种子点相关的代码分析 homework1_zzh

论文详细阐述了杜哈梅积分的原理，并用单自由度系统验证(The principle of duhami integral is expounded in detail, and the system is verified by single degree of freedom system.)

说明：  运用MVC方法将逐日NDVI合成为月合成NDVI(Daily NDVI is synthesized into monthly NDVI by MVC method)

说明：  对现存的主流分词方法进行了分析和比较，对前人的成果和经验进行了研究和改进,设计实现了一个基于词典和统计方法的在线中文分词系统。本文系统使用了基于一种主流分词方法的经典算法,双向最大匹配法。这种方法能够将正向最大匹配法得到的分词结果和逆向最大匹配法得到的结果进行比较，从而决定正确的分词方法。这类算法的优点是速度快，时间复杂度为O（n），实现简单。本系统为用户提供了一个中文分词的在线平台，有文本分词，输入分词内容，实时显示分词结果等功能可供使用，本系统在切词准确度和速度上的表现较为良好,基本完成了中文分词的工作,提供了较好的用户体验。本文系统的创新点在于，将python语言作为开发语言，并选择了较为热门的web框架Flask作为开发在线系统的框架，实现了一个在线中文分词系统。目的是方便用户进行分词，并实时观测结果。(This paper analyzes and compares the existing mainstream word segmentation methods, studies and improves the previous achievements and experiences, and designs and implements an online Chinese word segmentation system based on dictionary and statistical methods. In this paper, a classical algorithm based on a mainstream word segmentation method, bi-directional maximum matching method, is used. This method can compare the result of forward maximum matching method with that of reverse maximum matching method, so as to determine the correct segmentation method. This kind of algorithm has the advantages of high speed, O (n) time complexity and simple implementation.)

说明：  对于新手调试入手很快,通过32单片进行通信控制，进行WIFI设计透传。(For novice debugging start very quickly, through 32 monolithic communication control, WIFI design transmission.)

说明：  pcm2706+pcm1794同轴解码器(Pcm2706 + pcm1794 coaxial decoder)

这是一个语音识别提取特征参数的例子，提取lpcc和lpc特征参数并且计算了短时能量，这个是用来检测哭声可以使用的lpc-lpcc-short-time-energy(This is an example of speech recognition extraction feature parameters, extracting lpcc and lpc feature parameters and calculating short-term energy, which is used to detect the use of crying sounds.)

vista options控件！！没有任何限制可免费长期使用！-Control vista options! ! There are no restrictions on long-term use free of charge!

Ask the ETC control table

对于声音的采集，分析，频谱显示，滤波显示，适合初学者参考(labview)

俄罗斯方块源码，对提升VC是个不错的例子，望大家都能有所收获-Tetris source to enhance the VC is a good example, we hope all will be harvested