51ÂŇÂ×

Before joining 51ÂŇÂ× as the Stocker Visiting Professor of AI, Trevor Bihl served as a senior research engineer and program manager at the Air Force Research Laboratory (AFRL), Sensors Directorate (2016–2025), where he led intramural and extramural research across TRLs 1-9 (foundational to applied) in artificial intelligence, autonomous systems, machine learning, and signal processing.  Prior to AFRL, he spent a decade as a research contractor. He has also served as an adjunct professor at Shawnee State University, Wright State University's Boonshoft School of Medicine, and Marshall University.  

He is a senior member of the Institute of Electrical and Electronics Engineers (IEEE), and a senior member, and former board member, of the Institute for Operations Research and the Management Sciences (INFORMS).

Research Interests: Analogical learning; artificial intelligence; autonomous systems; biostatistics; computer vision; control systems; cyber Systems; cyber physical systems; cyber security; data mining; electricity theft; machine learning; neural networks; neuromorphic systems; operations research; planning, scheduling, and routing; optimization; power systems; reinforcement learning; remote sensing;  spiking neural networks; statistics; system identification; transfer learning;

All Degrees Earned: Ph.D., Electrical Engineering, Air Force Institute of Technology, 2015; M.S., Electrical Engineering, Ohio Univeristy; B.S., Electrical Engineering, 51ÂŇÂ×

Journal Articles (37)

1. Combs, K., Goble, I., Howlett, S., Adams, Y., and Bihl, T.  (2025) "Evaluating the Trade-off Between Analogical Reasoning Ability and Efficiency in Large Language Models" IEEE Transactions on Cognitive and Developmental Systems
2. Vicente-Sola, A., Manna, D.L., Kirkland, P., Di Caterina, G. and Bihl, T.J., (2025) “Spiking Neural Networks for event-based action recognition: A new task to understand their advantage,” Neurocomputing
3. Bihl, T. J., and Sands, T. “Dynamic System Modeling for Flexible Structures,” Online Journal of Robotics and Automation Technology
4. Utimula, K., Hayaschi, K-T., Bihl, T. J., Hongo, K., and Maezono, R., “Using reinforcement learning to autonomously identify sources of error for agents in group missions,” Frontiers in Control Engineering
5. Baietto, A., Stewart, C., and Bihl, T.J., (2024) “Dataset assembly for training Spiking Neural Networks,” Neurocomputing
6. Miller, N., Drumm, G., Champagne, L., Cox, B., and Bihl, T. J. â€śStrategies to Alleviate Flickering AI: Bayesian and Smoothing Methods for Deep Learning Classification in Video" Journal of Defense Analytics and Logistics
7. Combs, K., Moyer, A., and Bihl, T. J. (2024) â€śUncertainty in Visual Generative AI,” Algorithms 17(4)
8. Combs, K., Bihl, T. J., and Ganapathy, S., (2024) â€śUtilization of generative AI for the characterization and identification of visual unknowns,” Natural Language Processing Journal
9. Yue, Y., Baltes, M., Abuhajar, N., Sun, T., Karanth, A., Smith, C., Bihl, T.J., and Liu, J., (2023) “Spiking Neural Networks fine-tuning for brain image segmentation,” Frontiers in Neuroscience-Neuromorphic Engineering, 17
10. Combs, K., Bihl, T. J., (2022) “Question Optimization: Building Quiz Bowl Tournament Sets,” International Journal of Applied Management Science (IJAMS)
11. Bayless, S., Bihl, T. J., Rohan, C., Travers, J., Whitney, E., (2023) "Inappropriate Testing of Streptococcal Pharyngitis in Children Less than Three Years Old: Application of Statistical Process Control," Clinical Pediatrics, 62(4), pp. 309-315
12. McCloskey, B.J., Cox, B.A., Champagne, L. and Bihl, T.J., (2023) “Benefits of using blended generative adversarial network images to augment classification model training data sets,” The Journal of Defense Modeling and Simulation
13. Webb, T., Fu, S., Bihl, T., Holyoak, K.J. and Lu, H. (2023). “Zero-shot visual reasoning through probabilistic analogical mapping,” Nature Communications, 14(1)
14. Combs, K., Lu, H. and Bihl, T.J., (2023) “Transfer Learning and Analogical Inference: A Critical Comparison of Algorithms, Methods, and Applications,” Algorithms, 16(3), p.146.
