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The Award database is continually updated throughout the year. As a result, data for FY23 is not expected to be complete until September, 2024.

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. 3D Acoustic Model for Geometrically Constrained Environments

    SBC: HEAT, LIGHT, AND SOUND RESEARCH, INC.            Topic: N16AT018

    Systems that operate in constrained environments depend on the acoustics in several ways. Harbor defense systems detect intruders (peopleand/or vessels) by either listening for their noises (passively) or by pinging on them and detecting their echoes (actively). Furthermore, suchsystems may also form the equivalent of an underwater cell phone network using sound to carry the information. The acous ...

    STTR Phase II 2017 Department of DefenseNavy
  2. Conjugate heat transfer for LES of gas turbine engines

    SBC: CASCADE TECHNOLOGIES INC            Topic: N19BT027

    Current design tools for gas turbine engines invoke a variety of simplifying assumptions to estimate heat transfer to solid/metal engine components (e.g., isothermal boundary conditions). These approximations are often not valid, result in inaccurate predictions of heat transfer, and ultimately compromise the thermal integrity of propulsion and power systems. Wall-modeled large eddy simulation (WM ...

    STTR Phase I 2019 Department of DefenseNavy
  3. Comprehensive Surf Zone Modeling Tool

    SBC: Arete Associates            Topic: N19AT010

    Areté Associates, along with STTR partner Rochester Institute of Technology (RIT), are proposing a comprehensive software capability for scene generation, object insertion, and performance modeling for passive and active EO COBRA sensors over the surf zone. The Surf Zone Modeling Tool (SZT) will incorporate several technologies, including: open-source and Areté-designed SZ ocean physics models, ...

    STTR Phase I 2019 Department of DefenseNavy
  4. Compact and Low-cost High Performance Spectrometer Sensor based on Integrated Photonics Technology

    SBC: ULTRA-LOW LOSS TECHNOLOGIES LLC            Topic: N19AT023

    Ultra-Low Loss Technologies (ULL Technologies) is proposing in collaboration with Prof. Arka Majumdar from University of Washington (UW), to develop a compact, low-cost spectrometer module to be used for chemical sensing applications and to be fabricated using the process design kit (PDK) available through AIM Photonics multi-project wafer run (MPW). The team will combine ULL Technologies expertis ...

    STTR Phase I 2019 Department of DefenseNavy
  5. Cognitive Risk Management for UAS Missions

    SBC: Stottler Henke Associates, Inc.            Topic: N17BT035

    Enabling operators to command and control multiple UAVs will require higher levels of supervisory control, enabling vehicles to operate autonomously during larger portions of each mission. For the foreseeable future, however, critical portions of each mission will require operators to apply their superior knowledge, judgment, and skills to assess the situation, monitor execution more closely and, ...

    STTR Phase I 2017 Department of DefenseNavy
  6. Cognitive Adaptation and Mission Optimization (CAMO) for Autonomous Teams of UAS Platforms

    SBC: OPTO-KNOWLEDGE SYSTEMS INC            Topic: N17BT035

    OKSI and Professor Matthew Taylor will develop the Cognitive Adaptation and Mission Optimization (CAMO) command and control tool for teams of UAS platforms. CAMO will incorporate existing databases (e.g., NASA population maps, FAA airspace maps, etc.) as well as real-time data from UAS into a learning-based cognitive control solution that maximizes mission performance while minimizing risk for a t ...

    STTR Phase I 2017 Department of DefenseNavy
  7. Cognitive Adaptation and Mission Optimization (CAMO) for Autonomous Teams of UAS Platforms

    SBC: OPTO-KNOWLEDGE SYSTEMS INC            Topic: N17BT035

    The Navy needs cognitive control capabilities that enable an autonomous robotic team comprised of a ground control station node and a team of UAS platforms to operate independently (or with minimal human oversight) while carrying out complex missions. A cognitive control capability needs to be developed that concurrently optimizes the balance of mission risk / performance with respect to the Navy ...

    STTR Phase II 2019 Department of DefenseNavy
  8. Bonded Joint Analysis Method

    SBC: M4 ENGINEERING, INC.            Topic: N12AT004

    During Phase I and Phase II, M4 Engineering, Inc. and Sandia National Laboratories have created a unique bonded joint analysis methodology and associated software. During Phase II.5, the developed techniques will be further enhanced and a fully functional commercial analysis code (SIMULIA/Abaqus) plug-in will be created. The software plug-in will make the advanced technology accessible to all leve ...

    STTR Phase II 2016 Department of DefenseNavy
  9. Body-worn Wireless Physiological Monitoring Network

    SBC: Cognionics, Inc.            Topic: N13AT021

    This STTR Phase II proposal continues our work towards building a simple, high quality and unobtrusive mobile physiological sensor platform. The capabilities of the Phase I prototype will be expanded by adding sensors to further acquire SpO2 and respiration in addition to forming a body area network for data collection across multiple points on a subjects body. A software infrastructure will also ...

    STTR Phase II 2017 Department of DefenseNavy
  10. Blending Classical Model-Based Target Classification and Identification Approaches with Data-Driven Artificial Intelligence

    SBC: TOYON RESEARCH CORPORATION            Topic: N18BT033

    Toyon Research Corp. and the University of California propose to develop innovative algorithms to perform automatic target recognition (ATR), localization, and classification of maritime and land targets in EO/IR, LiDAR, and SAR imagery. The proposed algorithms are based on recent developments made at the University of California, which outline a strong mathematical framework for naturally blendin ...

    STTR Phase I 2019 Department of DefenseNavy
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