"Science is not so much to obtain new facts as to discover new ways of thinking about them." William Bragg (1862-1942)
Dr. Mohammed R. AL-MULLA
Born: Lebanon, Beirut.
Office Location: My
office is located in 31 KH on the Second Floor, room number 25b. (My office hours)
Interest: Pervasive computing, Signal analysis, AI, Data mining, Evolutionary computation, Localized Muscle Fatigue.
Mohammed R. Al-mulla finished his PhD from the School of Computer Science and Electronic Engineering at University of Essex (UK) . He received his first MSc in distributed systems at Napier University in Edinburgh and his second MSc was in pervasive computing from the University of Essex. He also has a Bachelor of Science obtained at Heriot-Watt University in Edinburgh .
Mohammed is interested in signal analysis, particularly in the biomedical field. In his research he uses a range of tools such as evolutionary computation, Fractals, Wavelets etc.
Mohammed R. Al-mulla's professional experience includes working as a teaching assistant from 2000 - 2005 in the Department of Computer Science, Kuwait University. He was also teaching at the School of Computer Science and Electronic Engineering at the University of Essex from 2006 to 2011. Mohammed is also the co-founder of the PhD CEEC conference series that was founded in 2009. Mohammed is currently an assistant professor in the Computer Science Department at Kuwait University, where he is also the coordinator of the MCC unit. Mohammed is also currently collaborating with Essex university research center to complete a funded project on wearable sports monitoring system.
Ph.D. Pervasive computing, Essex University, UK. (2006 - 2011)
MSc. Pervasive computing, Essex University, UK, (2006).
MSc. Distributed systems, Napier University, Edinburgh, UK ,(1998).
B.Sc. Computer science, Heriot Watt University, Edinburgh, UK, (1997).
2011- Assistant Professor, Department of Computer Science, Kuwait University.
2011- MCC Coordinator, Faculty of science, Kuwait University.
2000-2005 Teaching Assistant, Department of Computer Science, Kuwait University.
Wearable Sports Monitoring System (Funded by Essex University).
MDPI Journals (Computers).Editorial board member
WOLSIE 2012 : Workshop On Large Scale Intelligent Environments Organizing Committee member.
Journal of Medical Systems. Special issue, BAN for Healthcare Applications. Guest Editor
International Journal of Ubiquitous Computing (IJUC). Editorial board member
Lancaster University - Advanced Data mining course February 2009.
Mathworks- Advanced Matlab Training 2008.
GRADschool Training course for Team build activities, University of Essex (United Kingdom), July 2008.
Graduate Teaching Assistant Training, University of Essex (United Kingdom), March 2007.
Initial Research Training for PhD Studies, University of Essex (United Kingdom), December 2006.
(TBA ) Pervasive Computing
The Engineer magazine (2011),”Muscle-fatigue sensor could help athletes train harder”
1. M. R. Al-Mulla, Optimized Pseudo-Wavelet Function to decompose Surface Mechanomyogram signals towards Automated Classification of Localized Muscle Fatigue.(Under review)
2. M. R. Al-Mulla, F. Sepulveda and M. Colley (2010),Evolved Wavelet Function to Optimally Decompose sEMG for Classification of Localized Muscle Fatigue. Elsevier.Medical Engineering & Physics. Impact Factor 2.113.
3. M. R. Al-Mulla, F. Sepulveda. (2010), "Novel Feature Modeling the Prediction and Detection of sEMG Muscle Fatigue towards an Automated Wearable System."
Sensors10, no. 5: 4838-4854. Impact Factor 1.917.
4. M. R. Al-Mulla, F. Sepulveda and M. Colley (2011),An autonomous wearable system for predicting and detecting localized muscle fatigue.Sensors2011,11(2), 1542-1557; doi:10.3390/s110201542. Impact Factor 1.917.
5. M. R. Al-Mulla, F. Sepulveda and M. Colley(2011),An Evolved Feature to Optimally Classify Localised Muscle Fatigue during Dynamic Contractions Towards a Wearable Autonomous Fatigue Prediction System.(Under review).
6. M. R. Al-Mulla, F. Sepulveda and M. Colley(2011), Review paper, A Review of Non-Invasive Techniques to Detect and Predict Localised Muscle Fatigue. Sensors in Biomechanics and Biomedicine, Impact Factor 1.917.
1. M. R. Al-Mulla, F. Sepulveda and M. Colley (2011),’sEMG based Techniques to Detect and Predict Localised Muscle Fatigue . Electromyography, Mark Schwartz (Ed.), InTech, ISBN 978-953-307-1010-5.
1. M. R. Al-Mulla (2012). “Evolutionary Computation Extracts a Super sEMG Feature to Classify Localized Muscle Fatigue During Dynamic Contractions”, CEEC 2012, 4th Computer science and Electronic Engineering Conference 12th-13th September 2012, University of Essex. “In press”
2. James Dooley , Matthew Ball, M. R. Al-Mulla (2012),“Beyond Four Walls: Towards Large-Scale Intelligent Environments”, WOLSIE 2012 : Workshop On Large Scale Intelligent Environments, Guanajuato, Mexico.
Mulla, F. Sepulveda (2010), Predicting the time to localized muscle fatigue using ANN and evolved sEMG feature.IEEE International Conference¨ on Autonomous and Intelligent Systems (AIS 2010),Povoa de Varzim, Portugal.M. R. Al-
4. M. R. Al-Mulla, F. Sepulveda (2010),Novel Feature Assisting in the Prediction of sEMG Muscle Fatigue Towards Wearable Autonomous System.6th IEEE International Mixed-Signals, Sensors, and Systems Test Workshop" (IMS3TW'10), France.
5. M. R. Al-Mulla, F. Sepulveda, M. Colley, F. Al-Mulla (2009) 'Statistical Class Separation using sEMG Features Towards Automated Muscle Fatigue Detection and Prediction". CISP'09/BMEI'09, The 2nd International Conference IEEE, EMBS on Image and Signal Processing and The 2nd International Conference on Biomedical Engineering and Informatics, Tianjin, Pages 4469-4473.
6. M. R. Al-Mulla, F. Sepulveda, M. Colley and A. Kattan (2009), "Classification of localized muscle fatigue with Genetic Programming on sEMG during isometric contraction". 31st IEEE Engineering in Medicine and Biology Conference, Minneapolis, Pages 2633-2638.
7. Ahmed Kattan, M. R. AL-Mulla, Francisco Sepulveda and Riccardo Poli, Detecting Localised Muscle Fatigue during Isometric Contraction using Genetic Programming, International Conference on Evolutionary Computation, IJCCI Springer, Pages 292-296, Madeira“ Portugal, 2009.
8. M.R. Al-Mulla, Myoelectric Signal Analysis on localised Muscle to detect muscle fatigue during sustained isometric contraction, Master's Thesis, University of Essex,UK. 2006.