15. Baietto, A., Boubin, J., Farr, P., Bihl, T.J., Jones, A.M. and Stewart, C., 2022. Lean neural networks for autonomous radar waveform design. Sensors, 22(4), p.1317
16. Yerrapragada, S., Sawant, S., Chen, S., Bihl, T., Wang, J., Bihl, J., (2022) “The protective effects of miR-210 modified endothelial progenitor cells released exosomes in hypoxia/reoxygenation injured neurons,” Experimental Neurology, vol. 358
17. Vicente Sola, A., Manna, D. L., Kirkland, P., Di Caterina, G., Bihl, T. J., (2022) “Keys to accurate feature extraction using residual spiking neural networks,” Neuromorphic Computing and Engineering, vol. 2 044001
18. Bihl, J., Sawant, H., Nguyen, D., Bihl, T. and Iwuchukwu, I., (2022) “The profile of extracellular vesicles in intracerebral hemorrhage patients,” Journal of Cerebral Blood Flow and Metabolism, 42(1), pp. 294-295
19. Sawant, H., Bihl, T., Nguyen, D., Iwuchukwu, I. and Bihl, J., (2022) “The profile of inflammatory extracellular vesicles in intracerebral hemorrhage patients,” Frontiers in Stroke, 1, p.988081.
20. Manna, D.L., Vicente-Sola, A., Kirkland, P., Bihl, T.J. and Di Caterina, G., (2022) “Simple and complex spiking neurons: Perspectives and analysis in a simple STDP scenario,” Neuromorphic Computing and Engineering, 2(4), p.044009.
21. Bill, J., Champagne, L., Cox, B. and Bihl, T., (2021) “Meta-Heuristic Optimization Methods for Quaternion-Valued Neural Networks,” Mathematics, 9(9), p.938.
22. Schmeusser B, Borchers, C., Travers, J.B., Borchers, S., Trevino, J., Rubin, M., Donnelly, H., Kellawan, K., Carpenter, L., Bahl, S., Rohan, C., Mueenich, E., Guenthner, S., Hahn, H., Rkein, A., Darst, M., Mousdicas, N., Cates, E., Sunar, U., Bihl, T.J., (2020) Inter-and Intra-physician variation in quantifying actinic keratosis skin photodamage. Journal of Clinical and Investigative Dermatology. 8(2),
23. Bihl, T. J., Paciencia, T. J., Bauer, K. W., and Temple, M. A., (2020) “Cyber-Physical Security with RF Fingerprint Classification through Distance Measure Extensions of Generalized Relevance Learning Vector Quantization,” Security and Communications Networks, vol. 2020
24. Paciencia, T. J., Bihl, T. J., and Bauer, K. W., (2019) “Improved n-dimensional data visualization from hyper-radial values,” Journal of Algorithms and Computational Technology, vol. 13
25. Travers, J. B., Poon, C., Bihl, T. J., Borchers, C., Rohrbach, D. J., Borchers, S., Trevino, J., Rubin, M., Donnelly, H., Kellawan, K., Carpenter, L., Bahl, S., Rohan, C., Muennich, E., Guenthner, S., Hahn, H., Rkein, Al, Darst, M., Mousdicas, N., Cates, E., Sunar, U., (2019) “Quantifying skin photodamage with spatial frequency domain imaging: Statistical results,” Biomedical Optics Express, vol 10, no 9, pp. 4676-83
26. Butler, H. K., Friend, M. A., Bauer, K. W., Bihl, T. J., (2018) invited â€śThe Effectiveness of Using Diversity to Select Multiple Classifier Systems With Varying Classification Thresholds,” Journal of Algorithms and Computational Technology, 12(3), pp.187-199
27. Gutierrez, R. J., Bauer, K. W., Boehmke, B. C., Saie, C. M., and Bihl, T. J. (2018) invited "Cyber Anomaly Detection: Using Tabulated Vectors and Embedded Analytics for Efficient Data Mining" Journal of Algorithms and Computational Technology, 12(4) 293-310
28. Bihl, T. J., and Bauer, K.W., (2017) “Statistical Analysis of High-Level Features from State of the Union Addresses,” International Journal of Information Systems and Social Change (IJISSC), vol. 8, no. 2, pp. 50-73
29. Gutierrez, R. J., Boehmke, B. C., Bauer, K. W., Saie, C. M., and Bihl, T. J. â€śanomalyDetection: Implementation of Augmented Network Log Anomaly Detection Procedures,” R Journal, August 2017
