ex5FileIO-timePrandVal

本代码介绍了舰载相控阵雷达在海杂波背景下进行动目标检测的方法，性能稳定，杂波消除明显，目标信号提取有效，代码值得相关研究人员学习和参考。(This code introduces the method of how shipborne PAR radar detects moving object signal in strong sea clutter background with many good attributes.)

最大功率输出控制的光伏发电系统，通过输出功率作为反馈信号控制输入电压，是系统处于最大功率输出。这个是最大功率追踪部分。(Maximum power output of photovoltaic power generation system controlled by the output power control input voltage as a feedback signal, the system at the maximum power output. This is the maximum power point tracking section.)

matlab gui设计学习实例 实现小球旋转，反转，停止，调速等功能(example of matlab gui)

说明：  数字图像处理--同态滤波--matlab程序(Digital image processing- homomorphic filtering- matlab program)

一个GUI界面编辑 包括多种图像处理 方法 阈值 直方图均一化等(A GUI interface for editing include a variety of image processing methods, such as threshold histogram homogenization)

说明：  基于susan算法的图像边缘检测，内有主函数，和2个子函数，可出图。(Susan algorithm based on edge detection, within the main function, and 2 sub-functions, can be a map.)

无线定位代码，包括各种定位算法的基本函数，定位算法包括TDOA、TOA、AOA等(Wireless location code, including the basic function of a variety of localization algorithm, localization algorithm, including TDOA, TOA, AOA, etc.)

这是一个可以把四张图片合成一张图片的程序(This is a synthesis of four images can be a picture of the process)

DTW（Dynamic Time Warping，动态时间归整）算法，该算法基于动态规划（DP）的思想，解决了发音长短不一的模板匹配问题，是语音识别中出现较早、较为经典的一种算法。用于孤立词识别，DTW算法与HMM算法在训练阶段需要提供大量的语音数据，通过反复计算才能得到模型参数，而DTW算法的训练中几乎不需要额外的计算。所以在孤立词语音识别中，DTW算法仍然得到广泛的应用。 (DTW (Dynamic Time Warping, dynamic time warping) algorithm based on dynamic programming (DP) ideas, sounds of varying lengths to solve the template matching problem, is speech recognition appeared earlier, more classic kind of algorithm. For isolated word recognition, DTW algorithm and HMM algorithm in the training phase need to provide a large number of voice data, obtained by repeated calculations to model parameters, while the DTW algorithm is almost no additional training calculations. Therefore, in isolated word speech recognition, DTW algorithm is still widely used.)

Vapnik-Cheervonenkis (VC) Dimension 􀂇 Support Vector Machines 􀂇 SVM Applications 􀂇 Committee machines 􀂇 PAC Learning 􀂇 Boosting 􀂇 “No Free Lunch” Theorem(Vapnik-Cheervonenkis (VC) Dimension 􀂇 Support Vector Machines 􀂇 SVM Applications 􀂇 Committee machines 􀂇 PAC Learning 􀂇 Boosting 􀂇 " No Free Lunch" Theorem)