Results 1 to 10 of about 59 (42)
Transfer Learning for Radio Frequency Machine Learning: A Taxonomy and Survey [PDF]
Sensors, 2022 Transfer learning is a pervasive technology in computer vision and natural language processing fields, yielding exponential performance improvements by leveraging prior knowledge gained from data with different distributions.
Lauren J. Wong, Alan J. Michaels
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Transferring Learned Behaviors between Similar and Different Radios [PDF]
SensorsTransfer learning (TL) techniques have proven useful in a wide variety of applications traditionally dominated by machine learning (ML), such as natural language processing, computer vision, and computer-aided design.
Braeden P. Muller +3 more
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Evaluation of Confusion Behaviors in SEI Models [PDF]
SensorsRadio Frequency Machine Learning (RFML) has in recent years become a popular method for performing a variety of classification tasks on received signals.
Brennan Olds +2 more
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A Radio Frequency Region-of-Interest Convolutional Neural Network for Wideband Spectrum Sensing [PDF]
Sensors, 2023 Wideband spectrum sensing plays a crucial role in various wireless communication applications. Traditional methods, such as energy detection with thresholding, have limitations like detecting signals with low signal-to-noise ratio (SNR).
Adam Olesiński, Zbigniew Piotrowski
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Assessing the Value of Transfer Learning Metrics for Radio Frequency Domain Adaptation
Machine Learning and Knowledge ExtractionThe use of transfer learning (TL) techniques has become common practice in fields such as computer vision (CV) and natural language processing (NLP). Leveraging prior knowledge gained from data with different distributions, TL offers higher performance ...
Lauren J. Wong +3 more
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An Analysis of Radio Frequency Transfer Learning Behavior
Machine Learning and Knowledge ExtractionTransfer learning (TL) techniques, which leverage prior knowledge gained from data with different distributions to achieve higher performance and reduced training time, are often used in computer vision (CV) and natural language processing (NLP), but ...
Lauren J. Wong +3 more
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IEEE Open Journal of the Communications Society, 2021 While deep learning (DL) technologies are now pervasive in state-of-the-art Computer Vision (CV) and Natural Language Processing (NLP) applications, only in recent years have these technologies started to sufficiently mature in applications related to ...
Lauren J. Wong +5 more
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Quantifying Raw RF Dataset Similarity for Transfer Learning Applications
IEEE Open Journal of the Communications Society, 2022 Transfer learning (TL) has proven to be a transformative technology for computer vision (CV) and natural language processing (NLP) applications, offering improved generalization, state-of-the-art performance, and faster training time with less labelled ...
Lauren J. Wong +2 more
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Sensitivity Analysis of RFML Applications
IEEE AccessPerformance of radio frequency machine learning (RFML) models for classification tasks such as specific emitter identification (SEI) and automatic modulation classification (AMC) have improved greatly since their introduction, especially when measured ...
Braeden P. Muller +2 more
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IEEE Access, 2023 Automatic Modulation Classification (AMC) is a technique used in wireless communication systems to identify the modulation type of received signals at the receiver.
Kuchul Jung +2 more
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