Matlab

Matlab : technical computing environment
Higher Edu - Research dev card
Development from the higher education and research community
  • Creation or important update: 19/09/13
  • Minor correction: 19/09/13
  • Index card author: Eric Hivon (IAP)
  • Theme leader : Dirk Hoffmann (Centre de Physique des Particules de Marseille (CPPM-IN2P3))

HEALPix : data analysis, simulation and visualisation on the sphere

This software was developed (or is under development) within the higher education and research community. Its stability can vary (see fields below) and its working state is not guaranteed.
  • Web site
  • System:
  • Current version: 3.11 - April 2013
  • License(s): GPL - GPLv2
  • Status: stable release
  • Support: maintained, ongoing development
  • Designer(s): Eric Hivon; Martin Reinecke; Krzysztof M. Gorski; Anthony J. Banday; Benjamin D. Wandelt; Emmanuel Joliet; William O'Mullane; Cyrille Rosset; Andrea Zonca
  • Contact designer(s): hivon at iap.fr
  • Laboratory, service: MPA (Garching, Allemagne), Caltech (Pasadena, CA,Etats-Unis), TAC (Copenhague, Danemark), ESAC (Madrid, Espagne), JPL (Pasadena, CA, Etats-Unis), ESO (Garching, Allemagne)

 

General software features

The HEALPix software implements the HEALPix (Hierarchical Equal Area iso-Latitude Pixelation) pixelation of the sphere. Initially developed for the simulation and analysis of ESA Planck satellite observations (dedicated to the study of the Cosmic Microwave Background (CMB) anisotropies, whose first results were delivered in March 2013), this software and its pixelation algorithm have become standard tools in the simulation and analysis of data on the sphere, including the NASA WMAP satellite, also dedicated to CMB observation, and the Pierre Auger ground based observatory for high energy cosmic rays, and are used for other astrophysical and geological studies.

Main features of the pixelation

At a given resolution, all HEALPix pixels have the same surface area, even if their shape varies slightly. Thanks to the hierarchical feature of the pixelation, upgrading its resolution to the next level simply amounts to divide each pixel into four sub-pixel of the same area. This allows quick and efficient upgrading and downgrading operations of existing maps.

Since the pixels are regularly spaced on iso-latitude rings, Spherical Harmonics can be computed very efficiently. The synthesis or analysis up to multipole Lmax  of a spherical data set containing Npix pixels is reduced from    Npix Lmax2   to   Npix½ Lmax2  compared to non iso-latitude pixelation.

Features of the software package

The represents data on the sphere, and enables analysis or simulation of these maps in (scalar or spin-weighted) Spherical Harmonics, as well as various kinds of statistical analyses and processing. Portable FITS files are used for input and output. The list of available functions includes:

  • generation of random maps (gaussian or not) from an arbitrary angular power spectrum,
  • computation of the angular power spectrum (or angular correlation function) of a map,
  • convolution of a spherical map with an arbitrary circular window,
  • tessellation of the sphere and pixel processing supported down to a pixel size of 0.4 milliarcseconds (equivalent to 3.5 1018 pixels on the sphere),
  • median filtering of a map,
  • search of local extrema in a map,
  • query of pixels located in user defined disks, triangles, polygons, ...
  • processing of binary masks to identify 'holes' in order to fill them, or to apodize masks,
  • visualization of HEALPix sky maps either on the whole sky (using Mollweide or orthographic projections) or on a patch (gnomic or cartesian projections),
  • output in Google Map/Google Sky and DomeMaster format.

The most expensive operations, such a Spherical Harmonics Transform have been carefully optimised and benefit from a shared memory parallelisation based on OpenMP.

Contents of the software package

The software is available in C, C++, Fortran90, IDL/GDL, Java and python. The following modules are provided in each of these languages:

  • a library of tools (subroutines, functions, procedures, modules, classes, ...depending on languages) covering most of the functionnalities described above, as well as supporting ancillary tools (eg, parameter file parsing),
  • a set of stand-alone facilities based on the library above and each implementing one of HEALPix major features (map generation or analysis, filtering, resolution udgrade or downgrade, visualization). These applications are generally run via an interactive dialog or an ASCII parameter file. Their source code can be used as a starting point for user specific developments,
  • an extensive PDF and/or HTML documentation describing in details the API, inner working and limitations of each tool and application.

Finally, some tools (interactive script and Makefile) are provided to manage and facilitate the compilation and installation of one or several of the libraries and facilities, for most combinations of hardwares, operating systems, compilers, ...

