method_CA_2D

Hi this is matlab simulation

旅行商问题：全国省会二维坐标如图30所示，基于遗传算法设计从黑龙江到西藏，并遍历全国各省会(每个省会只经过一次）的最短路径(Traveling salesman problem: two-dimensional coordinates of the national capital as shown in Figure 30, based on genetic algorithm design from Heilongjiang to Tibet and traverse the provinces (each provincial capital only after a) the shortest path)

压缩感知可以用远低于奈奎斯特速率对信号进行采样，然后用重构算法对测量的信号进行重构，这程序就是CS的重构算法。(Compressed sensing can be used far less than the Nyquist sampling rate of the signal, and then use the measurement signal reconstruction algorithm to reconstruct this program is CS reconstruction algorithms.)

People can use this to build a MATLAB project for LQR control

雷达系统分析与设计 第三版 matlab_code(radar system analysis and design using matlab)

调制识别是非协作通信中的关键技术之一，在电子对抗、软件无线电等领域 应用十分广泛。该过程介于信号检测与解调之间，由于对传输信号没有过多的先 验信息以及在接收端有很多未知的参数，例如：信号功率，载波频率和相位偏移， 时间信息等，导致对调制方式的盲识别是一项很困难的任务。此外，实际中存在 多径衰落、频率选择性和时变信道等问题，使得这项任务变得更具有挑战性。 (The modulation recognition is one of the key technologies of the non-cooperative communication, such as electronic countermeasures and software defination radio, therefore it is such a significant discipline and orientation in the research of communication field. Obviously, without enough knowledge in transmitter and verified parameters about receivers, for example signal power, carrier frequency and phase offsets and so force, it is hard to get the modulation of blind identification. What is more, for the sake of the multipath fading, frequency-selective and time-varying channels in the real scenarios the task is even harder. )

说明：  基于匹配追踪的信号稀疏分解的一种新方法的仿真(Based on matching pursuit signal decomposition A new method for sparse)

对语音信号读取，并画出波形图，是语音识别的基础，对新手有一些帮助(Read the speech signal, and draw the waveform, is the basis of speech recognition, there is some help for novice)

this is a source code of matlab, implementing an algorithm called Seam Carving.

用于盲信号分离的原代码，可以根据输入源信号的基本统计特征,由观测数据进行信号分离,最终恢复出源信号。 (Blind signal processing has become an emerging subject in signal processing in these years, which depends on the source signal statistical characteristics to separate the signals from the observation data and recovers the source signals in the end. )