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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. Handoff Training for Combat Casualty Care (HTC3) Framework

    SBC: Perceptronics Solutions, Inc.            Topic: DHA17B001

    This proposal is to develop a Handoff Training for Combat Casualty Care (HTC3) Framework.Training is the crux of the handoff problem today. Patient handoffs are a crucial part of casualty care, both in military and civilian environments; and today handoffs are being performed in less than optimal fashion, with ineffective communications accounting for 80% of the handoff errors. Our new HTC3 Framew ...

    STTR Phase I 2018 Department of DefenseDefense Health Agency
  2. Combat Casualty Handoff Automated Trainer (CCHAT)

    SBC: SOAR TECHNOLOGY INC            Topic: DHA17B001

    Combat casualty handoffs are critical communication moments during which responsibility for the patient and important casualty information is transferred between providers. The nature of these handoffs requires specialized training, for which no standardized framework currently exists. The proposed effort aims to develop a capability, compatible with current DoD systems, that provides caregivers w ...

    STTR Phase I 2018 Department of DefenseDefense Health Agency
  3. CCHAT Handoff Protocol

    SBC: SOAR TECHNOLOGY INC            Topic: DHA17B002

    Research has identified that handoffs are particularly important communication processes, during which communication error can lead to patient safety situations. Organizations have created standard practices and training materials to encourage teamwork communication for handoffs, however these do not necessarily capture the needs of military medicine of combat casualty care. Combat casualty handof ...

    STTR Phase I 2018 Department of DefenseDefense Health Agency
  4. Griffon Test Suite

    SBC: SOAR TECHNOLOGY INC            Topic: DHA17C001

    In this proposal we support the development of a hypoxia test battery by designing and developing a domain general tool suite for processing, synchronizing, and evaluating data from cognitive, behavioral, and physiological measures.The proposed Griffon Tool Suite addresses many of the practical requirements demanded by a flexible test battery. The effort falls into three major thrusts.First, we pr ...

    STTR Phase I 2018 Department of DefenseDefense Health Agency
  5. Novel Structure-Preserving Algorithms for Accurate Rocket Trajectory Propagation

    SBC: OPTIMAL SYNTHESIS INC.            Topic: MDA17T002

    The Department of Defense uses large-scale high-resolution federated simulations to propagate rocket vehicle trajectories. Runge-Kutta methods have served as a de-facto standard while conducting such simulations. However, there are several challenges while using Runge-Kutta methods for this task. Firstly, there should be exact time-step matching between federates, otherwise the states have to be i ...

    STTR Phase I 2018 Department of DefenseMissile Defense Agency
  6. SmallSat Stirling Cryocooler for Missile Defense (SSC-X)

    SBC: WECOSO, INC.            Topic: MDA17T003

    West Coast Solutions (WCS), in collaboration with the Georgia Institute of Technology and Creare LLC, proposes an adaptation of our SmallSat Stirling Cryocooler (SSC) technology in response to STTR Topic MDA17-T003: High-Efficiency, Low-Volume, Space-Qualified Cryogenic-Coolers. In Phase 1 we will scale up a design currently in development for NASA to meet the Missile Defense Agency (MDA) topic re ...

    STTR Phase I 2018 Department of DefenseMissile Defense Agency
  7. Smallsat Cryocooler System

    SBC: IRIS TECHNOLOGY CORPORATION            Topic: MDA17T003

    The Iris Technology team which also include Northrop Grumman Aerospace Systems (NGAS) and the University of Wisconsin, is attacking the problem of high-efficiency, low-volume, space-qualified cryocooler systems.The team has a firm starting point by leveraging the Northrop Grumman Microcryocooler and the Iris Technology mLCCE (Miniature Low Cost Control Electronics).TMU enhancement will start with ...

    STTR Phase I 2018 Department of DefenseMissile Defense Agency
  8. Lightweight Structural Components of a Missile Body

    SBC: ALPHASTAR TECHNOLOGY SOLUTIONS LLC            Topic: MDA17T004

    The Ground-Based Interceptor (GBI) missile is the weapon component of the Ground-Based Midcourse Defense (GMD) system that consists of a rocket booster and kinetic kill vehicle. Recently, MDA has sought technologies to improve the performance of the booster vehicle (BV). To date, studies have shown that reductions in weight have a direct impact on overall effectiveness. The current proposal aims t ...

    STTR Phase I 2018 Department of DefenseMissile Defense Agency
  9. Hybrid DNN-based Transfer Learning and CNN-based Supervised Learning for Object Recognition in Multi-modal Infrared Imagery

    SBC: TOYON RESEARCH CORPORATION            Topic: 1

    On this effort Toyon Research Corp. and The Pennsylvania State University are developing deep learning-based algorithms for object recognition and new class discovery in look-down infrared (IR) imagery. Our approach involves the development of a hybrid classifier that exploits both transfer learning and semi-supervised paradigms in order to maintain good generalization accuracy, especially when li ...

    STTR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
  10. Algorithms for Look-down Infrared Target Exploitation

    SBC: SIGNATURE RESEARCH, INC.            Topic: 1

    Signature Research, Inc. (SGR) and Michigan Technological University (MTU) propose a Phase I STTR effort to develop a learning algorithm which exploits the spatio-spectral characteristics inherent within IR imagery and motion imagery.Our archive of modelled and labeled data sets will allow our team to thoroughly capture the variable elements that will drive machine learning performance.The overall ...

    STTR Phase I 2018 Department of DefenseNational Geospatial-Intelligence Agency
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