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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. Injection Locked "Cooker" Magnetron

    SBC: PHYSICAL SCIENCES INC.            Topic: N20AT015

    The Navy desires a compact and highly efficient S-band magnetron source with stabilized output capable of modulation over a narrow bandwidth. In this Phase I STTR proposal, Physical Sciences Inc (PSI) outlines the development of an injection locked “cooker” magnetron which can be used for frequency shift keying (FSK) or phase shift keying (PSK) in a portable high power transmission device. In ...

    STTR Phase I 2020 Department of DefenseNavy
  2. Frequency and Phase Locking of Magnetrons Using Varactor Diodes

    SBC: CALABAZAS CREEK RESEARCH, INC.            Topic: N20AT015

    Magnetrons are compact, inexpensive, and highly efficient sources of RF power used in many industrial and commercial applications. For most of these applications, the requirement is for RF power without regard to precise frequency or phase control, and noise riding on the RF signal is not important. For many accelerator, defense, and communications applications, however, these characteristics prev ...

    STTR Phase I 2020 Department of DefenseNavy
  3. Machine Learning for Simulation Environment

    SBC: ARETE ASSOCIATES            Topic: N20AT014

    Areté and the Machine Learning for Artificial Intelligence (MLAI) Lab at the University of Arizona (UofA) will develop an interactive scenario building tool capable of generating realistic synthetic 360° videos in real-time for use in training simulators for periscope operators .  We refer to this solution as RealSynth360.  This novel capability will be created by combining the latest advances ...

    STTR Phase I 2020 Department of DefenseNavy
  4. TIS: Trusted Sensor Integration

    SBC: OBJECTSECURITY LLC            Topic: N20AT011

    Condition-based maintenance plus (CBM+), and cyber-physical systems (CPS) in general, depend on correct sensor data for analysis, decision making and control loops. If the sensor data that arrives at the point of processing is not correct, or more accurate, is outside its accepted error range, then any further processing will be incorrect as well. This could result in, in the case of CBM+, not det ...

    STTR Phase I 2020 Department of DefenseNavy
  5. Marburg Virus Prophylactic Medical Countermeasure

    SBC: MAPP BIOPHARMACEUTICAL, INC.            Topic: CBD18A002

    There are currently no vaccines or therapeutics available for Marburg Virus Disease (MVD). Given the specter of weaponization and the terrible morbidity and high mortality rate of MVD, this represents a critical threat to the operational readiness of the Warfighter. While traditional vaccines have contributed greatly to public health, they have some limitations especially in the context of operati ...

    STTR Phase II 2020 Department of DefenseOffice for Chemical and Biological Defense
  6. PARTEL: Periscope video Analysis using Reinforcement and TransfEr Learning

    SBC: MAYACHITRA, INC.            Topic: N20AT007

    We propose a suite of video processing algorithms utilizing the machine learning (ML) techniques of artificial intelligence (AI) reinforcement learning, deep learning, and transfer learning to process submarine imagery obtained by means of periscope cameras. Machine learning (ML) can help in addressing the challenge of human failure of assessing the data of periscope imagery. Though pre-tuned blac ...

    STTR Phase I 2020 Department of DefenseNavy
  7. Machine Learning for Transfer Learning for Periscopes

    SBC: ARETE ASSOCIATES            Topic: N20AT007

    Areté and the Machine Learning for Artificial Intelligence (MLAI) Lab at the University of Arizona (UofA) will develop and demonstrate new approaches that improve the performance of in situ machine learning (ML) algorithms as they evolve over time, adapt to new environments, and are capable of transferring their learned experiences across platforms.  Technological advances that will be brought t ...

    STTR Phase I 2020 Department of DefenseNavy
  8. Joint User-Centered Planning Artificial Intelligence Tools Effective Mission Reasoning (JUPITER)

    SBC: CHARLES RIVER ANALYTICS, INC.            Topic: N19BT029

    Effective mission planning is critical for military strategy and execution. This process is complex as human operators must consider many variables (e.g., resource limitations, threats, risks) when formulating a plan to accomplish mission goals. Although powerful tools, such as the Navy’s Joint Mission Planning System (JMPS), provide advanced functionality, mission planning remains a hybrid acti ...

    STTR Phase II 2020 Department of DefenseNavy
  9. 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 II 2020 Department of DefenseNavy
  10. Electromagnetic Interference Resilient, Low Noise Figure, Wide Dynamic Range RF Photonic Link

    SBC: Photonic Systems, Inc.            Topic: N20AT012

    Photonic Systems, Inc. (PSI) and Harvard University propose to collaborate in Phases I and II of this STTR program towards the goal of demonstrating a broadband RF/photonic signal link with a specific combination of performance parameters and other features not available from present state-of-the-art links. The solicitation’s goal – specifically, an electromagnetic attack-resilient electro-op ...

    STTR Phase I 2020 Department of DefenseNavy
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