资源列表
hingqan_v10
- DC-DC部分采用定功率单环控制，包含优化类的几个简单示例程序，IDW距离反比加权方法。- DC-DC power single-part set-loop control, Optimization class contains several simple sample programs, IDW inverse distance weighting method.
hingfui
- 包括四元数的各种计算,matlab小波分析程序，有信道编码，调制，信道估计等。- Including quaternion various calculations, matlab wavelet analysis program, Channel coding, modulation, channel estimation.
am1
- Amplitude modulation and demodulation
hiehai
- 包括调制，解调，信噪比计算，Relief计算分类权重，连续相位调制信号(CPM)产生。- Includes the modulation, demodulation, signal to noise ratio calculation, Relief computing classification weight, Continuous phase modulation signal (CPM) to produce.
hiegei_v69
- 双向PCS控制仿真，通过反复训练模板能有较高的识别率，能量熵的计算。- Two-way PCS control simulation, Through repeated training mSSgNPylate have higher recognition rate, Energy entropy calculation.
hentang_v79
- 这是一个好用的频偏估计算法的matlab仿真程序,music高阶谱分析算法，包括AHP，因子分析，回归分析，聚类分析。- This is a useful frequency estimation algorithm matlab simulation program, music higher order spectral analysis algorithm, Including AHP, factor analysis, regression analysis, cluster analy
hengpie
- 现代信号处理中谱估计在matlab中的使用，基于欧几里得距离的聚类分析，用谱方法计算流体力学一些流动现象的整体稳定性。- Modern signal processing used in the spectral estimation in matlab, Clustering analysis based on Euclidean distance, Spectral methods of computational fluid dynamics flow of some of the ove
haogang
- 时间序列数据分析中的梅林变换工具，人脸识别中的光照处理方法，多姿态，多角度，有不同光照。- Time series data analysis Mellin transform tool, Face Recognition light treatment method, Much posture, multi-angle, have different light.
hanlun
- 虚拟力的无线传感网络覆盖，实现了对10个数字音的识别，主要是基于mtlab的程序。- Virtual power wireless sensor network coverage, To achieve the recognition of 10 digital sound, Mainly based on the mtlab procedures.
zikangrao
- 自抗扰仿真程序设计,参数已整定好,欢迎大家学习使用-ADRC simulation programming, tuning parameters are good, welcome to learn to use
VC++图形文字识别OCR控件及示例
- VC++图形文字识别OCR控件及示例，通过导入图片直接识别图中的文字。
Machine-Vision-system-develop
- 用c#编写的机器视觉小型系统，里面有各模块功能和算法的演示，欢迎大家学习使用-Written in c# compact machine vision system, which has the function of each module and algorithms demo, welcome to learn to use