飞机姿态控制系统设计

利用散度法计算垂直速度，主要是缺测值的处理(Calculated using the vertical velocity dispersion, mainly dealing with missing data values)

emprical mode decomposition( emd, find peaks)

基于机载的旋转式合成孔径雷达成像（包含点目标、多目标、时域脉压、频域脉压、RD算法）(Rotary Airborne Synthetic Aperture Radar imaging (including point target, multi-target, time domain pulse compression, frequency-domain pulse pressure, RD algorithm))

频率响应屏蔽技术（FRM，Frequency Response Masking）技术实现窄带滤波器(Frequency Response Masking technology to realize narrow band filter)

nice book for bigneer u can use it for developing extre concept

输入信号与输出信号进行对比，并给出均方误差曲线(Input and output signals are compared, and the mean square error curve is given)

test BPSK ofdm add awgn ber

说明：  基于非负矩阵分解（NMF）的人脸特征提取算法，NMF基本思想是找到一个线性子空间W，使的构成子空间的基本图像的像素点都是正值，而且人脸图像在子空间上的投影系数也是正数(Non-negative Matrix Factorization (NMF) of facial feature extraction algorithm, NMF basic idea is to find a linear sub-space W, so that the composition of sub-space of the basic image pixels are positive, and face image in the sub-space projection coefficient is positive)

T=[1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1] 输入向量的最大值和最小值 threshold=[0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1] net=newff(threshold,[31 3],{ tansig , logsig }, trainlm ) 训练次数为1000，训练目标为0.01，学习速率为0.1 net.trainParam.epochs=1000 net.trainParam.goal=0.01 LP.lr=0.1 net = train(net,P,T) 测试数据，和训练数据不一致 P_test=[0.2101 0.0950 0.1298 0.1359 0.2601 0.1001 0.0753 0.0890 0.0389 0.1451 0.0128 0.1590 0.2452 0.0512 0.1319 0.2593 0.1800 0.0711 0.2801 0.1501 0.1298 0.1001 0.1891 0.2531 0.0875 0.0058 0.1803 0.0992 0.0802 0.1002 (T = [1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1] ' of the maximum and minimum input vector threshold = [0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1] net = newff (threshold, [31 3], {' tansig' , ' logsig' }, ' trainlm' ) training times for the 1000 target of 0.01 training, learning rate of 0.1 net.trainParam.epochs = 1000 net. trainParam.goal = 0.01 LP.lr = 0.1 net = train (net, P, T) test data, and training data inconsistencies P_test = [0.2101 0.0950 0.1298 0.1359 0.2601 0.1001 0.0753 0.0890 0.0389 0.1451 0.0128 0.1590 0.2452 0.0512 0.1319 0.2593 0.1800 0.0711 0.2801 0.1501 0.1298 0.1001 0.1891 0.2531 0.0875 0.0058 0.1803 0.0992 0.0802 0.1002 )

源代码在matlab实现相邻帧间差分法对于视频中对移动目标的识别。附送视频，在matlab上能打开的视频只有无压缩的AVI视频。视频背景没有变化的情况下，处理效果很好。(The source code in matlab between adjacent frame difference method for the recognition of moving target in the video. Attached to the video, can open the video on the matlab only compressed AVI video. Does not change the background of video cases, the treatment effect is very good.)