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Toward Automated Utility Pole Condition Monitoring: A Deep Learning Approach.

, , , , , and . ISGT-Europe, page 255-259. IEEE, (2020)

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Harm Claim Thresholds: On the Use of Extreme Value Theory for Receiver Environment Characterization.. WCNC Workshops, page 1-6. IEEE, (2017)Random Spectral Sampling for Compliance Enforcement in Dynamic Spectrum Access Networks., and . Wirel. Pers. Commun., 96 (2): 2401-2425 (2017)Improving Student Engagement in Teaching Electric Machines Through Blended Learning., , , and . IEEE Trans. Educ., 62 (4): 297-304 (2019)Deep Learning Neural Receiver for Organic Communication Channels., and . LATINCOM, page 1-4. IEEE, (2023)Re-thinking compliance enforcement: Investigating random spectrum sampling techniques for temporal occupancy characterization., and . WOWMOM, page 1-8. IEEE Computer Society, (2015)Estimation of spectrum occupancy in heterogeneous radio access environments using random spectral sampling., and . Sarnoff Symposium, page 1-6. IEEE, (2012)A Survey of 3D Printing Technologies as Applied to Printed Electronics., and . IEEE Access, (2022)Out-of-band radiation: Opportunities for antenna-based RF energy harvesting in wireless devices?. OnlineGreenComm, page 53-58. IEEE, (2015)Energy-reduced non-contiguous spectrum sensing for compliance enforcement in dynamic spectrum access environments., , and . PACRIM, page 376-380. IEEE, (2015)Toward Automated Utility Pole Condition Monitoring: A Deep Learning Approach., , , , , and . ISGT-Europe, page 255-259. IEEE, (2020)