Tagir Fabarisov
Papers
2
Total Citations
6
H-Index
2
About
Tagir Fabarisov’s research sits at the intersection of cyber-physical systems (CPS), reliability engineering, and deep learning, with a focus on making safety-critical autonomous systems more robust. His most cited work introduces a deep learning-based error mitigation strategy for assistive exoskeletons, designed specifically for computational-resource-limited platforms and edge Tensor Processing Units—a practical solution for real-time fault tolerance in wearable robotics. This paper has garnered 4 citations, reflecting early interest in his novel approach to embedding AI-driven resilience into embedded systems. Fabarisov also pioneered an automated model-based reliability assessment framework for Software-Defined Manufacturing (SDM), addressing the growing complexity of software-intensive production systems and digital twins. This work, with 2 citations, tackles the challenge of frequent software updates in safety-critical environments. A key achievement is his development of FIBlock, a highly customizable Simulink block for model-based fault injection, enabling systematic testing of sensor, computing, and network faults in heterogeneous CPS components. Fabarisov’s contributions are particularly valuable for students and researchers exploring the integration of deep learning with reliability engineering in resource-constrained, edge-computing contexts.
Research Focus
Key Achievements
Top Papers
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- 2