Mohammad S. Alam

What is he best known for?

The fields of study he is best known for:

• Artificial intelligence
• Computer vision
• Optics

His scientific interests lie mostly in Automotive engineering, Proton exchange membrane fuel cell, Power station, Hybrid system and Power control. The Automotive engineering study combines topics in areas such as Transient and Electrical engineering. His Transient research incorporates themes from Control engineering, Peak demand and Efficient energy use.

His Power station research is multidisciplinary, incorporating elements of MATLAB, AC power, Mathematical optimization and Evolutionary programming. His Hybrid system study integrates concerns from other disciplines, such as Hybrid power, Electronic engineering and Electric power system. His study looks at the relationship between Electric power system and fields such as Wavelet, as well as how they intersect with chemical problems.

His most cited work include:

• Dynamic modeling, design, and simulation of a combined PEM fuel cell and ultracapacitor system for stand-alone residential applications (276 citations)
• Modeling, control and simulation of a PV/FC/UC based hybrid power generation system for stand-alone applications (273 citations)
• A dynamic model for a stand-alone PEM fuel cell power plant for residential applications (242 citations)

What are the main themes of his work throughout his whole career to date?

Mohammad S. Alam spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Optics. Artificial intelligence and Fourier transform are commonly linked in his work. He combines subjects such as Apodization, Spectral density, Wavelet and Optical filter with his study of Fourier transform.

Mohammad S. Alam has included themes like Clutter and Hyperspectral imaging in his Computer vision study. His work deals with themes such as Artificial neural network, Object detection, Facial recognition system and Distortion, which intersect with Pattern recognition. His work carried out in the field of Algorithm brings together such families of science as Phase, Subtraction, Arithmetic, Noise and Encryption.

He most often published in these fields:

• Artificial intelligence (48.21%)
• Computer vision (36.90%)
• Pattern recognition (23.81%)

What were the highlights of his more recent work (between 2012-2019)?

• Artificial intelligence (48.21%)
• Computer vision (36.90%)
• Pattern recognition (23.81%)

In recent papers he was focusing on the following fields of study:

His primary scientific interests are in Artificial intelligence, Computer vision, Pattern recognition, Hyperspectral imaging and Pattern recognition. The concepts of his Computer vision study are interwoven with issues in Clutter and Joint. His Pattern recognition research includes themes of Preprocessor and Filter.

The various areas that he examines in his Hyperspectral imaging study include Spectral signature and Receiver operating characteristic. His Tracking research integrates issues from Object and Image processing. His Filter study combines topics in areas such as Algorithm, Fourier transform, Spectral density and Phase correlation.

Between 2012 and 2019, his most popular works were:

• Correlation based efficient face recognition and color change detection (11 citations)
• Optimal intermittent dose schedules for chemotherapy using genetic algorithm (10 citations)
• Self-Organizing Hierarchical Particle Swarm Optimization of Correlation Filters for Object Recognition (9 citations)

In his most recent research, the most cited papers focused on:

• Artificial intelligence
• Computer vision
• Optics

His primary areas of study are Artificial intelligence, Computer vision, Optics, Pattern recognition and Tracking. His Artificial intelligence study incorporates themes from Algorithm and Nonlinear system. His studies deal with areas such as Clutter, Distortion, Metric and Cluster analysis as well as Algorithm.

His Computer vision research is multidisciplinary, incorporating perspectives in Fourier transform and Matched filter. His study in the field of Bandwidth, Circular polarization and Wideband also crosses realms of Motion detection. While the research belongs to areas of Pattern recognition, Mohammad S. Alam spends his time largely on the problem of Filter, intersecting his research to questions surrounding Infrared, Forward looking infrared, Pattern recognition, Cognitive neuroscience of visual object recognition and Particle swarm optimization.