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This paper presents ARGO, a smart eyewear platform for on-device machine learning that integrates a multimodal sensor suite and an optimized YOLOv11 model for real-time urban obstacle recognition, achieving privacy-preserving local processing with low latency and a memory footprint of 2.483 MB.
Proposes a lightweight neural architecture search performed directly on the deployment device for near-sensor computing, validated on sEMG sign language and fault diagnosis datasets, achieving improved accuracy and reduced RAM occupancy.
This paper presents an AI-driven framework for energy-efficient environmental monitoring in smart cities using edge intelligence and TinyML, which dynamically activates sensors based on spatiotemporal conditions to reduce energy consumption and extend sensor lifespan.