NSF Org:	TI Translational Impacts
Recipient:	YALE UNIV
Initial Amendment Date:	August 18, 2023
Latest Amendment Date:	March 15, 2024
Award Number:	2303328
Award Instrument:	Standard Grant
Program Manager:	Peter Atherton patherto@nsf.gov  (703)292-8772 TI  Translational Impacts TIP  Dir for Tech, Innovation, & Partnerships
Start Date:	August 1, 2023
End Date:	July 31, 2025 (Estimated)
Total Intended Award Amount:	$1,499,998.00
Total Awarded Amount to Date:	$1,519,986.00
Funds Obligated to Date:	FY 2023 = $1,499,998.00 FY 2024 = $19,988.00
History of Investigator:	Holly Rushmeier (Principal Investigator) rushmeier@cs.yale.edu Philip Townsend (Co-Principal Investigator) David Messinger (Co-Principal Investigator) Mario Parente (Co-Principal Investigator) Yingyun Liu (Co-Principal Investigator)
Recipient Sponsored Research Office:	Yale University 150 MUNSON ST NEW HAVEN CT  US  06511-3572 (203)785-4689
Sponsor Congressional District:	03
Primary Place of Performance:	Yale University 25 Science Park NEW HAVEN CT  US  06520-8327
Primary Place of Performance Congressional District:	03
Unique Entity Identifier (UEI):	FL6GV84CKN57
Parent UEI:	FL6GV84CKN57
NSF Program(s):	POSE
Primary Program Source:	01002425DB NSF RESEARCH & RELATED ACTIVIT 01002324DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):
Program Element Code(s):	211Y00
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.084

ABSTRACT

Traditional photography produces images for human perception with a simple representation of color. Hyperspectral Imaging (HSI) expands imaging beyond reproduction for human perception to precise spectral radiance recording ranging from the ultraviolet to the infrared wavelengths. The enhanced discernment enabled by HSI is used to benefit an abundance of applications across biology, medicine, pharmaceuticals, materials, cultural heritage preservation, and space operations. However, the application of HSI in the past has been inhibited by dispersed intellectual property, siloed and immature software tools and data, steep learning curves, and lack of industry standards. This project is releasing the potential of HSI by developing an open-source HSI ecosystem (HSI-OSE) with contributions from HSI instrument manufactures, academic researchers, software providers, and general users. The project?s novelties are a sustainable organizational structure for the HSI-OSE established around a tool and data hub that enables continuous, open, and asynchronous contributions from registered users in the community. The project's impacts are accelerating progress in application areas such as natural resource monitoring, climate change assessment, artifact authentication, cancer diagnostics, pathogen detection, and quality control in pharmaceuticals and seeds.

The HSI-OSE advances knowledge in the field of hyperspectral imaging by promoting synergy among the community of manufacturers, researchers, and users with tools and data sharing, as well as feature requests. With a central hub for HSI data resources, it enables further applications of the technology through machine learning (ML) and artificial intelligence (AI). Large amounts of data are generated in each HSI recording session depicting the attributes, properties, or characteristics of the imaging subject. Sophisticated software tools are required to process and interpret these data. This project creates a comprehensive HSI data resource and develops the protocols and standards for software development to enable data sharing across many machine ML/AI applications. The well-documented open-source software tools and hardware designs from the HSI-OSE also facilitate the entry of new adopters of the HSI technology.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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