output
20172020
most citedDistributed Deep Learning Models for Wireless Signal Classification with Low-Cost Spectrum Sensors

863 citations

5 papers

cs.CV2020★ 28 cited

SLAP: Improving Physical Adversarial Examples with Short-Lived Adversarial Perturbations

Giulio Lovisotto, Henry Turner, Ivo Sluganovic +2

Research into adversarial examples (AE) has developed rapidly, yet static adversarial patches are still the main technique for conducting attacks in the real world, despite being o…

eess.SP2018★ 4 cited

Nanosecond-precision Time-of-Arrival Estimation for Aircraft Signals with low-cost SDR Receivers

Roberto Calvo-Palomino, Fabio Ricciato, Blaz Repas +2

Precise Time-of-Arrival (TOA) estimations of aircraft and drone signals are important for a wide set of applications including aircraft/drone tracking, air traffic data verificatio…

cs.CR2017★ 3 cited

Towards Plausible Graph Anonymization

Yang Zhang, Mathias Humbert, Bartlomiej Surma +3

Social graphs derived from online social interactions contain a wealth of information that is nowadays extensively used by both industry and academia. However, as social graphs con…

cs.NI2017★ 863 cited

Distributed Deep Learning Models for Wireless Signal Classification with Low-Cost Spectrum Sensors

Sreeraj Rajendran, Wannes Meert, Domenico Giustiniano +2

This paper looks into the technology classification problem for a distributed wireless spectrum sensing network. First, a new data-driven model for Automatic Modulation Classificat…

cs.NI2017★ 176 cited

Electrosense: Open and Big Spectrum Data

Sreeraj Rajendran, Roberto Calvo-Palomino, Markus Fuchs +5

While the radio spectrum allocation is well regulated, there is little knowledge about its actual utilization over time and space. This limitation hinders taking effective actions…