loc_im_display

This is a superheterodyne reciever simulation, created in MATLAB - Simulink. The purpose is to study the effect of perturbations on the imaginary frequence and on the intermmediate frequence, in the context of incoherent demodulation.(This is a superheterodyne reciever simulation, created in MATLAB- Simulink. The purpose is to study the effect of perturbations on the imaginary frequence and on the intermmediate frequence, in the context of incoherent demodulation.)

这是一个非常完整并且正确的软件，里面dmc的所有有关程序，希望能够帮助到别人。(this is very important program，hope that it can help u guys)

基于MUSIC方法功率谱估计的matlab仿真，包含实验详细报告，源码，运行结果，及其分析。(MUSIC-based method of power spectrum estimation matlab simulation, including a detailed report on the experiment, source, run results, and analysis.)

lab work for begginers

matlab 模糊识别例子，方便大家研究、论文学习以及教学使用。(matlab Fuzzy recognition example, to facilitate the study, the paper research and teaching aids)

说明：  人脸检测的一个matlab程序，运行良好。(face detection based on matlab)

这是数模中有关层次分析的源码，相信有需要的人!(This is the model on several levels of analysis of the source code, I believe those who are in need!)

共轭MUSIC与平滑MUSIC，来实现波达方向估计(The conjugated MUSIC smoothing MUSIC DOA estimation)

说明：  近年来，基于启发式的多目标优化技术得到了很大的发展，研究表明该技术比经典方法更实用和高效。有代表性的多目标优化算法主要有NSGA、NSGA-II、SPEA、SPEA2、PAES和PESA等。粒子群优化(PSO)算法是一种模拟社会行为的、基于群体智能的进化技术，以其独特的搜索机理、出色的收敛性能、方便的计算机实现，在工程优化领域得到了广泛的应用，多目标PSO(MOPSO)算法应用到了不同的优化领域[9~11]，但存在计算复杂度高、通用性低、收敛性不好等缺点。 多目标粒子群（MOPSO）算法是由CarlosA. Coello Coello等在2004年提出来的(In recent years, heuristic-based multi-objective optimization technology has been greatly developed, and research shows that this technology is more practical and efficient than classical methods. Representative multi-objective optimization algorithms mainly include NSGA, NSGA-II, SPEA, SPEA2, PAES and PESA. Particle Swarm Optimization (PSO) algorithm is an evolutionary technology based on swarm intelligence that simulates social behavior. With its unique search mechanism, excellent convergence performance, and convenient computer implementation, it has been widely used in the field of engineering optimization. The objective PSO (MOPSO) algorithm is applied to different optimization fields [9~11], but it has shortcomings such as high computational complexity, low versatility, and poor convergence. The multi-objective particle swarm optimization (MOPSO) algorithm was proposed by Carlos A. Coello Coello et al. in 2004)

这是我自编的PQ分解法小程序，采用极坐标形式表示节点电压。(This is my small self of the PQ decomposition process is expressed by polar coordinates node voltage.)