| Targeted adversarial discriminative domain adaptation HM Chen, A Savakis, A Diehl, E Blasch, S Wei, G Chen Journal of Applied Remote Sensing 15 (3), 038504-038504, 2021 | 13 | 2021 |
| An end-to-end vechicle classification pipeline using vibrometry data A Smith, O Mendoza-Schrock, S Kangas, M Dierking, A Shaw SPIE Defense+ Security, 90790O-90790O-11, 2014 | 13 | 2014 |
| Transfer learning for aided target recognition: comparing deep learning to other machine learning approaches S Rivera, O Mendoza-Schrock, A Diehl Automatic Target Recognition XXIX 10988, 200-209, 2019 | 4 | 2019 |
| Remote vibrometry vehicle classification A Smith, S Goley, K Vongsy, A Shaw, M Dierking SPIE Defense+ Security, 94640S-94640S-13, 2015 | 2 | 2015 |
| Securing Machine Learning: A Red vs Blue Approach A Hildenbrandt, A Diehl NAECON 2021-IEEE National Aerospace and Electronics Conference, 337-340, 2021 | 1 | 2021 |
| Effects of fundamental frequency normalization on vibration-based vehicle classification A Smith, S Goley, K Vongsy, A Shaw, M Dierking SPIE Defense+ Security, 94741A-94741A-13, 2015 | 1 | 2015 |
| Sparse Feature-Persistent Hierarchical Classification A Diehl, J Ash NAECON 2024-IEEE National Aerospace and Electronics Conference, 147-152, 2024 | | 2024 |
| Sub-signal detection from noisy complex signals using deep learning and mathematical morphology J Wei, H Clouse, A Diehl Deep Learning and Its Applications for Vehicle Networks, 273-292, 2023 | | 2023 |
| Exploring the Removal of Bit-Planes for Increased Adversarial Robustness (Preprint) A Hildenbrandt, A Diehl, C Menart, R Canady, M Robertson, H Richards | | 2023 |
| End-to-End Classification Process for the Exploitation of Vibrometry Data AN Smith Wright State University, 2014 | | 2014 |
