Software Downloads for "Decoder P Dom X"

This being a very basic program for the demonstration of bubble and dew point calculations of a ideal binary VLE mixture obeying Raoult's. The basic purpose of this program is to demonstrate VLE P-T-x-y calculations of a ideal binary mixture and to get related data in graphical form for any such a system. It uses Raoult's law for the VLE calculations, the Antoine equation for the calculation of pure component vapour pressures at different temperatures and the Dalton's law for the calculation of total pressure of the vapor in equilibrium with the liquid.

  • Platform: Matlab, Scripts
  • Publisher: Suhas B. Ghugare
  • Date: 03-03-2013
  • Size: 61 KB

[L, C, D] = FKMEANS(X, k) partitions the vectors in the n-by-p matrix X into k (or, rarely, fewer) clusters by applying the well known batch K-means algorithm. Rows of X correspond to points, columns correspond to variables. The output k-by-p matrix C contains the cluster centroids. The n-element output column vector L contains the cluster label of each point. The k-element output column vector D contains the residual cluster distortions as measured by total squared distance of cluster members from the centroid.

  • Platform: Matlab, Scripts
  • Publisher: Tim Benham
  • Date: 24-05-2013
  • Size: 10 KB

The electric power P "consumed" by an electrical device is the product between the voltage V at the terminals (in Volts) and current I passing through it (in amps):

P = V x I

The SI unit of power is the Watt (W).
For direct current, P = V x I
For alternating current used with resistance type components (lamps, heaters), P = VRMSx IRMS where VRMS and IRMS are the RMS values of voltage and current.
The electrical energie consummed by an electrical appliance is the product of electrical power (P) and the duration of time used (t)
E = P x t
The unit of energy is the Joule (J).

  • Platform: Android 2.x, Android 3.x, Android 4.4, Android 4.x
  • Publisher: eduMedia
  • Date: 28-02-2014
  • Size: 521 KB

This app allows you to easily make computations over gaussian variables.

You can :
* choose the mean and the standard deviation.
* compute the following probabilities P(X < a), P(X > a) et P(a < X < b)
* find a such that for a given p you get P(X < a) = p or P(-a < X < a) = p

This app has just been released, and will be extended soon. If you find any mistake, bug, or have any suggestion, please contact me and I will consider your messages with the greatest attention.

NB : This app is ad-less certified, it will show no notification, nor ask any stupid right (for instance using the camera, turn the wifi on or send text messages).

  • Platform: Android 2.x, Android 3.x, Android 4.4, Android 4.x
  • Publisher:
  • Date: 04-07-2014
  • Size: 86 KB

The pack in now splitted in 3 files, the main pack, a wm module and a quicktime module. As explained on the decoder pack page, you need the wm module only if you don't have Windows Media Player or more installed. This pack works with pcs under windows only, it will never works under mac, linux, hardware divx players, mobiles phones.... Satsuki Decoder Pack is an audio/video auto-installable filters and decoder for windows 2k/XP/2k3/2k8/vista/seven (French, English, Serbian latin, Duch, Russian).

It calculates the Spearman rank correlation coefficient from 2 or more data sets, and the associated t-test and p-values. The code is adapted with major changes from the Numerical Recipes book ( Example: >> x = [1 2 3 3 3]'; >> y = [1 2 2 4 3; rand(1,5)]'; >> [r,t,p] = spear(x,y) >> [r,t,p]=spear(x,y) r = 0.8250 -0.6000 t = 2.5285 -1.2990 p = 0.0855 0.2848.

Find the projection of point P in R^n on the ellipsoid E = { x = x0 + U*(z.*radii) : |z| = 1 }, where U is orthogonal matrix of the orientation of E, radii are the axis lengths, and x0 is the center. Or on generalized conic E = { x : x'*A*x + b'*x + c = 0 }. The projection is the minimization problem: min | x - P | (or max | x - P|) for x in E. Method: solve the Euler Lagrange equation with respect to the Lagrange multiplier, which can be written as polynomial equation (from an idea by Roger Stafford).

