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REMOTE DETECTION TECHNOLOGIES

Description:

Maximum Phase I Award Amount: $200,000

Maximum Phase II Award Amount: $1,100,000

Accepting SBIR Phase I Applications: YES

Accepting STTR Phase I Applications: NO

 

The Remote Detection Program within the Office of Defense Nuclear Nonproliferation Research and Development (DNN R&D) has an objective to develop new technologies and methods for nuclear and radiological material security. Meeting this objective requires the improvement of current technology and the development of new tools for remote detection applications. These advances can be used to enable emergency response, safeguards, treaty verification, and other government applications. Research areas in the remote sensing program include: 1) the development of imaging and non-imaging systems (passive or active), 2) multi-modal detection technology, and 3) enhancing detection opportunities through computational methods. Grant applications are sought in the following subtopics:

 

a.      Extending Remote Gas Sensing Range

Fast spectral identification of multiple chemical species is essential when signals are short lived or in constant flux. In response to this, a variety of active sensing systems have been investigated in the past. For example, frequency comb technologies have been shown to provide large spectral coverage while maintaining high resolution [1]. 

 

Proposed efforts under this topic should seek to extend the range of sensing systems that can identify multiple gaseous species beyond 1km distance-to-target [2]. High priority should be given to the maximization of signal-to-noise ratios. 

 

Questions – Contact: Chris Ramos, Christopher.ramos@nnsa.doe.gov

 

b.      Networked Edge Sensing

Advances in neuromorphic engineering [1] and event-based sensing have demonstrated new paradigms for remote sensing science.  This is in part due to increased computation and analysis on-board the sensor (or ‘at the edge’). Additionally, these technologies are capable of reduced size, weight, and power requirements.

 

Proposed efforts under this topic should investigate the networking of edge sensing sensors to enhance persistence, increase range, or minimize noise. Modalities that are of interest include: EM, optical, or seismo-acoustic.

 

Questions – Contact: Chris Ramos, Christopher.ramos@nnsa.doe.gov

 

c.       Other

In addition to the specific subtopics listed above, grant applications in other areas relevant to this topic are invited.

 

Questions – Contact: Chris Ramos, Christopher.ramos@nnsa.doe.gov

 

References: Subtopic a:

1.      Kowligy, A., et al. “Infrared electric field sampled frequency comb spectroscopy.” Science Advances, 7 June 2019, https://advances.sciencemag.org/content/advances/5/6/eaaw8794.full.pdf    

 

2.      Rieker, G.B., Giorgetta, F.R., et al. “Frequency-comb-based remote sensing of greenhouse gases over

kilometer air paths.” Optica, Vol. 1,  Issue 5, pp. 290-298, 2014, https://www.nist.gov/publications/frequency-comb-based-remote-sensing-greenhouse-gases-over-kilometer-air-paths  

 

References: Subtopic b:

1.      Posch, C. “Bio-inspired vision.” Journal of Instrumentation, Volume 7, January 2012,

https://iopscience.iop.org/article/10.1088/1748-0221/7/01/C01054

 

2.      Leng, S., Posch, C., et al. “Asynchronous Neuromorphic Event-Driven Image Filtering”, Vol. 102, No. 10, October 2014 | Proceedings of the IEEE, https://www.neuromorphic-vision.com/public/publications/20/publication.pdf

 

3.      Corradi, F , Indiveri, G. “A neuromorphic event-based neural recording system for smart brain-machine-interfaces.” IEEE Transactions on Biomedical Circuits and Systems, 9(5):699 – 709, 2015 https://www.zora.uzh.ch/id/eprint/121693/6/8752947.pdf

 

4.      Indiveri, G., "Neuromorphic analog VLSI sensor for visual tracking: circuits and application examples,"  IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, vol. 46, no. 11, pp. 1337-1347, Nov. 1999, doi: 10.1109/82.803473,https://ieeexplore.ieee.org/abstract/document/803473  

 

5.      Koch, C., and Mathur, B., "Neuromorphic vision chips," IEEE Spectrum, vol. 33, no. 5, pp. 38-46, May 1996, doi: 10.1109/6.490055, https://ieeexplore.ieee.org/abstract/document/490055

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