pend

慢速跳频，调制样式为DPSK(DPSK/FH)，包括信号的发射接收模型，自己写的代码，仅供参考。(Slow frequency hopping, modulation pattern for DPSK (DPSK/FH), including the launch of signal reception model, write your own code, for reference purposes only.)

说明：  main是主函数，HK.m是分类算法函数，训练模式和待分类模式存放在Patterns.mat文件中，分为4个样本，其中w1和w3是训练模式，w2和w4是待分类的模式。

傅 立叶变换是数字信号处理领域一种很重要的算法。要知道傅立叶变换算法的 意义，首先要了解傅立叶原理的意义。傅立叶原理表明：任何连续测量的时序或 信号，都 可以表示为不同频率的正弦波信号的无限叠加。而根据该原理创立的 傅立叶变换算法利用直接测量到的原始信号，以累加方式来计算该信号中不同正 弦波信号的频 率、振幅和相位。 (The goals for the course are to gain a facility with using the Fourier transform, both specific techniques and general principles, and learning to recognize when, why, and how it is used. Together with a great variety, the subject also has a great coherence, and the hope is students come to appreciate both. )

此函数可以实现MATLAB，智能天线方向图函数程序的画法并且可以通过改变参数得到不同的方向图(Smart antenna pattern function MATLAB program)

雨滴检测 检测户外雨滴数量，大小，质心等用于预测雨量的大小(Raindrop raindrop detection Detection outdoor number, size, and so on for size prediction centroid rainfall)

corner detection a digital image processing project in matlab .m files included including presentation

file to estimate carbon flow

iterative EM MAP algorithm

main executing reference usage: usage_timeDifference.m Sample output: The time interval between earierTime [09-Sep-1900 09:09:18] and laterTime [31-Oct-2010 23:33:08] is 110 years, 79 days, 14 hours, 23 mins, 50.17 secs. The objective is to compute the time interval taking into account the leap days that are subsumed within the time interval. Note: The logic subsumes the time interval by yearEnd, monthEnd, dayEnd - timeEarlier (within that year) + time interval (excluding that 2 years) + timeLater - yearEnd, monthEnd, dayEnd (within that year) It differentiates the earlier time and later time, ie. timeDifference (earlierTime, laterTime) gives the same result as timeDifference (laterTime, earlierTime). leapDayHasPassed.m illustrates a possible approach to determine if a certain day of concern is already over at a given time. Caveat: does not include leap secs

对数据进行EMD分析时，如果数据不是纯的白噪声,则分析过程中一些尺度将会丢失,出现模态混叠现象。EEMD通过附加均匀分布 在整个时频空间的白噪声，可有效抑制这种现象的发生。(EEMD is insufficient for the EMD method, we propose a noise-assisted data analysis methods. EEMD decomposition principle is: when the additional white noise uniformly distributed throughout the time-frequency space, the time-frequency space is carved up by the filter component of the composition at different scales.-)