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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. Accelerated Burn-In Process for High Power Quantum Cascade Lasers to Reduce Total Cost of Ownership

    SBC: IRGLARE LLC            Topic: N20BT029

    The development of a burn-in process for high power MWIR and LWIR Quantum Cascade Lasers (QCLs) is proposed. The new burn-in process will be rooted in extensive statistical experimental data to be collected in Phase I and Phase II of this program. This will allow us to extract all the critical empirical parameters required for mean-time-to-failure (MTTF) projections for high power MWIR and LWIR de ...

    STTR Phase I 2021 Department of DefenseNavy
  2. Accelerated Burn-In Process for High Power Quantum Cascade Lasers to Reduce Total Cost of Ownership

    SBC: ADTECH PHOTONICS, INC.            Topic: N20BT029

    Quantum Cascade Lasers (QCLs) are one of the most versatile sources of radiation in the mid-infrared range and have found applications in a variety of fields. Despite their widespread adoption, one of the main hurdles holding QCLs back from large volume manufacturing is the large cost of ownership. While QCLs, like most semiconductor devices based on III-V compounds, can leverage the economies of ...

    STTR Phase I 2021 Department of DefenseNavy
  3. A Chemometric Approach to Categorize HTPB and Reliably Predict Outcomes in Gum stock and Propellant Formulations

    SBC: HELICON CHEMICAL COMPANY LLC            Topic: N23AT018

    This Phase I proposal will utilize multivariate regression, including cluster and principal component analysis to categorize lots of HTPB R-45M based on their physicochemical properties. Artificial intelligence-driven pattern recognition analysis will be performed on a variety of HTPB characterization data and spectra. Categorized data will be assessed against measured outcomes (pot life, mechanic ...

    STTR Phase I 2023 Department of DefenseNavy
  4. Acoustic Intercept Receiver for Naval Special Warfare Undersea Vehicles

    SBC: Sonatech, Inc.            Topic: N09T012

    Naval Special Warfare (NSW) teams often use underwater vehicles (UVs) for clandestine transport into highly sensitive littoral areas. The current sonar suites on the existing vehicles do not include a system for detecting and identifying active sonar transmissions, making the vehicle and the SEALs susceptible to detection by active sonar systems on manned and unmanned surface and subsurface surve ...

    STTR Phase I 2009 Department of DefenseNavy
  5. 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 I 2009 Department of DefenseNavy
  6. Acoustic Pattern Recognition for Security Breaching Noise Detection

    SBC: SCIENTIFIC APPLICATIONS & RESEARCH ASSOCIATES, INC.            Topic: N06T036

    A proposed acoustic pattern recognition system will be based around an array of acoustic sensors to allow localization of sound events and cancellation of background noise. The number of sensors could be adjusted to vary the coverage area depending on the site. The measured signals will provide the input to a sophisticated signal processing algorithm that will separate the acoustic signals into ...

    STTR Phase I 2006 Department of DefenseNavy
  7. 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
  8. Active Transfer Learning for Intelligent Tutoring

    SBC: SOAR TECHNOLOGY INC            Topic: N15AT013

    We propose an intelligent assessment concept that will discover and reason about KSAs in multiple instruc-tional domains and settings. SoarTech will team with world-class researchers from the University of Califor-nia, Davis and the University of Memphis to research and develop TARGET: Transfer via Active Requests to Generalize Effective Training. TARGET will enhance SoarTechs assessment system DA ...

    STTR Phase I 2015 Department of DefenseNavy
  9. Adaptable Sensor Processing for Passive Targets Using Neuro

    SBC: Nova Research, Inc. DBA Nova Sensors            Topic: N/A

    Work to be performed in this program is based upon years of innovative technology development invested by personnel at Nova Research. Techniques proposed here are patterned after biological principles which, when applied to finding targets which are moving against highly cluttered infrared backgrounds, will be shown to exhibit remarkable performance. Sensor and data fusion techniques may ...

    STTR Phase I 1998 Department of DefenseNavy
  10. Adaptive Behavior Modeling of Asymmetric Tactics (ABMAT)

    SBC: STOTTLER HENKE ASSOCIATES, INC            Topic: OSD07T005

    In order for simulation based training to help prepare warfighters for modern asymmetric tactics, opponent models of behavior must become more dynamic and contemporary and challenge trainees with adaptive threats consistent with those increasingly encountered by the military. The proposed Adaptive Behavior Modeling of Asymmetric Tactics (ABMAT) system will apply artificial intelligence machine le ...

    STTR Phase I 2007 Department of DefenseNavy
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