MANJU MOHAN
RL Automotive, Aston University, Hindustan Institute of Technology and Science
About
Embedded Systems Engineer and former KTP Associate with experience in applied machine learning, optimisation, and signal-processing methods onto real-world sensing and automation problems. Strong record of peer-reviewed publications and cross-disciplinary collaboration across non-destructive evaluation (NDE), structural health monitoring, embedded systems, machine vision, and robotics. Experienced in interdisciplinary delivery and producing publishable research outputs, with a strong background in higher-education teaching support including content development, assessment support, learner progress monitoring, and management of technical laboratories and learning resources.
Employment
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RL Automotive Embedded System Engineer2024 - Present
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Aston University Embedded System Engineer2024 - 2024
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Hindustan Institute of Technology and Science Assistant Professor2015 - 2023
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Hindustan Institute of Technology and Science Lecturer2009 - 2011
Education
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Hindustan Institute of Technology and Science Ph. D2015 - 2023
Projects & Funding
Projects & funding information is unavailable.
Publications (18)
- Project-based learning and its influence on teaching and learning in the first-year core mechatronic engineering course Save
- Design and Development of IoT-Based Smart Tea Vending Machine Save
- Design and development of knee rehabilitation robot Save
- Temperature measurement of overlapped fiber Bragg grating sensors in a series fiber sensor network Save
- A Generalized Classification Framework with Simultaneous Feature Weighting and Selection Using Antlion Optimization Algorithm Save
- Design and evaluation of various knee assistive mechanisms for rehabilitation Save
- Design and analysis of a tail sitter (VTOL) UAV composite wing Save
- Classification of process conditions in martensitic stainless steel: A machine learning approach on magnetic Barkhausen emission signals Save
- Weed identification using KNN algorithm for paddy field based on machine vision Save
- A machine learning approach to nonlinear ultrasonics for classifying annealing conditions in austenitic stainless steel Save