  • Platform: Matlab, Scripts
  • Publisher: Bruno Luong
  • Date: 19-05-2013
  • Size: 31 KB

r2 = rsquare(y,f) RSQUARE computes the coefficient of determination (R-square) value from actual data Y and model data F. INPUTS Y : Actual data F : Model fit OUTPUT R2 : Coefficient of determination EXAMPLE x = 0:0.1:10; y = 2.*x + 1 + randn(size(x)); p = polyfit(x,y,1); f = polyval(p,x); r2 = rsquare(y,f); figure; plot(x,y,'b-'); hold on; plot(x,f,'r-'); Jered R Wells 11/17/11 jered [dot] wells [at] duke [dot] edu.

  • Platform: Matlab, Scripts
  • Publisher: Jered Wells
  • Date: 17-04-2013
  • Size: 10 KB

Wrapper for pcolor that behaves similarly to imagesc. Squash OS X Preview's "blurry image" bug! SANEPCOLOR simple wrapper for pcolor Unlike the built-in pcolor command, this function does not cut off the last row and column of the input matrix. In this way, sanePColor is intended to be as easy to use as imagesc, but allows the user to specify the x and y coordinates of each cell if desired. This function is also useful as an alternative means of generating images to print to PDF that are compatible with OS X's "Preview" PDF viewer (imagesc outputs are "blurred" when printing to a PDF as a vector graphic when viewed using Preview).

  • Platform: Matlab, Scripts
  • Publisher: Jeremy
  • Date: 26-01-2013
  • Size: 10 KB

GUIPDFTK - merge, split, decrypt, crypt, re-compress, decompress repair PDF files If PDF is electronic paper, then pdftk is an electronic staple-remover, hole-punch, binder, secret-decoder-ring, and X-Ray-glasses. Pdftk is a useful utility for handling PDF documents. Every PDF user should have one in the top drawer of his/her desktop. Use it to:Merge PDF DocumentsSplit PDF Pages into a New DocumentDecrypt Input as Necessary (Password Required)Encrypt Output as DesiredBurst a PDF Document into Single PagesReport on PDF Metrics, including Metadata and BookmarksUncompress and Re-Compress Page StreamsRepair Corrupted PDF (Where Possible)Pdftk is also an example of how to use a library of Java classes in a stand-alone C++ program.

  • Platform: WinOther
  • Publisher:
  • Date: 12-10-2009
  • Size: 109 KB

Multi-Knapsack solver by two stochastic solvers : i) by Cross-Entropy Method and ii) by Botev-Kroese Method for the following problem max S(X)=(p^{t}X) st. WX <= c Please run the demo files : test_ce_knapsack.m test_cemcmc_knapsack.m NB. You may need to recompile mex-files. Please open run "mexme_mks" to compile on your own platform..

  • Platform: Matlab, Scripts
  • Publisher: Sebastien Paris
  • Date: 18-01-2013
  • Size: 61 KB

StructTetraMesh is a tool to build structured tetreaedral mesh on cuboid dataset. This is usefull in avoiding delaunay 3D triangulation that for this kind of dataset are particularly slow and numerically unstable. This tool allows a huge speed improvement against delaunayn for 3D points, the output triangulation will also be 'slivers-less', this is particularly important for programs that need a conform triangulation like fem analysis and search structure algorithms. INPUT: x,y,z must be vectors, they represent the axis of the cuboid.

  • Platform: Matlab, Scripts
  • Publisher: Luigi Giaccari
  • Date: 09-05-2013
  • Size: 10 KB

GMM-GMR is a set of Matlab functions to train a Gaussian Mixture Model (GMM) and retrieve generalized data through Gaussian Mixture Regression (GMR). It allows to encode efficiently any dataset in Gaussian Mixture Model (GMM) through the use of an Expectation-Maximization (EM) iterative learning algorithms. By using this model, Gaussian Mixture Regression (GMR) can then be used to retrieve partial output data by specifying the desired inputs. It then acts as a generalization process that computes conditional probability with respect to partially observed data.

  • Platform: Matlab, Scripts
  • Publisher: Sylvain Calinon
  • Date: 20-02-2013
  • Size: 41 KB

Like polyfit.m but includes weighting of each data point. Summary ---------------- Find a least-squares fit of 1D data y(x) with an nth order polynomial, weighted by w(x). Usage ----- P = polyfitweighted(X,Y,N,W) finds the coefficients of a polynomial P(X) of degree N that fits the data Y best in a least-squares sense. P is a row vector of length N+1 containing the polynomial coefficients in descending powers, P(1)*X^N + P(2)*X^(N-1) +...+ P(N)*X + P(N+1). W is a vector of weights. Vectors X,Y,W must be the same length.

