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AM-FM-Handcrafted-Features-vs-Learned-Features-in-RF-Modulation-Classification
AM-FM-Handcrafted-Features-vs-Learned-Features-in-RF-Modulation-Classification PublicWe quantify the value of classical AM/FM and spectral moments. SHAP analyses over the classical stack, (ii) per-family ablations, and (iii) SNR-stratified deltas.
Python
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Ensemble-Size-vs-Latency-and-Energy-on-CPU-GPU-for-RF-Modulation-Ensembles
Ensemble-Size-vs-Latency-and-Energy-on-CPU-GPU-for-RF-Modulation-Ensembles PublicEnsemble modulation classifiers promise robustness against domain shift and label noise, but each added model increases inference latency and energy consumption. For realtime RF spectrum surveillan…
Python
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Hierarchical-vs-Flat-Ensembles-in-RF-Modulation-Classification
Hierarchical-vs-Flat-Ensembles-in-RF-Modulation-Classification PublicWe quantify when a parent HierarchicalMLClassifier beats a flat ensemble and vice versa. We report per-class win profiles, confusion deltas, and latency trade-offs, with code paths mapped to super(…
Python
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IQ-Length-Normalization-Policies-for-RF-Modulation-Classifiers
IQ-Length-Normalization-Policies-for-RF-Modulation-Classifiers PublicTemporal RF models typically require fixed-length IQ sequences, yet real-world bursts arrive at variable durations and sampling rates. In RF–QUANTUM–SCYTHE, the temporal input builder _create_tempo…
Python
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