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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. A High-Speed Digital Holocamera for the 3-D Analysis of Flow Interaction with High Speed Flows

    SBC: METROLASER, INCORPORATED            Topic: N20AT020

    In hypersonic flight, airborne particles such as water or ice can penetrate and alter the bow shock and flow field, enhance erosion mechanisms and alter aerodynamics. Particles break up as they pass through the shock wave, impact the surface, erode and increase surface roughness, increase turbulence and heat transfer, and augment heating that can destroy heat shields prematurely. Many tests and th ...

    STTR Phase I 2020 Department of DefenseNavy
  2. High Efficiency Propeller for Small Unmanned X Systems using Advanced Composite Materials

    SBC: CATTO PROPELLERS            Topic: N20AT006

    In the proposed STTR study, Catto Propellers, Inc. (Catto) and the University of North Dakota (UND) will create an efficient new propeller design utilizing advanced composite materials for use on small unmanned x systems.  During Phase I, a comprehensive study will be conducted to develop a new propeller design in order to increase propeller efficiency, reduce aerodynamic noise and utilize innova ...

    STTR Phase I 2020 Department of DefenseNavy
  3. 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
  4. Electromagnetic Interference (EMI) Resilient, Low Noise Figure, Wide Dynamic Range of Radio Frequency to Photonic (RF Photonic) Link

    SBC: APPLIED NANOFEMTO TECHNOLOGIES LLC            Topic: N20AT012

    EMI resilient RF Photonic Links are critical for connecting remote antennas in the next generation Navy electronics warfare (EW) architecture. Current commercially available RF/photonic link technologies have deficiencies in dynamic range, noise figure, and SWaP performance. For a solution, this STTR project aims to develop a novel wide dynamic range, low noise RF photonic link, where the key comp ...

    STTR Phase I 2020 Department of DefenseNavy
  5. Development of Precision Alignment Techniques for Millimeter Wave Sources

    SBC: DYMENSO LLC            Topic: N20AT013

    High power generation at millimeter wave (mm-wave) frequencies is expensive and the concurrent need for wide bandwidths at these frequencies creates an extremely challenging problem. Currently the most stringent requirements for mm-wave power and bandwidth can only be practically met by vacuum electronics (VE) technology. At present, vacuum amplifiers with the required performance are prohibitivel ...

    STTR Phase I 2020 Department of DefenseNavy
  6. 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
  7. Analog Optical Link using Novel Record Performance Laser, Modulator and Photodiode Technology

    SBC: FREEDOM PHOTONICS LLC            Topic: N20AT012

    In this program, Freedom Photonics and its research partner institution will demonstrate an analog optical link using novel record performance laser, modulator and photodiode technology. Preliminary designs for a miniature, deployable implementation will be conducted as well in Phase I.

    STTR Phase I 2020 Department of DefenseNavy
  8. 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
  9. 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
  10. 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
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