  • Platform: Matlab, Scripts
  • Publisher: Salman Rogers
  • Date: 06-01-2013
  • Size: 10 KB

easyfitGUI(varargin) fits real data Y = f(X) easyfitGUI open a figure with uimenus devoted to process the data. VARARGIN: one or several matrix [X, Y] having: first column = vector of the independant variable (X) second column = vector of the dependant variable (Y). The Y-data are plotted versus the X-data. for instance: x1=0:0.1:3;x1=x1(:); y1=5*x1.^1.2+1+randn(size(x1)); easyfitGUI([x1,y1]) x2=-3:0.1:3;x2=x2(:); y2=5*x2.^2+1+randn(size(x2)); x3=0:0.1:pi;x3=x3(:); y3=-40*sin(x3); easyfitGUI([x2,y2],[x3,y3]) The available uimenus are : 'SELECTLINE' uimenu Select the line to be fitted.

  • Platform: Matlab, Scripts
  • Publisher: Jean-Luc Dellis
  • Date: 03-06-2013
  • Size: 10 KB

anneal Minimizes a function with the method of simulated annealing (Kirkpatrick et al., 1983) ANNEAL takes three input parameters, in this order: LOSS is a function handle (anonymous function or inline) with a loss function, which may be of any type, and needn't be continuous. It does, however, need to return a single value. PARENT is a vector with initial guess parameters. You must input an initial guess. OPTIONS is a structure with settings for the simulated annealing. If no OPTIONS structure is provided, anneal uses a default structure.

Leave-one-out cross-validation for PLS regression or discriminant analysis pls_cv = plscv(x,y,vl,'da') input: x (samples x descriptors) for cross-validation y (samples x variables) for regression or (samples x classes) for discriminant analysis. Classes numbers must be >0. vl (1 x 1) number of latent variables to compute in cross-validation 'da' (char) to indicate PLS-discriminant analysis (in PLS regression it is no used) output: pls_cv struct with: Ypcv (samples x variables x vl) predicted variables or (samples x classes x vl) predicted classes for cross-validation Tcv (samples x vl) x-scores for cross-validation samples For PLS-R: RMSEcv (variables x vl) Root Mean Square Error for cross-validation R2cv (variables x vl) Correlation Coefficient for cross-validation For PLS-DA: Succv (1 x vl) Success (%) of classification for...

  • Platform: Matlab, Scripts
  • Publisher: Cleiton Nunes
  • Date: 22-03-2013
  • Size: 10 KB

function [C,d]=eliminateConstraints(A,b) eliminates variables from a problem with linear equality constraints to give an unconstrained problem. This is useful e.g. when solving a problem with linear constraints and a nonlinear objective or further nonlinear constraints; eliminating the linear constraints makes the problem easier. Where the original constrained problem has: - variable vector x (length n). - linear constraint A*x=b, where the matrix A and vector b are eliminateConstraints's arguments.

  • Platform: Matlab, Scripts
  • Publisher: Andrew Jackson
  • Date: 07-02-2013
  • Size: 10 KB

vector generalization of matlab standard function randn() with correlations. inputs: Rpp - pXp correlation matrix nSamp - number of samples outputs: data matrix of size [p rows X nSamp cols] sample correlation matrix example generate a vector of 1000 pairs which are correlated as [1 -0.3] [-0.3 1] R=[1 -0.3; -0.3, 1]; n = 1000; [x, sampR]=correlatedGaussianNoise(R,n); disp(sampR).

  • Platform: Matlab, Scripts
  • Publisher: michaelB brost
  • Date: 22-06-2013
  • Size: 10 KB

Other hypothesis used with the Mauchly's sphericity test is on the orthonormalized contrasts, H0: C'SC = s^2I , where C is any full-rank (p-1) x p matrix of orthonormal contrasts. A is the nonorthonormalized contrasts matrix. C = GMO(A') is a (p-1) doOCo p column-orthonormal matrix (by the Gram-Schmidt orthonormalization procedure) L = q^q|C'SC|/[tr(C'SC)]^q LL = - (N 1 ((2q^2 + q + 2)/(6q)) ln L = - (N 1 ((2q^2 + q + 2)/(6q)) [ln|C'SC| q ln [tr(C'SC)/q]] This statistic has an approximate Chi-square distribution with 0.

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