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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. A Clinical 3D Movement Analysis System for Assessing Lower Extremity Injury Risk and Recovery in Athletes

    SBC: BIONIKS            Topic: NIAMS

    DESCRIPTION provided by applicant The mission of Bioniks is to develop and commercialize accurate low cost movement analysis systems for clinicians ergonomists athletic trainers and other professionals interested in quantifying human movement Our initial focus is on developing computer enhancements for inexpensive D cameras like the Microsoft Kinect These enhancements surpass the accura ...

    STTR Phase I 2016 Department of Health and Human ServicesNational Institutes of Health
  2. Acoustically/Vibrationally Enhanced High Frequency Electromagnetic Detector for Buried Landmines

    SBC: AKELA INC            Topic: A16AT004

    Laboratory investigations have suggested that acoustically or vibrationally inducing motion in buried targets can aid in improving target detectability through a characteristic response related to differential target motion. This gain is realized by adding an additional degree of freedom, modulation due to motion in the GPR return signal, to use as a discriminating feature. The AKELA team is propo ...

    STTR Phase I 2016 Department of DefenseArmy
  3. Activated Reactants to Reduce Fuel Cell Overpotentials

    SBC: JSJ Technologies, LLC            Topic: A10AT011

    The current produced in electrochemical galvanic cells is primarily dependent on the rate of the electrode reactions where the cell's anode is less negative, supplying less energy than thermodynamically predicted, and the cell's cathode is less positive, supplying less energy than thermodynamically predicted. Reduction of electrochemical overpotentials in electrochemical systems has been the prim ...

    STTR Phase I 2010 Department of DefenseArmy
  4. Adaptive Fleet Synthetic Scenario Research

    SBC: KAB LABORATORIES INC.            Topic: N10AT044

    Synthetic scenario-based training of Navy personnel in the use of Navy SIGINT/IO systems has helped to reduce training costs, and it has enabled the personnel to be trained in an environment that sufficiently approximates real-world situations that could not otherwise be accomplished within the class room. However, scenario development is highly complex and involves a great deal of human effo ...

    STTR Phase I 2010 Department of DefenseNavy
  5. Adaptive Learning for Stall Pre-cursor Identification and General Impending Failure Prediction

    SBC: Frontier Technology Inc.            Topic: N10AT008

    Frontier Technology, Inc. (FTI) and Northeastern University propose to investigate and develop an innovative approach to predict stall events of aircraft engines prior to occurrence and in sufficient time to allow the FADEC controller to adjust engine variables. The team will utilize vector quantization and neural network techniques to develop accurate models of engine behavior that will be used t ...

    STTR Phase I 2010 Department of DefenseNavy
  6. Adaptive Quantum-Dot Photodetectors with Bias-Tunable Barriers

    SBC: ESENSORS INC.            Topic: AF08BT02

    The proposed research program focuses on design, fabrication, and characterization of quantum-dot infrared photodetectors (QDIPs) which features bias-tunable parameters, including the spectral response, optical gain, and operating time. Wide variations of detector parameters can be realized through the bias-tunable potential barriers surrounding quantum dots. Changes in bias will transform the ba ...

    STTR Phase I 2010 Department of DefenseAir Force
  7. Additive Manufacturing for Microwave Vacuum Electron Device Cost Reduction

    SBC: RADIABEAM TECHNOLOGIES, LLC            Topic: N16AT010

    The Department of the Navy has a need for the development of an additive manufacturing (AM) process for key vacuum electronic device components to meet on-demand, flexible, and affordable manufacturing requirements. The developed manufacturing method has a potential to reduce cost of vacuum electronics by as much as 70% as well as simplify and hence expedite production process of these devices by ...

    STTR Phase I 2016 Department of DefenseNavy
  8. Advanced Computational Methods for Study of Electromagnetic Compatibility

    SBC: HYPERCOMP INC            Topic: AF09BT13

    The leakage of electromagnetic (EM) energy into air vehicles, and particularly into ordnance, poses a hazard that requires careful evaluation. Under current guidelines, such evaluations are primarily to be carried out through extensive testing of items under possible field conditions, a process that can be both time-consuming and costly. The scope of this STTR Phase I activity is to implement a h ...

    STTR Phase I 2010 Department of DefenseAir Force
  9. Advanced Computational Methods for Study of Electromagnetic Compatibility

    SBC: MATHEMATICAL SYSTEMS & SOLUTIONS, INC.            Topic: AF09BT13

    The present text proposes development of efficient, accurate and rapidly-convergent algorithms for the simulation of propagation and scattering of electromagnetic fields within and around structures that (i) Consist of complex combinations of penetrable materials as well as perfect and imperfect conductors, and, (ii) Possess complex geometrical characteristics, including open surfaces, metallic c ...

    STTR Phase I 2010 Department of DefenseAir Force
  10. Advanced Data Processing, Storage and Visualization Algorithms for Structural Health Monitoring Sensor Networks of Naval Assets

    SBC: ACELLENT TECHNOLOGIES INC            Topic: N10AT042

    Acellent Technologies Inc. and Prof. F. G. Yuan at North Carolina State University (NCSU) are proposing to develop a Hybrid Distributed Sensor Network Integrated with Self-learning Symbiotic Diagnostic Algorithms and Models to determine materials state awareness and its evolution, including identification of precursors, detection of microdamages and flaws near high stress area or in a distributed ...

    STTR Phase I 2010 Department of DefenseNavy
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