Advanced vehicle simulator
This project holds the latest releases for canonical versions of the ADVISOR® Software and "Advanced Vehicle Simulator". ADVISOR is a MATLAB/Simulink based simulation program for rapid analysis of the performance and fuel economy of light and heavy-duty vehicles with conventional (gasoline/diesel), hybrid-electric, full-electric, and fuel cell powertrains. New features are developed under the title "Advanced Vehicle Simulator". Periodically, stable versions of Advanced Vehicle Simulator will be submitted for consideration as the next canonical version of the ADVISOR software. ADVISOR is a registered trademark of the Alliance for Sustainable Energy, LLC, the manager and operator of the National Renewable Energy Laboratory for the United States Department of Energy. Trademark used under license.
This project, developed at UCL London, provides code for tomographic reconstruction. NiftyRec is written in C and has Python and Matlab extensions. Computationally intensive functions have a GPU accelerated version based on CUDA.
A Matlab toolkit for analyzing EEG/ERP datasets, especially PCA. If you run into a problem, please send me a note and I'll fix it. The tutorial is in the documentation folder and the tutorial data is a separate download (tutorial data.zip).
Dynamics of quantum systems, controlled by external fields
WavePacket is a program package for numerical simulation of quantum-mechanical wavepacket dynamics for distinguishable particles. It can be used to solve one or more (i.e. coupled channels) time-independent or time-dependent (linear) Schrödinger and Liouville-von Neumann-equations. Optionally accounting for the interaction with external electric fields within the semiclassical dipole approximation, WavePacket can simulate modern experiments using ultrashort light pulses in photo-induced physics or chemistry, including quantum optimal control. The graphical capabilities allow visualization 'on the fly', including Wigner transforms to phase space. A description of WavePacket can be found in our manuscript at http://dx.doi.org/10.1016/j.cpc.2016.12.007. For examples / movies, see also the Wiki pages of the WavePacket main project. First established in 2004, the present Matlab version is in a stable, mature state. Further development mainly by Burkhard Schmidt at Free University Berlin
GLPKMEX - a Matlab MEX interface for the GLPK library Note: GLPKMEX is not currently compatible with glpk-4.49, or later. please use with glpk 4.40 - 4.48.
SegyMAT is a set of Matlab/Octave m-files to read and write SEG Y data following SEG Y Revision 0 and 1
The Auditory Modeling Toolbox is a community effort to build a simple and well tested toolbox for doing auditory modelling in Matlab, Octave, Python and C. Over 30 models and data sets from the area of hearing science are provided.
A Matlab/Octave toolbox to design, simulate, and analyze optical communication systems. Open source, fast (using MEX), user-friendly and customizable, it includes cutting-edge solutions for: modulation formats, performance estimation, fiber propagation.
Spatially Oriented Format for Acoustics
In this project we work on the (S)patially (O)riented (F)ormat for (A)coustics: SOFA. The file format is intended for reading, saving, and describing spatially oriented data of acoustic systems. Examples of data we consider are head-related transfer functions (HRTFs), binaural room impulse responses (BRIRs), multichannel measurements such as done with microphone arrays, or directionality data of loudspeakers. The format specifications are the major issue, but we also aim in providing APIs for reading and writing the data in SOFA.
A MATLAB program for 2D particle tracking or 3D DHM based tracking.
We present a versatile and fast MATLAB program (UmUTracker) that automatically detects and tracks particles by analyzing long video sequences acquired by either light microscopy or digital holography microscopy (DHM). Our program finds the 2D particle center position using an isosceles triangle transform and the axial position by a fast implementation of Rayleigh-Sommerfeld numerical reconstruction algorithm using a one dimensional radial intensity profile. *Updates v1.1: 2017/11/21- Bug fix & Blob detection functions. Please visit our homepage for more information: http://www.physics.umu.se/english/research/biological-physics/the-biophysics-and-biophotonics-group/ *Please cite our work using the following information: Zhang, H., T. Stangner, K. Wiklund, A. Rodriguez, and M. Andersson. 2017. UmUTracker: A versatile MATLAB program for automated particle tracking of 2D light microscopy or 3D digital holography data. Comput. Phys. Commun. 219: 390–399.
An easy to install and use toolbox for octave for the calculation purpose of power system along with features like Economic load dispatch, load flow analysis, transmission line parameter and swing equation etc
Yet Another Audio Feature Extractor is a toolbox for audio analysis. Easy to use and efficient at extracting a large number of audio features simultaneously. WAV and MP3 files supported, or embedding in C++, Python or Matlab applications.
The Janelia Automated Animal Behavior Annotator
The Janelia Automatic Animal Behavior Annotator (JAABA) is a machine learning-based system that enables researchers to automatically compute interpretable, quantitative statistics describing video of behaving animals. Through our system, users encode their intuition about the structure of behavior by labeling the behavior of the animal, e.g. walking, grooming, or following, in a small set of video frames. JAABA uses machine learning techniques to convert these manual labels into behavior detectors that can then be used to automatically classify the behaviors of animals in large data sets with high throughput. JAABA combines an intuitive graphical user interface, a fast and powerful machine learning algorithm, and visualizations of the classifier into an interactive, usable system for creating automatic behavior detectors. Documentation is available at: http://jaaba.sourceforge.net/
Fast Finite Element Method implementation in native MATLAB.
