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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. Single Use Uncrewed Aircraft with Oceanic Range

    SBC: DRAGOON TECHNOLOGY LLC            Topic: 92

    Tropical Cyclone meteorological data from the Atlantic main development region (MDR) is difficult to collect due to the remoteness of the location and difficulty of deploying manned aircraft to the region. Data collection is frequent upon development of a major hurricane but many tropical disturbances in the MDR leading up to hurricane development are of interest. Being able to collect meteorologi ...

    SBIR Phase I 2022 Department of CommerceNational Oceanic and Atmospheric Administration
  2. Measuring Stormwater Overflow with Synthetic Aperture Radar

    SBC: WOLVERINE RADAR COMPANY            Topic: 93

    Many municipalities in the United States have begun to use impermeability measurements to incentivize storm water retention as part of the architectural and landscaping design process. These measurements are performed through analysis of high-resolution aerial photography and updated on an annual or semi-annual basis. The practice of using high resolution aerial or even satellite photography is pr ...

    SBIR Phase I 2022 Department of CommerceNational Oceanic and Atmospheric Administration
  3. Design of a Modular Platform for Advanced Synthesis of Energetic Materials​

    SBC: NALAS ENGINEERING SERVICES INC            Topic: OSD21C003

    Modular continuous processes are the future of chemical process development and chemical manufacturing. They enable faster time to market, improved safety, lower costs, and enhanced flexibility over traditional batch processes. Proposed work under this topic includes the design of a modular system for synthesis of many energetic materials, energetic precursors, and critical chemicals. Modules that ...

    STTR Phase I 2022 Department of DefenseOffice of the Secretary of Defense
  4. Underwater Adhesive for Coral Restoration

    SBC: METNA CO            Topic: 9201

    Coral transplantation is a primary management option for rehabilitation of degraded reefs. Stabilization (via adhesion, etc.) of transplants on existing reef or artificial substrates notably improves their survival rate. Improved underwater adhesives are needed for expedient and convenient stabilization of coral transplants with improved survival rate. A new hybrid organic-inorganic adhesive is pr ...

    SBIR Phase I 2020 Department of CommerceNational Oceanic and Atmospheric Administration
  5. OPEN WORLDS NOAA Portal

    SBC: MICHIGAN AEROSPACE CORP            Topic: 9302

    Many people recognize how critical it is to understand our weather, climate, and environment. Educators already use NOAA data and models in working with students and their communities as part of their teaching and outreach. With the exponential growth in data, however, many of them struggle with accessing and managing that data. Evolving data formats and computing requirements make it very challen ...

    SBIR Phase I 2020 Department of CommerceNational Oceanic and Atmospheric Administration
  6. NOAA Aerielle SONAR UAS

    SBC: HYDRONALIX INC            Topic: 9401

    This proposed concept is a direct response to NOAA Topic 9.4.01, “Unmanned Aircraft System: Rapid Response for Natural Disaster” and will develop a Hybrid Unmanned Air System (UAS) called “Aerielle” equipped with a Humminbird SONAR imaging system that is commonly used by first responders. Aerielle will perform currently developed disaster response missions for UAS and additional under wate ...

    SBIR Phase I 2020 Department of CommerceNational Oceanic and Atmospheric Administration
  7. Scalable Low-Cost AESA Transmitter with Phase-Only Nulling

    SBC: EMAG TECHNOLOGIES, INC.            Topic: SCO182002

    In this SBIR project, EMAG Technologies Inc. proposes to develop a compact, low-cost, scalable, transmit-only X-band active phased array antenna with phase-only nulling capability based on our proven VISAT architecture. The proposed AESA will use commercial PCB manufacturing platform and will utilize commercial off-the-shelf (COTS) parts and components for the entire multilayer stack-up. The propo ...

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
  8. High Velocity Gun-Launched Projectile and Sabot Structures

    SBC: SIMULATIONS, LLC            Topic: SCO182003

    The U.S. military is increasingly focused on long-range warfare by delivering time-critical guided hypersonic projectiles to troops on the ground. Additionally, in recent years long range precision fires have become a priority for ranges on the order of 250 nm. On the development path to reaching these extended ranges, the U.S. DoD, Strategic Capabilities Office, Office of Navy Research, Navy and ...

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
  9. Reinforcement Learning with Intelligent Context-based Exploration (RL-ICE)

    SBC: SOAR TECHNOLOGY INC            Topic: SCO182006

    State of the art object detection in satellite imagery currently requires large quantities of hand-labeled satellite images. But what if there exists only very limited satellite imagery of the object, perhaps a single pass? Current deep learning solutions can not learn effective models with this extremely limited data. If, however, there exists model of the object that can be used to synthesize mo ...

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
  10. QUINN (Quantum INspired Neural Networks)

    SBC: SOAR TECHNOLOGY INC            Topic: SCO183001

    Machine learning models are susceptible to adversarial attacks that make modifications to the input data in order to cause misclassifications. The root cause is the linearity of the decision boundaries of machine learning models in relation to their inputs. One promising direction is to represent the input data as a distribution. Quantum information science entails techniques for working with wave ...

    SBIR Phase I 2019 Department of DefenseOffice of the Secretary of Defense
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