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intelligent Frequency Modulated Continuous Wave (iFMCW)

Award Information
Agency: Department of Defense
Branch: Army
Contract: W58RGZ-23-C-0042
Agency Tracking Number: A2-9452
Amount: $1,144,286.49
Phase: Phase II
Program: STTR
Solicitation Topic Code: A21C-T013
Solicitation Number: 21.C
Solicitation Year: 2021
Award Year: 2023
Award Start Date (Proposal Award Date): 2023-08-08
Award End Date (Contract End Date): 2025-08-07
Small Business Information
7262 Governors West Drive, Ste 102
Huntsville, AL 35806-1111
United States
DUNS: 615378796
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Jeffery Craven
 (256) 224-5846
Business Contact
 Michele Kochoff Platt
Phone: (256) 682-6261
Research Institution
 Auburn University
 Robert Dean
200 Broun Hall
Auburn, AL 36849
United States

 (334) 844-1809
 Federally Funded R&D Center (FFRDC)

Technical Abstract: Operational availability, reliability, and performance of Army weapons systems platforms are key factors in achieving mission success.  Army maintainers and depot artisans have a requirement for an intelligent toolset to quickly detect, locate, characterize/classify, and predict wire and connector faults in interconnect cables.  AVNIK Defense Solutions, Inc. (AVNIK) has conducted Phase I of an Army Small Business Technology Transfer (STTR) project to develop an intelligent Frequency Modulated Continuous Wave (iFMCW) hand-held toolset to detect, diagnose, and predict cable faults in aircraft and missile systems. We have worked with expert subcontract team members at Auburn University, the University of Alabama at Huntsville (UAH), and Instrumental Sciences, Inc. (ISI) to research cable properties, evaluate iFMCW waveform options, evaluate candidate data analytics methods for cable fault characterization, and demonstrate key elements of the toolset in the laboratory. Primary objectives of Phase II of the project research are to design, build, test, and demonstrate an engineering prototype with (1) enhanced capabilities of the prior AVNIK iFMCW hand-held cable fault identification tool prototypes, (2) applied data analytics and artificial intelligence techniques to identify and quantify cable and connector fault characteristics, and (3) use of statistical methods to maximize fault detection/prediction accuracy of the iFMCW tool technology for cable field data sets.

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

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