Creates a number of samples from a specified number of dimensions and centers them around a given mean, and within a given covariance range. You might not find it very useful, but hey, I need something to do this so why not.
You need to generate 1000 samples from a 3 dimensional Gaussian distribution with a mean m = [4,5,6], and with a covariance sigma = [9 0 0;0 9 0;0 0 9].
If you have the freedom to choose your abscissas and your integrand is smooth or has
a log singularity, then this script is for you. It computes the definite integral of a user
defined function over the interval [a, b]. The user can...
Some curve fitting or smoothing tools can benefit from knowledge of the noise variance to expect on your data. Kalman filters use this information, also some spline fitting tools. So I wrote a function to extract the noise variance from a signal...
ECR is a new method for regression analysis, which employs a supervising alpha to supervise the X-matrix decomposition. When alpha=0, ECR coincides with principal component regression (PCR), when alpha=1, ECR coincides with partial least squares...
To compute the LU factorization under default settings:
[L U p q] = lucp(A)
This produces a factorization such that L*U = A(p,q). Vectors p and q permute the rows and columns, respectively.
The pivot tolerance can...
Reduce image noise by measuring local pixel statistics and remapping intensities.
Tristan Ursell (c)
Relative Noise Transform
The Keywords Widget collects the query strings from Google, Bing and Yahoo. It lists the keywords as a sidebar widget so that users might click on them and find information using the WordPress built-in search. In this way a user might find more...
* 1.5 - Allows all users who are logged in to see all Private posts
* show_private_posts() is now a widget
* Merged with Partial Private Post (See below)
This plugin is a full featured private post...
This Matlab program Solve N-equation with Gauss elimination method and check results with Matlab Function.
GAUSSian REALIZation of a random variable with standard deviation sigma and correlation length lambda
This program generates filtered BPSK with proper pulse shaping filters such as ideal Nyquist, Raised cosine, Square root raised cosine and Gaussian filters. Determine the power spectrum of filtered BPSK signals.
This program simulates Gaussian beam propagation through a Fabry-Perot interferometer with adjustable error angles. The result is the intensity behind the cavity at a screen perpendicular to the propagation direction. It contains a GUI for setting...
Comparison with ground truth and triangulation provided, with varying amounts of gaussian noise added in train and test data.
GUI is in Portuguese, but self-explanatory. English version will be provided soon.
This is a little script that adds several Gaussian or Lorentzian functions with the appropriate full width at half max and height to generate and plot a spectrum. The input is an array containing frequency vs oscillator strength, desired fwhm of...
em_ghmm : Expectation-Maximization algorithm for a HMM with Multivariate Gaussian measurement
[logl , PI , A , M , S] = em_ghmm(Z , PI0 , A0 , M0 , S0 , [options]);
solves the linear least squares problem with nonnegative variables using the block principal pivoting algorithm in:
Portugal, Judice and Vicente, A comparison of block pivoting and interior point algorithms for linear least squares problems...
A fast generator of gaussian mixture samples with a general ND dimensional calling syntax.
Permit to sample from a simple multivariate process to several gaussian mixture in a easy way.
Please run mexme_sample_mvgm.m to...
Solves the linear least squares problem with nonnegative variables using the newton's algorithm in:
Portugal, Judice and Vicente, A comparison of block pivoting and interior point algorithms for linear least squares problems with nonnegative...
Solves the linear least squares problem with nonnegative variables using the predictor-corrector algorithm in:
Portugal, Judice and Vicente, A comparison of block pivoting and interior point algorithms for linear least squares problems with...
The algorithm takes in a 1-D signal and finds the Gaussian derivative of it with the given kernel size and sigma, to provide the zero crossings. The zero crossings are then analyzed to give peaks and valleys. In order to remove very closely...