ZernikePolynomialsForTheWeb

2维的FDTD C程序(2-dimensional FDTD C procedures)

fluent软件的UDF程序，是焊接过程的热源，热源类型为双椭球空间热源。(fluent udf heat source)

预处理CG算法，在进行共轭梯度法之前先对系数矩阵进行预处理，再进行迭代(pretreatment congruent gradient)

说明：  基于AOA的室外雷达定位算法仿真，基于TDOA的室外雷达定位算法仿真，基于AOA doa的室外雷达定位算法仿真，三维(Simulation of outdoor radar location algorithm based on AOA, simulation of outdoor radar location algorithm based on TDOA, simulation of outdoor radar location algorithm based on AOA TDOA, 3d)

abaqus中扩展有限元（UEL）计算疲劳断裂(extended finite element method (uel)for fracture problems within the finite element software ABAQUS)

用切比雪夫多项式逼近已知函数 用勒让德多项式逼近已知函数 用帕德形式的有理分式逼近已知函数 用列梅兹算法确定函数的最佳一致逼近多项式 求已知函数的最佳平方逼近多项式 用傅立叶级数逼近已知的连续周期函数 离散周期数据点的傅立叶逼近 用自适应分段线性法逼近已知函数 用自适应样条逼近（第一类）已知函数 离散试验数据点的多项式曲线拟合 离散试验数据点的线性最小二乘拟合 离散试验数据点的正交多项式最小二乘拟合 (By using Chebyshev polynomial approximation of the known functionApproximation of the known functions by Legendre polynomialsApproximation by rational fraction of known function in the form of PadmaTo determine the best uniform function polynomial approximation with the Lemez algorithmThe best known polynomial square for function approximationContinuous periodic function with Fourier series approximation of the knownApproximation of discrete data points in the Fu Liye cycleApproximation of the known function with adaptive piecewise linear methodAdaptive spline approximation ( first class ) known functionPolynomial curve fitting of discrete data pointsLinear least squares fitting of discrete data pointsOrthogonal polynomial least squares fitting of discrete data points)

Matlab编写的最小二乘法最优化计算方法，能自动完成多维非线性方程组的最小值计算(Matlab Min_LM math tools for calculating the min-value of formulars)

java编写九九乘法表 1*1=1 2*1=2 2*2=4 3*1=3 3*2=6 3*3=9 4*1=4 4*2=8 4*3=12 4*4=16 5*1=5 5*2=10 5*3=15 5*4=20 5*5=25 6*1=6 6*2=12 6*3=18 6*4=24 6*5=30 6*6=36 7*1=7 7*2=14 7*3=21 7*4=28 7*5=35 7*6=42 7*7=49 8*1=8 8*2=16 8*3=24 8*4=32 8*5=40 8*6=48 8*7=56 8*8=64 9*1=9 9*2=18 9*3=27 9*4=36 9*5=45 9*6=54 9*7=63 9*8=72 9*9=81 (Multiplication table 1*1=1 2*1=2 2*2=4 3*1=3 3*2=6 3*3=9 4*1=4 4*2=8 4*3=12 4*4=16 5*1=5 5*2=10 5*3=15 5*4=20 5*5=25 6*1=6 6*2=12 6*3=18 6*4=24 6*5=30 6*6=36 7*1=7 7*2=14 7*3=21 7*4=28 7*5=35 7*6=42 7*7=49 8*1=8 8*2=16 8*3=24 8*4=32 8*5=40 8*6=48 8*7=56 8*8=64 9*1=9 9*2=18 9*3=27 9*4=36 9*5=45 9*6=54 9*7=63 9*8=72 9*9=81 )

信号处理与通信中的凸优化理论，从纯数学的角度介绍了凸优化理论的基本原理和方法(Convex Optimization in Signal Processing and Communication)

用牛顿迭代法估计正弦信号的幅度，相位，频率，选用多次montecalor算法估计参数均值，并与理论上参数估计的CRLB下限进行比较。(Newton iterative method estimates the sinusoidal signal amplitude, phase, frequency, use multiple montecalor algorithm to estimate the parameters mean, and with the theoretical limit of parameter estimation CRLB compare.)