30. Moore, K.L., Bihl, T. J., Bauer, K. W., and Dube, T. E., (2017) “Cyber Data Feature-Extraction and Artificial Neural Network Based Feature-Selection for Classifying Cyber-Traffic Threats,” Journal of Defense Modeling and Simulation, vol. 13, no. 3, pp. 217-231
31. Bihl, T. J., Bauer, K.W., and Temple, M. A., (2016) “Feature Selection for RF Fingerprinting With Multiple Discriminant Analysis and Using ZigBee Device Emissions,” IEEE Transactions on Information Forensics and Security, vol. 11, no. 8, pp. 1862-1874
32. Situ, J.X., Friend, M.A., Bauer, K.W., and Bihl, T.J., (2016) “Contextual Features and Bayesian Belief Networks for Improved Synthetic Aperture Radar Combat Identification,” Military Operations Research Journal, vol. 21, no. 1, pp. 89-106
33. Bihl, T. J., Young, W. A., and G. R. Weckman, (2016) “Defining, Understanding and Addressing Big Data,” International Journal of Business Analytics (IJBAN), vol. 3, no. 2, pp. 1-32
34. Jablonski, J. A., Bihl, T. J., Bauer, K. W., (2015) “Principal Component Reconstruction Error for Hyperspectral Anomaly Detection,” IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 8, pp. 1725-1729 (Impact Factor: 5.43, H-Index: 138)
35. Friesen, K. D., Bihl, T. J., Bauer, K.W., Friend, M. A., (2013) “Contextual Anomaly Detection Cueing Methods for Hyperspectral Target Recognition,” American Journal of Science and Engineering, vol. 2, no. 1, pp. 9-16
36. Williams, J., Bihl, T., and Bauer, K., (2013) "Towards the mitigation of correlation effects in anomaly detection for hyperspectral imagery,” Journal of Defense Model and Simulation, vol. 10, no. 3, pp. 263-273
37. Ryer, D.M., Bihl, T.J., Bauer, K.W., and Rogers, S.K., (2012) “QUEST Hierarchy for Hyperspectral Face Recognition,” Advances in Artificial Intelligence, vol. 2012, pp. 1-13

Books (2)

1. Zobaa, A. and Bihl, T. J. (2019) Big Data Analytics in Future Power Systems, CRC Press, 8 chapters, 174 pages
2. Bihl, T. J. (2017) Biostatistics Using JMP: A Practical Guide, SAS Press, 14 chapters, 356 pages

Patent Submissions (6)

1. Bihl, T. J., Cox, C., Frimel, S., Morales-Colon, E., Tran, B., Plug and play ooda loop for airborne resource management using sensor fusion (ARMS), US 18/303,878
2. Bihl, T. J., Boubin, J., Baietto, A., Stewart, C., and Jones, A., (2024) Method of Analyzing and Correcting a Dynamic Waveform Using Multivariate Error Loss Functions,  US 18/418,576
3. Bihl, T. J., Boubin, J., Baietto, A., Stewart, C., and Jones, A., (2024) Method of Analyzing and Correcting a Complex Waveform by Real and Imaginary Partitioning and Recombination
4. Combs, K. and Bihl, T. J., (2023) Method and System for the Input Recognition Through Analogical Reasoning Algorithm, US18/068,589
5. Bihl, T. J., (2023) Methods and system for analogical learning in machine learning processes, non-provisional patent filed Dec. 2023, US18/068,925
6. Bihl, T. J., and Cox, C., (2023) Autonomous Airborne Mission Navigation and Tasking System, US17/899,244

Confernce papers (70)

1. Bihl, T. J., Boland, M., Turner, D., and Sarkisian, C., (2025) “Topological Data Analysis for Time Series Classification of NFL Track Data,” IEEE NAECON
2. Bihl, T. J. (2025) “A review of gravity offloading,” IEEE NAECON
3. Simpkins, D., Scheller, C., Bihl, T. J., Witherell, J., Miller, A., “Rapid Game Development with AI Demonstration,” IEEE NAECON
4. Simpkins, D., Rhodes, S., Bihl, T.J. (2025), “Developing a Modular Food Label Reader with Application to Vegan/Vegetarian Products,” IEEE NAECONBresciani, C., Lavazza, L., Cominelli, M., Han, L., Dong, G., Gringoli, F., Kaplan, L. M., Srivastava, M. B., Bihl, T. J., Blasch, E. P., Knutson, F. J., and Cerutti, F., (2025) “ Preliminary Insights into Resource-Constrained Neuro-Symbolic Causal Complex Event Processing” IEEE Fusion Conferenc
