Nirali Patel
Papers
1
Total Citations
7
H-Index
1
About
Nirali Patel’s research lies at the intersection of embedded systems and computer vision, with a focus on enabling real-time visual intelligence on resource-constrained devices. Her most-cited work, “A Hardware/Software Co-Design Approach for Real-Time Object Detection and Tracking on Embedded Devices” (2018, 7 citations), addresses a critical challenge in embedded vision: balancing algorithmic accuracy with the limited processing power and energy budgets of platforms like mobile robots, surveillance cameras, and industrial sensors. Patel proposes a co-design methodology that partitions object detection and tracking tasks between hardware accelerators and software routines, achieving real-time performance without sacrificing reliability. This approach is foundational for applications ranging from autonomous navigation to smart manufacturing, where low-latency visual feedback is essential. While her citation count is modest, her contributions are notable for their practical, systems-level perspective—bridging the gap between theoretical vision algorithms and deployable embedded solutions. Patel’s work is particularly valuable for students and engineers seeking to understand how to implement robust computer vision on edge devices, making her a key voice in the growing field of embedded AI.
Research Focus
Key Achievements
Top Papers
- 1