M
Mohammad S. Alam
Researcher at Texas A&M University–Kingsville
Publications - 499
Citations - 7269
Mohammad S. Alam is an academic researcher from Texas A&M University–Kingsville. The author has contributed to research in topics: Image processing & Hyperspectral imaging. The author has an hindex of 39, co-authored 452 publications receiving 6613 citations. Previous affiliations of Mohammad S. Alam include University of Dayton & University of South Alabama.
Papers
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Modeling, control and simulation of a PV/FC/UC based hybrid power generation system for stand-alone applications
TL;DR: In this paper, the integration of photovoltaic (PV), fuel cell (FC) and ultra-capacitor (UC) systems for sustained power generation is discussed.
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Dynamic modeling, design, and simulation of a combined PEM fuel cell and ultracapacitor system for stand-alone residential applications
Mehmet Uzunoglu,Mohammad S. Alam +1 more
TL;DR: In this article, a new dynamic model and design methodology for an FC and ultracapacitor-based energy source for stand-alone residential applications has been developed using MATLAB, Simulink and SimPowerSystems environments based on the mathematical and dynamic electrical models developed for the proposed system.
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A dynamic model for a stand-alone PEM fuel cell power plant for residential applications
TL;DR: In this paper, a dynamic electrochemical simulation model of a grid independent proton exchange membrane (PEM) fuel cell power plant is presented, which includes the methanol reformer, the PEM stack, and the power conditioning unit.
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Dynamic modeling, design and simulation of a wind/fuel cell/ultra-capacitor-based hybrid power generation system
TL;DR: In this article, the authors proposed a dynamic model, design and simulation of a wind/FC/UC hybrid power generation system with power flow controllers, where when the wind speed is sufficient, the wind turbine can meet the load demand while feeding the electrolyzer.
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Fringe-adjusted joint transform correlation.
TL;DR: Improved correlation discrimination is achieved by using a fringe-adjusted joint transform correlator (JTC) found to yield significantly better correlation output than the classical and binary JTC's for input scenes involving single as well as multiple objects.