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S-Isomap
- Descr iption: S-ISOMAP is a manifold learning algorithm, which is a supervised variant of ISOMAP. Reference: X. Geng, D.-C. Zhan, and Z.-H. Zhou. Supervised nonlinear dimensionality reduction for visualization and classification. IEEE Transactio
matlab7.x
- 《精通MATLAB7.0混合编程》系统地介绍MATLAB 7.0的混合编程方法和技巧。全书共分为13章。第1章和第2章介绍MATLAB的基础知识，第3章简要介绍MATLAB混合编程，第4章至第9章分别介绍几种典型的混合编程方法，包括C-MEX、MATLAB引擎、MAT数据文件共享、Mideva、Matrix和Add-in。第10章、第11章介绍MATLAB与Delphi和Excel的混合编程。第12章介绍MATLAB COM Builder，第13章以图像处理为例介绍了一个综合应用实例。
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- 空间后方交汇求解相机外方位元素，变量如下 ％ x，y 控制点像点坐标 ％ X，Y，Z 控制点空间坐标 ％f焦距 ％X0，Y0，Z0，a，b，c六个外方位元素 ％x0，y0，-f内方位元素：光心坐标 ％cha，chb，chc：外方位角元素改正数 ％count 记录迭代次数 ％R 旋转矩阵 ％A 线性化的偏导系数矩阵 ％L 常数项矩阵 ％M0 外方位元素矩阵 ％M1 外方位元素改正数矩阵-meeting space for rear camera po
piecewise_eval
- LINEINTRGAL Line Integral in a 2D Vector Field. LINEINTRGAL(X,Y,U,V,C) computes the line integral along the lines given in cell array C. X and Y define the coordinates of a rectangular grid over which U and V are defined. X and Y must be monoto
BIQPBOX
- ％BIQPBOX Bisection reflective line search for sqpbox ％ [nx,nsig,alpha] = BIQPBOX(s,c,strg,x,y,sigma,l,u,... ％ oval,po,normg,DS,mtxmpy,data,H) ％ returns the new feasible point nx, the corresponding sign vector nsig, ％ and the step size of the
DEFINEV
- ％DEFINEV Scaling vector and derivative ％ ％ [v,dv]= DEFINEV(g,x,l,u) returns v, distances to the ％ bounds corresponding to the sign of the gradient g, where ％ l is the vector of lower bounds, u is the vector of upper ％ bounds. Vector dv is
4-parameter
- 可以进行曲线回归拟合算法的四参数算法。函数为 y = (a-d)/(1+(x/c)^b) +d . ec50.m 为其主要函数
TVL1_HCS_v1
- % May 2010 % This matlab code implements TVL1 based Hybrid Compressive Sensing using LSQR. % Only suitable the small scale data due to the costly storage and computation. % % A - M x N measurement matrix: random sampling alone or hybrid sampling (ran
ssim_index
- Zhou Wang proposed a new idea of Structural Similarity Index (SSIM )for image quality measurement based on the assumption that the HVS is highly adapted to extract structural information from the viewing field. The Mean Structural Similarity Index (M
hunhebianma
- VC + + 与MATLAB 混合编程,可以将VC 优秀的可视化编程能力和MATLAB 强大的计算能力结合起来。 分析了C 语言与MATLAB7. x 在接口的特点,详细介绍了VC + + 6. 0 与MATLAB7. x 混合编程的三种方法: MATLAB 引擎、MATLAB 编译器和MATLAB COM 编译器-VC++ Mixed with MATLAB programming, can be excellent VC Visualization MATLAB programmi
2finish
- matlab的基本实现函数有图 例如求代数方程3x5+4x4+7x3+2x2+9x+12=0的所有根。（利用roots函数）-The basic realization of matlab function has a plan such as the algebraic equation 3x5+4 x4+7 x3+2 x2+9 x+12 = 0 for all roots. (Using the roots function)
ex
- 自己写的 c 语言的e的x次方函数 精度还可以的-Write their own language, e c x-th power function of accuracy can also be the
FMINCON
- C=CURVEFIT( 拟合模型 ,参数C初值,X数据,Y数据) 求参数C使得 sum {(FUN(C,X数据)-Y数据).^2}最小化-C = CURVEFIT ( fitting model and parameter C, initial X, Y data) for data parameters that sum {there (C) (C, X-ray data- Y j ^ 2) minimizing}
SphereDecoder
- 用于MIMO检测的sphere decoder程序- This function implements a soft output sphere decoder. Based on the paper: C. Studer, M. Wenk, A. Burg, and H. Blocskei: "Soft-Output Sphere Decoding: Performance and Implementation Aspects", Asilomar 2006
[M]
- 原始单纯形法（大M法，无需给出初始基变量） Programmed by Liyang(faruto s Studio~!) BNU MATH Email:liyangbnu@mail.bnu.edu.cn QQ:516667408 last modified 2008.4.27 求解标准型线性规划:max C*X s.t. A*X=b (b>=0) X>=0 输入：C是n维行向量，A是m*n的系数矩阵，b是m维列向量- Of the origina
text2
- 离散随机信号的计算机仿真(验证性实验)---仿真协方差函数为C（x)的高斯过程-Computer simulation of discrete stochastic signal validation experiments--- Simulation covariance function C (x) of the Gaussian process
L01p_e
- [x,f]= L01p_e(c,A,b,N)用枚举法求解下列 0-1线性规划问题min f=c *x, s.t. A*x<=b,x的分量全为整数0或1, 其中N表示约束条件 Ax ≤ b中的前N个是等式，N= 0时可以省略。 返回结果x是最优解，f是最优解处的函数值。 -[X, f] = L01p_e (c, A, b, N) with the following enumeration method for solving linear programming problems 0
SimpleMthd
- 单纯形法matlab程序 求解标准型线性规划:max c*x s.t. A*x=b x>=0 本函数中的A是单纯初始表，包括:最后一行是初始的检验数，最后一列是资源向量b N是初始的基变量的下标 输出变量sol是最优解, 其中松弛变量（或剩余变量）可能不为0 输出变量val是最优目标值，kk是迭代次数- example：max 2*x1+3*x2 s.t. x1+2*x2<=8 4*x1<=16 4*x
kubik_app_alg
- cubic curve(in form y= f(x) = a*x^3 + b*x^2 + c*x + d) approximation by set of lines with given error
X射线相机内校正
- X射线影像增强器相机内校正算法，Matlab+C++实现。