Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 10 of 10 for “"rfml"”.
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Real-World Considerations for RFML Applications
Radio Frequency Machine Learning (RFML) is the application of ML techniques to solve problems in the RF domain as an alternative to traditional digital-signal processing (DSP) techniques. Notable among these are the tasks of specific emitter identification (SEI), determining source identity of a …
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Sensitivity Analysis of RFML-based SEI Algorithms
Radio Frequency Machine Learning (RFML) techniques for the classification tasks of Specific Emitter Identification (SEI) and Automatic Modulation Classification (AMC) have seen rapid improvements in recent years. The applications of SEI, a technique used to associate a received signal to an …
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Adversarial RFML: Evading Deep Learning Enabled Signal Classification
… termed Radio Frequency Machine Learning (RFML), to synthesize new waveforms, control radio resources, as well as detect and classify signals. While there are numerous advantages to RFML, this thesis answers the question "is it secure?" DNNs have been shown, in other applications such as …
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Enhancing Communications Aware Evasion Attacks on RFML Spectrum Sensing Systems
… high threat environment. RF machine learning (RFML) systems contribute to this threat as they have been shown to be successful in gleaning information from intercepted signals, through the use of learning-enabled eavesdroppers. This thesis focuses on a method of defense against such …
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Foundations of Radio Frequency Transfer Learning
… field known as Radio Frequency Machine Learning (RFML), has the potential to provide increased performance and flexibility when compared to traditional signal processing techniques and has broad utility in both the commercial and defense sectors. Existing RFML systems predominately utilize …
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The Importance of Data in RF Machine Learning
… space of Radio Frequency Machine Learning (RFML), which we take to mean the application of Machine Learning (ML) as close to the sampled baseband signal directly after digitization as is possible, while allowing for preprocessing when reasonably defined and justified. After constraining the …
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Intelligently Leveraging Multi-Channel Image Processing Neural Networks for Multi-View Co-Channel Signal Detection
… deconfliction is a known challenge in RFML. We use YOLO, a deep learning algorithm, to deconflict the outputs from different channelizers to avoid overreporting. YOLO's ability to handle three channels makes it ideal for our study as we also use three channelizers. While our approach is …
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Metadata representation and management for context mediation
… representations include RDF, RuleML, and RFML. The transformation between the different representations is done through XSLT. An intuitive user interface is also created to aid the creation of metadata for context mediation. The user interface features textual and graphical components that …
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On the Use of Convolutional Neural Networks for Specific Emitter Identification
… ways. Recent work in RF Machine Learning (RFML) and Convolutional Neural Networks (CNNs) has shown the capability to perform signal processing tasks such as modulation classification, without the need for pre-defined expert features. Given this success, the work presented in this thesis …
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One Size Does Not Fit All: Optimizing Sequence Length with Recurrent Neural Networks for Spectrum Sensing
… the field of Radio Frequency Machine Learning (RFML), techniques like deep neural networks and reinforcement learning have been used to develop more complex spectrum sensing systems that are not reliant on expert features. Architectures like Convolutional Neural Networks (CNN) and Recurrent …