MOKA

Block Compressed Sensing of Images Using Directional Transforms

control system program using mat lab

using terlis code to generate stbc

减摇水舱对船舶参数横摇的抑制，4自由度模型，包括垂荡‘纵摇和水舱状态(The anti rolling tank on the inhibition of Parametric Rolling of ships, model of 4 degrees of freedom, including heave pitching and water tank to state )

应用MATLAB语言处理数字信号与数字图像，里面 有matlab调用c的方法(Application of MATLAB language processing digital signal with the digital image, which has matlab call method c)

运用matlab编程求最短路径的算法a=xlsread( D:数据.xls ) 调入数据 n=size(a,1) for i=1:n for j=1:n if a(i,j)==1000 a(i,j)==inf 不相邻点之间的距离赋值为无穷大 (The shortest path)

有关matlab的图像融合与图像配准程序。并附带了程序运行说明，简单明了。(About matlab image fusion and image registration procedures. And comes with a description of the program to run, plain and simple.)

OFDMA 物理层开发的matlab 源码.飞思卡尔提供.包括manger和SP两个部分。802.16e仿真，MATLAB环境(OFDMA physical layer development matlab source code. Freescale offers. The development of the physical layer was a great help, too spent to buy the rice yo! ! !)

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)

DFT和FIR宽带波束形成源程序 可以自行设置参数 如载频强度 入射角度 信噪比等(DFT and FIR broadband beamforming source code can set the parameters such as the intensity of the incident angle of the carrier frequency signal to noise ratio, etc.)