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Found 6 papers, 0 papers with code
e-G2C: A 0.14-to-8.31 $μ$J/Inference NN-based Processor with Continuous On-chip Adaptation for Anomaly Detection and ECG Conversion from EGM
This work presents the first silicon-validated dedicated EGM-to-ECG (G2C) processor, dubbed e-G2C, featuring continuous lightweight anomaly detection, event-driven coarse/precise conversion, and on-chip adaptation.
Towards Length-Versatile and Noise-Robust Radio Frequency Fingerprint Identification
During the inference, a multi-packet inference approach is further leveraged to improve the classification accuracy in low SNR scenarios.
Towards Receiver-Agnostic and Collaborative Radio Frequency Fingerprint Identification
In this paper, we propose a receiver-agnostic RFFI system that is not sensitive to the changes in receiver characteristics; it is implemented by employing adversarial training to learn the receiver-independent features.
Radio Frequency Fingerprint Identification for Security in Low-Cost IoT Devices
Data augmentation is adopted to improve low SNR RFFI performance.
RT-RCG: Neural Network and Accelerator Search Towards Effective and Real-time ECG Reconstruction from Intracardiac Electrograms
To close this gap and make a heuristic step towards real-time critical intervention in instant response to irregular and infrequent ventricular rhythms, we propose a new framework dubbed RT-RCG to automatically search for (1) efficient Deep Neural Network (DNN) structures and then (2)corresponding accelerators, to enable Real-Time and high-quality Reconstruction of ECG signals from EGM signals.
Towards Scalable and Channel-Robust Radio Frequency Fingerprint Identification for LoRa
Radio frequency fingerprint identification (RFFI) is a promising device authentication technique based on the transmitter hardware impairments.
