Yuyang Sha
Papers
1
Total Citations
11
H-Index
1
About
Yuyang Sha is a rising researcher in the fields of edge computing, lightweight deep learning, and intelligent consumer electronics. His work focuses on making advanced convolutional neural networks (CNNs) practical for resource-constrained devices such as smartphones and educational hardware. Sha’s most cited paper, "A Novel Fusion Pruning-Processed Lightweight CNN for Local Object Recognition on Resource-Constrained Devices" (2024, 11 citations), introduces an innovative fusion pruning technique that significantly reduces model size and computational load without sacrificing accuracy. This contribution addresses a critical bottleneck in deploying AI on everyday devices, enabling real-time local object recognition without cloud dependency. By bridging the gap between high-performance deep learning and limited hardware, Sha’s research has immediate implications for smart education tools, mobile applications, and the broader Internet of Things ecosystem. His work is gaining traction among engineers and academics seeking efficient, deployable AI solutions, marking him as a promising voice in the push toward accessible, on-device intelligence.
Research Focus
Key Achievements
Top Papers
- 1