CCHP_CO2

本程序是对V-BLAST系统及其检测算法的仿真，可采用BPSK,QPSK,16QAM,64QAM调制。检测算法为ML，MMSE,ZF,以及采用迫零的连续干扰消除检测算法。(This procedure is the V-BLAST detection algorithm and the simulation system can be used BPSK, QPSK, 16QAM, 64QAM modulation. Detection algorithm for the ML, MMSE, ZF, and the use of zero-forcing successive interference cancellation detection algorithm.)

The second line simply displays the matched image, the third line gets the histogram of the matched image, and the last line plots the histogram, which should match the specified PDF. Remember the PDF is supplied to the histogram equalization function, not a CDF, it automatically creates a CDF from the PDF supplied to it.

一个关于四轴飞行器的国外源码，bitcraze-crazyflie，很出名的一个国外飞行器开源组织开发的源代码，已经通过测试，其中包含卡尔曼滤波等自平衡算法(A source of foreign aircraft on four axes, bitcraze-crazyflie, very famous aircraft of a foreign organization to develop open-source source code, has passed the test, which includes self-balancing algorithm Kalman filtering)

二维FDTD仿真soft source入射到有限区域边界后的传播，区域中间放置了一块pec，并且设定了ABC边界条件，可以自动生成关于Ez，Hx和Hy的.avi文件，播放脉冲动态传输。(Dimensional FDTD simulation soft source is incident to spread limited area boundary after placing a piece of the middle region pec, and set the ABC boundary conditions, can automatically generate .avi files on Ez, Hx and Hy, and play dynamic pulse transmission.)

合成孔径雷达成像程序，使用CS算法，对点目标进行仿真，实现点目标的成像(Synthetic aperture radar imaging procedures, the use of CS algorithm, the point target simulation to achieve point target imaging)

说明：  这是马尔科夫链预测的一个非常好的例子，值得大家学习与借鉴。(This is a very good example of Markov chain prediction, which is worth learning and using for reference.)

matlab多目标优化问题实现，可以参考一下啊(matlab multi-objective optimization, you can refer to ah)

this is matlab user-guide.

一个基于matlab的运动目标跟踪的资料，包括源代码和视频样本(A moving target tracking based on matlab, including source code and video samples )

扩频同步时频域二维分布 R=[x,y] surf(R) x:-5:0.1:5 y:-0.01:0.005:0.1(Synchronous time-frequency domain spread spectrum two-dimensional distribution of R = [x, y] surf (R) x:-5:0.1:5 y:-0.01:0.005:0.1)