Personal profile
Research profile
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.
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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SDG 3 Good Health and Well-being
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Projects
- 1 Finished
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TTW P16-25 (Project 1): Deep Learning as a Service (DLaaS)
Stuijk, S. (Project Manager), Hosseinibijikola, S. (Project member), van der Hagen, D. (Project communication officer), de Mol-Regels, M. (Project communication officer) & Karami, S. (Project member)
1/01/19 → 31/12/23
Project: Research direct
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Real-time Print Anomaly Detection Using Deep Neural Networks
Karami, M. H., 9 Nov 2023, Eindhoven: Technische Universiteit Eindhoven.Research output: Thesis › EngD Thesis
Open AccessFile41 Downloads (Pure) -
A Deep Convolutional Neural Network for Melanoma Recognition in Dermoscopy Images
Haghighi, S. N., Danyali, H., Helfroush, M. S. & Karami, M. H., 31 Dec 2020, 2020 10th International Conference on Computer and Knowledge Engineering (ICCKE). Institute of Electrical and Electronics Engineers, p. 453-456 4 p. 9303684Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
4 Link opens in a new tab Citations (Scopus) -
An Entanglement-Inspired Action Selection and Knowledge Sharing Scheme for Cooperative Multi-agent Q-Learning Algorithm used in Robot Navigation
Karami, M. H., Aghababa, H. & Keyhanipour, A. H., 31 Dec 2020, 2020 10th International Conference on Computer and Knowledge Engineering, ICCKE 2020. Institute of Electrical and Electronics Engineers, p. 617-622 6 p. 9303636Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic
1 Link opens in a new tab Citation (Scopus)
Press/Media
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National Research Council of Canada Reports Findings in Computers (Real-time simulation of viscoelastic tissue behavior with physics-guided deep learning)
12/01/23
1 item of Media coverage
Press/Media: Expert Comment