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Radio frequency interference detection using machine learning
2016 IEEE Radio and Antenna Days of the Indian Ocean (RADIO), 2016 Radio frequency interference (RFI) has plagued radio astronomy which potentially might be as bad or worse by the time the Square Kilometre Array (SKA) comes up. RFI can be either internal (generated by instruments) or external that originates from intentional or unintentional radio emission generated by man.
Olorato Mosiane +2 more
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Machine Learning-Powered Radio Frequency Sensing: A Review
IEEE Sensors JournalThis article delves into the transformative potential of machine learning (ML) in radio frequency (RF) sensing applications. We focus on pivotal domains such as device localization, occupancy detection, activity monitoring, and biometric sensing, showcasing how ML is redefining the boundaries of what is possible.
Avik Santra +7 more
exaly +4 more sources
Machine learning method for radio frequency identification
, 2023 The increasing interest in exploring robust transmitter authentication methods to enhance digital communication security has led to the emergence of physical layer authentication, a technique leveraging the intrinsic characteristics of a device to generate its fingerprints. This research investigates the feasibility of employing RF fingerprinting, as a
Da Huang (18353895)
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Environmental Research, 2019
This paper applies Machine Learning (ML) algorithms to peer-reviewed publications in order to discern whether there are consistent biological impacts of exposure to non-thermal low power radio-frequency electromagnetic fields (RF-EMF). Expanding on previous analysis that identified sensitive plant species, we extracted data from 45 articles published ...
Malka N, Halgamuge, Devra, Davis
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This paper applies Machine Learning (ML) algorithms to peer-reviewed publications in order to discern whether there are consistent biological impacts of exposure to non-thermal low power radio-frequency electromagnetic fields (RF-EMF). Expanding on previous analysis that identified sensitive plant species, we extracted data from 45 articles published ...
Malka N, Halgamuge, Devra, Davis
openaire +4 more sources
Using Machine Learning for the detection of Radio Frequency Interference
2019 URSI Asia-Pacific Radio Science Conference (AP-RASC), 2019Radio Astronomy, by its very nature, detects extremely faint cosmic radio signals and is therefore very susceptible to Radio Frequency Interference (RFI). We present some initial results of our work to identify RFI using a Machine Learning (ML) based approach.
Kevin Vinsen +2 more
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Machine learning for radio frequency interference mitigation using polarization
2017 IEEE Radio and Antenna Days of the Indian Ocean (RADIO), 2017Radio frequency interference (RFI) is electromagnetic interference (EMI) from signals in the radio frequencies of the electromagnetic spectrum. RFI reduces the sensitivity of radio telescope and produces artefacts in the observed data. We present the result of applying machine learning techniques to detect confidently man made RFI.
Olorato Mosiane +2 more
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Machine Learning for Radio Frequency Interference Flagging
, 2021 The field of radio frequency interference (RFI) flagging involves the identification of corrupted data within radio astronomy measurements. This work explores the application of supervised machine learning algorithms for RFI flagging, trained on real measurement data and simulated data with simulated RFI.
Harrison, Kyle
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Remote Patient Monitoring Using Radio Frequency Identification (RFID) Technology and Machine Learning for Early Detection of Suicidal Behaviour in Mental Health Facilities [PDF]
Sensors, 2021 Remote Patient Monitoring (RPM) has gained great popularity with an aim to measure vital signs and gain patient related information in clinics. RPM can be achieved with noninvasive digital technology without hindering a patient’s daily activities ...
Xiaohui Tao +2 more
exaly +2 more sources
Resilience Improvements for Space-Based Radio Frequency Machine Learning
2020 International Symposium on Networks, Computers and Communications (ISNCC), 2020Recent work has quantified the degradations that occur in convolutional neural nets (CNN) deployed in harsh environments like space-based image or radio frequency (RF) processing applications. Such degradations yield a robust correlation and causality between single-event upset (SEU) induced errors in memory weights of on-orbit CNN implementations ...
Lauren J. Wong +8 more
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