The MATLAB Astronomy & Astrophysics Toolbox (MAAT) is a collection of functions and classes for astronomy and astrophysics experimental and theoretical research.
The toolbox is organized in several packages and sub packages that allow easy-to-navigate platform, as well as several "container" classes (e.g., image, catalog, time containers) with their own functions (methods), as well as static classes. The toolbox contains detailed documentation and examples, as well as a detailed help section for each function (see documentation).
The current MAAT reference is Ofek (2014).
MAAT documentation is available as part of the MAAT distribution, in the +manual package. Online documentation is available at: https://webhome.weizmann.ac.il/home/eofek/matlab/
The MAAT toolbox has functions and classes covering the following topics:
* Documentation: A package containing live documents (documentation with example code) covering many aspects of this toolbox with examples (see documentation).
* Astronomical image processing: A complete set of functions to reduce and analyze astronomical images, from bias removal to image subtraction, source extraction and PSF photometry.
* Astronomical spectra processing: A set of functions to reduce and analyze astronomical spectral data.
* Astronomical catalogs and images access, search and manipulation: Tools to store and manipulate astronomical catalogs as well as functions for fast access of astronomical catalogs stored locally, and access of external image databases and catalogs (e.g., VizieR, MAST, IRSA, SDSS, Chandra).
* Astronomical spectra access, fitting and manipulation: Manipulation, matching and fitting of astronomical spectra and astronomical filters. Including a local database of spectral templates.
* Time series analysis : Tools for time series analysis.
* Celestial coordinates, ephemeris and time: Large number of functions for celestial coordinates, time, celestial mechanics, and ephemeris.
* Fitting and statistics utilities: Tools for statistics, data analysis and signal processing.
* Cosmology: Functions for cosmology.
* Binaries: Binary stars orbit fitting and eclipsing binary.
* Occultations: Diffractive stellar occultations.
* GRB: Gamma Ray Bursts related functions.
* Supernovae: Shock cooling, radioactive decay and tools for SN research. Stars and galaxies:
* General utilities: Astronomical constants, units conversions, strings manipulation, files and IO related functions, and functions for manipulating matlab objects.
* ds9 control: Full interaction with ds9 image viewer, including easy creation and display of region files, imexam-like functionality, and interaction with the image processing tools.
* FITS images manipulation: Additional functions (to those available as part of the matlab CFITSIO library) to read, write and manipulate FITS files.
* Telescope optics: Signal-to-noise calculation, zernikie polynomials, scintillation and wavefront simulations, and telescope control.
* Plotting: Additional functions for plotting.
* WWW access: Powerful functions for world wide web downloading and searching.
Eran (2021). MATLAB Astronomy & Astrophysics Toolbox (MAAT) (https://github.com/EranOfek/MAAT), GitHub. Retrieved .
If you are using this code or products in your scientific publication please give a reference to Ofek (2014; ascl.soft 07005).
Contact for code and support firstname.lastname@example.org
Whatsapp +91 9464894829
3. Image encryption by generating Halton sequences
5. Text-Encryption Matlab code for AES,DES,Hybrid AES-DES and AES w/ chaos
6. Image encryption and decryption using chaotic key sequence generated by sequence of logistic map and sequence of states of Linear Feedback Shift Register
7. A New Approach of Image Encryption Using 3D Chaotic Map to Enhance Security of Multimedia Component
8. MATLAB was used for the implementation of Chaotic Digital Image Encryption.
10. Cryptanalyzing an Image Scrambling Encryption Algorithm of Pixel Bits.
11. Novel Image Compression encryption hybrid algorithm based on a key-controlled measurement matrix in compressive sensing.
12. An Image Encryption Scheme Based on a Hybrid Model of DNA Computing, Chaotic Systems and Hash Functions.
13. Colour image encryption algorithm combining Arnold map, DNA sequence operation, and a Mandelbrot set.
14. Advanced Encryption Standard
16. Image Encryption and Decryption Using Logistic Map Equation and Linear Feedback Shift.
17. A simple Matlab implementation of the algorithm presented in the paper: "Reversible-data-hiding-in-Encrypted-image"
17. Image encryption and encoding methods
19. DES 64bit Encryption and Decryption
20. Matlab project on blind digital watermarking and encryption.
21. Encrypting an image using Salient Object Detection and K-Means Clustering.
22. Recurrent Scale Approximation for Object Detection in CNN.
23. Object detection via a multi-region & semantic segmentation-aware CNN model.
24. R-FCN: Object Detection via Region-based Fully Convolutional Networks
25. Adversarial Examples for Semantic Segmentation and Object Detection.
26. Object Detection in Videos toolkit for VisDrone2019
27. Computational biology and medical image processing scripts and programs.
28. A MATLAB library/toolbox providing access to image registration suitable for use with medical images.
29. Lung medical image analysis and visualisation software for Matlab.
30. Medical ultrasound image processing.Carotid ultrasoung segmentation using RF data.
31. A Phase Congruency and Local Laplacian Energy Based Multi-Modality Medical Image Fusion Method in NSCT Domain.
32. Automatic tool for landmark localisation in 3D medical images.
34. Recognizing and Refining the location of Individual Vessels in Segmented Retinal Images.
35. Image segmentation method on medical image is provided and tested.
36. 3D non-rigid image registration for medical and synthetic images using truncated hierarchical B-splines (THB-Splines).
37. Laplacian Re-Decomposition for Multimodal Medical Image Fusion[J]. IEEE Transactions on Instrumentation and Measurement, 2020.
38. Medical software for Processing multi-Parametric images Pipelines.
39. Prostate cancer segmentation based on MRI and PET images.
40. Image segmentation methods for biomedical purposes such as cell segmentation, blood vessel segmentation (eye blood vessels), and segmentation of brain tumors.
41. Medical Image Analysis Breast Cancer Lesion Detection.
42. Medical image enhancement based on nonlinear technique and logarithmic transform coefficient histogram matching.
43. Machine Learning: A Bayesian and Optimization Perspective.
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