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ecg-kit

This toolbox is a collection of Matlab tools that I used, adapted or developed during my PhD and post-doc work with the Besicos group at University of Zaragoza, Spain and at the National Technological University of Buenos Aires, Argentina. The ecg-kit has tools for reading, processing and presenting results. The main feature of the this toolbox is the possibility to use several popular algorithms for ECG processing, such as:
- Algorithms from Physionet's WFDB software package
- QRS detectors, such as gqrs, wqrs, wavedet, ecgpuwave, Pan & Tompkins, EP limited
- Wavedet ECG delineator
- Pulse wave detectors as wabp and wavePPG
- a2hbc and EP limited heartbeat classifiers.
And other scritps for inspecting, correcting and reporting all these results.

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TIGRE: Tomographic Iterative GPU-based Reconstruction Toolbox

TIGRE: Tomographic Iterative GPU-based Reconstruction Toolbox
TIGRE is a GPU accelerated software for big scale 3D tomographic reconstruction, being capable of reconstructing geometries such as Cone Beam Computed Tomography and Parallel Beam Computed Tomography.
TIGRE additionally includes a big set of iterative methods for image reconstruction, methods that can achieve better images with less projections. The algorithms included are:

-FDK (inverse radon)
-SART, OS-SART, SIRT (Gradient descend-based)
-CGLS (Krylov subspace based)
-ADS-POCS, OSC-POCS, B-ADS-POCS-β, SART-TV (Total variation regularization)
-MLEM (Statistical inversion)

TIGRE is released by University of Bath and CERN under a modified BSD license, making it available for everyone to use and modify, and the authors do encourage people to contribute to the code bug fixes, new methods or anything that can help it be better!

Visit TIGRE's Github page for more information.

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ebertolazzi/Clothoids

G1fitting [](https://travis-ci.org/ebertolazzi/G1fitting)
### G1 and G2 fitting with clothoids
**by Enrico Bertolazzi and Marco Frego**

The script `buildClothoid` implements the algorithm described in the paper

*G1 fitting with clothoids*, Mathematical Methods in the Applied Sciences, John Wiley & Sons, (2014), Ltd,. http://onlinelibrary.wiley.com/doi/10.1002/mma.3114/abstract

**Description:**
Given two points and two direction associated with the points,
a clothoid, i.e. a curve with linear varying curvature is computed
in such a way it pass to the points with the prescribed direction.
The solution in general is not unique but chosing the one for
which the angle direction variation is less than `2*pi` the solution
is unique.
The sofware solves the nonlinear system associated to the fitting problem
computing initial curvature and its derivative with the lenght of the curve.
An additional routine for the computation of the points along a clothoid
curve is added for convenience.

**Usage:**
To compute curvature and length of a clothoid passing throught points
P0=`(x0,y0)`, P1=`(x1,y1)` with angles `theta0` and `theta1` use the
function `buildClothoid`

`clot = buildClothoid( x0, y0, theta0, x1, y1, theta1, tol ) ;`

The parameter `tol` (usually `1e-10`) is a tolerance parameter
used to stop Newton iteration.
The resulting curve can be described by the 5 parameters

- `(clot.x0, clot.y0)` initial point
- `clot.theta0` initial direction (angle)
- `clot.k0` initial curvature of the curve
- `clot.dk` derivative of the cuvature along arc length

plus a 6th parameter `clot.L`

- `clot.L` total length of the curve connecting P0 and P1

to compute points along a clothoid curve use the function `pointsOnClothoid`

`XY = pointsOnClothoid( x0, y0, theta0, k, dk, 0:L/npts:L ) ;

or

`XY = pointsOnClothoid( clot, 0: clot.L/npts:clot.L ) ;`

This function uses the 5 parameters `x0`, `y0`, `theta0`, `k`, `dk`
which indentify the curve. The parameter `L` is used to determine length
of the portion of the curve to compute. The parameter `npts` is the number
of points computed along the curve.

`XY` is a `2 x npts` matrix whose columns are the points along the curve.

To plot the computed curve use MATLAB `plot` command as usual:

`plot( XY(1,:), XY(2,:), '-r' ) ;`

Four sample scripts: TestN0, TestN1, TestN2 and TestN3 shows how to use the functions.

