Ali Mohammad Alqudah

Papers

Total Citations

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About

Ali Mohammad Alqudah is a researcher whose work sits at the intersection of computer vision, pattern recognition, and sensor-based localization. His primary research areas include handwritten numeral recognition using deep learning and 3D object localization with ultrasonic sensor arrays. Alqudah’s most cited paper, "Recognition of Handwritten Arabic and Hindi Numerals Using Convolutional Neural Networks" (2021, 22 citations), addresses the critical challenge of automating the detection and classification of handwritten numerals—a task with broad applications in document processing, banking, and postal automation. This work demonstrates his ability to apply convolutional neural networks to culturally significant script recognition problems. In a different vein, his paper "Triad system for object's 3D localization using low-resolution 2D ultrasonic sensor array" (2020) explores innovative approaches to 3D localization, which has important implications for collision avoidance systems, robotic guidance, and autonomous navigation. While still early in his career, Alqudah’s contributions bridge traditional sensor-based methods with modern deep learning techniques, showcasing a versatile skill set. His work is particularly valuable for researchers interested in the intersection of embedded systems and artificial intelligence for real-world automation tasks.