Third Party Developements

One can distinguish two kinds of third party developements (defined as not (yet) being part of the official HEALPix package described above):

  • new functionalities, for instance many tools based on Minkowski functionals, wavelets (iSAP, MRS, S2LET, SphereLab), or structure identification (DisPerSE) developed by various research teams can be applied to data stored in HEALPix format,
  • translations, re-implementations or wrapping of (some of) existing functionalities, for instance in Matlab/Octave (Mealpix) and Yorick (YHeal) are available. (See (almost) exhaustive list.)

Context in which the software is used

Software used for the analysis of Planck satellite data.
Data format supported by Aladin visualisation software to represent diffuse astronomical data on the sky.

Publications related to the software

Higher Edu - Research dev card
Development from the higher education and research community
  • Creation or important update: 11/09/13
  • Minor correction: 11/09/13

Signal separation : generation and separation of digital signals

This software was developed (or is under development) within the higher education and research community. Its stability can vary (see fields below) and its working state is not guaranteed.
  • System:
  • Current version: 2012
  • License(s): Proprietary licence
  • Status: internal use
  • Support: not maintained, no ongoing development
  • Designer(s): Elena Florian, Antoine Chevreuil, Philippe Loubaton.
  • Contact designer(s): Philippe.Loubaton @ univ-mlv.fr
  • Laboratory, service:

 

General software features

This sofware generates various kinds of signals produced by standard digital communication systems, and simulates their propagation into a multi-channel multi-paths propagation channel. A number of blind source separation algorithms are also implemented.

Context in which the software is used

This software has been released for the industrial contract Aintercom, this software is not distributed otherwise.

Publications related to the software
  • Elena Florian, Antoine Chevreuil, Philippe Loubaton. Blind source separation of convolutive mixtures of non circular linearly modulated signals with unknown baud rates. Signal Processing, 2012, 92, pp. 715-726.

  • P. Jallon, Antoine Chevreuil, Philippe Loubaton. Separation of digital communication mixtures with the CMA: case of various unknown baud rates. Signal Processing, 2010, 90 (9), pp. 2633-2647.

Higher Edu - Research dev card
Development from the higher education and research community
  • Creation or important update: 17/05/13
  • Minor correction: 17/07/13

LSMM : Majorize-Minimize LineSearch for logarithmic barrier function optimization

This software was developed (or is under development) within the higher education and research community. Its stability can vary (see fields below) and its working state is not guaranteed.
  • Web site
  • System:
  • Current version: 1.0 - mars 2013
  • License(s): CeCILL-B
  • Status: stable release
  • Support: maintained, no ongoing development
  • Designer(s): Emilie Chouzenoux (LIGM), Saïd Moussaoui (IRCCyN)
  • Contact designer(s): emilie.chouzenoux @ univ-mlv.fr
  • Laboratory, service:

 

General software features

This toolbox allows to determine a suitable stepsize in iterative descent algorithms applied to the minimization of a criterion containing a logarithmic barrier function associated to linear constraints. A Majorization-Minimization (MM) scheme is adopted. It is based on the derivation of a log-quadratic majorant function well suited to approximate the criterion containing barrier terms. The convergence of classical descent algorithms when this linesearch strategy is employed is ensured.

A demo file illustrates the efficiency of the MM linesearch on the Newton minimization of the barrier criterion associated to a random quadratic programming (QP) test problem.

Context in which the software is used

Linearly constrained optimization.

Publications related to the software
  • E. Chouzenoux, S. Moussaoui and J. Idier. "Majorize-Minimize Linesearch for Inversion Methods Involving Barrier Function Optimization." Inverse Problems, Vol. 28, No. 6, 2012.

  • E. Chouzenoux, S. Moussaoui and J. Idier. "Efficiency of Line Search Strategies in Interior Point Methods for Linearly Constrained Optimization." In Proceedings of the IEEE Workshop on Statistical Signal Processing (SSP 2011), pages 101-104, Nice, France, 28-30 juin 2011.

  • E. Chouzenoux, S. Moussaoui and J. Idier. "A Majorize-Minimize Line Search Algorithm for Barrier Function Optimization." In Proceedings of the 17th European Signal Processing Conference (EUSIPCO 2009), pages 1379-1383, Glasgow, UK, 24-28 août 2009. EURASIP Press.

