TinyML Empowered Transfer Learning on the Edge
Author:
Affiliation:
1. Department of Electrical Engineering, Faculty of Engineering, The Hashemite University, Zarqa, Jordan
2. School of Electronic and Electrical Engineering, University of Leeds, Leeds, U.K
Funder
Engineering and Physical Sciences Research Council (EPSRC) CHEDDAR
UK Research and Innovation
MSCA Horizon EU
Abdul Hameed Shoman Foundation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/8782661/10362961/10459233.pdf?arnumber=10459233
Reference53 articles.
1. Human Action Recognition From Various Data Modalities: A Review
2. Human Activity Recognition With Smartphone and Wearable Sensors Using Deep Learning Techniques: A Review
3. AHAR: Adaptive CNN for Energy-Efficient Human Activity Recognition in Low-Power Edge Devices
4. A Joint Energy and Latency Framework for Transfer Learning Over 5G Industrial Edge Networks
5. Unlocking Edge Intelligence Through Tiny Machine Learning (TinyML)
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