optimal_capacity

对序列进行李亚普诺夫指数的运算，判断初始序列何时进入混沌状态(Lyapunov exponent of the sequence of operations, to determine when to enter the chaotic state of the initial sequence)

有关DVB—S2的技术特点及应用，及其相关的重要技术，如信道编码，LDPC码和BCH码的编译码算法(Technical characteristics and application of DVB-S2, and the important technology, such as channel coding, LDPC code and BCH code encryption algorithm)

边界元算法求解正方形导热问题实例。一个简单的二维导热模型，用BEM算法求解，用于学习边界元编程。(Boundary element method for solving square conduction problem instances. A simple two-dimensional heat conduction model, using BEM algorithm for learning boundary element programming.)

This Matlab file is intended to demonstrate that the application of Kalman filters to basic linear system is quite easy and its application is optimal under certain conditions.

通过卷积码的学习，建立了卷积码的两种译码方式的simulink仿真模型！(in this part we can use the simulink to realize the convolutional decoding by harddecision and softdecision.)

这是一个用来计算2维光子带的matlab程序。可分别计算TM 和TE模式平面波入射下的场分布。(This is a program to calculate the Photonic Bands of two dimension。The field excited by TM and TE mode plane wave incident can be calculated.)

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)

关于fem算法及其源代码的一篇文章，后附有源代码，以查找无重复(fem with matlab and its instructions)

Principle Component Analysis

Simulink_BFSK_RicianFading,simulink环境下RicianFading的BFSK误码率仿真(Simulink_BFSK_RicianFading, simulink environment RicianFading the BFSK bit error rate simulation)