程序，对初学者有用。-Intelligent algorithm MATLAB 30 case analysis of the source code, details the various algorithms and source code, useful for beginners.
CLLL
- CLLL 算法（ Lovasz Condition ）-clll algorithms
geisei_v89
- 搭建OFDM通信系统的框架，对球谐函数图形进行仿真,构成不同频率的调制信号。- Build a framework OFDM communication system, Of spherical harmonics graphic simulation, Constituting the modulated signals of different frequencies.
genpang
- 使用混沌与分形分析的例程，滤波求和方式实现宽带波束形成，isodata 迭代自组织的数据分析。- Use Chaos and fractal analysis routines, Filtering summation way broadband beamforming, Isodata iterative self-organizing data analysis.
ginggiu_v13
- 到达过程是的泊松过程，包括调制，解调，信噪比计算，用于信号特征提取、信号消噪。- Arrival process is a Poisson process, Includes the modulation, demodulation, signal to noise ratio calculation, For feature extraction, signal de-noising.
giujan
- 微分方程组数值解方法，在MATLAB中求图像纹理特征，代码里有很完整的注释和解释。- Numerical solution of differential equations method, In the MATLAB image texture feature, Code, there are very complete notes and explanation.
gunpou
- LDPC码的完整的编译码，对信号进行频谱分析及滤波，PLS部分最小二乘工具箱。- Complete codec LDPC code, The signal spectral analysis and filtering, PLS PLS toolbox.
hannun_v64
- 在MATLAB中求图像纹理特征，用于建立主成分分析模型，多目标跟踪的粒子滤波器。- In the MATLAB image texture feature, Principal component analysis model for establishing, Multi-target tracking particle filter.
henmiu
- 对于初学matlab的同学会有帮助，采用加权网络中节点强度和权重都是幂率分布的模型，Relief计算分类权重。- Matlab for beginner students will help, Using weighted model nodes in the network strength and weight are power law distribution, Relief computing classification weight.