Higher Edu - Research dev card
Development from the higher education and research community
  • Creation or important update: 06/05/13
  • Minor correction: 06/05/13

RestoVMFB_Lab : Matlab toolbox for image restauration with the Variable Metric Forward-Backward algorithm

This software was developed (or is under development) within the higher education and research community. Its stability can vary (see fields below) and its working state is not guaranteed.
  • Web site
  • System:
  • Current version: 1.0 - avril 2013
  • License(s): CeCILL-B
  • Status: stable release
  • Support: maintained, no ongoing development
  • Designer(s): Audrey Repetti (LIGM), Emilie Chouzenoux (LIGM)
  • Contact designer(s): audrey.repetti @ univ-mlv.fr
  • Laboratory, service:

 

General software features

This Matlab toolbox allows to restore an image degraded by a linear operator and Gaussian Dependant noise with variance depending linearly on the image. The considered criterion is composed with the neg-log-likelihood of the noise distribution as data fidelity term, the indicator function allowing to constraint the dynamic range and the isotropic total variation favorizing piecewise constant images.

The restoration process uses the Majorize-Minimize Variable Metric Forward-Backward Algorithm.

Context in which the software is used

Image restauration

Publications related to the software

E. Chouzenoux, J.-C. Pesquet and A. Repetti. "Variable Metric Forward-Backward Algorithm for Minimizing the Sum of a Differentiable Function and a Convex Function" Submitted, 2013. Available online at http://www.optimization-online.org/DB_FILE/2013/01...

Higher Edu - Research dev card
Development from the higher education and research community
  • Creation or important update: 30/01/12
  • Minor correction: 30/01/12

SURELET-DECONV : SURE-LET based restoration software (Matlab toolbox)

This software was developed (or is under development) within the higher education and research community. Its stability can vary (see fields below) and its working state is not guaranteed.
  • Web site
  • System:
  • Current version: v1.0 - 20/01/2012
  • License(s): CeCILL-B
  • Status: stable release
  • Support: maintained, no ongoing development
  • Designer(s): J.-C. Pesquet, C. Chaux
  • Contact designer(s): caroline.chaux@univ-mlv.fr
  • Laboratory, service:

 

General software features

This Matlab toolbox allows to restore (blur + noise) an image using SURE-LET.

Context in which the software is used

Image deconvolution of an image corrupted by a blur and an additive Gaussian noise.

Publications related to the software

A SURE Approach for Digital Signal/Image Deconvolution Problems
Jean-Christophe Pesquet, Amel Benazza-Benyahia, Caroline Chaux
IEEE Transactions on Signal Processing, Vol. 57, No. 12, Dec. 2009, pp. 4616-4632.
http://arxiv.org/abs/0810.4807
http://www-syscom.univ-mlv.fr/~chaux/publications....

Higher Edu - Research dev card
Development from the higher education and research community
  • Creation or important update: 28/09/11
  • Minor correction: 28/09/11

RestoMMMG_Lab : Matlab toolbox for image restoration (degradation modeled as a linear operator and a Gaussian noise)

This software was developed (or is under development) within the higher education and research community. Its stability can vary (see fields below) and its working state is not guaranteed.
  • Web site
  • System:
  • Current version: v1.0 - Sep. 2011
  • License(s): CeCILL-B
  • Status: stable release
  • Support: maintained, no ongoing development
  • Designer(s): E. Chouzenoux
  • Contact designer(s): emilie.chouzenoux@univ-mlv.fr
  • Laboratory, service:

 

General software features

This toolbox allows to restore an image degraded by a linear operator and Gaussian noise. The considered criterion is composed with a least square function as data fidelity term, a quadratic distance function allowing to constraint the dynamic range of the restored image into [xmin,xmax], a quadratic elastic net term allowing to ensure the existence of the solution and a regularization term favorizing piecewise constant images.
The restoration process uses the Majorize-Minimize Memory Gradient Algorithm.

Context in which the software is used

Image restoration (degradation modeled as a linear operator and a Gaussian noise).

Publications related to the software
  • E. Chouzenoux, J. Idier and S. Moussaoui. A Majorize-Minimize Strategy for Subspace Optimization Applied to Image Restoration. IEEE Transactions on Image Processing, Vol. 20, No. 18, pages 1517-1528, juin 2011.
  • E. Chouzenoux, J.-C. Pesquet, H. Talbot and A. Jezierska. A Memory Gradient Algorithm for l2-l0 Regularization with Applications to Image Restoration. IEEE ICIP 2011.
  • E. Chouzenoux, Recherche de pas par Majoration-Minoration. Application à la résolution de problèmes inverses, Thèse, 2010.
  • http://www-syscom.univ-mlv.fr/~chouzeno/Recherche....
Higher Edu - Research dev card
Development from the higher education and research community
  • Creation or important update: 28/03/11
  • Minor correction: 21/05/19