5. Bihl, T.J. and Lemming, G. (2025) “Developing a Framework for Biological Intelligence Modules in Autonomous Social Robots” Ubiquitous Robotics 2025
6. Bihl, T.J., Oroszi, T., Ganapathy, S., and Travers, J. (2025) “Improving Statistics Education at Wright State University with Design Project Based Learning, Problem Solving, and Peer Review” ASEE North Central Section (NCS) Annual Conference
7. Nagura, D., Bihl, T.J., and Liu, J. (2025) “Reinforcement Learning with Human Experience (RLHE) for Racing Car Game” ASEE North Central Section (NCS) Annual Conference,
8. Hoffman, A., and Bihl, T.J., (2025) “West Virginia’s Power Systems Industry: History, Directions, and Future” ASEE North Central Section (NCS) Annual Conference
9. Sizemore, E., and Bihl, T.J., (2025) “Gamification in Power System Education” ASEE North Central Section (NCS) Annual Conference
10. Stevenson, E. S., Cook. J., and Bihl, T.J. (2025) “Understanding Electricity Theft: Causes, Consequences, and AI-Based Detection” ASEE North Central Section (NCS) Annual Conference, accepted
11. Juric,R., Ronchieri, E., Sanfilippo, F. and Bihl, T. J. â€śIntroduction to the Minitrack on Intelligent Edge Computing,“2025 Hawaii International Conference on System Sciences (HICSS), pp. 7247
12. Carrizales, C., Zhang, X., and Bihl, T. J. (2025) “Reinforcement Learning for Adversarial Environments,” 2025 Hawaii International Conference on System Sciences (HICSS), pp. 826-835
13. Combs, K., Bihl, T. J., Howlett, S., and Adams, Y. (2025) “Zero-shot Comparison of Large Language Models (LLMs) Reasoning Abilities on Long-text Analogies,” 2025 Hawaii International Conference on System Sciences (HICSS) , pp. 1614-1623
14. Combs, K., and Bihl, T. J. (2025) “The Visual Analogs of Linguistic Concepts and Their Implications on Generative AI,” 2025 Hawaii International Conference on System Sciences, pp. 718-727
15. Baeitto, A., and Bihl, T. J. (2025) “Generative Data for Neuromorphic Computing,” 2025 Hawaii International Conference on System Sciences (HICSS), pp. 7248-7257
16. Goulart, B., Bihl, T. J., and Travers, J. (2024) “Trends in Advanced Tabletop Digital DJ Media Players from 2000-2023,” IEEE NAECON, Dayton, OH
17. Alzapiedi, L., and Bihl, T. J. (2024) “Trend Analysis Through Large Language Models,” IEEE NAECON, Dayton, OH
18. Nagura, D., Bihl, T. J., Liu, J. (2024) “Boosting Race Car Performance Through Reinforcement Learning from Ai Feedback (RLAIF),” IEEE NAECON, Dayton, OH
19. Cominelli, M., Gringoli, F., Kaplan, L., Srivastava, M., Bihl, T., Blasch, E., Iyer, N., and Cerutti, F., (2024)  â€śNeuro-symbolic fusion of Wi-Fi sensing data for passive radar with inter-model knowledge transfer,” IEEE FUSION, Venice, IT
20. Kumar, D. P., Rathinam, S., Darbha, S., and Bihl, T. J. (2024)  "Heuristic for Min-Max Heterogeneous Multi-Vehicle Multi-Depot Traveling Salesman Problem," AIAA SciTech 2024 Forum
21. Moorthy, K., Kumar, D. P., Darbha, S., Rathinam, S., and Bihl, T. J. (2024)  "Minimum-Cost Routing of two UAVs with Communication Constraints," AIAA SciTech 2024 Forum,
22. Bihl, T. J., Farr, P., Di Caterina, G., Kirkland, P., Vicente Sola, A., Manna, D., Liu, J., and Combs, K., (2024) “Exploring Spiking Neural Networks (SNN) for Low Size, Weight, and Power (SWaP) Benefits,” Hawaii International Conference on System Sciences (HICSS) , pp. 7561-7570
23. Combs, K., Bihl, T. J., and Pennington, J.*, (2024) “Interoperability for Autonomy,” Hawaii International Conference on System Sciences (HICSS) , pp. 903-912
24. Combs, K. and Bihl, T. J. (2024) “A Preliminary Look at Generative AI for the Creation of Abstract Verbal-to-visual Analogies,” Hawaii International Conference on System Sciences (HICSS) , pp. 1180-1189
