Leslie M. Collins
Professor of Electrical and Computer EngineeringLeslie M. Collins earned the BSEE degree from the University of Kentucky, and the MSEE, and PhD degrees from the University of Michigan, Ann Arbor. From 1986 through 1990 she was a Senior Engineer at Westinghouse Research and Development Center in Pittsburgh, PA. She joined Duke in 1995 as an Assistant Professor and was promoted to Associate Professor in 2002 and to Professor in 2007. Her research interests include physics-based statistical signal processing, subsurface sensing, auditory prostheses and pattern recognition. She is a member of the Tau Beta Pi, Sigma Xi, and Eta Kappa Nu honor societies. Dr. Collins has been a member of the team formed to transition MURI-developed algorithms and hardware to the Army HSTAMIDS and GSTAMIDS landmine detection systems. She has been the principal investigator on research projects from ARO, NVESD, SERDP, ESTCP, NSF, and NIH. Dr. Collins was the PI on the DoD UXO Cleanup Project of the Year in 2000. As of 2015, Dr. Collins has graduated 15 PhD students.
Appointments and Affiliations
- Professor of Electrical and Computer Engineering
- Professor of Biomedical Engineering
- Faculty Network Member of the Duke Institute for Brain Sciences
- Faculty Network Member of The Energy Initiative
- Office Location: 3461 CIEMAS, Durham, NC 27708
- Office Phone: (919) 660-5260
- Email Address: email@example.com
- Ph.D. University of Michigan at Ann Arbor, 1995
- M.Sc.Eng. University of Michigan at Ann Arbor, 1986
- B.S.E. University of Kentucky at Lexington, 1985
This laboratory's research is in the area of physics-based statistical signal processing algorithms, and we are actively engaged in two general application areas: (1) Investigating human auditory perception and developing remediation strategies for the hearing impaired; (2) developing sensor-based algorithms for the detection of hazardous buried objects, such as unexploded ordnance (UXO) and landmines. Our research methodology is distinguished in two fundamental ways. First, we place an emphasis on incorporating the physics or phenomenology that governs the specific application directly into the signal processing framework, and we consider both experimental and theoretical issues. Second, we maintain an interactive collaboration with the end-user community that provides necessary feedback to the development process and validates the real-world utility of our research efforts. Our work in these application areas has improved quality of life and safety of life as a result of the development of novel signal processing algorithms.
Sensing and Sensor Systems
Land Mine Detection
- BME 493: Projects in Biomedical Engineering (GE)
- BME 494: Projects in Biomedical Engineering (GE)
- ECE 280L9: Signals and Systems - Lab
- ECE 280L: Introduction to Signals and Systems
- ECE 391: Projects in Electrical and Computer Engineering
- ECE 392: Projects in Electrical and Computer Engineering
- ECE 493: Projects in Electrical and Computer Engineering
- ECE 494: Projects in Electrical and Computer Engineering
- ECE 590: Advanced Topics in Electrical and Computer Engineering
- ECE 899: Special Readings in Electrical Engineering
- ENERGY 395: Connections in Energy: Interdisciplinary Team Projects
- ENERGY 396: Connections in Energy: Interdisciplinary Team Projects
- ENERGY 590: Special Topics in Energy
- ENERGY 795: Connections in Energy: Interdisciplinary Team Projects
- ENERGY 796: Connections in Energy: Interdisciplinary Team Projects
In the News
- Smart Meters: Duke Engineers Seek Energy Insights by Reading a Building's Electrical Signatures (Oct 4, 2016)
- Pratt Researchers Are Using Deep Learning to Distinguish Solar Panels from Swimming Pools (Aug 31, 2016)
- Malof, JM; Bradbury, K; Collins, LM; Newell, RG; Serrano, A; Wu, H; Keene, S, Image features for pixel-wise detection of solar photovoltaic arrays in aerial imagery using a random forest classifier, 2016 IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2016 (2017), pp. 799-803 [10.1109/ICRERA.2016.7884446] [abs].
- Knox, M; Rundel, C; Collins, L, Sensor fusion for buried explosive threat detection for handheld data, Proceedings of SPIE - The International Society for Optical Engineering, vol 10182 (2017) [10.1117/12.2263013] [abs].
- Sakaguchi, R; Morton, KD; Collins, LM; Torrione, PA, A Comparison of Feature Representations for Explosive Threat Detection in Ground Penetrating Radar Data, IEEE Transactions on Geoscience and Remote Sensing (2017), pp. 1-10 [10.1109/TGRS.2017.2732226] [abs].
- Reichman, D; Collins, LM; Malof, JM, On Choosing Training and Testing Data for Supervised Algorithms in Ground-Penetrating Radar Data for Buried Threat Detection, IEEE Transactions on Geoscience and Remote Sensing (2017), pp. 1-11 [10.1109/TGRS.2017.2750920] [abs].
- Camilo, JA; Collins, LM; Malof, JM, A Large Comparison of Feature-Based Approaches for Buried Target Classification in Forward-Looking Ground-Penetrating Radar, IEEE Transactions on Geoscience and Remote Sensing (2017), pp. 1-12 [10.1109/TGRS.2017.2751461] [abs].