Mohammad Karami is a Professional Doctorate In Engineering (PDEng) Candidate at the Technical University of Eindhoven, Department of Electrical Engeenirg, Electronic System group. He is mainly focused on developing a print artifact detection system based on Deep Learning Anomaly Detection (DLAD) methods. He aims to develop a highly-accurate optimized deep learning artifact detection system that can perform in real-time, assuring that any visible artifacts within the prints are detected, and the compensation process conducted before the print leaves the printer. His project covers the research and design process from the high level of abstraction to the low level of abstraction.
An Entanglement-Inspired Action Selection and Knowledge Sharing Scheme for Cooperative Multi-agent Q-Learning Algorithm used in Robot Navigation(2020)
A Deep Convolutional Neural Network for Melanoma Recognition in Dermoscopy Images(2020)
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