**Mex files for fast computation:**

In directory `src_mex` you find a C++ implementation of the proposed algorithm
with `mex` interface. To compile run `Compile` from MATLAB window.
After compilation the compiled version of the scripts

- buildClothoid
- evalClothoid
- FresnelCS
- GeneralizedFresnelCS
- pointsOnClothoid

are available in the `matlab` directory.

**Additional mex**

The mex implementation of the script:

- intersectClothoid
- TriTriOverlap
- biarc

are added to the library. The first compute all the intersections between two clothoids. The second check if two triangles (planar) overlap (used in the intersection computation).
The third compute a biarc given G1 data.

The experimental script

- buildClothoid2arcG2
- buildClothoid3arcG2

implements the algorithms described in the paper
*On the G2 Hermite Interpolation Problem with Clothoids*, submitted for publication.

**Authors:**

Enrico Bertolazzi and Marco Frego
Department of Industrial Engineering
University of Trento enrico.bertolazzi@unitn.it m.fregox@gmail.com

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neuropoly/axonseg

Segment axon and myelin from microscopy data. Written in Matlab. The compiled versions are also available for those who do not have the necessary processing toolboxes.

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Jorsorokin/HDBSCAN

This is a MATLAB implementation of HDBSCAN, a hierarchical version of DBSCAN. HDBSCAN is described in Campello et al. 2013 and Campello et al. 2015. Please see the extensive documentation in the github repository. Suggestions for improvement / collaborations are encouraged!

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The HDR Toolbox

The HDR Toolbox provides functions for processing HDR images and videos for different tasks such as tone mapping, inverse tone mapping, reverse tone mapping, expansion, HDR compression, image-based lighting, handling HDR videos, color transforms, etc.
The HDR Toolbox is part of the book "Advanced High Dynamic Range Imaging", where you can find extra documentation about the code and more details on HDR imaging. Please visit the website of the book at the following URL:
http://www.advancedhdrbook.com/
Note that the GitHub repository of the HDR Toolbox can be found at the following URL:
https://github.com/banterle/HDR_Toolbox

HOW TO INSTALL:
1) Unzip the file HDRToolbox.zip in a FOLDER on your PC/MAC
2) Run Matlab
3) Set the FOLDER as the current directory
4) Write the command:
installHDRToolbox
in the Command Window, and wait for the installation process to end.

NOTE ON TONE MAPPING:
The majority of TMOs return tone-mapped images with linear values. This means that gamma encoding needs to be applied to the output of these TMOs before visualization or before writing tone mapped images on the disk; otherwise, these images may appear dark. A few operators (e.g. Mertens et al.'s operator) return gamma-encoded values, so there is no need to apply gamma to them; in this case, a message (e.g. a Warning) is displayed after tone mapping alerting that there is no need of gamma encoding.

NOTE ON EXPANSION OPERATORS (INVERSE/REVERSE TONE MAPPING):
The majority of EOs require having as input LDR images in the range [0,1] that are LINEARIZED. This means that the camera response function (CRF) or the gamma encoding has been removed. This operation is MANDATORY in order to generate fair comparisons; please do use the gammaRemoval parameter to remove gamma if you do not have the CRF of the input image. RAW files do not require this step because they are already linear.

REFERENCE:
Please reference the book in your work or papers if you use this toolbox:
@book{Banterle:2017,
author = {Banterle, Francesco and Artusi, Alessandro and Debattista, Kurt and Chalmers, Alan},
title = {Advanced High Dynamic Range Imaging (2nd Edition)},
year = {2017},
month={July},
isbn = {9781498706940},
publisher = {AK Peters (CRC Press)},
address = {Natick, MA, USA},
}

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serg3y/MatLab-digiCamControl

DSLR Camera Controller (MatLab wrapper for digiCamControl)

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Jorsorokin/SingularSpectrum

Singular spectrum analysis (SSA) is a non-parametric spectral decomposition technique for time series, akin to fourier or wavelet analysis, in which a time-series is decomposed into a time-frequency matrix. However, SSA does not rely on strict parametric forms and is able to pull out non-stationary and complex components from time-series in a data-dependent manner. Please refer to the documentation in the methods of SSA.m for details.