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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. Additive Manufacturing Sensor Fusion Technologies for Process Monitoring and Control.

    SBC: ARCTOS Technology Solutions, LLC            Topic: DLA18A001

    Universal Technology Corporation (UTC) has teamed with the University of Dayton Research Institute (UDRI), Stratonics, and Macy Consulting to demonstrate not only the transitionability into commercial systems, but also to develop the data analytics and monitoring and control requirements to extract the full value fromseveral sensors, including the Stratonics ThermaViz, acoustic and profilometry se ...

    STTR Phase I 2018 Department of DefenseDefense Logistics Agency
  2. Bounding generalization risk for Deep Neural Networks

    SBC: Euler Scientific            Topic: NGA20A001

    Deep Neural Networks have become ubiquitous in the modern analysis of voluminous datasets with geometric symmetries. In the field of Particle Physics, experiments such as DUNE require the detection of particle signatures interacting within the detector, with analyses of over a billion 3D event images per channel each year; with typical setups containing over 150,000 different channels.  In an ...

    STTR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
  3. 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
  4. 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
  5. Oxygen Production and Delivery on Demand

    SBC: GLOBAL RESEARCH & DEVELOPMENT INC            Topic: DHA17B005

    This proposal is in response to the Defense Health Agency 2017 Phase I SBIR topic 17B-005.The approach is the use of a membrane oxygen pump using newly developed nano-thickness membranes with all the layers less than 1 micron total.Nanometer thickness membranes enable more oxygen output per surface area at temperatures of 300-600 C than current state-of-the -art 600-800 C membranes that are 50-300 ...

    STTR Phase I 2018 Department of DefenseDefense Health Agency
  6. SHAPE-BASED GENERALIZATION BOUNDS FOR DEEP LEARNING

    SBC: GEOMETRIC DATA ANALYTICS INC.            Topic: NGA20A001

    We propose to develop a theoretical understanding of the relationship between intrinsic geometric structure in both training and latent data and characteristics of functions learned from that data for deep neural network (DNN) architectures. Along the way we propose to also understand the structure of the neural networks that are best trained on a given data set. Both of these theories will lead t ...

    STTR Phase I 2020 Department of DefenseNational Geospatial-Intelligence Agency
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