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Mohammed S. Al-Rawi


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Group (before 2015) Signal Processing Laboratory
Transverse activity (before 2015) Transverse Activity on Innovative Biomedical Technologies
Status Former member
Country Portugal
Former PhD students
Former masters students
Past projects
Events CAIP2011, ECCB12, ENS 2011, HBM 2011, ICIAR 2012, ICIP 2012, ISI proceedings
Proposals GOLD - Genome Wide Association, PermuTest
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My current research is a mix between pattern recognition and neuroimaging.The purpose is using machine learning and pattern recognition algorithms on functional magnetic resonance imaging data to decode brain states, and/or analyzing structural brain data. I am also interested in developing computer vision techniques to detect 2D and 3D objects, applying evolutionary algorithms, and developing/using permutation testing methods. Besides music, (soccer) video games, scientific research is my other favorite pastime.


- Functional and Structural (human) Brain Analysis 
- Medical Image Processing
- Multi-voxel Pattern Analysis
- 2D and 3D object recognition, invariant to affine and rigid transformations
- Pseudo Zernike and Zernike Moments
- Multi-objective Optimization using Evolutionary Algorithms / Genetic algorithms
- Higher Order Neural Networks
- Pattern Classification
- Machine Learning
- Diabetic Retinopathy
- Genome wide association studies
- Music Generation via Computer Algorithms
- Iris Recognition and Clustering
- Permutation Testing


IEEE Signal Processing Society (SPS)

Human Brain Mapping (2010)

Other links

Contribution to Open Source

Which contains:

1- Armitage’s Trend Test, usually used in genome-wide association studies (GWAS)
2- Finding SNP markers contingency tables 
3- Fast generation of Zernike radial polynomials via q-recursion

4- Fast generation of pseudo Zernike radial polynomials via p-recursion

5- Kintner's method to generate Zernike radial polynomials

6- Prata's method  to generate Zernike radial polynomials

7-  2D, 3D, ND Trapezoidal integrals

Matlab versus C/C++ language, which one is faster?

To test for the speed issue, and how fast each environment is, I computed the so called pseudo Zernike radial polynomials for 1 milion r values, up to the order 99, using an efficient method widely known as the p-recursive.

Running the C++ executable in release mode:

[alrawi@srv Release]$Pzmp                                              
Number of r values is: 1000,000 (1M)
execution time (s) of Pseudo Zernike Polynomials values is: 3.25 seconds
[alrawi@srv Release]$

As for matlab, the execution for the same size problem, by using vectorized technique, was nearly twice as that of the C function.
>> tic; R = pseudo_zernike_polynomials_p_recursive(99,0, x); toc
Elapsed time is 6.589477 seconds.

Result: For the above task, C is twice faster than matlab.



Articles in international journals listed in the ISI

Other articles in journals

Chapters in books

Articles in conference proceedings


Visitors map.