Breaking the Performance Gap of Fully and Semisupervised Learning in Electromagnetic Signature Recognition
Author:
Affiliation:
1. School of Electrical and Information Engineering, Tianjin University, Tianjin, China
2. Department of Electrical and Electronics Engineering, Kadir Has University, Istanbul, Turkey
Funder
National Natural Science Foundation of China
Key Research and Development Program of Tibet Autonomous Region
Science and Technology Major Project of Tibet Autonomous Region
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing
Link
http://xplorestaging.ieee.org/ielx7/6488907/10384482/10184115.pdf?arnumber=10184115
Reference64 articles.
1. TT-SVD: An Efficient Sparse Decision-Making Model With Two-Way Trust Recommendation in the AI-Enabled IoT Systems
2. Device Fingerprinting in Wireless Networks: Challenges and Opportunities
3. Joint Motion Classification and Person Identification via Multitask Learning for Smart Homes
4. Survey of automatic modulation classification techniques: classical approaches and new trends
5. Cognitive radio networking and communications: an overview
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