ECG Authentication Hardware Design With Low-Power Signal Processing and Neural Network Optimization With Low Precision and Structured Compression-Reference-Cited by-同舟云学术

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ECG Authentication Hardware Design With Low-Power Signal Processing and Neural Network Optimization With Low Precision and Structured Compression

Published:2020-04 Issue:2 Volume:14 Page:198-208
ISSN:1932-4545
Container-title:IEEE Transactions on Biomedical Circuits and Systems
language:
Short-container-title:IEEE Trans. Biomed. Circuits Syst.

Author:

Cherupally Sai Kiran^ORCID,Yin Shihui^ORCID,Kadetotad Deepak,Srivastava Gaurav,Bae Chisung,Kim Sang Joon^ORCID,Seo Jae-sun

Funder

NSF

Samsung Advanced Institute of Technology

C-BRIC, one of six centers in JUMP

Defense Advanced Research Projects Agency

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Biomedical Engineering

Link

http://xplorestaging.ieee.org/ielx7/4156126/9049430/09000713.pdf?arnumber=9000713

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1. Noise suppression zeroing neural network for online solving the time-varying inverse kinematics problem of four-wheel mobile manipulators with external disturbances;Artificial Intelligence Review;2024-07-20

2. An Event-Driven Cardiac Monitoring System Based on a Low-Power Atrial-Fibrillation Detection ASIC With a Sensitivity of 92.5%;IEEE Sensors Letters;2024-03

3. Accurate authentication based on ECG using deep learning;Journal of Computer Security;2024-01-16

4. X-MyoNET: Biometric Identification Using Deep Processing of Dynamic Surface Electromyography;IEEE Transactions on Instrumentation and Measurement;2024

5. Stream Processing Architectures for Continuous ECG Monitoring Using Subsampling- Based Classifiers;IEEE Transactions on Very Large Scale Integration (VLSI) Systems;2024-01

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