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STTR Phase I: FlatCam: Inexpensive, Compact Lensless Cameras for IoT Applications

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 1914252
Agency Tracking Number: 1914252
Amount: $224,995.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: I
Solicitation Number: N/A
Timeline
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-07-15
Award End Date (Contract End Date): 2020-06-30
Small Business Information
5214 La Branch St
HOUSTON, TX 77004
United States
DUNS: 116816155
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: Yes
Principal Investigator
 Jesse Adams
 (904) 554-4138
 flatcamimaging@gmail.com
Business Contact
 Jesse Adams
Phone: (904) 554-4138
Email: flatcamimaging@gmail.com
Research Institution
 William Marsh Rice University
 Ashok Veeraraghavan
 
6100 MAIN ST
Houston, TX 77005
United States

 Nonprofit College or University
Abstract

The broader impact of this Small Business Technology Transfer (STTR) Phase I project is the development of a new imaging platform technology that has the potential to affect many areas including consumer imaging, medical imaging, spectroscopy, astronomy, surveillance, and defense. Transitioning this technology into real applications will mean this technology can be used for personalized experiences, improved quality of life, and increased safety. The STTR Phase I proposed project will develop inexpensive, lensless imaging devices (contrary to the current state-of-art cameras, that rely on lenses to form a focused image), that can be integrated with internet-of-things (IoT) devices to gather visual data. Since the lens in a camera accounts for the vast majority of the cost and the weight, these devices can provide order of magnitude reductions in cost, allowing cameras to be integrated into a much larger array of home, auto, and city-scale smart devices. The research tasks in this project are: (1) developing fast, real-time algorithms for image reconstruction exploiting advances in optimization and machine learning (2) developing face detection, recognition, and tracking algorithms that operate with the lensless imaging platform for IoT applications like personalization, and (3) improving data communication (wired or wireless) to meet current and future IoT needs by exploring end-to-end system integration and optimization. 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.

* Information listed above is at the time of submission. *

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