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The Award database is continually updated throughout the year. As a result, data for FY24 is not expected to be complete until March, 2025.

Download all SBIR.gov award data either with award abstracts (290MB) or without award abstracts (65MB). A data dictionary and additional information is located on the Data Resource Page. Files are refreshed monthly.

The SBIR.gov award data files now contain the required fields to calculate award timeliness for individual awards or for an agency or branch. Additional information on calculating award timeliness is available on the Data Resource Page.

  1. Acoustic Intercept Receiver for Naval Special Warfare Undersea Vehicles

    SBC: INFORMATION SYSTEMS LABORATORIES INC            Topic: N09T012

    Information Systems Laboratories (ISL) and Florida Atlantic University (FAU) propose to develop and test a system that uses existing signal processing algorithms coupled with innovative construction technology developed ISL under our E-Field sensor programs and FAU under UUV programs. The Challenge is to develop a small system package with the capability to intercept active threat emissions early ...

    STTR Phase II 2010 Department of DefenseNavy
  2. Active Imaging through Fog

    SBC: SA PHOTONICS, LLC            Topic: N18AT021

    Active imaging systems are used to for imaging in degraded visual environments like that found in marine fog and other environments with a high level of attenuation and scattering from obscurants like fog, rain, smoke, and dust.These systems are still limited in range and resolution. SA Photonics is taking advantage of multiple image enhancement techniques, like wavelength tunability, pulse contro ...

    STTR Phase I 2018 Department of DefenseNavy
  3. Adaptive Fleet Synthetic Scenario Research

    SBC: KAB LABORATORIES INC.            Topic: N10AT044

    Synthetic scenario-based training of Navy personnel in the use of Navy SIGINT/IO systems has helped to reduce training costs, and it has enabled the personnel to be trained in an environment that sufficiently approximates real-world situations that could not otherwise be accomplished within the class room. However, scenario development is highly complex and involves a great deal of human effo ...

    STTR Phase I 2010 Department of DefenseNavy
  4. Adaptive Fleet Synthetic Scenario Research

    SBC: KAB LABORATORIES INC.            Topic: N10AT044

    Synthetic scenario-based training of Navy personnel in the use of Navy SIGINT/IO systems has helped to reduce training costs, and it has enabled the personnel to be trained in an environment that sufficiently approximates real-world situations that could not otherwise be accomplished within the classroom. However, scenario development is highly complex and involves a great deal of human effort and ...

    STTR Phase II 2012 Department of DefenseNavy
  5. Adaptive Learning for Stall Pre-cursor Identification and General Impending Failure Prediction

    SBC: Frontier Technology Inc.            Topic: N10AT008

    Frontier Technology, Inc. (FTI) and Northeastern University propose to investigate and develop an innovative approach to predict stall events of aircraft engines prior to occurrence and in sufficient time to allow the FADEC controller to adjust engine variables. The team will utilize vector quantization and neural network techniques to develop accurate models of engine behavior that will be used t ...

    STTR Phase I 2010 Department of DefenseNavy
  6. Adaptive Optics for Nonlinear Atmospheric Propagation of Laser Pulses

    SBC: ADVANCED SYSTEMS & TECHNOLOGIES INC            Topic: N17AT024

    Filamentation of ultra-short laser pulse propagation in non-linear media offers significant potentials allowing to address numerous problems in military and commercial sectors. However, practical implementation of this requires an ability to control the USLP at its propagation through inhomogeneous media, like turbulent atmosphere. On the basis of our approach for combating turbulence effects on p ...

    STTR Phase II 2018 Department of DefenseNavy
  7. Additive Manufacturing for Microwave Vacuum Electron Device Cost Reduction

    SBC: RADIABEAM TECHNOLOGIES, LLC            Topic: N16AT010

    The Department of the Navy has a need for the development of an additive manufacturing (AM) process for key vacuum electronic device components to meet on-demand, flexible, and affordable manufacturing requirements. The goal of this Phase II effort is to develop and validate Electron Beam Melting (EBM) additive manufacturing (AM) technology for the production of vacuum electronic devices meeting t ...

    STTR Phase II 2018 Department of DefenseNavy
  8. Additive Manufacturing Sensor Fusion Technologies for Process Monitoring and Control.

    SBC: ARCTOS Technology Solutions, LLC            Topic: DLA18A001

    Universal Technology Corporation (UTC) has teamed with the University of Dayton Research Institute (UDRI), Stratonics, and Macy Consulting to demonstrate not only the transitionability into commercial systems, but also to develop the data analytics and monitoring and control requirements to extract the full value fromseveral sensors, including the Stratonics ThermaViz, acoustic and profilometry se ...

    STTR Phase I 2018 Department of DefenseDefense Logistics Agency
  9. Additive Manufacturing Sensor Fusion Technologies for Process Monitoring and Control.

    SBC: X-Wave Innovations, Inc.            Topic: DLA18A001

    Additive Manufacturing (AM) is a modern and increasingly popular manufacturing process for metallic components, but suffers from well known problems of inconsistent quality of the finished product. Process monitoring and feedback control are therefore crucial research areas with a goal of solving this problem. To address this concern, X-wave Innovations, Inc. (XII) and the University of Dayton Res ...

    STTR Phase I 2018 Department of DefenseDefense Logistics Agency
  10. Additive Manufacturing Sensor Fusion Technologies for Process Monitoring and Control.

    SBC: ARCTOS Technology Solutions, LLC            Topic: DLA18A001

    This Phase II project aims to assemble the key set of sensor modalities that are needed to reliably view the key process anomalies and properties of laser powder bed fusion. The research team will down-select from the Phase I sensors investigated and integrate the sensors into a sensor fusion software package that facilitates data collection and synchronization, and eventually feedback control of ...

    STTR Phase II 2019 Department of DefenseDefense Logistics Agency
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