Prof. Dr. Mahmoud Moghavvemi
Department Of Electrical Engineering
Faculty Of Engineering
mahmoud@um.edu.my| View CV | |
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| Biography | |
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Professor Dr. Mahmoud Moghavvemi received the Bsc. ,MEngSc. and PhD from State University of New York at Buffalo New York, University of Bridgeport Connecticut USA and University of Malaya Malaysia. He worked as test and design engineer for CTI Electronics USA and director of Informatics school of engineering prior to joining Department of Electrical Engineering of University of Malaya where he is currently a full Professor. His contributions can be seen in more than 30 patents, more than 200 refereed journals and conferences articles and many post graduate students. Professor Mahmoud Moghavvemi is the reviewer for several distinguished journals in his field of expertise. He is the founder and currently the director of the center for Research in Applied Electronics (CRAE). |
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Publication
Finance
| Grant | Progress | Status |
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| GPF059A-2018 |
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new |
| FP003-2014B |
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end |
| RU004Q-2016 |
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end |
| PG094-2012B |
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end |
| TA028-2012A |
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end |
| This information is generated from Research Grant Management System | ||
Investigation of the CAEP Components in Different Human Ethnic Groups Based On Their Native Languages
A Novel approach for classifying human ethnicity according to the auditory brain responses
The Past, Present and Prospective on UV-VIS-NIR Skin Photonics and Spectroscopy - A Wavelength Guide
A New Surgical Robotic System Model for Neuro-endoscopic Surgery
Multi-Hole Waveguide directional couplers
Advanced Microwave circuits and systems, Electromagnetic solutions for the agricultural problems
Behavior of electromagnetic in different Media and structures", Exposure to EMF
Electromagnetic solution to various pest problem in agriculture
Simulation of a Three-links Planar Redundant Manipulator for Obstacle Avoidance Using Neural Networks
Sinusoidal Input Describing Function Analysis for Coupled Tanks System
Modelling of Human Cardiovascular System in Ventricular Assist Device Simulation
Improved Architecture of the Feedforward Neural Network for Image Recognition