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- Title
Effects of RF Signal Eventization Encoding on Device Classification Performance.
- Authors
Smith, Michael J.; Temple, Michael A.; Dean, James W.
- Abstract
The results of first-step research activity are presented for realizing an envisioned "event radio" capability that mimics neuromorphic event-based camera processing. The energy efficiency of neuromorphic processing is orders of magnitude higher than traditional von Neumann-based processing and is realized through synergistic design of brain-inspired software and hardware computing elements. Relative to event-based cameras, the development of event-based hardware devices supporting Radio Frequency (RF) applications is severely lagging and considerable interest remains in obtaining neuromorphic efficiency through event-based RF signal processing. In the Operational Technology (OT) protection arena, this includes efficient software computing capability to provide reliable device classification. A Random Forest (RndF) classifier is considered here as a reliable precursor to obtaining Spiking Neural Network (SNN) benefits. Both 1D and 2D eventized RF fingerprints are generated for bursts from NDev = 8 WirelessHART devices. Average correct classification (%C) results show that 2D fingerprinting is best overall using detected events in burst Gabor transform responses. This includes %C ≥ 90% under multiple access interference conditions using an average of NEPB ≥ 400 detected events per burst. This is sufficiently promising to motivate next-step activity aimed at (1) reducing fingerprint dimensionality and minimizing the required computational resources, and (2) transitioning to a neuromorphic-friendly SNN classifier—two significant steps toward developing the necessary computing elements to achieve the full benefits of neuromorphic processing in the envisioned RF event radio.
- Subjects
VON Neumann, John, 1903-1957; RADIO frequency; GABOR transforms; HUMAN fingerprints; BIOLOGICALLY inspired computing; DESIGN software; RANDOM forest algorithms; CLASSIFICATION; SIGNAL processing
- Publication
Electronics (2079-9292), 2024, Vol 13, Issue 11, p2020
- ISSN
2079-9292
- Publication type
Article
- DOI
10.3390/electronics13112020