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[matlab] TVL1_HCS_v1 DL : 0: % 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 (random sampling and low resolution sampling) % % gamma - weight for L1-norm of the diagonal gradients % % tol - tolerance for stopping criterion. % - DEFAULT 1e-3 if omitted. % % maxIter - maximum number of iterations % - DEFAULT 50, if omitted. %Original TVL1 based Hybrid Compressive Sensing problem: % min ||gx ||_1 + ||gy ||_1 +\gamma *(||gxy ||_1 + ||gyx ||_1) % s.t. A*I = b and I >=0 %It can be solved using PDCO as follows % min c*x s.t. Phi * x =B % x = [I; gx; -gx; gy; -gy; gxy; -gxy; gyx; -gyx] % Phi and B enforce the measurement (A) and constrant among the elements of x; % Xianbiao Shu, May 2010. Questions? xshu2@uiuc.edu; % Copyright: Computer Vision and Robotics Lab, University of Illinois, Urbana-Champaign % Acknowledgement: Primal-Dual interior method for Convex Objectives (PDCO) % http://www.stanford.edu/group/SOL/software/pdco.html
Date : 2011-04-21 Size : 895.62kb User : li123kai@126.com
[matlab] S-Isomap DL : 0: Description: 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 Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2005, vol.35, no.6, pp.1098-1107.-Description: 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 Transactions on Systems, Man, and Cybernetics- Part B: Cybernetics, 2005, vol.35, no.6, pp.1098-1107.
Date : 2025-12-21 Size : 31kb User : 修宇
[matlab] duoduixiang DL : 0: 多对象的旋转，实现用户自定义输入各个点的位置，并可以自己修改运动速度，轨迹A,B,C的径长。-more object rotation, user-defined input all points on the location and movement can change their speed, trajectory A, B, C-Path.
Date : 2025-12-21 Size : 2kb User : 淡林
[matlab] exm1001_1 DL : 0: 对于传递函数为G=1/(s*s+2*&*s+1)归一化二阶系统，制作一个能绘制该系统单位阶跃响应的图形用户界面。本例演示：（A）图形界面的大致生成过程；（B）静态文本和编辑框的生成；（C）坐标方格控制键的形成；（D）如何使用该界面。-for the transfer function of G = 1/(s* s* 2
Date : 2025-12-21 Size : 1kb User : djs
[matlab] sdir2cas DL : 0: s平面中直接形式到级联形式的转换％适合模拟滤波器的％C为增益系数％B为包含各bk的K乘3维实系数矩阵％A为包含各ak的K乘3维实系数矩阵％b为直接形式的分子多项式系数％a为直接形式的分母多项式系数-s-plane in the direct form and cascade form of analog filters suitable for conversion of C for the gain coefficient B to include the bk of the K x 3-dimensional real coefficient matrix A to include all of K by ak real 3-dimensional coefficient matrix b for direct forms of molecular polynomial coefficients a direct form of the denominator polynomial coefficients
Date : 2025-12-21 Size : 1kb User : 吴江华
[matlab] 231226 DL : 0: 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 position outside elements, as follows% variable x, y control point pixel coordinates% X, Y, Z coordinates control room focal length f%% X0, Y0, Z0, a, b, c 6 exterior orientation elements% x0, y0,- f position within elements : Optical Center coordinates% cha, chb, chc : Foreign elements azimuth correction% record count the number of iterations rotation matrix R%% A linear partial derivative of the coefficient matrix% L constant Matrix% M0 Orientation% M1 matrix elements of exterior orientation correction matrix
Date : 2025-12-21 Size : 1kb User : 王立钊
[matlab] equal-area-critirea DL : 0: E=input( enter the generator voltage: ) V=input( enter the infinite bus voltage: ) Gx=input( enter the reactance of generator: ) L1x=input( enter the line reactance: ) L2x=input( enter the line reactance: ) L3x=input( enter the line reactance: ) L4x=input( enter the line reactance: ) Pi=input( enter the input power: ) X1=Gx+L1x+((L2x*L3x)/(L2x+L3x))+L4x Pm1=E*V/X1 DO=asin(Pi/Pm1) A=((L2x*L3x)/(2*(L2x+L3x))) C=A B=((L3x/2)^2)/(L2x+L3x) X2=(Gx+L1x+A)+(L4x+A)+(Gx+L1x+A)*(A+L4x)/B Pm2=(E*V)/X2 X3=Gx+L1x+L2x+L4x Pm3=(E*V)/X3 DC=input( enter the fault clearing angle: ) Dm=(3.14-asin(Pi/Pm3)) a1=quad( sin ,DO,DC) A1=(Pi*(DC-DO)-Pm2*a1) a2=quad( sin ,DC,Dm) A2=Pm3*a2-Pi*(Dm-DC) if(A2>=A1) disp( the system is stable ) else disp( the system is unstable ) end Dcc=acos((Pi*(Dm-DO)-Pm2*(cos(DO))+Pm3*(cos(Dm)))/(Pm3-Pm2)) disp( critical clearing angle: ) D-E=input( enter the generator voltage: ) V=input( enter the infinite bus voltage: ) Gx=input( enter the reactance of generator: ) L1x=input( enter the line reactance: ) L2x=input( enter the line reactance: ) L3x=input( enter the line reactance: ) L4x=input( enter the line reactance: ) Pi=input( enter the input power: ) X1=Gx+L1x+((L2x*L3x)/(L2x+L3x))+L4x Pm1=E*V/X1 DO=asin(Pi/Pm1) A=((L2x*L3x)/(2*(L2x+L3x))) C=A B=((L3x/2)^2)/(L2x+L3x) X2=(Gx+L1x+A)+(L4x+A)+(Gx+L1x+A)*(A+L4x)/B Pm2=(E*V)/X2 X3=Gx+L1x+L2x+L4x Pm3=(E*V)/X3 DC=input( enter the fault clearing angle: ) Dm=(3.14-asin(Pi/Pm3)) a1=quad( sin ,DO,DC) A1=(Pi*(DC-DO)-Pm2*a1) a2=quad( sin ,DC,Dm) A2=Pm3*a2-Pi*(Dm-DC) if(A2>=A1) disp( the system is stable ) else disp( the system is unstable ) end Dcc=acos((Pi*(Dm-DO)-Pm2*(cos(DO))+Pm3*(cos(Dm)))/(Pm3-Pm2)) disp( critical clearing angle: ) Dcc
Date : 2025-12-21 Size : 7kb User : tkspandy
[matlab] vcc_mex DL : 0: 一般情况下，我们都是在MATLAB命令行或DOS命令行下编译MEX程序。所用的命令就是：mex filename.c 这有很多不方便的地方： a. 虽然mex也可以编译C++的mex程序，但是它的主框架仍是C的 a. 当程序有多个模块时，需要多次使用mex命令，操作很麻烦 b. 不能利用VC特有的ClassWizard自动创建和维护类 c. 不能用MFC类库编写应用程序 d. 不能方便地进行类似VC的项目管理本文详细解说如何在IDE中编译MEX程序-Under normal circumstances, we are all in the MATLAB command line or DOS command line program to compile MEX. Command is used by: mex filename.c This has a lot of inconvenient places: a. Although the mex can also compile C++ The mex program, but it is still the main frame C of a. When the procedure has a number of modules the need to repeatedly use the mex command, the operation is cumbersome b. Can not use VC-specific ClassWizard automatically create and maintain the category c. can not use MFC class library to write applications easily d. should not conduct a similar project management VC a detailed explanation of how this article IDE compiling MEX procedures
Date : 2025-12-21 Size : 11kb User : Dean
[matlab] 4-parameter DL : 0: 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-Can curve fitting algorithm of the four-parameter algorithm. Function y = (ad)/(1+ (X/c) ^ b)+ D. Ec50.m its main function
Date : 2025-12-21 Size : 2kb User : 2213
[matlab] periodogramestimate DL : 0: Generate 100 samples of a zero-mean white noise sequence with variance , by using a uniform random number generator. a Compute the autocorrelation of for . b Compute the periodogram estimate and plot it. c Generate 10 different realizations of , and compute the corresponding sample autocorrelation sequences , and . Compute the average autocorrelation sequence as and the corresponding periodogram for . d Compute and plot the average periodogram using the Bartlett method. e Comment on the results in parts (a) through (d). -Generate 100 samples of a zero-mean white noise sequence with variance, by using a uniform random number generator.a Compute the autocorrelation of for. B Compute the periodogram estimate and plot it. C Generate 10 different realizations of, and compute the corresponding sample autocorrelation sequences, and. Compute the average autocorrelation sequence as and the corresponding periodogram for. d Compute and plot the average periodogram using the Bartlett method. e Comment on the results in parts (a) through (d).
Date : 2025-12-21 Size : 1kb User : 冀晗
[matlab] Cspline DL : 0: 这是一个三次样条插值的.m程序输入的是一个二维数组A(Nx2) 插值方法为: S(x) = A(J) + B(J)*( x - x(J) ) + C(J)*( x - x(J) )**2 +D(J) * ( x - x(J) )**3 for x(J) <= x < x(J + 1) -This is a cubic spline interpolation. M program input is a two-dimensional array A (Nx2) interpolation method: S (x) = A (J)+ B (J)* (x- x (J ))+ C (J)* (x- x (J))** 2+ D (J)* (x- x (J))** 3 for x (J)
Date : 2025-12-21 Size : 1kb User : 朱与心
[matlab] nnToolKit_cb DL : 0: C++BUILDER与MATLAB实现混合编程源代码-C++ BUILDER realize mixed language programming with the MATLAB source code
Date : 2025-12-21 Size : 1.56mb User : 魏建明
[matlab] nurbsR2006b DL : 0: 非均匀有理B样条的matlab程序，其中用到了C的混合编程。对于学习数据融合技术的人很有帮助！-Non-uniform rational B-spline matlab program, which uses a mixture of C programming. Data fusion technology for the study of people very helpful!
