Trisha Das Mou
Papers
2
Total Citations
14
H-Index
2
About
Trisha Das Mou is a rising researcher whose work bridges robotics and computer vision, with a focus on practical, low-cost solutions for real-world challenges. Her most cited paper, “A Dynamic Approach to Low-Cost Design, Development, and Computational Simulation of a 12DoF Quadruped Robot” (2023, 10 citations), addresses the prohibitive cost and complexity of legged robots. By introducing a dynamic, affordable design and simulation framework, she opens doors for applications in education, assistance, security, and surveillance—fields long interested in legged robotics but hindered by expense. In parallel, her work on “Multi-Range Sequential Learning Based Dark Image Enhancement with Color Upgradation” (2023, 4 citations) tackles a critical problem in low-light imaging: noise, blurring, and poor contrast that impede object detection. She proposes a novel convolutional neural network integrating a dual attention unit and selective kernel feature synthesis, advancing image enhancement for robust visual systems. Together, these contributions showcase her ability to innovate across domains—making sophisticated technology more accessible while improving perception under adverse conditions. Trisha’s research is particularly valuable for students and engineers seeking cost-effective, high-impact solutions in autonomous systems and image processing.
Research Focus
Key Achievements
Top Papers
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- 2