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ORCIDScott Kuzdeba
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2020 – today
- 2024
[c22]Scott Kuzdeba:
Federated Learning of Things - Expanding the Heterogeneity in Federated Learning. AAAI Spring Symposia 2024: 307- 2023
- [c21]Scott Kuzdeba, Josh Robinson:
A Conditional Analysis of RF ML Latent Features. MILCOM 2023: 931-936 - 2022
- [c20]Amit Bhatia, Josh Robinson, Joseph M. Carmack, Scott Kuzdeba:
FPGA Implementation of Radio Frequency Neural Networks. CCWC 2022: 613-618 - [c19]Scott Kuzdeba, Josh Robinson:
Data-Centric Approaches to Radio Frequency Machine Learning. MILCOM 2022: 362-367 - [c18]Scott Kuzdeba, Josh Robinson, Joseph M. Carmack, David Couto:
Systems View to Designing RF Fingerprinting for Real-World Operations. WiseML@WiSec 2022: 33-38 - 2021
- [c17]Scott Kuzdeba, Joseph M. Carmack, Josh Robinson:
RF Fingerprinting with Dilated Causal Convolutions-An Inherently Explainable Architecture. ACSCC 2021: 292-299 - [c16]Joseph M. Carmack, Scott Kuzdeba:
RiftNet Reconstruction Model for Radio Frequency Domain Waveform Representation and Synthesis. AIIoT 2021: 303-312 - [c15]Scott Kuzdeba, Josh Robinson, Joseph M. Carmack:
Transfer Learning with Radio Frequency Signals. CCNC 2021: 1-9 - [c14]Josh Robinson, Scott Kuzdeba:
RiftNet: Radio Frequency Classification for Large Populations. CCNC 2021: 1-6 - [c13]Josh Robinson, Scott Kuzdeba:
Novel device detection using RF fingerprints. CCWC 2021: 648-654 - [c12]James Stankowicz
, Scott Kuzdeba:
Unsupervised Emitter Clustering through Deep Manifold Learning. CCWC 2021: 732-737 - [c11]Josh Robinson, Joseph M. Carmack, Amit Bhatia, John Majewski, Scott Kuzdeba, Joseph Farkas, Brandon Hombs, Thomas Koch:
Neural Network Signal Processing for LTE Receiver Application. DySPAN 2021: 309-314 - [c10]Joseph M. Carmack, Amit Bhatia, Josh Robinson, John Majewski, Scott Kuzdeba:
Neural Network Generative Models for Radio Frequency Data. UEMCON 2021: 577-582 - [c9]Joseph M. Carmack, Steve Schmidt, Scott Kuzdeba:
Multi-Agent Reinforcement Learning Approaches to RF Fingerprint Enhancement. WiseML@WiSec 2021: 67-72 - [c8]Steve Schmidt, James Stankowicz, Joseph M. Carmack, Scott Kuzdeba:
RiftNeXt™: Explainable Deep Neural RF Scene Classification. WiseML@WiSec 2021: 79-84 - 2020
- [c7]Josh Robinson, Scott Kuzdeba, James Stankowicz, Joseph M. Carmack:
Dilated Causal Convolutional Model For RF Fingerprinting. CCWC 2020: 157-162 - [c6]T. Scott Brandes, Scott Kuzdeba, Jessee McClelland, Neil Bomberger, Andrew Radlbeck:
RF Waveform Synthesis Guided by Deep Reinforcement Learning. WIFS 2020: 1-6
2010 – 2019
- 2019
- [c5]Scott Kuzdeba, Brandon Hombs, Jeremy D. Greenlee, Frank H. Guenther:
Kalman Filter Changepoint Detection and Trend Characterization. MLSP 2019: 1-6 - 2018
- [c4]Scott Kuzdeba, Andrew Radlbeck, Matthew Anderson:
Performance Metrics for Cognitive Electronic Warfare - Electronic Support Measures. MILCOM 2018: 1-9 - 2017
- [c3]Krishna Karra, Scott Kuzdeba, Josh Petersen:
Modulation recognition using hierarchical deep neural networks. DySPAN 2017: 1-3 - 2013
- [c2]Scott Kuzdeba, Alexander M. Wyglinski, Brandon Hombs:
Prototype implementation of a visual communication system employing video imagery. CCNC 2013: 184-189 - 2011
- [c1]Scott Kuzdeba, Joseph Farkas, Brandon Hombs:
Impact of power spread constraints on a multiuser detection enabled ad hoc network. MILCOM 2011: 2233-2237
Coauthor Index
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last updated on 2024-06-04 21:31 CEST by the dblp team
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