25. Baietto, A., Stewart, C., and Bihl, T. J. (2023) “Dataset Augmentation for Robust Spiking Neural Networks,” IEEE International Conference on Autonomic Computing and Self-Organizing Systems, Toronto, Canada
26. Manna, D.L., Vicente-Sola, A., Kirkland, P., Bihl, T.J. and Di Caterina, G.. (2023) "Frameworks for SNNs: a review of data science-oriented software and an expansion of SpykeTorch." International Conference on Engineering Applications of Neural Networks, pp. 227-238.
27. Bihl, T.J., Cox, C., Tran, B., Morales, E., Frimel, S., Combs, K., and Arlint, A., (2023) ”Airborne Resource Management Using Sensor Fusion,” 10th NATO Military Sensing Symposium (SET-311), London, UK
28. Manna, D., Vicente-Sola, A., Kirkland, P. Bihl, T. J., and Di Caterina, G. (2023) “Time Series Forecasting with Spiking Neural Networks,” 10th NATO Military Sensing Symposium (SET-311), London, UK
29. Yue, Y., Batles, M., Abujahar, N., Sun, T., Smith, C.D., Bihl, T., and Liu, J., “Hybrid Spiking Neural Network Fine-tuning for Hippocampus Segmentation,” IEEE International Symposium on Biomedical Imaging (ISBI), Cartagena, April 2023
30. Bihl, T., Cox, C., Adams, Y., Pennington, J., (2023) “Autonomous Search and Rescue with Modeling and Simulation and Metrics,” Hawaii International Conference on System Sciences (HICSS), pp. 1768-1777 accepted with no revision
31. Combs, K., Bihl, T., Ganapathy, S., (2023) “Integration of Computer Vision with Analogical Reasoning for Characterizing Unknowns” Hawaii International Conference on System Sciences (HICSS), pp. 960-969
32. Combs, K., Bihl, T., (2023) “Clustering and Topological Data Analysis: Comparison and Application” Hawaii International Conference on System Sciences (HICSS), pp. 815-824
33. Manna, D. L., Vicente Sola, A.*, Kirkland, P., Bihl, T. J., Di Caterina, G., (2022) “Frameworks for SNNs: a review of data science-oriented software and an expansion of SpykeTorch,” International Conference On Neuromorphic Systems
34. Baietto, A., Boubin, J., Farr, P., Bihl, T. J., (2022) “Lean Neural Networks for Real-time Embedded Spectral Notching Waveform Design,” IEEE 31st International Symposium on Industrial Electronics
35. Combs, K., Bihl, T., Ganapathy, S., Staples, D., (2022) “Analogical Reasoning: A Comparison of Algorithms for Self-Learning Natural Language Processing” Hawaii International Conference on System Sciences (HICSS)
36. Bihl, T., Jones, A., Farr, P., Straub, K., Bontempo, B., Jones, F., (2022) “Assessing Multi-Agent Reinforcement Learning Algorithms for Autonomous Sensor Resource Management” Hawaii International Conference on System Sciences (HICSS)
37. Swize, E., Champagne, L., Cox, B., Bihl, T., (2022) “Bayesian Augmentation of Deep Learning to Improve Video Classification” Hawaii International Conference on System Sciences (HICSS)
38. Bihl, T.J., Martin, J. A., Gross, K. C., Bauer, K. W. (2021) "Data and Feature Fusion Approaches for Anomaly Detection in Polarimetric Hyperspectral Imagery"  IEEE National Aerospace and Electronics Conference (NAECON), pp 157 – 163
39. Hawkes, T. D., Bihl, T. J,  (2021) "Symbols to represent AI systems" IEEE National Aerospace and Electronics Conference (NAECON), pp 61 – 68
40. Bihl, T., Schoenbeck, J., Rondeau, C., Jones, A., & Adams, Y. (2021) “Tuning Hyperparameters for DNA-based Discrimination of Wireless Devices.” Hawaii International Conference on System Sciences (HICSS), pp. 6965-6974
41. Hampo, M., Fan, D., Jenkins, T., DeMange, A., Westberg, S., Bihl, T. and Taha, T., 2020, July. “Associative Memory in Spiking Neural Network Form Implemented on Neuromorphic Hardware.” International Conference on Neuromorphic Systems 2020 (pp. 1-8).
