myLee

bom provide the orginal details before calculation

离散坐标法解辐射传递方程的SRPAN离散程序(Discrete ordinates method for solving radiative transfer equation SRPAN discrete program)

用c#编写了一套FIR窗口滤波器，其中数据段的提取来自于matlab的混合编程(Written in c# a FIR filter window, including data extracted from the mixture of matlab programming)

Matlab与VPI联合仿真光OFDM传输系统，其中VPI只完成光纤传输其余都用matlab完成。系统包括了上下变频，clipping等模块。(Matlab co-simulation with VPI optical OFDM transmission system, wherein the optical fiber transmission VPI only complete rest are completed using matlab. System includes a down conversion, clipping and other modules.)

说明：  联通区域的标记 matlab 的bwlabel功能(matlab bwlabel )

MOVEMENT DETECTOR: Get video from webcam and detect the movement.

分别用三种不同的差分近似公式（前向差分、后项差分和中心差分）来技术函数的近似值(Respectively with three different difference approximation formula (forward difference, backward difference and central difference) approximation to the technical function)

说明：  kmeans算法，用于聚类，简单matlabcode(kmeans code)

模拟退火算法——组合优化 模拟退火算法主要用于解决组和优化问题，它是模拟物理中晶体物质的退火过程而开发的一种优化算法。在对固体物质进行模拟退火处理时，通常先将它加温熔化，使其中的粒子可自由运动，然后随着温度的逐渐下降，粒子也逐渐形成了低能态的晶格。若在凝结点附近的温度下降速率足够慢，则固体物质一定会形成最低能态的基态。 对于组合优化问题来说，它也有这样的类似过程。组合优化问题解空间中的每一点都代表一个具有不同目标函数值的解。所谓优化，就是在解空间中寻找目标函数最小（大）解的过程。若把目标函数看成能量函数，某一控制参数视为温度 T，解空间当作形态空间，那么寻找基态的过程也就是求目标函数极小值的优化过程。 (Simulated annealing algorithm- combinatorial optimization Simulated annealing algorithm used to solve optimization problems and the group, it is the physical simulation of the crystal material in the annealing process and the development of an optimization algorithm. Solid substances in the simulated annealing process, it is usually heated it first melts, so that the particles are free to exercise, then gradually decreased as the temperature of the particles gradually formed a lower state of the lattice. If the temperature drops near the condensation rate slow enough, the solids will form the lowest energy state of the ground state. For combinatorial optimization problems, it also has such a similar process. Combinatorial optimization problem the solution space of each point represents a different objective function value of the solution. The so-called optimization, is in the solution space to find the minimum objective function (large) solution process. If the objective function a)

Particle swarm optimization is a derivative-free global optimum search algorithm based on the collective intelligence of a large group of intercommunicating entities. The individual particles are simple and primitive, knowing only their own current locations and fitness values, their personal best locations, and the swarm s best location. Each particle continually adjusts its trajectory based this information, moving towards the global optimum with each iteration. The swarm as a whole displays a remarkable level of coherence and coordination despite the simplicity of its individual particles. The coordinated behavior of the swarm has been compared with that of a flock of birds or a school of fish.