| NSF Org: |
DMS Division Of Mathematical Sciences |
| Recipient: |
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| Initial Amendment Date: | July 31, 2023 |
| Latest Amendment Date: | July 31, 2023 |
| Award Number: | 2307827 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Stacey Levine
slevine@nsf.gov (703)292-2948 DMS Division Of Mathematical Sciences MPS Directorate for Mathematical and Physical Sciences |
| Start Date: | August 15, 2023 |
| End Date: | July 31, 2026 (Estimated) |
| Total Intended Award Amount: | $198,963.00 |
| Total Awarded Amount to Date: | $198,963.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
601 S COLLEGE RD WILMINGTON NC US 28403-3201 (910)962-3167 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
601 S COLLEGE RD WILMINGTON NC US 28403-3201 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | APPLIED MATHEMATICS |
| Primary Program Source: |
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| Program Reference Code(s): | |
| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.049 |
ABSTRACT
High dimensional data sensing, representation, and recovery that utilize the data?s intrinsic low dimensionality have become pervasive in many applications, including signal processing, computer vision, and machine learning. This project involves the development and analysis of new solutions for sensing and analyzing high dimensional data, inspired by the electrodermal activity (EDA) signal decomposition problem. A cleaner EDA decomposition allows scientists and data analysts to extract better features to serve a variety of tasks such as market research, seizure detection, human stress analysis, and emotion recognition, leading to social and economical benefits. In particular, this project aims to increase the recovery rate of medication-assisted treatment in rehabilitation centers. Many of the expected results will also be applicable to other imaging modalities as well as machine learning applications. Undergraduate and master students from diverse backgrounds will be mentored as part of this project. The students will also have opportunities to work with peers and researchers to better understand and contribute to real world applications.
This project aims to recover structured signals that are intrinsically low dimensional with significantly subsampled measurements. The project involves an analysis of the sensing conditions needed for the subsampled measurements and how they benefit the structured signals expressed in terms of few atoms. A framework for a generalized matrix decomposition will also be developed, which will be suitable for the EDA decomposition setting. Efficient algorithms for solving the associated novel optimization problems will also be developed and implemented. The new framework developed in this project combined with rigorous theoretical guarantees are expected to strengthen existing partnerships with other disciplines such as psychology and industry.
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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