Caleb Parks Source Confirmed

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

Researcher

University of Arkansas at Fayetteville

unknown

2 h-index 6 pubs 40 cited

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Biography and Research Information

OverviewAI-generated summary

Caleb Parks' research focuses on the application of deep learning techniques, particularly neural networks, to challenges in synthetic data generation and analysis for Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR). His work investigates methods for bridging the gap between synthetic and measured SAR data, exploring techniques like style and content splitting, and enforcing feature correlation on generated data using generative adversarial networks (GANs). Parks has also examined the value of phase information in SAR ATR and developed novel attention mechanisms for neural network architectures, such as OrthoNets and WaveNets. He has co-authored six publications, achieving an h-index of 2 with 40 citations. His collaborators include Susan Gauch from the University of Arkansas at Fayetteville, with whom he shares three publications.

Metrics

  • h-index: 2
  • Publications: 6
  • Citations: 40

Selected Publications

  • Assessing the value of phase to deep SAR ATR via noninferiority testing (2025) DOI
  • Graph pretraining approach to utilize synthetic data for SAR ATR (2024) DOI
  • OrthoNets: Orthogonal Channel Attention Networks (2023) DOI
  • Bridging the synthetic to measured SAR gap by splitting style and content (2023) DOI
  • WaveNets: Wavelet Channel Attention Networks (2022) DOI
  • Enforcing feature correlation on cycle-consistent GAN generated functions: a first step in closing the synthetic measured gap found in SAR images (2022) DOI

Collaborators

Researchers in the database who share publications

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