A methodology to rank importance of frequencies and channels in electromyography data with Decision Tree classifiers

2026-04-20T08:51:39Z2bb0bec9ba9469a6ce482f7a4b0e90c4136a7fec1fc97b6cdd6569a36ab93a4d
EMGFP-ANetFR3MRI-privacyPEILRISTHzTX-noiseambient-backscatterartificial-noisechannel-estimationcovert-communicationsintegrated-sensing-and-communicationinverse-learningmassive-MIMOmmWavemulti-site-radarprivacyradarsub-THztransmitter-privacyultrasound-FWI

What happened

Collection of recent arXiv submissions (April 20, 2026) covering sensing, communications, and inverse learning with several papers that raise potential security and privacy concerns. Notable items: (1) a RIS‑aided transmitter privacy framework that formalizes how a malicious sensor can estimate transmitter–sensor channels and proposes active/passive beamforming and transmit‑side artificial noise to degrade unauthorized channel estimation; (2) a dual‑function radar enabling ambient backscatter where a tag exploits radar reverberation as an ambient carrier—this architecture could enable covert/协

Why it matters

A reviewed impact interpretation has not been published for this record.

Evidence and limitations

Source ID
arxiv_eess_sp
Record identifier
2bb0bec9ba9469a6ce482f7a4b0e90c4136a7fec1fc97b6cdd6569a36ab93a4d
Enrichment time
2026-04-20T08:51:39Z
AI-assisted enrichment
Yes

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Record · A methodology to rank importance of frequencies and channels in electromyography data with Decision Tree classifiers · Baitaphish