FFT

关于电磁波散射的时域有限差分法计算程序 很好的(a code about finite difference time domain method for electromagnetic waves scattering)

最大频率选择小波的实现的经典论文之一，非常有启发性(The realization of maximum frequency selective wavelets, one of the classic papers, very enlightening)

生成30位的m序列的matlab仿真源代码(M-generated sequence of 30 simulation source code matlab)

perform Jacobi method iterations

基于MATLAB的FIR数字低通滤波器的实现设计(The realization of the FIR digital low-pass filter based on MATLAB design )

final SerializableAssert exComparator.

地震信号处理，虚同相轴方法预测层间多次波，将数据分成上下两部分，利用相关和褶积的原理预测出层间多次波。预测信号和原始信号在相位和振幅上存在差异，用L1范数匹配法进行匹配，其中，提供了两种方法解病态方程，分别为高斯-赛德尔方法和正则化方法。(Seismic signal processing, predicte internal multiples by construct virtual events .The data is divided into two parts, using the principle of correlation and convolution to predicte multiples. There are differences between the prediction signal and the original signal on the phase and amplitude, so we use the L1 norm method to match the singal.We provide two methods for solving ill equation, respectively Gauss- Seidel method and regularization method.)

edge detector using Sobel and Gradient Operator in Matlab

蕞优分组周游路线问题是一类物流智能调度问题，是众多的计算难度极大的组合爆炸问题之一。自从运输问题最早是由Hitchcock在1941年提出的[2][4]后，人们对这一类问题给予了极大的关注并进行了大量的研究，提出了有效解决问题的优化算法——单纯形法的变形[2]。但由于问题规模的扩大，纯数学方法在允许的时间和空间上都很难找到问题的最优解，于是人们开始转向寻找问题的近似最优解，遗传算法以其在求解最优化问题中的独特的自组织性、自适应性很快进入人们的视野。 经典的遗传算法对于求解多目标的NP完全性问题非常有效，但对于有多个限制条件的多目标最优化问题缺显得有点力不从心，很难得到稳定度较高、收敛较快的解。本文提出的双层遗传算法模型给这一类问题提供了一个很好的解决方法，虽然建模条件限制了描述的问题的通用性，有待推广，但它能有效利用问题的领域知识，提高了求解效率，保证了解的可行性和最优性，算法思想极具推广潜力，可在其它物流智能调度问题求解算法设计中借鉴。(The most optimal packet travel route problem is a kind of intelligent logistic scheduling problem is one of combinatorial calculation difficult problem many. Since the transportation problem was first proposed by Hitchcock in 1941 after the [2][4], the people of this class of problems given great attention and made a lot of research, the deformation of [2] optimization, simplex method algorithm is effective to solve the problem of. But due to the expansion of the scale of the problem, the pure mathematical methods in the allowed time and space are very difficult to find the optimal solution of the problem, so people begin to turn to the approximate optimal solution for the problem, genetic algorithm in solving optimization problems with its unique in the self-organizing, adaptive soon entered people s field of vision. Very effective classical genetic algorithm for solving multi-objective NP complete problems, but for the multi-objective optimization problem with multiple constraints. I)

相控阵雷达的MIMO的仿真,对于雷达中的仿真(MIMO simulation of phased array radar, radar simulation)