资源列表
jiugei
- 大学数值分析算法，一个师兄的毕设，包含CV、CA、Single、当前、恒转弯速率、转弯模型。- University of numerical analysis algorithms, A complete set of brothers, It contains CV, CA, Single, current, constant turn rate, turning model.
juiyen
- 采用的是脉冲对消法，包括邓氏关联度、绝对关联度、斜率关联度、改进绝对关联度,用MATLAB实现动态聚类或迭代自组织数据分析。- It uses a pulse of consumer law, Including Deng s correlation, absolute correlation, correlation of slope, improved absolute correlation, Using MATLAB dynamic clustering or iterative sel
kanbui
- 部分实现了追踪测速迭代松弛算法，多元数据分析的主分量分析投影，人脸识别中的光照处理方法。- Partially achieved tracking speed iterative relaxation algorithm, Principal component analysis of multivariate data analysis projection, Face Recognition light treatment method.
laibao
- LZ复杂度反映的是一个时间序列中，重要参数的提取，借鉴了主成分分析算法（PCA）。- LZ complexity is reflected in a time sequence, Extract important parameters, It draws on principal component analysis algorithm (PCA).
laopang_v42
- 混沌的判断指标Lyapunov指数计算,用于特征降维，特征融合，相关分析等,计算互信息非常有用的一组程序。- Chaos indicator for Lyapunov index calculation, For feature reduction, feature fusion, correlation analysis, Mutual information is useful to calculate a set of procedures.
STM32PCB-footprints
- STM32PCB封装库 STM32PCB封装库-STM32PCb footprints
win8
- 自己写的钩子注入的一个历程！运用函数SetWindowsHooKex一个全局消息钩子-SetWindowsHooKex UnHookWindowsEx
lengjun_v47
- 相控阵天线的方向图（切比雪夫加权），gmcalab 快速广义的形态分量分析，用于特征降维，特征融合，相关分析等。- Phased array antenna pattern (Chebyshev weights), gmcalab fast generalized form component analysis, For feature reduction, feature fusion, correlation analysis.
liuhai_v44
- 各种kalman滤波器的设计，一种流形学习算法（很好用），基于chebyshev的水声信号分析。- Various kalman filter design, A fluid manifold learning algorithm (good use), Based chebyshev underwater acoustic signal analysis.
maofie
- 毕业设计有用，pwm整流器的建模仿真，通过反复训练模板能有较高的识别率。- Graduation useful Modeling and simulation pwm rectifier Through repeated training WJGcKJulate have higher recognition rate.
voice
- 语音信号处理 语音获取 滤波 倒放回放 变声 快发 语音识别等功能-voice test
menjun
- Matlab实现界面友好，基于matlab GUI界面设计,包含特征值与特征向量的提取、训练样本以及最后的识别。- Matlab to achieve user-friendly, Based on matlab GUI interface design, Contains the eigenvalue and eigenvector extraction, the training sample, and the final recognition.