one_desiel_gen2014a

Automatic voltage regulator uncompensated and compensated case matlab simulation with results

语音分离的研究在语音通信、声学目标检测、声音信号增强等方面有着重要的理论意义和实用价值。而将语音分离技术应用到智能机器人中。让机器人具有智能的听觉，实现声源定位和分离，确定说话人个数，进行人机对话等方面更具有广阔的应用前景。 (Separation of speech in voice communications, acoustic target detection, sound signal enhancement, and so has important theoretical significance and practical value. The voice separation technology applied to intelligent robots. Intelligent robot to the hearing, to achieve sound localization and separation, to determine the number of speaker, the man-machine dialogues in a more broad application prospects.)

ACCV的一篇论文以及源代码，不是我写的只是搬运到这里而已，具体请访问论文中作者的网页。改进Harris的角点检测，主要改进在：1、是在加权模板选择了各向异性的模板代替了各向同性的高斯模板；2、选择使用多尺度滤波器对图像进行处理，使算法具有一定的多尺度特性。(This is paper with code,which published on ACCV.I m not the author of this paper.If want to know more,please vistor the home page of the author.Major improvements of this paper is focus on: 1 Using a bilateral filters insteal of the isotropic Gaussian kernel for weighted template selection 2、Using Multi-Scale filter which make the algorithm can detect the multi-scale corner in the image)

机械方面基于matlab的连杆分析程序，凸轮设计程序(Mechanical matlab-based linkage analysis program, the cam design program)

雷达影像后向散射系数计算,并绘制后向散射系数图(backscattering coefficients of Radar image, show the figure of backscatter coefficients)

matlab调用计算机声卡，实时显示计算机播放的声强级，使用数据获取工具箱，强烈推荐(call matlab computer sound cards, real-time display of the computer to play sound intensity level, the use of data acquisition toolbox, strongly recommend)

说明：  This is code to simulation of AM, FM, PSK, QAM modulation

基于ARIMA时间序列模型对风速进行拟合，预测短时间的风速(Based on ARIMA time series model fitting for wind speed forecasting wind speed for a short period of time )

一个雷达距离像对齐的小程序，其中有距离像的归一化，对齐等四个步骤，我的源码，欢迎下载(radar distance as a small alignment procedures, as there is a gap between the uniform, the four alignment steps My source, welcomed downloaded)

用MATLAB编写的人脸识别算法，对PCA主成分分析进行了改进(Face recognition algorithm using MATLAB, the PCA principal component analysis has been improved)