Muhammad Tayyab

Université Joseph Fourier, Taylor's University, Centre National de la Recherche Scientifique

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About

Muhammad Tayyab’s research bridges computational neuroscience, image processing, and sustainable manufacturing. His early work focused on modeling biological vision mechanisms—specifically lateral inhibition in neural networks—to develop contrast and contour operators for image processing and robotics. His 2009 paper on “Understanding Physiological and Degenerative Natural Vision Mechanisms” (11 citations) introduced dynamical flows for image contrasting, while his related work on retinal degenerative disease modeling (3 citations) applied Markov random fields to neural network simulations. These contributions demonstrate how biological principles can inspire robust computational algorithms. More recently, Tayyab has pivoted toward Industry 4.0 and sustainability, co-authoring “Smart Factories Greener Future” (2024, 6 citations), which explores how smart manufacturing technologies can drive environmental goals. He also designed an autonomous SLAM-based forklift robot (2017, 2 citations) for dynamic warehouse environments, showcasing practical robotics applications. With a career spanning bio-inspired computing, medical imaging, and green manufacturing, Tayyab exemplifies interdisciplinary research that translates neural mechanisms into real-world technologies, from vision systems to autonomous logistics.