42. Farr, P., Jones, A.M., Bihl, T., Boubin, J. and DeMange, A., (2020) “Waveform Design Implemented on Neuromorphic Hardware,” IEEE International Radar Conference (RADAR), pp. 934-939
43. Bihl, T. J., Gutierrez, R., Bauer, K. W., Boehmke, B. C., and Saie, C., (2020) “Topological Data Analysis for Enhancing Embedded  Analytics for Enterprise Cyber Log Analysis and Forensics,” Hawaii International Conference on System Sciences (HICSS), Maui, HI, pp. 1937 – 1946.
44. Bihl, T. J., Jones, A., Schoenbeck, J., Steeneck, D. and Jordan, J., (2020) “Finding Algorithm Settings: Easy and Efficient Hyperparameter Optimization to Address Some Artificial Intelligence “ilities”,” Hawaii International Conference on System Sciences (HICSS), Maui, HI, pp. 943 – 952
45. Bihl, T. J., and Talbert, M., (2020) “Analytics for Autonomous C4ISR within e-Government: a Research Agenda,” Hawaii International Conference on System Sciences (HICSS), pp. 2218 - 2227
46. Boubin, J., Jones, A.M. and Bihl, T., (2019) “Neurowav: Toward real-time waveform design for vanets using neural networks,” IEEE Vehicular Networking Conference (VNC), pp. 1-4
47. John-Baptiste, P., Smith, G.E., Jones, A.M. and Bihl, T., (2019) “Rapid Waveform Design Through Machine Learning,” IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp. 659-663
48. Ramirez, J., Armitage, T., Bihl, T. and Kramer, R., (2019) “Topological Learning for Semi-Supervised Anomaly Detection in Hyperspectral Imagery” IEEE National Aerospace and Electronics Conference (NAECON), pp. 560-564
49. Bihl, T.J., Cox, C. and Machin, T., (2019) “Towards a Taxonomy of Planning for Autonomous Systems” IEEE National Aerospace and Electronics Conference (NAECON), pp. 74-79
50. Berthold, B., Bihl, T. J., Cox, C., Jenkins, T.A., and Leland, L., (2019) “Probabilistic reasoning for real-time UAV decision and control,“ 2019 SPIE Defense and Commercial Sensing, Baltimore, MD
51. Bihl, T. J., Jenkins, T., Cox, C., DeMange, A., Hill, K., and Zelnio, E., (2019) “From Lab to Internship and Back Again: Learning Autonomous Systems through Creating a Research and Development Ecosystem” Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, vol. 33, pp. 9635-9643
52. Moore, D.*, Bihl, T.J., Jenkins, T.A. and Archibald, C., (2018) “Accommodating Plan Revisions with Multiple Agents for Local Search in Road Networks.” IEEE National Aerospace and Electronics Conference (pp. 52-59), Dayton, OH.