Date : 2025-12-21 Size : 210kb User : zhanglin
[matlab] work DL : 0: Matlab实现： Erlang B model(M/M/n/n)与 Erlang C model排队系统的模拟，并画出阻塞概率(P)与负载(A=lamda/miu in Erlang)的关系图。用法：运行RunMe-Matlab achieve: Erlang B model (M/M/n/n) and the Erlang C model simulation queuing system, and draw blocking probability (P) and load (A = lamda/miu in Erlang) the relationship between the map. Usage: run RunMe
Date : 2025-12-21 Size : 2kb User : Zheng Xiao
[matlab] 5 DL : 0: 一道程序编译顺序的考题，涉及到函数调用的先后顺序及运算符号的优先级等问题。下面我展开给你讲。 C的程序编译总是从main函数开始的，这道题的重点在“fun（（int）fun（a+c,b）,a-c）） ”语句。系统首先要确定最外层 fun()函数的实参，第一个参数的确定需要递归调用fun()函数（不妨称其为内层函数）。内层函数的两个参数分别为x=a+b=2+8=10、y=b=5,执行函数体x+y=10+5=15,于是得外层函数的参数x=15。其另一个参数y=a-c=2-b=-6,再次执行函数体,得最终返回值x+y=15+(-6)=9。 -Compiling together the sequence of test procedures, involving the sequence of function calls and operator symbols, such as the priority problem. Now I give you to start speaking. Procedures for C compiler always start from the main function and at这道题the focus of " fun ((int) fun (a+ c, b), ac)) " statement. System must first determine the most outer layer of fun () function of real parameters, the first parameters of recursive calls required fun () function (may be called the inner function). Inner function separately for the two parameters x = a+ b = 2+8 = 10, y = b = 5, to execute the function body x+ y = 10+5 = 15, then the outer function parameters were x = 15 . Its another parameter y = ac = 2-b =- 6, once again to execute the function body may eventually return the value of x+ y = 15+ (-6) = 9.
Date : 2025-12-21 Size : 2kb User : 蜗蜗牛
[matlab] bloqueo DL : 0: Calculo funciones erlang b y erlang c
Date : 2025-12-21 Size : 2kb User : supertaty96
[matlab] queueing_theory DL : 0: 计算Erlang B,C 模型中阻塞率与输入负载随服务器数量变化的数值关系并绘图。模拟一个M/M/k排队系统。该资料为通信系统排队理论matlab实验内容。-Using the iterative scheme, calculate the blocking probability of the Erlang B and C model. Draw the relationship of the blocking probability and offered traffic while sever number varies. Simulate a M/M/k queue system with input parameters of λ, μ, k
Date : 2025-12-21 Size : 2kb User : Aurora
[matlab] Erlang DL : 0: 根据Erlang B公式进行程序设计：（1）输入：话务量、信道数；输出：PC （2）输入：PC、信道数；输出：最大话务量根据Erlang C公式进行程序设计：（1）输入：话务量、信道数；输出：PW[延迟>0] （2）输入：PW[延迟>0]、信道数；输出：最大话务量 -According to Erlang B formula for program design: (1) Input: traffic, number of channels output: PC (2) input: PC, number of channels output: maximum traffic based on Erlang C formula for program design: (1) Input: traffic, number of channels output: PW [Delay> 0] (2) Input: PW [Delay> 0], the number of channels output: maximum traffic
Date : 2025-12-21 Size : 3kb User : 依依
[matlab] fangzhen DL : 0: 赶火车过程仿真一列火车从A站经过B站开往C站，某人每天赶往B站乘这趟火车。已知火车从A站到B站运行时间为均值30分钟、标准差为2分钟的正态随机变量.火车大约在下午1点离开A站。离开时刻的频率分布为 -Process simulation, a train to catch the train from A through B station station station bound for C, B Station tour someone rushed to the train every day. Known to the train station from station A to B as the average running time 30 minutes, standard deviation of 2 minutes of normal random variables. Trains leave about 1:00 pm A station. The frequency distribution for the leave time
Date : 2025-12-21 Size : 11kb User : 李振东
[matlab] LSM DL : 0: 最小二乘法拟合function [TrainingTime, TestingTime, TrainingAccuracy, TestingAccuracy] = LSM(Inputs, Targets, No_of_Output) a1=[]; a2=[]; a3=[]; a4=[]; %%%%%%%%%%%%% Selecte training set randomly a = Inputs; b = Targets; c = [ b' a']; [M,N] = size(c); id = randperm(M,floor(0.7*M)).'; train1 = c(id,:); c(id',:) = []; test1 = c;(function [TrainingTime, TestingTime, TrainingAccuracy, TestingAccuracy] = LSM(Inputs, Targets, No_of_Output) a1=[]; a2=[]; a3=[]; a4=[]; %%%%%%%%%%%%% Selecte training set randomly a = Inputs; b = Targets; c = [ b' a']; [M,N] = size(c); id = randperm(M,floor(0.7*M)).'; train1 = c(id,:); c(id',:) = []; test1 = c;)
Date : 2025-12-21 Size : 249kb User : 吴炳福

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