Monolix : analysis of non linear mixed effects models

This software was developed (or is under development) within the higher education and research community. Its stability can vary (see fields below) and its working state is not guaranteed.
  • Web site
  • System:
  • Current version: 3.1 - 10/2009
  • License(s): CeCILL
  • Status: validated (according to PLUME), under development
  • Support: maintained, ongoing development
  • Designer(s): Marc Lavielle, Hector Mesa, Kaelig Chatel, Benoît Charles, Eric Blaudez
  • Contact designer(s): Marc.Lavielle@math.u-psud.fr
  • Laboratory, service:

 

General software features

MONOLIX is a free software dedicated to the analysis of non linear mixed effects models. The objective of this software is to perform: parameter estimation, model selection, goodness of fit plots and, data simulation.

Context in which the software is used
  • Research in statistic: University of Paris 5, 11 and 13
  • Research in pharmacology: INSERM - P7
  • Research in microbiology : INRA
Publications related to the software

SAEM algorithm

  • Delyon B., Lavielle M., and Moulines E. "Convergence of a stochastic approximation version of the EM algorithm" The Annals of Stat., vol 27, no. 1, pp 94-128, 1999.
  • Kuhn E., Lavielle M. "Coupling a stochastic approximation version of EM with a MCMC procedure" ESAIM P&S, vol.8, pp 115-131, 2004.
  • Kuhn E., Lavielle M. "Maximum likelihood estimation in nonlinear mixed effects models" Computational Statistics and Data Analysis, vol. 49, No. 4, pp 1020-1038, 2005.
  • Lavielle M., Meza C. "A Parameter Expansion version of the SAEM algorithm" Statistics and Computing, vol. 17, pp 121-130, 2007.
  • Donnet S., Samson A. "Estimation of parameters in incomplete data models defined by dynamical systems" Jour. of Stat. Planning and Inference, vol. 137, no. 9, pp 2815-2831, 2007.
  • Meza C., Jaffrezic F., Foulley J.L. "REML estimation of variance parameters in non linear mixed effects models using the SAEM algorithm" The Biometrical Journal 49, 1-13, 2007.
  • Donnet S., Samson A. "Parametric inference for mixed models defined by stochastic differential equations" ESAIM P&S, 12:196-218, (2008).

Applications

  • Makowski D., Lavielle M. "Using SAEM to estimate parameters of models of response to applied fertilizer" Journal of agricultural, Biological and Enviromental Statistics, vol. 11, n. 1, pp. 45-60, 2006.
  • Samson A., Lavielle M., Mentré F. "Extension of the SAEM algorithm to left-censored data in non-linear mixed-effects model: application to HIV dynamics models" Computational Statistics and Data Analysis, vol. 51, pp. 1562--1574, 2006.
  • Jaffrezic F., Meza C., Lavielle M., Foulley J.L. "Genetics analysis of growth curves using the SAEM algorithm" Genetics Selection Evolution, vol. 38, pp. 583--600, 2006.
  • Lavielle M., Mentré F. "Estimation of population pharmacokinetic parameters of saquinavir in HIV patients and covariate analysis with the SAEM algorithm" Journal of Pharmacokinetics and Pharmacodynamics, vol. 34, pp. 229--49, 2007.
  • Comets E, Verstuyft C, Lavielle M, Jaillon P, Becquemont L, Mentré F. Modelling the influence of MDR1 polymorphism on digoxin pharmacokinetic parameters. European Journal of Clinical Pharmacology, 63, pp. 437-49, 2007.
  • Samson A., Lavielle M., Mentré F. "The SAEM algorithm for group comparison tests in longitudinal data analysis based on nonlinear mixed-effects model" Statistics in Medicine, vol. 26, pp 4860-4875, 2007.
Higher Edu - Research dev card
Development from the higher education and research community
  • Creation or important update: 04/03/11
  • Minor correction: 25/03/11

TexGeoPPXA_Lab : Matlab toolbox for geometry/texture decomposition

This software was developed (or is under development) within the higher education and research community. Its stability can vary (see fields below) and its working state is not guaranteed.
  • Web site
  • System:
  • Current version: v1.0 - octobre 2010
  • License(s): CeCILL-B
  • Status: stable release
  • Support: maintained, no ongoing development
  • Designer(s): Nelly Pustelnik
  • Contact designer(s): nelly.pustelnik_@_ims-bordeaux.fr
  • Laboratory, service:

 

General software features

This software performs geometry/texture decomposition from a degraded observation. By degradation we mean convolution operator and Poisson noise.
The method is based on convex criterion minimization. The criterion contains 4 terms :

  • Kullback-Leibler divergence
  • indicator function
  • l1 norm applied on frame coefficients (texture component)
  • total variation applied on geometry component

The PPXA (Parallel ProXimal Algorithm) is used to perform the minimization.