53. Bihl, T. J., Cox, C. and Jenkins, T. (2018) “Finding Common Ground by Unifying Autonomy Indices to Understand Needed Capabilities,” 2018 SPIE Defense and Commercial Sensing, Orlando, FL
54. Bihl, T. J. and Steeneck, D. W. (2018) “Multivariate Stochastic Approximation to Tune Neural Network Hyperparameters for Critical Infrastructure Communication Device Identification,” 2018 Hawaii International Conference on System Sciences (HICSS), pp. 2225-2234
55. Bihl, T. J. and Hajjar, S. (2017) “Electricity Theft Concerns within Advanced Energy Technologies,” 2017 IEEE National Aerospace and Electronics Conference (NAECON),  Dayton, OH
56. Steeneck, D. W. and Bihl, T. J., (2017) “Stochastic Approximation for Learning Rate Optimization for Generalized Relevance Learning Vector Quantization,” 2017 IEEE National Aerospace and Electronics Conference (NAECON), Dayton, OH
57. Bihl, T. J., Temple, M. A., Bauer, K.W., (2017) “An Optimization Framework for Generalized Relevance Learning Vector Quantization with Application to Z-Wave Device Fingerprinting,” 2017 Hawaii International Conference on System Sciences (HICSS), pp. 360 – 365
58. Hawkes, T., Bihl, T. J., and Rogers, S., (2016), “Poster: Qualia Exploitation of Sensing Technology (QuEST) for Vehicular Network Optimization,” 2016 IEEE Vehicular Networking Conference (VNC), pp. 1-2
59. Bihl, T. J., Temple, M. A., and Bauer, K.W., (2016) “Feature Selection Fusion (FSF) for Aggregating Relevance Ranking Information with Application to ZigBee Radio Frequency Device Identification,” 2016 IEEE National Aerospace and Electronics Conference (NAECON),  Dayton, OH, pp. 80-87
60. Hawkes, T. D., Bihl, T. J., and Woolacott, M. H. (2016) "Interacting Systems Approach to Investigation of Human Cognitive Capacity," 3rd Annual Cincinnati-Dayton INFORMS Conference, Dayton, OH, pp. 32-39
61. Bihl, T. J., Temple, M. A., Bauer, K.W., and Ramsey, B., (2015) “Dimensional Reduction Analysis for Physical Layer Device Fingerprints with Application to ZigBee and Z-Wave Devices,” IEEE Military Communications Conference (MILCOM), Tampa, FL, pp. 360-365
62. Carbino, T. J., Temple, M. A., and Bihl, T. J., (2015) “Ethernet Card Discrimination Using Unintentional Cable Emissions and Constellation-Based Fingerprinting,” International Conference on Computing, Networking and Communications (ICNC), Anaheim, CA, pp. 369-373
63. Ward, M. R., Bihl, T. J., Bauer, K. W., (2014) “Vibrometry-based vehicle identification framework using nonlinear autoregressive neural networks and decision fusion,” IEEE National Aerospace and Electronics Conference (NAECON),  Dayton, OH, pp. 180-185
64. Bihl, T. J., Mitchell, J.R., and Irwin, R. D., (2013) “Hybrid System Identification for MIMO Control-System Design,” 19th IFAC (International Federation of Automatic Control) Symposium on Automatic Control in Aerospace, WĂĽrzburg, Germany, pp. 411-416
65. Ryer, D.M., Bihl, T.J., Bauer, K.W., and Rogers, S.K., (2011) “QUEST Hierarchy for Hyperspectral Face Recognition,” SPIE Symposium on Defense & Security Symposium, Orlando, FL, pp. 1-9
66. Mindrup, F., Bihl, T.J., and Bauer, K., (2010) "Modeling Noise in a Framework to Optimize the Detection of Anomalies in Hyperspectral Imaging," Intelligent Engineering Systems Through Artificial Neural Networks, St. Louis, MO, V20, pp. 517-524  
67. Williams, J., Bihl, T.J., and Bauer, K., (2010) "Mitigation of Correlation and Heterogeneity Effects in Hyperspectral Data," Intelligent Engineering Systems Through Artificial Neural Networks, V20, St. Louis, MO, pp. 501-507
68. Bihl, T.J., Heidenreich, J., Allen, D., and Hunt, K., (2009) “SPECTTRA:  A Space Power System Modeling and Simulation Tool,” AIAA 7th International Energy Conversion Engineering Conference, Denver, CO, pp. 1-14
69. Bihl, T.J., Pham, K.D., and Murphey, T.W., (2007) “Modeling and Control of Active Gravity Off-Loading for Deployable Space Structures,” SPIE Symposium on Defense & Security Symposium, Orlando, FL, pp. 1-10
70. Bihl, T.J., Manning, W.J., Mitchell, J.R., and Bukley, A.P., (2007) “Controller Development and Embedded Controller Implementation for Active Gravity Off-Loading of Deployable Space Structures,” 30th Annual AAS Guidance and Control Conference,  Breckenridge, CO, pp. 1-9