A shared memory parallel sparse matrix library including Sparse BLAS.
librsb is a library for sparse matrix computations featuring the Recursive Sparse Blocks (RSB) matrix format. This format allows cache efficient and multi-threaded (that is, shared memory parallel) operations on large sparse matrices. The most common operations necessary to iterative solvers are available, e.g.: matrix-vector multiplication, triangular solution, rows/columns scaling, diagonal extraction / setting, blocks extraction, norm computation, formats conversion. The RSB format is especially well suited for symmetric and transposed multiplication variants. On these variants, librsb has been found to be faster than Intel MKL's implementation for CSR. Most numerical kernels code is auto generated, and the supported numerical types can be chosen by the user at build time. librsb implements the Sparse BLAS standard, as specified in the BLAS Forum documents.
A neural network package for Octave! Goal is to be as compatible as possible to the one of MATLAB(TM).
C++, Matlab and Python library for Hidden-state Conditional Random Fields. Implements 3 algorithms: LDCRF, HCRF and CRF. For Windows and Linux, 32- and 64-bits. Optimized for multi-threading. Works with sparse or dense input features.
Real time signature verification using MATLAB and C#
Online Signature Verification technology requires primarily a digitizing tablet and a special pen connected to the USB port of a computer. An individual can sign on the digitizing tablet using the special pen regardless of his signature size and position. The signature is characterized as pen-strokes consisting x-y coordinates and pressure with the data being stored in a signature database. Dynamic time warping (DTW) and quadratic discriminant analysis (QDA) is used to get results where the system accepts 2% of forged signatures and rejects 5% of true signatures
a Small (Matlab/Octave) Toolbox for Kriging
The STK is a (not so) Small Toolbox for Kriging. Its primary focus in on the interpolation / regression technique known as kriging, which is very closely related to Splines and Radial Basis Functions, and can be interpreted as a non-parametric Bayesian method using a Gaussian Process (GP) prior. The STK also provides tools for the sequential and non-sequential design of experiments. Even though it is, currently, mostly geared towards the Design and Analysis of Computer Experiments (DACE), the STK can be useful for other applications areas (such as Geostatistics, Machine Learning, Non-parametric Regression, etc.).
XFEM implementation in MATLAB
The extended finite element method (XFEM) classified, one of the partition of unity method (PUM), allows discontinuities to be simulated independently of the mesh. This is possible by adding appropriate functions to the FE approximation basis, for example, the Heaviside function. The discontinuities can evolve in time, without a need for a conforming mesh. A MATLAB implementation of the XFEM written by VP Nguyen, is given here. The interaction of cracks and crack-inclusion interaction is modelled with XFEM framework. The elements intersected by discontinuity surface are sub-divided into quadrature subcells aligned with the discontinuity and higher order quadrature is adopted. The implementation is described in the following article: Meshless methods: a review and computer implementation aspects VP Nguyen, T Rabczuk, S Bordas, M Duflot, Mathematics and computers in simulation 79 (3), 763-813.
A high-level python to matlab(tm) bridge. Let's matlab look like a normal python library.
Random Tree Generator for MatLab
RandTree is a MatLab based tree simulator program where the algorithm is based on Honda's model. We have used probabilistic generation of branches in order to simulate visually realistic tree structures. This program is designed to generate branching structures with bifurcation branching pattern (sympodial branching). By changing the probabilities and branching angles, you can generate different shapes of trees.
A MATLAB Automatic Differentiation Tool
ADiGator is a source transformation via operator overloading tool for the automatic differentiation of mathematical functions written in MATLAB. Given a user written file, together with information on the inputs of said file, ADiGator uses forward mode automatic differentiation to generate a new file which contains the calculations required to compute the numeric derivatives of the original user function. Furthermore, these calculations are written entirely in the native MATLAB language, and thus the process may be repeated to obtain nth order derivative files. The package is particularly appealing for applications where the same derivative must be found at multiple different points, i.e. non-linear root finding/optimization, stiff ode integration, etc.
Face Recognition System Matlab source code
Research on automatic face recognition in images has rapidly developed into several inter-related lines, and this research has both lead to and been driven by a disparate and expanding set of commercial applications. The large number of research activities is evident in the growing number of scientific communications published on subjects related to face processing and recognition. Index Terms: face, recognition, eigenfaces, eigenvalues, eigenvectors, Karhunen-Loeve algorithm.
Locally Weighted Projection Regression (LWPR)
Locally Weighted Projection Regression (LWPR) is a fully incremental, online algorithm for non-linear function approximation in high dimensional spaces, capable of handling redundant and irrelevant input dimensions. At its core, it uses locally linear models, spanned by a small number of univariate regressions in selected directions in input space. A locally weighted variant of Partial Least Squares (PLS) is employed for doing the dimensionality reduction. Please cite:  Sethu Vijayakumar, Aaron D'Souza and Stefan Schaal, Incremental Online Learning in High Dimensions, Neural Computation, vol. 17, no. 12, pp. 2602-2634 (2005).  Stefan Klanke, Sethu Vijayakumar and Stefan Schaal, A Library for Locally Weighted Projection Regression, Journal of Machine Learning Research (JMLR), vol. 9, pp. 623--626 (2008). More details and usage guidelines on the code website.