Context in which the software is used

This software performs geometry/texture decomposition from a degraded (convolution + Poisson noise) observation.

Publications related to the software
  • N. Pustelnik, Méthodes proximales pour la résolution de problèmes inverses. Application à la Tomographie par Emission de Positrons. Thèse Université Paris-Est, 2010.
  • L. M. Briceno-Arias, P. L. Combettes, J.-C. Pesquet, and N. Pustelnik, Proximal method for geometry and texture image decomposition, International Conference on Image Processing (ICIP) , Honk Kong, 26-29 Septembre 2010.
  • http://nellypustelnik.perso.sfr.fr/
Higher Edu - Research dev card
Development from the higher education and research community
  • Creation or important update: 04/03/11
  • Minor correction: 08/09/11

RestoPPXA_Lab : Matlab toolbox for image restoration

This software was developed (or is under development) within the higher education and research community. Its stability can vary (see fields below) and its working state is not guaranteed.
  • Web site
  • System:
  • Current version: v2.0 - August 2011
  • License(s): CeCILL-B
  • Status: stable release
  • Support: maintained, no ongoing development
  • Designer(s): Nelly Pustelnik
  • Contact designer(s): nelly.pustelnik_@_ims-bordeaux.fr
  • Laboratory, service:

 

General software features

This software allows to restore images degraded by a convolution operator and a noise (Poisson or Gaussian). The method behind is based on convex criterion minimization. This criterion includes a data fidelity term (Kullback-Leibler divergence or l2 norm), an indicator function (e.g. pixel range constraint) and a regularization term that can be:

  • a l1 norm applied on frame (DTT) coefficients
  • a total variation term (TV)
  • an hybrid regularization (l1 + TV)

PPXA (Parallel ProXimal Algorithm) is used to minimize the resulting criterion.

Context in which the software is used

This software allows to restore images degraded by a convolution operator and a noise (Poisson or Gaussian).

Publications related to the software
  • N. Pustelnik, Méthodes proximales pour la résolution de problèmes inverses. Application à la Tomographie par Emission de Positrons. Thèse Université Paris-Est, 2010.
  • N. Pustelnik, C. Chaux, and J.-C. Pesquet, Hybrid regularization for data restoration in the presence of Poisson noise, European Signal Processing Conference (EUSIPCO), Glasgow, Scotland, August 24-28, 2009.
  • http://nellypustelnik.perso.sfr.fr/
Higher Edu - Research dev card
Development from the higher education and research community
  • Creation or important update: 04/03/11
  • Minor correction: 04/03/11

Blind source separation : Matlab toolbox

This software was developed (or is under development) within the higher education and research community. Its stability can vary (see fields below) and its working state is not guaranteed.
  • Web site
  • System:
  • Current version: 2.0.2 - Jul. 2010
  • License(s): not yet chosen
  • Status: stable release
  • Support: maintained, no ongoing development
  • Designer(s): Pierre Jallon, Marc Castella
  • Contact designer(s): Marc.Castella_@_it-sudparis.eu
  • Laboratory, service:

 

General software features

This toolbox is designed for Matlab and can be used to separate convolutive mixtures of signals.

Context in which the software is used

Cyclo-stationnary source separation.

Publications related to the software
  • Deflation and kurtosis based contrasts
    • C. Simon, Ph. Loubaton and C. Jutten, Separation of a class of convolutive mixtures: a contrast function approach Signal Processing, Volume 81, Issue 4, April 2001, pp.883-887.
  • Quadratic contrasts
    • M. Castella, S. Rhioui, E. Moreau and J.-C. Pesquet, Quadratic Higher-Order Criteria for Iterative Blind Separation of a MIMO Convolutive Mixture of Sources. IEEE Trans. Signal Processing, Vol. 55, Issue 1, Jan. 2007 pp.218-232.
    • M. Castella, E. Moreau and J.-C. Pesquet, A quadratic MISO contrast function for blind equalization. Proc. of ICASSP 2004, pp.681-684, Vol.2, Montréal, Canada.
    • M. Castella, S. Rhioui, E. Moreau and J.-C. Pesquet, Source separation by quadratic contrast functions: a blind approach based on any higher-order statistics. Proc. of ICASSP 2005, pp.569-572, Vol.3, Philadelphia, USA.
  • Other references at: http://www-public.int-evry.fr/~castella/toolbox/references.php
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